DETAILED ACTION
Status of Claims
In the communication filed on 09/18/2025, claims 1-20 are pending. Claims 1, 3, 10-11, 16, 18, and 20 have been amended.
Response to Arguments
The prior drawing objections are maintained. Additional discussion is included infra.
The prior objections to the specification and claims are withdrawn due to the amendments.
The prior rejections under U.S.C. 112(b) are withdrawn due to the amendments.
The prior rejections under U.S.C. 101 are withdrawn due to the amendments.
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the arguments do not apply to the combination of references being used in the current rejection.
Drawings
The drawings, Figures 2-10, are objected to because the unlabeled rectangular box(es) shown in the drawings should be provided with descriptive text labels. Although the boxes in the figures are numbered which allows a correlation to each box as one reads the specification, the numbers by themselves do not allow one to quickly ascertain the concept of the invention which is desirable during a later search of analogous art. The numbers should be complimented with words spelled out to facilitate future searches. Replacement drawings in compliance with 37 CFR 1.84 and 37 CFR 1.121(d) are required.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) and/or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, line 6 recites “based on the learned patterns”, which should be revised to “based on the learned patterns of periodic charging and periodic usage”. This suggested change will align the language with that of claim 1, lines 11-12, and avoids a potential antecedent basis issue.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-4, 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), and Schwarz et al. (US 2012/0200257 A1).
Regarding Claim 1, Saita discloses a method (flowchart of Fig. 3) for determining an optimal state-of-charge (SOC) operating window (range of operating SOC values from lower value “SOC low” to upper value “second charging recommendation threshold value”; annotated Fig. 4 included infra) for a battery (30; Fig. 1) for use in an electric vehicle (electric vehicle 10; Fig. 1), comprising the following.
PNG
media_image1.png
905
1391
media_image1.png
Greyscale
Saita further discloses learning a pattern of periodic charging (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “number of charges, one-time charging amount” per ¶ [70]; may be once per day or once every multiple days per ¶ [68]) of the battery (30) for a plurality of time periods (¶ [70]: based on “n days” where “n is an integer greater than or equal to 2 and less than or equal to 31”; ¶ [197]: “on a per day basis”).
Saita further discloses learning a pattern of periodic usage (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “daily travel distance” per ¶ [70]) of the battery (30) for the plurality of time periods (“n days”).
Saita further discloses determining a periodic energy requirement (“ΔSOCe (median)” of the “one-day estimated consumption amount ΔSOCe” per ¶ [70-71]; Fig. 3 step S4; Fig. 4) for the battery (30) for the plurality of time periods (“n days”) based on the learned patterns (“usage patterns”; includes both patterns of periodic charging and pattern of periodic usage per ¶ [70]) using a statistical model (“statistical processing” per ¶ [70-71]).
Saita further discloses setting a maximum SOC level (“second charging recommendation threshold value”; Fig. 3 step S6; Fig. 4) and a minimum SOC level (“SOC_low”; Fig. 4; set per ¶ [74, 82-86]) for the SOC operating window (range from “SOC_low” to “second charging recommendation threshold value”; Fig. 4).
Saita further discloses the setting of the maximum SOC level (“second charging recommendation threshold value”) is based on the learned patterns (ΔSOCe is based on “usage patterns” per ¶ [70]; thus, the “second charging recommendation threshold value” is set based on “usage patterns”) of periodic charging (“number of charges, one-time charging amount” included in “usage patterns” per ¶ [70]) and periodic usage (“daily travel distance” included in “usage patterns” per ¶ [70]).
Saita further discloses controlling, by a controller (“charging control apparatus 22”; Figs. 1-2; ¶ [18]), a vehicle charger (“charging equipment 14”; Fig. 1) to charge the battery (30) to a state-of-charge within the set maximum SOC level (“second charging recommendation threshold value”; Fig. 4) and minimum SOC level (“SOC_low”; Fig. 4), thereby prolonging a service life (¶ [136]: “extending the lifetime of the battery 30”) of the battery (30).
Saita does not disclose “deriving an adjusted battery energy requirement by modifying the periodic energy requirement with an anxiety factor, wherein the anxiety factor is calculated based on a frequency of the periodic charging and an availability of charging locations for the battery”.
As addressed supra, Saita discloses the setting of the maximum SOC level for the SOC operating window is based on the learned patterns of periodic charging and periodic usage. However, Saita further does not disclose the setting of the maximum SOC level is “based on two or more of the adjusted battery energy requirement, the learned patterns of periodic charging and periodic usage, and a battery chemistry of the battery”.
As addressed supra, Saita discloses setting the minimum SOC level for the SOC operating window. However, Saita further does not disclose the setting of the minimum SOC level is “based on two or more of the periodic energy requirement, the learned patterns of periodic charging and periodic usage, and a battery chemistry of the battery”.
Matt teaches deriving an adjusted battery energy requirement (¶ [1]: “determining the energy requirement of a vehicle for a journey”; determined after step 18 of Fig. 3) by modifying (“energy requirement” is modified per the iterative method of Fig. 3) the periodic energy requirement (“energy requirement” is associated with a “refuelling/recharging interval”, per ¶ [2]) with an anxiety factor (“factor F”; Fig. 3, step 15; ¶ [24]: “combining the weighted factors”).
Matt further teaches the anxiety factor (“factor F”) is calculated based on a frequency of the periodic charging (¶ [2]: “refuelling/recharging interval”; data for these intervals is part of the “vehicle data and/or journey data” obtained in step 10 of Fig. 3; ¶ [24]: “each factor derived from the vehicle data and/or journey data”) and an availability of charging locations (¶ [25]: “factors may include … location of re-fuelling [sic] station”; ¶ [2]: “refuelling/recharging stations”) for the battery (“battery” to power an “electric car” per ¶ [44]; ¶ [10]: “energy store includes one or more batteries”).
Matt further teaches the adjusted battery energy requirement to improve accuracy of predictions of the required energy for future trips (¶ [7]), particularly when the recharging intervals are long and where recharging stations are sparse (¶ [2]).
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by Saita to modify the periodic energy requirement with an anxiety factor to derive an adjusted battery energy requirement, as taught by Matt, to improve accuracy of predictions of the required energy for future trips, particularly when the recharging intervals are long and where charging locations are less available.
Duan teaches setting the minimum SOC level (“SOCend_opt”; Fig. 3 step 380) based on the learned patterns of periodic charging and periodic usage (“vehicle used history” per Fig. 3 step 320; includes “charging data” and “trip data” per ¶ [44]) of the battery (“traction battery 101”; Fig. 1).
Duan teaches this method of setting the minimum SOC level for the advantage of improving battery life (¶ [8, 59]). Duan further teaches this for the advantage of providing a travel distance margin/cushion (¶ [9]), which improves user experience.
It would have been obvious to one of ordinary skill in the art to modify the method and the minimum SOC level disclosed by the combination of Saita and Matt to set the minimum SOC level based on the learned patterns, as taught by Duan, for the advantages of improving battery life and/or user experience.
Schwarz teaches setting a maximum SOC level (“upper SOC limit 204”; Fig. 3) and a minimum SOC level (“lower SOC limit 206”; Fig. 3) for the SOC operating window (“desired SOC range 202”; Fig. 3).
Schwarz further teaches these SOC levels (204, 206) are set based on a battery chemistry (¶ [2]: “desirable for certain types of high-voltage batteries, like those based on lithium-ion chemistries, to be maintained in a certain SOC range”; ¶ [3]: “if it is very cold out … a lithium-ion chemistry may not have enough power to start”; ¶ [3]: “manage the … SOC … to account for this”; ¶ [17]: “memory device 50 may also store pertinent battery characteristics … battery’s cell chemistry” ; ¶ [13] lists possible battery chemistries; ¶ [25]: “temperature thresholds can depend on the chemistry”) of the battery (“battery pack 30”; Fig. 1).
Schwarz further teaches setting SOC levels based on battery chemistry to reduce degradation effects on the battery and improve battery life (¶ [2, 11, 33]). Schwarz further teaches that certain battery chemistries have different performance across temperatures, and thus the battery chemistry needs to be considered in setting the SOC range to ensure the vehicle has enough power to start at low temperatures (¶ [2-3])
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by the combination of Saita, Matt, and Duan to set the SOC maximum level and SOC minimum level based on battery chemistry, as taught by Schwarz, to improve battery life and improve electric vehicle performance at low temperatures.
Regarding Claim 2, the combination of Saita, Matt, Duan, and Schwarz teaches the method of claim 1.
Saita discloses the periodic charging (“usage patterns” calculated per ¶ [197]; includes “number of charges, one-time charging amount” per ¶ [70]), the periodic usage (“usage patterns” calculated per ¶ [197]; includes “daily travel distance” per ¶ [70]) and the periodic energy requirement (“ΔSOCe (median)”) each have a periodicity of daily (“usage patterns” are calculated “on a per day basis” per ¶ [197]; “ΔSOC” and “ΔSOCe (median)” are calculated each day per ¶ [66-71]).
Regarding Claim 3, the combination of Saita, Matt, Duan, and Schwarz teaches the method of claim 1.
Saita discloses the statistical model (“statistical processing” per ¶ [70-71]) is a positively skewed (probability data of Fig. 9 is positively skewed to lower values of ΔSOC; positive skew also shown in Fig. 12 for example ΔSOC data) nonparametric distribution (median calculation method described in ¶ [70-71] does not rely on any assumptions about the underlying distribution of the ΔSOC data; thus, a nonparametric distribution is used for the “statistical processing”).
Regarding Claim 4, the combination of Saita, Matt, Duan, and Schwarz teaches the method of claim 1.
Saita discloses the learning step (Fig. 3 steps S2-S6; ¶ [70-71]) comprises receiving a plurality of charging instances (“number of charges” per ¶ [70]; assumed to be once per day, but can be multiple days between charges per ¶ [68]) and a plurality of usage instances (“plurality of one-time consumption amounts” per ¶ [67]) for the plurality of time periods (“n days”).
Saita further discloses establishing the patterns (“usage patterns” per ¶ [197]) of periodic charging (“usage patterns” include “number of charges” per ¶ [70]) and periodic usage (“usage patterns” include “daily travel distance”; based on adding together “a plurality of one-time consumption amounts” per ¶ [67]) based on the received pluralities of charging instances (“number of charges”) and usage instances (“plurality of one-time consumption amounts”), respectively.
Regarding Claim 6, the combination of Saita, Matt, Duan, and Schwarz teaches the method of claim 1.
Saita discloses accumulating additional instances (method is repeated once per day per ¶ [70]; thus, additional instances “for the most recent n days” are accumulated and recorded in “storage section 40”) of the periodic charging (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “number of charges, one-time charging amount” per ¶ [70]; may be once per day or once every multiple days per ¶ [68]) and the periodic usage (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “daily travel distance” per ¶ [70]) of the battery (30).
Saita further discloses utilizing a statistical analysis method (“statistical processing” per ¶ [70-71]) to derive an updated maximum SOC level (“second charging recommendation threshold value”; Fig. 3 step S6; Fig. 4; updated each day when the method is repeated per ¶ [70]) for the SOC operating window (range from “SOC_low” to “second charging recommendation threshold value”; Fig. 4) based on the additional instances of periodic charging (additional data for “usage patterns” including new “number of charges, one-time charging amount” per ¶ [70]) and periodic usage (additional data for “usage patterns” including new “daily travel distance” per ¶ [70]).
As addressed supra, Saita discloses utilizing a statistical analysis method “to derive an updated maximum SOC level for the SOC operating window based on the additional instances of periodic charging and periodic usage”. However, Saita does not disclose “utilizing a machine learning method to derive an updated maximum SOC level and an updated minimum SOC level for the SOC operating window based on the additional instances of periodic charging and periodic usage”.
Duan teaches utilizing a machine learning method (¶ [35]: “may use any processing strategies including … machine learning, neural networks, and the like to process the vehicle trip data and related data to determine the charge settings”) to derive an updated minimum SOC level (“SOCend_opt” calculated in step 380; gets updated each time the charge cord is connected in step 210; Figs. 2-3) for the SOC operating window (range from “SOCend_opt” to “SOCcharge”; Fig. 3) based on the additional instances (new “vehicle used history” is loaded in step 320 each time the charge cord is connected in step 210; Figs. 2-3) of periodic charging (“vehicle used history” includes “charging data” per ¶ [48]; periodic on a daily basis per ¶ [48]) and periodic usage (“vehicle used history” includes “trip data” per ¶ [48]; periodic on a daily basis per ¶ [48]).
Duan teaches this method of setting the minimum SOC level for the advantage of improving battery life (¶ [8, 59]). Duan further teaches this for the advantage of providing a travel distance margin/cushion (¶ [9]), which improves user experience. Duan further teaches the updating of the minimum SOC level to weigh the most recent data (¶ [48]). Duan further teaches the use of machine learning for the advantage of optimizing the SOC levels via self-learning (¶ [8, 30, 35, 48]), further improving battery life and user experience.
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by the combination of Saita, Matt, Duan, and Schwarz to utilize machine learning to update the minimum SOC level based on the additional instances of periodic charging and periodic usage, as further taught by Duan, for the advantages of optimizing battery life and/or user experience via self-learning based on the most recent data.
Regarding Claim 12, the combination of Saita, Matt, Duan, and Schwarz teaches the method of claim 1.
Saita discloses the periodic energy requirement (“ΔSOCe (median)”; Fig. 4) is one of a plurality of individual energy requirements (one of the “ΔSOC” values recorded in “40” per ¶ [70]).
Saita further discloses each of the individual energy requirements (each “ΔSOC” value) corresponds to a respective one of the plurality of time periods (each “ΔSOC” value corresponds to one of the “most recent n days” per ¶ [70]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), and O'Gorman et al. (US 2021/0138928 A1).
Regarding Claim 5, the combination of Saita, Matt, Duan, and Schwarz teaches the method of claim 4.
Saita discloses each charging instance (each of the “number of charges” per ¶ [70]; assumed to be once per day, but can be multiple days between charges per ¶ [68]), but includes few details of the stored data for each charging instance.
Saita further discloses each usage instance (each of the “plurality of one-time consumption amounts” per ¶ [67]) includes a respective total energy use amount (“one day of electricity consumption ΔSOC” per ¶ [66-68]).
Saita does not disclose “each charging instance includes two or more of a respective charging start time, a respective charging end time, a respective charging duration, a respective charging level, a respective beginning battery charge level and a respective ending battery charge level, and wherein each usage instance includes two or more of a respective usage start time, a respective usage end time, a respective usage duration, a respective average energy use amount and a respective total energy use amount”.
O’Gorman teaches each charging instance (“plug-in event” stored as “historical vehicle data 210”; ¶ [36-37]; Fig. 2) includes a respective charging start time (¶ [37]: “time at which the plug-in charging initiates”) and a respective charging end time (¶ [37]: “time at which the vehicle registers as unplugged from the charge station”).
O’Gorman further teaches each usage instance (each “trip” in the “series of trips” recorded in the “historical vehicle data 210” per ¶ [41]) includes a respective usage duration (“durations of travel” per ¶ [41]).
O’Gorman teaches that these characterizations of each charging instance and usage instance are recorded for the advantage of minimizing the cost of charging (¶ [5-6]).
It would have been obvious to one of ordinary skill in the art to modify the method and each charging instance disclosed by the combination of Saita, Matt, Duan, and Schwarz to include respective charging start/end times, as taught by O’Gorman, to minimize the cost of charging. It further would have been obvious to one of ordinary skill in the art to modify the method and each usage instance disclosed by the combination of Saita, Matt, Duan, and Schwarz to include a respective usage duration, as taught by O’Gorman, also to minimize the cost of charging.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), and Nieto et al. (US 2022/0140625 A1).
Regarding Claim 7, the combination of Saita, Matt, Duan, and Schwarz teaches the method of claim 6.
The combination of Saita, Matt, Duan, and Schwarz discloses the machine learning method (incorporated from Duan as addressed supra: “machine learning” per Duan ¶ [35]).
Saita does not disclose “the machine learning method is a neural network”.
Nieto discloses the machine learning method (“first machine learning algorithm” in block 502; Fig. 5) is a neural network (¶ [26]: “the embodiments may … employ one or more neural networks for the machine learning”).
Nieto teaches the use of a neural network for the advantage of applying weighting factors between nodes (¶ [26, 77-78, 83-84]) so as to optimize a state of charge level (¶ [55]) and increase the battery lifetime (¶ [29]).
It would have been obvious to one of ordinary skill in the art to modify the machine learning method disclosed by the combination of Saita, Matt, Duan, and Schwarz to incorporate a neural network, as taught by Nieto, to increase the battery lifetime using an improved machine learning algorithm with weighting factors.
Regarding Claim 8, the combination of Saita, Matt, Duan, Schwarz, and Nieto teaches the method of claim 7.
The combination of Saita, Matt, Duan, Schwarz, and Nieto teaches the neural network (incorporated from Nieto: “neural networks for the machine learning” per ¶ [26]).
Saita does not disclose “the neural network is a recurrent neural network”.
Nieto teaches the neural network is a recurrent neural network (¶ [27]: “the one or more neural networks … may comprise … one or more recurrent neural networks”).
Nieto teaches the use of a recurrent neural network for the advantage of including loops to allow information to be maintained (¶ [27]), which further supports learning to optimize a state of charge level (¶ [55]) and increase the battery lifetime (¶ [29]).
It would have been obvious to one of ordinary skill in the art to modify the neural network in the method disclosed by the combination of Saita, Matt, Duan, Schwarz, and Nieto to be a recurrent neural network, as further taught by Nieto, to increase the battery lifetime using an improved machine learning algorithm with loops to maintain information.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), and Katanoda (US 2014/0132214 A1; hereinafter “Kata”), and as evidenced by a Börger paper (Alexander Börger, Thermal runaway and thermal runaway propagation in batteries: What do we talk about?, 2019-06-05, Journal of Energy Storage, Volume 24).
Regarding Claim 9, the combination of Saita, Matt, Duan, and Schwarz teaches the method of claim 1.
Saita discloses the step of setting the maximum (Fig. 3 steps S6; “second charging recommended threshold value”; Fig. 4; ¶ [77-79]) and minimum (“SOC_low”; Fig. 4; set per ¶ [74, 82-86]) SOC levels comprises the following.
Saita further discloses selecting a candidate maximum SOC level (“second charging recommended threshold value”; Fig. 3 step S6).
Saita further discloses selecting, as a candidate minimum SOC level (“SOC_low” is set to 10% by default; ¶ [82]), a recommended minimum SOC level (“SOC_low” of 10% per ¶ [82]; Fig. 4) based on the battery capacity model (Fig. 4 shows a model where the battery capacity is modeled from 0% to 50%; the “SOC_low” value is set by default as 10% based on this model, per ¶ [82]) for the battery (30).
Saita further discloses deriving a battery energy requirement (“second charging recommendation threshold value” represents the required energy stored by the battery; Fig. 4) by adding a factor (“SOC_low” is added as a factor with “ΔSOCe (median)” to derive “second charging recommendation threshold value”; Fig. 4; ¶ [77-79] Expressions 3 + 4) to the periodic energy requirement (“ΔSOCe (median)”; Fig. 4).
Alternatively, Saita further discloses deriving a battery energy requirement (“second charging recommendation threshold value” represents the required energy stored by the battery; Fig. 4) by multiplying the periodic energy requirement (“ΔSOCe (median)”; Fig. 4) by a multiplier (multiplier “m” in Expression 3, set as integer 2 in Expression 4; represents a number of days; ¶ [77-79]).
Saita further discloses the factor (“SOC_low”) and the multiplier (“m”) are each based on the periodic charging (“SOC_low” can be changed to optimize based on usage patterns per ¶ [85] and is thus based on “number of charges” ¶ [70]; “m” represents number of days until charging, and is thus based on “number of charges” per ¶ [70, 77-78]) of the battery (30) and an availability of charging locations (“SOC_low” and “m” can both be modified based on “usage patterns” per ¶ [70, 77-78, 85]; the “usage patterns” are based on availability of charging locations because the charging locations are included in the usage pattern data per ¶ [62]; thus, both “SOC_low” and “m” are based on availability of charging locations; see also step S1 of Fig. 3) for the battery (30).
Saita further discloses adjusting one or both of the candidate minimum (“SOC_low” adjusted per ¶ [85-86]) and maximum (“second charging recommended threshold value” adjusts based on “m” value per ¶ [77-79]) SOC levels to establish the minimum (“SOC_low”; Fig. 4) and maximum SOC levels (“second charging recommended threshold value”; Fig. 4), respectively.
Saita further discloses establishing these levels so as to enable the battery (30) to supply the battery energy requirement (battery energy requirement of “second charging recommended threshold value” is enabled to be supplied following charging to the maximum SOC level “second charging recommended threshold value”; Fig. 4).
As addressed supra, Saita discloses selecting a candidate maximum SOC level. However, Saita does not disclose “selecting, as a candidate maximum SOC level, a lesser of a first recommended maximum SOC level based on a battery capacity model for the battery and a second recommended maximum SOC level based on a point of diminishing returns for thermal propagation performance for the battery”.
Kata teaches selecting, as a candidate maximum SOC level (“reference upper limit value”; ¶ [79]), a lesser (“Smax2” is less than “Smax1” per ¶ [83]; first embodiment selects the “long life mode” associated with “Smax2” per ¶ [89]) of a first recommended maximum SOC level (“Smax1”; annotated Fig. 4 provided infra) and a second recommended maximum SOC level (“Smax2”; annotated Fig. 4 provided infra).
PNG
media_image2.png
555
1266
media_image2.png
Greyscale
Kata further teaches the first recommended maximum SOC level (“Smax1”) is based on a battery capacity model (Fig. 4 shows a model where the battery capacity is modeled from 0% to 100%; the “Smax1” value is set to a value less than 100% to prevent overcharging based on this model, per ¶ [82]) for the battery (“power storage device 10” in “vehicle 5”; Fig. 1; ¶ [41]: “lithium ion battery or a nickel hydride battery”).
Kata further teaches the second recommended maximum SOC level (“Smax2”) is based on a point of diminishing returns for thermal propagation performance (“Smax2” is selected at an inflection point of increasing internal resistance, per ¶ [60]; the inflection point of increasing internal resistance is inherently a point of diminishing returns for thermal propagation performance, as evidenced infra) for the battery (10).
NOTE: Börger provides evidence that increased internal resistance in a battery is associated with a thermal runaway condition in a battery. Börger teaches that thermal runaway propagates based on heat produced from internal resistance in the battery (page 4, aspect 2 + section 3.2 Classification of TRs). Thus, it is well-known in the art that an inflection point of increasing internal resistance (as taught by Kata) is inherently a point of diminishing returns for thermal propagation performance.
Kata teaches this selection of the candidate maximum SOC level to improve the charging method to suppress progression of deterioration of the battery (¶ [54]).
It would have been obvious to one of ordinary skill in the art to modify the selection of the candidate maximum SOC level, as disclosed by the combination of Saita, Matt, Duan, and Schwarz, to be the lesser of two levels based on an incorporated battery capacity model and a point of diminishing returns for thermal propagation performance, as taught by Kata (with evidence from Börger), to suppress deterioration of the battery.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), Katanoda (US 2014/0132214 A1; hereinafter “Kata”), as evidenced by the Börger paper (Alexander Börger, Thermal runaway and thermal runaway propagation in batteries: What do we talk about?, 2019-06-05, Journal of Energy Storage, Volume 24), and in further view of a Zhou paper (Wenlu Zhou, Review on the Battery Model and SOC Estimation Method, 2021-09-20, MDPI Processes).
Regarding Claim 10, the combination of Saita, Matt, Duan, Schwarz, and Kata (evidenced by Börger) teaches the method of claim 9.
The combination of Saita, Matt, Duan, Schwarz, and Kata teaches the battery capacity model (incorporated model from Kata Fig. 4 to base the setting of the first recommended maximum SOC level “Smax1”).
Saita does not explicitly disclose “the battery capacity model is based on the battery chemistry of the battery”.
Zhou teaches the battery capacity model (“battery model” in title used to model “SOC” of a battery) is based on the battery chemistry (“electrochemical mechanism model” evaluates the “chemical properties”; section 2.1, pages 3-4) of the battery (“power battery” in a “vehicle”; Abstract).
Zhou teaches this for the advantages of a more accurate battery model that improves the energy management control strategy to improve the reliability of the vehicle (Abstract).
It would have been obvious to one of ordinary skill in the art to modify the battery capacity model disclosed by the combination of Saita, Matt, Duan, Schwarz, and Kata to be based on the battery’s chemistry, as taught by Zhou, to improve the reliability of the vehicle.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), Katanoda (US 2014/0132214 A1; hereinafter “Kata”), as evidenced by the Börger paper (Alexander Börger, Thermal runaway and thermal runaway propagation in batteries: What do we talk about?, 2019-06-05, Journal of Energy Storage, Volume 24), and in further view of Li et al. (US 2019/0226859 A1).
Regarding Claim 11, the combination of Saita, Matt, Duan, Schwarz, and Kata (evidenced by Börger) teaches the method of claim 9.
Saita does not disclose “the availability of charging locations for the battery is based on a range within which the battery may be utilized to locomotively power the electric vehicle”.
Li teaches the availability of charging locations (“charging stations 31” with availability displayed as “image 64” on the “HMI 54”; Figs. 3-4; ¶ [38]) for the battery (“high power battery pack 24”; Figs. 1-2) is based on a range (“predetermined distance”; ¶ [37-39, 47]; Fig. 5 steps 108, 110) within which the battery (24) may be utilized to locomotively power (¶ [37]: “may be charged at a selected one of these charge stations 31 before the high power battery pack 24 discharges to a minimum state-of-charge”) the electric vehicle (“electric vehicle 10”; Figs. 1, 4).
Li teaches basing the availability of charging locations based on this range to help the driver find available charging locations they can drive to without running out of charge (¶ [37]), which improves user experience.
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by the combination of Saita, Matt, Duan, Schwarz, and Kata for the availability of charging locations to be based on a feasible driving range, as taught by Li, to improve user experience.
Claims 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), and Nieto et al. (US 2022/0140625 A1).
Regarding Claim 13, Saita discloses a method (flowchart of Fig. 3) for determining an optimal state-of-charge (SOC) operating window (range of operating SOC values from lower value “SOC_low” to upper value “second charging recommendation threshold value”; annotated Fig. 4 included supra) for a battery (30; Fig. 1) for use in an electric vehicle (electric vehicle 10; Fig. 1), comprising the following.
Saita further discloses receiving a plurality of charging instances (“number of charges” per ¶ [70]; assumed to be once per day, but can be multiple days between charges per ¶ [68]) of the battery (30) for a plurality of time periods (¶ [70]: based on “n days” where “n is an integer greater than or equal to 2 and less than or equal to 31”; ¶ [197]: “on a per day basis”) and a plurality of usage instances (“plurality of one-time consumption amounts” per ¶ [67]) of the battery (30) for the plurality of time periods (“n days”).
Saita further discloses establishing a pattern of periodic charging (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “number of charges, one-time charging amount” per ¶ [70]) of the battery (30) based on the received plurality of charging instances (“number of charges”).
Saita further discloses establishing a pattern of periodic usage (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “daily travel distance” per ¶ [70]) of the battery (30) based on the received plurality of usage instances (“plurality of one-time consumption amounts”)
Saita further discloses determining a periodic energy requirement (“ΔSOCe (median)” of the “one-day estimated consumption amount ΔSOCe” per ¶ [70-71]; Fig. 3 step S4; Fig. 4) for the battery (30) for the plurality of time periods (“n days”) based on the learned patterns of periodic charging and periodic usage (“usage patterns”; includes both patterns of periodic charging and of periodic usage per ¶ [70]) using a positively skewed (probability data of Fig. 9 is positively skewed to lower values of ΔSOC; positive skew also shown in Fig. 12 for example ΔSOC data) nonparametric distribution (median calculation method described in ¶ [70-71] does not rely on any assumptions about the underlying distribution of the ΔSOC data; thus, a nonparametric distribution is used for the “statistical processing”).
Saita further discloses setting a maximum SOC level (“second charging recommendation threshold value”; Fig. 3 step S6; Fig. 4) and a minimum SOC level (“SOC_low”; Fig. 4; set per ¶ [74, 82-86]) for the SOC operating window (range from “SOC_low” to “second charging recommendation threshold value”; Fig. 4).
Saita further discloses the setting of the maximum SOC level (“second charging recommendation threshold value”) is based on the learned patterns (ΔSOCe is based on “usage patterns” per ¶ [70]; thus, the “second charging recommendation threshold value” is set based on “usage patterns”) of periodic charging (“number of charges, one-time charging amount” included in “usage patterns” per ¶ [70]) and periodic usage (“daily travel distance” included in “usage patterns” per ¶ [70]).
Saita further discloses accumulating additional instances (method is repeated once per day per ¶ [70]; thus, additional instances “for the most recent n days” are accumulated and recorded in “storage section 40”) of the periodic charging (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “number of charges, one-time charging amount” per ¶ [70]; may be once per day or once every multiple days per ¶ [68]) and the periodic usage (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “daily travel distance” per ¶ [70]) of the battery (30).
Saita further discloses utilizing a statistical analysis method (“statistical processing” per ¶ [70-71]) to derive an updated maximum SOC level (“second charging recommendation threshold value”; Fig. 3 step S6; Fig. 4; updated each day when the method is repeated per ¶ [70]) for the SOC operating window (range from “SOC_low” to “second charging recommendation threshold value”; Fig. 4) based on the additional instances of periodic charging (additional data for “usage patterns” including new “number of charges, one-time charging amount” per ¶ [70]) and periodic usage (additional data for “usage patterns” including new “daily travel distance” per ¶ [70]).
Saita further discloses controlling, by a controller (“charging control apparatus 22”; Figs. 1-2; ¶ [18]), a vehicle charger (“charging equipment 14”; Fig. 1) to charge the battery (30) to a state-of-charge within the set SOC operating window (within minimum “SOC_low” and maximum “second charging recommendation threshold value”; Fig. 4) or the updated SOC operating window (“second charging recommendation threshold value” is updated each day when the method is repeated per ¶ [70]), thereby prolonging a service life (¶ [136]: “extending the lifetime of the battery 30”) of the battery (30).
Saita does not disclose “deriving an adjusted battery energy requirement by modifying the periodic energy requirement with an anxiety factor, wherein the anxiety factor is calculated based on a frequency of the periodic charging and an availability of charging locations for the battery”.
As addressed supra, Saita discloses the setting of the maximum SOC level for the SOC operating window is based on the learned patterns of periodic charging and periodic usage. However, Saita further does not disclose the setting of the maximum SOC level is “based on two or more of the adjusted battery energy requirement, the learned patterns of periodic charging and periodic usage, and a battery chemistry of the battery”.
As addressed supra, Saita discloses setting the minimum SOC level for the SOC operating window. However, Saita further does not disclose the setting of the minimum SOC level is “based on two or more of the periodic energy requirement, the learned patterns of periodic charging and periodic usage, and a battery chemistry of the battery”.
As addressed supra, Saita discloses utilizing a statistical analysis method “to derive an updated maximum SOC level for the SOC operating window based on the additional instances of periodic charging and periodic usage”. However, Saita does not disclose “utilizing a recurrent neural network to derive an updated maximum SOC level and an updated minimum SOC level for the SOC operating window based on the additional instances of periodic charging and periodic usage”.
Matt teaches deriving an adjusted battery energy requirement (¶ [1]: “determining the energy requirement of a vehicle for a journey”; determined after step 18 of Fig. 3) by modifying (“energy requirement” is modified per the iterative method of Fig. 3) the periodic energy requirement (“energy requirement” is associated with a “refuelling/recharging interval”, per ¶ [2]) with an anxiety factor (“factor F”; Fig. 3, step 15; ¶ [24]: “combining the weighted factors”).
Matt further teaches the anxiety factor (“factor F”) is calculated based on a frequency of the periodic charging (¶ [2]: “refuelling/recharging interval”; data for these intervals is part of the “vehicle data and/or journey data” obtained in step 10 of Fig. 3; ¶ [24]: “each factor derived from the vehicle data and/or journey data”) and an availability of charging locations (¶ [25]: “factors may include … location of re-fuelling [sic] station”; ¶ [2]: “refuelling/recharging stations”) for the battery (“battery” to power an “electric car” per ¶ [44]; ¶ [10]: “energy store includes one or more batteries”).
Matt further teaches the adjusted battery energy requirement to improve accuracy of predictions of the required energy for future trips (¶ [7]), particularly when the recharging intervals are long and where recharging stations are sparse (¶ [2]).
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by Saita to modify the periodic energy requirement with an anxiety factor to derive an adjusted battery energy requirement, as taught by Matt, to improve accuracy of predictions of the required energy for future trips, particularly when the recharging intervals are long and where charging locations are less available.
Duan teaches setting the minimum SOC level (“SOCend_opt”; Fig. 3 step 380) based on the learned patterns of periodic charging and periodic usage (“vehicle used history” per Fig. 3 step 320; includes “charging data” and “trip data” per ¶ [44]) of the battery (“traction battery 101”; Fig. 1).
Duan teaches this method of setting the minimum SOC level for the advantage of improving battery life (¶ [8, 59]). Duan further teaches this for the advantage of providing a travel distance margin/cushion (¶ [9]), which improves user experience.
It would have been obvious to one of ordinary skill in the art to modify the method and the minimum SOC level disclosed by the combination of Saita and Matt to set the minimum SOC level based on the learned patterns, as taught by Duan, for the advantages of improving battery life and/or user experience.
Schwarz teaches setting a maximum SOC level (“upper SOC limit 204”; Fig. 3) and a minimum SOC level (“lower SOC limit 206”; Fig. 3) for the SOC operating window (“desired SOC range 202”; Fig. 3).
Schwarz further teaches these SOC levels (204, 206) are set based on a battery chemistry (¶ [2]: “desirable for certain types of high-voltage batteries, like those based on lithium-ion chemistries, to be maintained in a certain SOC range”; ¶ [3]: “if it is very cold out … a lithium-ion chemistry may not have enough power to start”; ¶ [3]: “manage the … SOC … to account for this”; ¶ [17]: “memory device 50 may also store pertinent battery characteristics … battery’s cell chemistry” ; ¶ [13] lists possible battery chemistries; ¶ [25]: “temperature thresholds can depend on the chemistry”) of the battery (“battery pack 30”; Fig. 1).
Schwarz further teaches setting SOC levels based on battery chemistry to reduce degradation effects on the battery and improve battery life (¶ [2, 11, 33]). Schwarz further teaches that certain battery chemistries have different performance across temperatures, and thus the battery chemistry needs to be considered in setting the SOC range to ensure the vehicle has enough power to start at low temperatures (¶ [2-3])
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by the combination of Saita, Matt, and Duan to set the SOC maximum level and SOC minimum level based on battery chemistry, as taught by Schwarz, to improve battery life and improve electric vehicle performance at low temperatures.
Duan further teaches utilizing a neural network (¶ [35]: “may use any processing strategies including … machine learning, neural networks, and the like to process the vehicle trip data and related data to determine the charge settings”) to derive an updated minimum SOC level (“SOCend_opt” calculated in step 380; gets updated each time the charge cord is connected in step 210; Figs. 2-3) for the SOC operating window (range from “SOCend_opt” to “SOCcharge”; Fig. 3) based on the additional instances (new “vehicle used history” is loaded in step 320 each time the charge cord is connected in step 210; Figs. 2-3) of periodic charging (“vehicle used history” includes “charging data” per ¶ [48]; periodic on a daily basis per ¶ [48]) and periodic usage (“vehicle used history” includes “trip data” per ¶ [48]; periodic on a daily basis per ¶ [48]).
Duan further teaches the updating of the minimum SOC level to weigh the most recent data (¶ [48]). Duan further teaches the use of a neural network / machine learning for the advantage of optimizing the SOC levels via self-learning (¶ [8, 30, 35, 48]), further improving battery life and user experience.
It would have been obvious to one of ordinary skill in the art to modify the statistical analysis method disclosed by the combination of Saita, Matt, Duan, and Schwarz to utilize a neural network to update the minimum SOC level based on the additional instances of periodic charging and periodic usage, as further taught by Duan, to optimize battery life and/or user experience via self-learning based on the most recent data.
Nieto teaches utilizing a recurrent neural network (“first machine learning algorithm” in block 502 of Fig. 5; ¶ [26]: “the embodiments may … employ one or more neural networks for the machine learning”; ¶ [27]: “the one or more neural networks employed in embodiments may comprise … one or more recurrent neural networks”) to derive SOC levels (¶ [55]).
Nieto teaches the use of a recurrent neural network for the advantage of including loops to allow information to be maintained (¶ [27]), which further supports learning to optimize a state of charge level (¶ [55]) and increase the battery lifetime (¶ [29]).
It would have been obvious to one of ordinary skill in the art to modify the method and neural network disclosed by the combination of Saita, Matt, Duan, and Schwarz to utilize a recurrent neural network to derive the updated SOC levels, as taught by Nieto, to increase battery lifetime using an improved machine learning algorithm with loops to maintain information.
Regarding Claim 17, the combination of Saita, Matt, Duan, Schwarz, and Nieto teaches the method of claim 13.
Saita discloses the periodic energy requirement (“ΔSOCe (median)”; Fig. 4) is one of a plurality of individual energy requirements (one of the “ΔSOC” values recorded in “40” per ¶ [70]).
Saita further discloses each of the individual energy requirements (each “ΔSOC” value) corresponds to a respective one of the plurality of time periods (each “ΔSOC” value corresponds to one of the “most recent n days” per ¶ [70]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), Nieto et al. (US 2022/0140625 A1), and O’Gorman et al. (US 2021/0138928 A1).
Regarding Claim 14, the combination of Saita, Matt, Duan, Schwarz, and Nieto teaches the method of claim 13.
Saita discloses each charging instance (each of the “number of charges” per ¶ [70]; assumed to be once per day, but can be multiple days between charges per ¶ [68]), but includes few details of the stored data for each charging instance.
Saita further discloses each usage instance (each of the “plurality of one-time consumption amounts” per ¶ [67]) includes a respective total energy use amount (“one day of electricity consumption ΔSOC” per ¶ [66-68]).
Saita does not disclose “each charging instance includes two or more of a respective charging start time, a respective charging end time, a respective charging duration, a respective charging level, a respective beginning battery charge level and a respective ending battery charge level, and wherein each usage instance includes two or more of a respective usage start time, a respective usage end time, a respective usage duration, a respective average energy use amount and a respective total energy use amount”.
O’Gorman teaches each charging instance (“plug-in event” stored as “historical vehicle data 210”; ¶ [36-37]; Fig. 2) includes a respective charging start time (¶ [37]: “time at which the plug-in charging initiates”) and a respective charging end time (¶ [37]: “time at which the vehicle registers as unplugged from the charge station”).
O’Gorman further teaches each usage instance (each “trip” in the “series of trips” recorded in the “historical vehicle data 210” per ¶ [41]) includes a respective usage duration (“durations of travel” per ¶ [41]).
O’Gorman teaches that these characterizations of each charging instance and usage instance are recorded for the advantage of minimizing the cost of charging (¶ [5-6]).
It would have been obvious to one of ordinary skill in the art to modify the method and each charging instance disclosed by the combination of Saita, Matt, Duan, Schwarz, and Nieto to include respective charging start/end times, as taught by O’Gorman, to minimize the cost of charging. It further would have been obvious to one of ordinary skill in the art to modify the method and each usage instance disclosed by the combination of Saita, Matt, Duan, Schwarz, and Nieto to include a respective usage duration, as taught by O’Gorman, also to minimize the cost of charging.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), Nieto et al. (US 2022/0140625 A1), and Katanoda (US 2014/0132214 A1; hereinafter “Kata”), and as evidenced by a Börger paper (Alexander Börger, Thermal runaway and thermal runaway propagation in batteries: What do we talk about?, 2019-06-05, Journal of Energy Storage, Volume 24).
Regarding Claim 15, the combination of Saita, Matt, Duan, Schwarz, and Nieto teaches the method of claim 13.
Saita discloses the step of setting the maximum (Fig. 3 steps S6; “second charging recommended threshold value”; Fig. 4; ¶ [77-79]) and minimum (“SOC_low”; Fig. 4; set per ¶ [74, 82-86]) SOC levels comprises the following.
Saita further discloses selecting a candidate maximum SOC level (“second charging recommended threshold value”; Fig. 3 step S6).
Saita further discloses selecting, as a candidate minimum SOC level (“SOC_low” is set to 10% by default; ¶ [82]), a recommended minimum SOC level (“SOC_low” of 10% per ¶ [82]; Fig. 4) based on the battery capacity model (Fig. 4 shows a model where the battery capacity is modeled from 0% to 50%; the “SOC_low” value is set by default as 10% based on this model, per ¶ [82]) for the battery (30).
Saita further discloses deriving a battery energy requirement (“second charging recommendation threshold value” represents the required energy stored by the battery; Fig. 4) by adding a factor (“SOC_low” is added as a factor with “ΔSOCe (median)” to derive “second charging recommendation threshold value”; Fig. 4; ¶ [77-79] Expressions 3 + 4) to the periodic energy requirement (“ΔSOCe (median)”; Fig. 4).
Alternatively, Saita further discloses deriving a battery energy requirement (“second charging recommendation threshold value” represents the required energy stored by the battery; Fig. 4) by multiplying the periodic energy requirement (“ΔSOCe (median)”; Fig. 4) by a multiplier (multiplier “m” in Expression 3, set as integer 2 in Expression 4; represents a number of days; ¶ [77-79]).
Saita further discloses the factor (“SOC_low”) and the multiplier (“m”) are each based on the periodic charging (“SOC_low” can be changed to optimize based on usage patterns per ¶ [85] and is thus based on “number of charges” ¶ [70]; “m” represents number of days until charging, and is thus based on “number of charges” per ¶ [70, 77-78]) of the battery (30) and an availability of charging locations (“SOC_low” and “m” can both be modified based on “usage patterns” per ¶ [70, 77-78, 85]; the “usage patterns” are based on availability of charging locations because the charging locations are included in the usage pattern data per ¶ [62]; thus, both “SOC_low” and “m” are based on availability of charging locations; see also step S1 of Fig. 3) for the battery (30).
Saita further discloses adjusting one or both of the candidate minimum (“SOC_low” adjusted per ¶ [85-86]) and maximum (“second charging recommended threshold value” adjusts based on “m” value per ¶ [77-79]) SOC levels to establish the minimum (“SOC_low”; Fig. 4) and maximum SOC levels (“second charging recommended threshold value”; Fig. 4), respectively.
Saita further discloses establishing these levels so as to enable the battery (30) to supply the battery energy requirement (battery energy requirement of “second charging recommended threshold value” is enabled to be supplied following charging to the maximum SOC level “second charging recommended threshold value”; Fig. 4).
As addressed supra, Saita discloses selecting a candidate maximum SOC level. However, Saita does not disclose “selecting, as a candidate maximum SOC level, a lesser of a first recommended maximum SOC level based on a battery capacity model for the battery and a second recommended maximum SOC level based on a point of diminishing returns for thermal propagation performance for the battery”.
Kata teaches selecting, as a candidate maximum SOC level (“reference upper limit value”; ¶ [79]), a lesser (“Smax2” is less than “Smax1” per ¶ [83]; first embodiment selects the “long life mode” associated with “Smax2” per ¶ [89]) of a first recommended maximum SOC level (“Smax1”; annotated Fig. 4 provided supra) and a second recommended maximum SOC level (“Smax2”; annotated Fig. 4 provided supra).
Kata further teaches the first recommended maximum SOC level (“Smax1”) is based on a battery capacity model (Fig. 4 shows a model where the battery capacity is modeled from 0% to 100%; the “Smax1” value is set to a value less than 100% to prevent overcharging based on this model, per ¶ [82]) for the battery (“power storage device 10” in “vehicle 5”; Fig. 1; ¶ [41]: “lithium ion battery or a nickel hydride battery”).
Kata further teaches the second recommended maximum SOC level (“Smax2”) is based on a point of diminishing returns for thermal propagation performance (“Smax2” is selected at an inflection point of increasing internal resistance, per ¶ [60]; the inflection point of increasing internal resistance is inherently a point of diminishing returns for thermal propagation performance, as evidenced infra) for the battery (10).
NOTE: Börger provides evidence that increased internal resistance in a battery is associated with a thermal runaway condition in a battery. Börger teaches that thermal runaway propagates based on heat produced from internal resistance in the battery (page 4, aspect 2 + section 3.2 Classification of TRs). Thus, it is well-known in the art that an inflection point of increasing internal resistance (as taught by Kata) is inherently a point of diminishing returns for thermal propagation performance.
Kata teaches this selection of the candidate maximum SOC level to improve the charging method to suppress progression of deterioration of the battery (¶ [54]).
It would have been obvious to one of ordinary skill in the art to modify the selection of the candidate maximum SOC level, as disclosed by the combination of Saita, Matt, Duan, Schwarz, and Nieto, to be the lesser of two levels based on an incorporated battery capacity model and a point of diminishing returns for thermal propagation performance, as taught by Kata, to suppress deterioration of the battery.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), Nieto et al. (US 2022/0140625 A1), Katanoda (US 2014/0132214 A1; hereinafter “Kata”), as evidenced by a Börger paper (Alexander Börger, Thermal runaway and thermal runaway propagation in batteries: What do we talk about?, 2019-06-05, Journal of Energy Storage, Volume 24), in further view of a Zhou paper (Wenlu Zhou, Review on the Battery Model and SOC Estimation Method, 2021-09-20, MDPI Processes), and Li et al. (US 2019/0226859 A1).
Regarding Claim 16, the combination of Saita, Matt, Duan, Schwarz, Nieto, and Kata (evidenced by Börger) teaches the method of claim 15.
The combination of Saita, Matt, Duan, Schwarz, Nieto, and Kata discloses the battery capacity model (incorporated model from Katanoda Fig. 4 to base the setting of the first recommended maximum SOC level “Smax1”).
Saita does not disclose “the battery capacity model is based on a battery chemistry of the battery; and wherein the availability of charging locations for the battery is based on a range within which the battery may be utilized to locomotively power the electric vehicle”.
Zhou teaches the battery capacity model (“battery model” in title used to model “SOC” of a battery) is based on a battery chemistry (“electrochemical mechanism model” evaluates the “chemical properties”; section 2.1, pages 3-4) of the battery (“power battery” in a “vehicle”; Abstract).
Zhou teaches this for the advantages of a more accurate battery model that improves the energy management control strategy to improve the reliability of the vehicle (Abstract).
It would have been obvious to one of ordinary skill in the art to modify the battery capacity model disclosed by the combination of Saita, Matt, Duan, Schwarz, Nieto, and Kata to be based on the battery’s chemistry, as taught by Zhou, to improve the reliability of the vehicle.
Li teaches the availability of charging locations (“charging stations 31” with availability displayed as “image 64” on the “HMI 54”; Figs. 3-4; ¶ [38]) for the battery (“high power battery pack 24”; Figs. 1-2) is based on a range (“predetermined distance”; ¶ [37-39, 47]; Fig. 5 steps 108, 110) within which the battery (24) may be utilized to locomotively power (¶ [37]: “may be charged at a selected one of these charge stations 31 before the high power battery pack 24 discharges to a minimum state-of-charge”) the electric vehicle (“electric vehicle 10”; Figs. 1, 4).
Li teaches basing the availability of charging locations based on this range to help the driver find available charging locations they can drive to without running out of charge (¶ [37]), which improves user experience.
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by the combination of Saita, Matt, Duan, Schwarz, Nieto, Kata, and Zhou for the availability of charging locations to be based on a feasible driving range, as taught by Li, to improve user experience.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), and Nieto et al. (US 2022/0140625 A1).
Regarding Claim 18, Saita discloses a method (flowchart of Fig. 3) for determining an optimal state-of-charge (SOC) operating window (range of operating SOC values from lower value “SOC_low” to upper value “second charging recommendation threshold value”; annotated Fig. 4 included supra) for a battery (30; Fig. 1) for use in an electric vehicle (electric vehicle 10; Fig. 1), comprising the following.
Saita further discloses learning a pattern of periodic charging (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “number of charges, one-time charging amount” per ¶ [70]; may be once per day or once every multiple days per ¶ [68]) of the battery (30) for a plurality of time periods (¶ [70]: based on “n days” where “n is an integer greater than or equal to 2 and less than or equal to 31”; ¶ [197]: “on a per day basis”).
Saita further discloses learning a pattern of periodic usage (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “daily travel distance” per ¶ [70]) of the battery (30) for the plurality of time periods (“n days”).
Saita further discloses determining a periodic energy requirement (“ΔSOCe (median)” of the “one-day estimated consumption amount ΔSOCe” per ¶ [70-71]; Fig. 3 step S4; Fig. 4) for the battery (30) for the plurality of time periods (“n days”) based on the learned patterns of periodic charging and periodic usage (“usage patterns”; includes both patterns of periodic charging and of periodic usage per ¶ [70]) using a positively skewed (probability data of Fig. 9 is positively skewed to lower values of ΔSOC; positive skew also shown in Fig. 12 for example ΔSOC data) nonparametric distribution (median calculation method described in ¶ [70-71] does not rely on any assumptions about the underlying distribution of the ΔSOC data; thus, a nonparametric distribution is used for the “statistical processing”).
Saita further discloses setting a maximum SOC level (“second charging recommendation threshold value”; Fig. 3 step S6; Fig. 4) and a minimum SOC level (“SOC_low”; Fig. 4; set per ¶ [74, 82-86]) for the SOC operating window (range from “SOC_low” to “second charging recommendation threshold value”; Fig. 4).
Saita further discloses the setting of the maximum SOC level (“second charging recommendation threshold value”) is based on the learned patterns (ΔSOCe is based on “usage patterns” per ¶ [70]; thus, the “second charging recommendation threshold value” is set based on “usage patterns”) of periodic charging (“number of charges, one-time charging amount” included in “usage patterns” per ¶ [70]) and periodic usage (“daily travel distance” included in “usage patterns” per ¶ [70]).
Saita further discloses accumulating additional instances (method is repeated once per day per ¶ [70]; thus, additional instances “for the most recent n days” are accumulated and recorded in “storage section 40”) of the periodic charging (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “number of charges, one-time charging amount” per ¶ [70]; may be once per day or once every multiple days per ¶ [68]) and the periodic usage (“usage patterns” calculated by “judging section 42A” per ¶ [197]; includes “daily travel distance” per ¶ [70]) of the battery (30).
Saita further discloses utilizing a statistical analysis method (“statistical processing” per ¶ [70-71]) to derive an updated maximum SOC level (“second charging recommendation threshold value”; Fig. 3 step S6; Fig. 4; updated each day when the method is repeated per ¶ [70]) for the SOC operating window (range from “SOC_low” to “second charging recommendation threshold value”; Fig. 4) based on the additional instances of periodic charging (additional data for “usage patterns” including new “number of charges, one-time charging amount” per ¶ [70]) and periodic usage (additional data for “usage patterns” including new “daily travel distance” per ¶ [70]).
Saita further discloses controlling, by a controller (“charging control apparatus 22”; Figs. 1-2; ¶ [18]), a vehicle charger (“charging equipment 14”; Fig. 1) to charge the battery (30) to a state-of-charge within the set SOC operating window (within minimum “SOC_low” and maximum “second charging recommendation threshold value”; Fig. 4) or the updated SOC operating window (“second charging recommendation threshold value” is updated each day when the method is repeated per ¶ [70]), thereby prolonging a service life (¶ [136]: “extending the lifetime of the battery 30”) of the battery (30).
Saita does not disclose “deriving an adjusted battery energy requirement by modifying the periodic energy requirement with an anxiety factor, wherein the anxiety factor is calculated based on a frequency of the periodic charging and an availability of charging locations for the battery”.
As addressed supra, Saita discloses the setting of the maximum SOC level for the SOC operating window is based on the learned patterns of periodic charging and periodic usage. However, Saita further does not disclose the setting of the maximum SOC level is “based on two or more of the adjusted battery energy requirement, the learned patterns of periodic charging and periodic usage, and a battery chemistry of the battery”.
As addressed supra, Saita discloses setting the minimum SOC level for the SOC operating window. However, Saita further does not disclose the setting of the minimum SOC level is “based on two or more of the periodic energy requirement, the learned patterns of periodic charging and periodic usage, and a battery chemistry of the battery”.
As addressed supra, Saita discloses utilizing a statistical analysis method “to derive an updated maximum SOC level for the SOC operating window based on the additional instances of periodic charging and periodic usage”. However, Saita does not disclose “utilizing a recurrent neural network to derive an updated maximum SOC level and an updated minimum SOC level for the SOC operating window based on the additional instances of periodic charging and periodic usage”.
Matt teaches deriving an adjusted battery energy requirement (¶ [1]: “determining the energy requirement of a vehicle for a journey”; determined after step 18 of Fig. 3) by modifying (“energy requirement” is modified per the iterative method of Fig. 3) the periodic energy requirement (“energy requirement” is associated with a “refuelling/recharging interval”, per ¶ [2]) with an anxiety factor (“factor F”; Fig. 3, step 15; ¶ [24]: “combining the weighted factors”).
Matt further teaches the anxiety factor (“factor F”) is calculated based on a frequency of the periodic charging (¶ [2]: “refuelling/recharging interval”; data for these intervals is part of the “vehicle data and/or journey data” obtained in step 10 of Fig. 3; ¶ [24]: “each factor derived from the vehicle data and/or journey data”) and an availability of charging locations (¶ [25]: “factors may include … location of re-fuelling [sic] station”; ¶ [2]: “refuelling/recharging stations”) for the battery (“battery” to power an “electric car” per ¶ [44]; ¶ [10]: “energy store includes one or more batteries”).
Matt further teaches the adjusted battery energy requirement to improve accuracy of predictions of the required energy for future trips (¶ [7]), particularly when the recharging intervals are long and where recharging stations are sparse (¶ [2]).
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by Saita to modify the periodic energy requirement with an anxiety factor to derive an adjusted battery energy requirement, as taught by Matt, to improve accuracy of predictions of the required energy for future trips, particularly when the recharging intervals are long and where charging locations are less available.
Duan teaches setting the minimum SOC level (“SOCend_opt”; Fig. 3 step 380) based on the learned patterns of periodic charging and periodic usage (“vehicle used history” per Fig. 3 step 320; includes “charging data” and “trip data” per ¶ [44]) of the battery (“traction battery 101”; Fig. 1).
Duan teaches this method of setting the minimum SOC level for the advantage of improving battery life (¶ [8, 59]). Duan further teaches this for the advantage of providing a travel distance margin/cushion (¶ [9]), which improves user experience.
It would have been obvious to one of ordinary skill in the art to modify the method and the minimum SOC level disclosed by the combination of Saita and Matt to set the minimum SOC level based on the learned patterns, as taught by Duan, for the advantages of improving battery life and/or user experience.
Schwarz teaches setting a maximum SOC level (“upper SOC limit 204”; Fig. 3) and a minimum SOC level (“lower SOC limit 206”; Fig. 3) for the SOC operating window (“desired SOC range 202”; Fig. 3).
Schwarz further teaches these SOC levels (204, 206) are set based on a battery chemistry (¶ [2]: “desirable for certain types of high-voltage batteries, like those based on lithium-ion chemistries, to be maintained in a certain SOC range”; ¶ [3]: “if it is very cold out … a lithium-ion chemistry may not have enough power to start”; ¶ [3]: “manage the … SOC … to account for this”; ¶ [17]: “memory device 50 may also store pertinent battery characteristics … battery’s cell chemistry” ; ¶ [13] lists possible battery chemistries; ¶ [25]: “temperature thresholds can depend on the chemistry”) of the battery (“battery pack 30”; Fig. 1).
Schwarz further teaches setting SOC levels based on battery chemistry to reduce degradation effects on the battery and improve battery life (¶ [2, 11, 33]). Schwarz further teaches that certain battery chemistries have different performance across temperatures, and thus the battery chemistry needs to be considered in setting the SOC range to ensure the vehicle has enough power to start at low temperatures (¶ [2-3])
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by the combination of Saita, Matt, and Duan to set the SOC maximum level and SOC minimum level based on battery chemistry, as taught by Schwarz, to improve battery life and improve electric vehicle performance at low temperatures.
Duan further teaches utilizing a neural network (¶ [35]: “may use any processing strategies including … machine learning, neural networks, and the like to process the vehicle trip data and related data to determine the charge settings”) to derive an updated minimum SOC level (“SOCend_opt” calculated in step 380; gets updated each time the charge cord is connected in step 210; Figs. 2-3) for the SOC operating window (range from “SOCend_opt” to “SOCcharge”; Fig. 3) based on the additional instances (new “vehicle used history” is loaded in step 320 each time the charge cord is connected in step 210; Figs. 2-3) of periodic charging (“vehicle used history” includes “charging data” per ¶ [48]; periodic on a daily basis per ¶ [48]) and periodic usage (“vehicle used history” includes “trip data” per ¶ [48]; periodic on a daily basis per ¶ [48]).
Duan further teaches the updating of the minimum SOC level to weigh the most recent data (¶ [48]). Duan further teaches the use of a neural network / machine learning for the advantage of optimizing the SOC levels via self-learning (¶ [8, 30, 35, 48]), further improving battery life and user experience.
It would have been obvious to one of ordinary skill in the art to modify the statistical analysis method disclosed by the combination of Saita, Matt, Duan, and Schwarz to utilize a neural network to update the minimum SOC level based on the additional instances of periodic charging and periodic usage, as further taught by Duan, to optimize battery life and/or user experience via self-learning based on the most recent data.
Nieto teaches utilizing a recurrent neural network (“first machine learning algorithm” in block 502 of Fig. 5; ¶ [26]: “the embodiments may … employ one or more neural networks for the machine learning”; ¶ [27]: “the one or more neural networks employed in embodiments may comprise … one or more recurrent neural networks”) to derive SOC levels (¶ [55]).
Nieto teaches the use of a recurrent neural network for the advantage of including loops to allow information to be maintained (¶ [27]), which further supports learning to optimize a state of charge level (¶ [55]) and increase the battery lifetime (¶ [29]).
It would have been obvious to one of ordinary skill in the art to modify the method and neural network disclosed by the combination of Saita, Matt, Duan, and Schwarz to utilize a recurrent neural network to derive the updated SOC levels, as taught by Nieto, to increase battery lifetime using an improved machine learning algorithm with loops to maintain information.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), Nieto et al. (US 2022/0140625 A1), and O’Gorman et al. (US 2021/0138928 A1).
Regarding Claim 19, the combination of Saita, Matt, Duan, Schwarz, and Nieto teaches the method of claim 18.
Saita discloses the learning step (Fig. 3 steps S2-S6; ¶ [70-71]) comprises receiving a plurality of charging instances (“number of charges” per ¶ [70]; assumed to be once per day, but can be multiple days between charges per ¶ [68]) and a plurality of usage instances (“plurality of one-time consumption amounts” per ¶ [67]) for the plurality of time periods (“n days”).
Saita discloses each charging instance (each of the “number of charges” per ¶ [70]; assumed to be once per day, but can be multiple days between charges per ¶ [68]), but includes few details of the stored data for each charging instance.
Saita further discloses each usage instance (each of the “plurality of one-time consumption amounts” per ¶ [67]) includes a respective total energy use amount (“one day of electricity consumption ΔSOC” per ¶ [66-68]).
Saita further discloses establishing the patterns (“usage patterns” per ¶ [197]) of periodic charging (“usage patterns” include “number of charges” per ¶ [70]) and periodic usage (“usage patterns” include “daily travel distance”; based on adding together “a plurality of one-time consumption amounts” per ¶ [67]) based on the received pluralities of charging instances (“number of charges”) and usage instances (“plurality of one-time consumption amounts”), respectively.
Saita does not disclose “each charging instance includes two or more of a respective charging start time, a respective charging end time, a respective charging duration, a respective charging level, a respective beginning battery charge level and a respective ending battery charge level, and wherein each usage instance includes two or more of a respective usage start time, a respective usage end time, a respective usage duration, a respective average energy use amount and a respective total energy use amount”.
O’Gorman teaches each charging instance (“plug-in event” stored as “historical vehicle data 210”; ¶ [36-37]; Fig. 2) includes a respective charging start time (¶ [37]: “time at which the plug-in charging initiates”) and a respective charging end time (¶ [37]: “time at which the vehicle registers as unplugged from the charge station”).
O’Gorman further teaches each usage instance (each “trip” in the “series of trips” recorded in the “historical vehicle data 210” per ¶ [41]) includes a respective usage duration (“durations of travel” per ¶ [41]).
O’Gorman teaches that these characterizations of each charging instance and usage instance are recorded for the advantage of minimizing the cost of charging (¶ [5-6]).
It would have been obvious to one of ordinary skill in the art to modify the method and each charging instance disclosed by the combination of Saita, Matt, Duan, Schwarz, and Nieto to include respective charging start/end times, as taught by O’Gorman, to minimize the cost of charging. It further would have been obvious to one of ordinary skill in the art to modify the method and each usage instance disclosed by the combination of Saita, Matt, Duan, Schwarz, and Nieto to include a respective usage duration, as further taught by O’Gorman, also to minimize the cost of charging.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Saita et al. (US 2021/0218073 A1) in view of Matthews et al. (EP 3501881 A1; hereinafter “Matt”), Duan et al. (US 2021/0218073 A1), Schwarz et al. (US 2012/0200257 A1), Nieto et al. (US 2022/0140625 A1), Katanoda (US 2014/0132214 A1; hereinafter “Kata”), as evidenced by a Börger paper (Alexander Börger, Thermal runaway and thermal runaway propagation in batteries: What do we talk about?, 2019-06-05, Journal of Energy Storage, Volume 24), in further view of a Zhou paper (Wenlu Zhou, Review on the Battery Model and SOC Estimation Method, 2021-09-20, MDPI Processes), and Li et al. (US 2019/0226859 A1).
Regarding Claim 20, the combination of Saita, Matt, Duan Schwarz, and Nieto teaches the method of claim 18.
Saita discloses the step of setting the maximum (Fig. 3 steps S6; “second charging recommended threshold value”; Fig. 4; ¶ [77-79]) and minimum (“SOC_low”; Fig. 4; set per ¶ [74, 82-86]) SOC levels comprises the following.
Saita further discloses selecting a candidate maximum SOC level (“second charging recommended threshold value”; Fig. 3 step S6).
Saita further discloses selecting, as a candidate minimum SOC level (“SOC_low” is set to 10% by default; ¶ [82]), a recommended minimum SOC level (“SOC_low” of 10% per ¶ [82]; Fig. 4) based on the battery capacity model (Fig. 4 shows a model where the battery capacity is modeled from 0% to 50%; the “SOC_low” value is set by default as 10% based on this model, per ¶ [82]) for the battery (30).
Saita further discloses deriving a battery energy requirement (“second charging recommendation threshold value” represents the required energy stored by the battery; Fig. 4) by adding a factor (“SOC_low” is added as a factor with “ΔSOCe (median)” to derive “second charging recommendation threshold value”; Fig. 4; ¶ [77-79] Expressions 3 + 4) to the periodic energy requirement (“ΔSOCe (median)”; Fig. 4).
Alternatively, Saita further discloses deriving a battery energy requirement (“second charging recommendation threshold value” represents the required energy stored by the battery; Fig. 4) by multiplying the periodic energy requirement (“ΔSOCe (median)”; Fig. 4) by a multiplier (multiplier “m” in Expression 3, set as integer 2 in Expression 4; represents a number of days; ¶ [77-79]).
Saita further discloses the factor (“SOC_low”) and the multiplier (“m”) are each based on the periodic charging (“SOC_low” can be changed to optimize based on usage patterns per ¶ [85] and is thus based on “number of charges” ¶ [70]; “m” represents number of days until charging, and is thus based on “number of charges” per ¶ [70, 77-78]) of the battery (30) and an availability of charging locations (“SOC_low” and “m” can both be modified based on “usage patterns” per ¶ [70, 77-78, 85]; the “usage patterns” are based on availability of charging locations because the charging locations are included in the usage pattern data per ¶ [62]; thus, both “SOC_low” and “m” are based on availability of charging locations; see also step S1 of Fig. 3) for the battery (30).
Saita further discloses adjusting one or both of the candidate minimum (“SOC_low” adjusted per ¶ [85-86]) and maximum (“second charging recommended threshold value” adjusts based on “m” value per ¶ [77-79]) SOC levels to establish the minimum (“SOC_low”; Fig. 4) and maximum SOC levels (“second charging recommended threshold value”; Fig. 4), respectively.
Saita further discloses establishing these levels so as to enable the battery (30) to supply the battery energy requirement (battery energy requirement of “second charging recommended threshold value” is enabled to be supplied following charging to the maximum SOC level “second charging recommended threshold value”; Fig. 4).
As addressed supra, Saita discloses selecting a candidate maximum SOC level. However, Saita does not disclose “selecting, as a candidate maximum SOC level, a lesser of a first recommended maximum SOC level based on a battery capacity model for the battery and a second recommended maximum SOC level based on a point of diminishing returns for thermal propagation performance for the battery”.
Saita further does not disclose “the battery capacity model is based on the battery chemistry of the battery, and wherein the availability of charging locations for the battery is based on a range within which the battery may be utilized to locomotively power the electric vehicle”.
Kata teaches selecting, as a candidate maximum SOC level (“reference upper limit value”; ¶ [79]), a lesser (“Smax2” is less than “Smax1” per ¶ [83]; first embodiment selects the “long life mode” associated with “Smax2” per ¶ [89]) of a first recommended maximum SOC level (“Smax1”; annotated Fig. 4 provided supra) and a second recommended maximum SOC level (“Smax2”; annotated Fig. 4 provided supra).
Kata further teaches the first recommended maximum SOC level (“Smax1”) is based on a battery capacity model (Fig. 4 shows a model where the battery capacity is modeled from 0% to 100%; the “Smax1” value is set to a value less than 100% to prevent overcharging based on this model, per ¶ [82]) for the battery (“power storage device 10” in “vehicle 5”; Fig. 1; ¶ [41]: “lithium ion battery or a nickel hydride battery”).
Kata further teaches the second recommended maximum SOC level (“Smax2”) is based on a point of diminishing returns for thermal propagation performance (“Smax2” is selected at an inflection point of increasing internal resistance, per ¶ [60]; the inflection point of increasing internal resistance is inherently a point of diminishing returns for thermal propagation performance, as evidenced infra) for the battery (10).
NOTE: Börger provides evidence that increased internal resistance in a battery is associated with a thermal runaway condition in a battery. Börger teaches that thermal runaway propagates based on heat produced from internal resistance in the battery (page 4, aspect 2 + section 3.2 Classification of TRs). Thus, it is well-known in the art that an inflection point of increasing internal resistance (as taught by Kata) is inherently a point of diminishing returns for thermal propagation performance.
Kata teaches this selection of the candidate maximum SOC level to improve the charging method to suppress progression of deterioration of the battery (¶ [54]).
It would have been obvious to one of ordinary skill in the art to modify the selection of the candidate maximum SOC level, as disclosed by combination of Saita, Matt, Duan Schwarz, and Nieto, to be the lesser of two levels based on an incorporated battery capacity model and a point of diminishing returns for thermal propagation performance, as taught by Kata (evidenced by Börger), to suppress deterioration of the battery.
The combination of Saita, Matt, Duan Schwarz, Nieto, and Kata teaches the battery capacity model (incorporated model from Katanoda Fig. 4 to base the setting of the first recommended maximum SOC level “Smax1”).
Zhou teaches the battery capacity model (“battery model” in title used to model “SOC” of a battery) is based on the battery chemistry (“electrochemical mechanism model” evaluates the “chemical properties”; section 2.1, pages 3-4) of the battery (“power battery” in a “vehicle”; Abstract).
Zhou teaches this for the advantages of a more accurate battery model that improves the energy management control strategy to improve the reliability of the vehicle (Abstract).
It would have been obvious to one of ordinary skill in the art to modify the battery capacity model disclosed by the combination of Saita, Matt, Duan Schwarz, Nieto, and Kata to be based on the battery’s chemistry, as taught by Zhou, to improve the reliability of the vehicle.
Li teaches the availability of charging locations (“charging stations 31” with availability displayed as “image 64” on the “HMI 54”; Figs. 3-4; ¶ [38]) for the battery (“high power battery pack 24”; Figs. 1-2) is based on a range (“predetermined distance”; ¶ [37-39, 47]; Fig. 5 steps 108, 110) within which the battery (24) may be utilized to locomotively power (¶ [37]: “may be charged at a selected one of these charge stations 31 before the high power battery pack 24 discharges to a minimum state-of-charge”) the electric vehicle (“electric vehicle 10”; Figs. 1, 4).
Li teaches basing the availability of charging locations based on this range to help the driver find available charging locations they can drive to without running out of charge (¶ [37]), which improves user experience.
It would have been obvious to one of ordinary skill in the art to modify the method disclosed by the combination of Saita, Matt, Duan Schwarz, Nieto, Kata, and Zhou for the availability of charging locations to be based on a feasible driving range, as taught by Li, to improve user experience.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Daniel P McFarland whose telephone number is (571) 272-5952. The examiner can normally be reached Monday-Friday, 7:30 AM - 4:00 PM Eastern.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Drew Dunn can be reached at 571-272-2312. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL P MCFARLAND/ Examiner, Art Unit 2859
/DREW A DUNN/ Supervisory Patent Examiner, Art Unit 2859
Prosecution Insights