Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
2. This office action is in response to the filing with the office dated 06/27/2024.
Information Disclosure Statement
3. The information disclosure statements (IDS) submitted on 10/03/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections – 35 U.S.C. 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
4. Claims 1-4, 8, 9, 15-18 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Adetola et al (US 2019/0277647 A1).
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Regarding independent claim 1, Adetola et al (US 2019/0277647 A1) teaches, A method of charging a battery system for a transport refrigeration unit (“TRU”) installed on a transport (figures 1-6) , the method comprising: determining a location and/or a travel route for the TRU (figure 6, paragraph [0007], [0066]-[0068]); analyzing a weather pattern for the location and/or the travel route to determine an expected environment (figures 3, 6, paragraphs [0060], [0062], [0066]-[0068]); determining a maximum state of charge for the battery system as a function of the expected environment (paragraphs [0065], [0070]); and charging the battery system with a power supply to obtain the maximum state of charge (paragraphs [0058], [0065], [0069]).
Regarding dependent claim 2, Adetola et al (US 2019/0277647 A1) teaches the method of Claim 1.
Adetola et al (US 2019/0277647 A1) further teaches, wherein the maximum state of charge is determined as a function of a time at the location and/or the travel before reaching a next power supply (paragraphs [0056]-[0058], [0065], [0069]).
Regarding dependent claim 3, Adetola et al (US 2019/0277647 A1) teaches the method of Claim 1.
Adetola et al (US 2019/0277647 A1) further teaches, downloading the weather pattern as forecast data from a weather service (paragraph [0060], The weather parameters 386 may be obtained from an external online data base (e.g. AccuWeather, weather.org . . . etc.). The external online database may provide current and future weather information along all possible routes between a start location and a destination location).
Regarding dependent claim 4, Adetola et al (US 2019/0277647 A1) teaches the method of Claim 1.
Adetola et al (US 2019/0277647 A1) further teaches, wherein the maximum state of charge is determined by comparing the weather pattern to historical weather patterns and battery use for the TRU (paragraphs [0065], [0068]).
Regarding dependent claim 8, Adetola et al (US 2019/0277647 A1) teaches the method of Claim 1.
Adetola et al (US 2019/0277647 A1) further teaches, further comprising determining an expected power generation by an onboard vehicle power system, and adjusting the maximum state of charge for the expected power generation (Paragraphs [0051], [0058], [0065], [0069]).
Regarding dependent claim 9, Adetola et al (US 2019/0277647 A1) teaches the method of Claim 1.
Adetola et al (US 2019/0277647 A1) further teaches, wherein the power supply connects to the battery system using an electric vehicle charger, and further comprising operating TRU components with the power supply after reaching to the maximum state of charge and the electric vehicle charger is still connected (Paragraphs [0051], [0058]).
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Regarding independent claim 15, Adetola et al (US 2019/0277647 A1) teaches, A power system for a transport refrigeration unit (“TRU”) installed on a transport (figure 6), the power system comprising: at least one battery (paragraph [0049]); a charging connector in electrical supply combination with the at least one battery (paragraphs [0051], [0058], [0065], [0069]) ; a controller (element 82, paragraph [0048]) in combination with the charging connector and configured to control a charge level of the at
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least one battery paragraph [0048]), wherein: the controller comprises a network connection adapted to connect with transport systems and external weather data (paragraph [0053]), and the controller is configured to determine a location and/or a travel route for the TRU, analyze a weather pattern
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for the location and/or the travel route to determine an expected environment (figures 3, 6, paragraphs [0060], [0062], [0066]-[0068]), determine a maximum state of charge for the battery system as a function of the expected environment (paragraphs [0065], [0070]), and charge the at least one battery via the charging connector to obtain the maximum state of charge (paragraphs [0058], [0065], [0069]).
Regarding dependent claim 16, Adetola et al (US 2019/0277647 A1) teaches the system of Claim 15.
Adetola et al (US 2019/0277647 A1) further teaches, wherein the maximum state of charge is determined as a function of a time at the location and/or the travel before reaching a next power supply (paragraphs [0056]-[0058], [0065], [0069]).
Regarding dependent claim 17, Adetola et al (US 2019/0277647 A1) teaches the system of Claim 15.
Adetola et al (US 2019/0277647 A1) further teaches, wherein the controller automatically downloads forecast data from a weather service (paragraph [0060], figures 4 and 5, The weather parameters 386 may be obtained from an external online data base (e.g. AccuWeather, weather.org . . . etc.). The external online database may provide current and future weather information along all possible routes between a start location and a destination location).
Regarding dependent claim 18, Adetola et al (US 2019/0277647 A1) teaches the system of Claim 15.
Adetola et al (US 2019/0277647 A1) further teaches, wherein the maximum state of charge is determined by comparing the weather pattern to historical weather patterns and battery use for the TRU (paragraphs [0065], [0068]).
Claim Rejections – 35 U.S.C. 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claims 5-7, 11-14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Adetola et al (US 2019/0277647 A1) and in view of Zeng et al (US 2020/0294323 A1).
Regarding dependent claim 5, Adetola et al (US 2019/0277647 A1) teaches the method of Claim 1.
Adetola et al (US 2019/0277647 A1) further teaches, determining the maximum state of charge for the battery system as a function of the expected environment (paragraphs [0065], [0070]).
Adetola et al is silent about further determining a thermal loss quotient for the transport; and determining the maximum state of charge for the battery system as a function of the expected environment and the thermal loss quotient.
Zeng et al (US 2020/0294323 A1) teaches, ([0045] The temperature-based energy loss parameter (e.g., energy loss as a result of external factors) can be calculated using E.sub.lo=μ.sub.lo.Math.t+K.sub.lo, where μ.sub.lo is the learned energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate), t is the trip period time, and K.sub.lo is the learned extra energy usage for bringing the current vehicle temperature up to the target temperature (transient energy usage). The value of μ.sub.lo is obtained from the lookup Table 1 which includes learned parameters. The input to the lookup table is the outside temperature T.sub.out. Again, μ.sub.lo is determined according to a relationship μ.sub.lo=μ.sub.lo(T.sub.out), in some embodiments. If the trip is long and the temperature change is significant along the trip due to time and location change, the average efficiency from multiple temperature values can be used. The following equation can be used to calculate the average value [00003]μlo=μlo(Tout,1)+μlo(Tout,2)+.Math.+μlo(Tout,n)n, where the value of K.sub.lo is obtained from a lookup Table 2 of the learned parameters. The inputs to the lookup table are the initial cabin temperature T.sub.cab and the outside temperature T.sub.out. Again, K.sub.lo=K.sub.lo(T.sub.cab, T.sub.out), in some embodiments. [0055] According to some embodiments, the temperature-based energy loss parameter can be calculated using the following equation: E.sub.lo=μ.sub.lo.Math.t+N.Math.K.sub.lo, where μ.sub.lo is the learned energy rate (kWh/s), t (s) is the expected driving time, and K.sub.lo (kWh) is the learned transient extra energy usage. N is the expected number of trips with cold starts in this time window. The value of μ.sub.lo is obtained from a one-dimensional lookup table of the learned parameters. The input to the lookup table is the outside temperature T.sub.out at the time of the time window, which is obtained from a third-party weather data service or system. Thus, μ.sub.lo=μ.sub.lo(T.sub.out), in some embodiments, where the value of t is obtained from the learned weekly operation pattern table. The value of N is obtained from the learned weekly operation pattern table, and the value of K.sub.lo is obtained from a two-dimensional lookup table of the learned parameters (see Table 2 above). The inputs to the lookup table are the initial cabin temperature T.sub.cab and the outside temperature T.sub.out. In various embodiments, K.sub.lo=K.sub.lo(T.sub.cab, T.sub.out), where the cabin temperature is obtained using T.sub.out and the expected temperature difference ΔT is obtained from the learned weekly operation pattern table. In sum, T.sub.cab=T.sub.out+ΔT, in various embodiments. [0056] The energy rate for the post-trip period (e.g., beyond-the-trip horizon) can be calculated using the total energy consumption in the post-trip period prediction horizon divided by the distance of the post-trip period prediction horizon using the following equation: [00005]ηb.Math.e.Math.y.Math.o.Math.n.Math.d=Eb.Math.e.Math.y.Math.o.Math.n.Math.dDb.Math.e.Math.y.Math.o.Math.n.Math.d. [0063] In some embodiments, the onboard machine learning module 114 can learn energy rater (kWh/s) for accessories, which is a learned number μ.sub.acc. In one or more embodiments, the onboard machine learning module 114 can learn steady state energy rate loss as a result of external factors (e.g., low temperature) (kWh/s) under different outside temperatures. To be sure, these data is stored in a one-dimensional lookup table for μ.sub.lo(T.sub.out). The onboard machine learning module 114 can learn extra transient energy usage loss as a result of external factors (e.g., low temperature), which can be stored in a two-dimensional lookup table for K.sub.lo(T.sub.cab, T.sub.out). In general, these various learned parameters can be determined using the onboard machine learning module 114, which monitors signal output from each of the drivetrain system 105, the climate control system 107, and the individual vehicle accessories 109).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Adetola et al by providing for calculating the temperature-based energy loss parameter (e.g., energy loss as a result of external factors) as taught by Zeng et al (paragraphs [0045]-, [0055], [0056], [0063]).
One of the ordinary skill in the art would have been motivated to make such a modification so that the energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate) can be calculated as taught by Zeng et al (paragraph [0045]).
Regarding dependent claim 6, Adetola et al (US 2019/0277647 A1) and Zeng et al (US 2020/0294323 A1) teach the method of Claim 5.
Adetola et al (US 2019/0277647 A1) further teaches, wherein the thermal loss quotient is determined from a cooling system coefficient of performance and/or transport information selected from transport volume, transport insulation value, and transport door type ([0059] Container parameters 384 may include insulation capabilities (e.g. R-value) of the container 24 and the solar reflectance index (e.g. solar gain) of the container 24. For example, a TRU 26 may not have to work as hard to keep perishable goods cool in a container 24 with a higher R-value, thus reducing energy consumption of the energy storage device 62 to operate the TRU 26).
Regarding dependent claim 7, Adetola et al (US 2019/0277647 A1) and Zeng et al (US 2020/0294323 A1) teach the method of Claim 5.
Adetola et al further teaches, ([0048] Referring to FIGS. 2 and 3, the energy storage device 62 may be configured to selectively power the compressor motor 60, the condenser fan motors 90, the evaporator fan motors 98, the controller 82, and other components 99 (see FIG. 3) that may include various solenoids and/or sensors) via, for example, electrical conductors 106. The controller 82 through a series of data and command signals over various pathways 108 may, for example, control the electric motors 60, 90, 98 as dictated by the cooling needs of the refrigeration unit 26. In one embodiment, the energy storage device 62 may be secured to the underside of the bottom wall 32 of the container 24 (see FIG. 1). The operation of the energy storage device 62 may be managed and monitored by an energy storage management system 63. The energy management system 63 is configured to determine a status of charge of the energy storage device 62 and a state of health of the energy storage device 62. Examples of the energy storage device 62 may include a battery system (e.g. a battery or bank of batteries), fuel cells, and others capable of storing and outputting electric energy that may be direct current (DC)).
Adetola et al does not verbatim teach, establishing a baseline thermal loss quotient for the transport; determining a cooling efficiency of the TRU over time; and adjusting the baseline thermal loss quotient based upon the determined cooling efficiency.
Zeng et al (US 2020/0294323 A1) teaches, ([0045] The temperature-based energy loss parameter (e.g., energy loss as a result of external factors) can be calculated using E.sub.lo=μ.sub.lo.Math.t+K.sub.lo, where μ.sub.lo is the learned energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate), t is the trip period time, and K.sub.lo is the learned extra energy usage for bringing the current vehicle temperature up to the target temperature (transient energy usage). The value of μ.sub.lo is obtained from the lookup Table 1 which includes learned parameters. The input to the lookup table is the outside temperature T.sub.out. Again, μ.sub.lo is determined according to a relationship μ.sub.lo=μ.sub.lo(T.sub.out), in some embodiments. If the trip is long and the temperature change is significant along the trip due to time and location change, the average efficiency from multiple temperature values can be used. The following equation can be used to calculate the average value [00003]μlo=μlo(Tout,1)+μlo(Tout,2)+.Math.+μlo(Tout,n)n, where the value of K.sub.lo is obtained from a lookup Table 2 of the learned parameters. The inputs to the lookup table are the initial cabin temperature T.sub.cab and the outside temperature T.sub.out. Again, K.sub.lo=K.sub.lo(T.sub.cab, T.sub.out), in some embodiments. [0055] According to some embodiments, the temperature-based energy loss parameter can be calculated using the following equation: E.sub.lo=μ.sub.lo.Math.t+N.Math.K.sub.lo, where μ.sub.lo is the learned energy rate (kWh/s), t (s) is the expected driving time, and K.sub.lo (kWh) is the learned transient extra energy usage. N is the expected number of trips with cold starts in this time window. The value of μ.sub.lo is obtained from a one-dimensional lookup table of the learned parameters. The input to the lookup table is the outside temperature T.sub.out at the time of the time window, which is obtained from a third-party weather data service or system. Thus, μ.sub.lo=μ.sub.lo(T.sub.out), in some embodiments, where the value of t is obtained from the learned weekly operation pattern table. The value of N is obtained from the learned weekly operation pattern table, and the value of K.sub.lo is obtained from a two-dimensional lookup table of the learned parameters (see Table 2 above). The inputs to the lookup table are the initial cabin temperature T.sub.cab and the outside temperature T.sub.out. In various embodiments, K.sub.lo=K.sub.lo(T.sub.cab, T.sub.out), where the cabin temperature is obtained using T.sub.out and the expected temperature difference ΔT is obtained from the learned weekly operation pattern table. In sum, T.sub.cab=T.sub.out+ΔT, in various embodiments. [0056] The energy rate for the post-trip period (e.g., beyond-the-trip horizon) can be calculated using the total energy consumption in the post-trip period prediction horizon divided by the distance of the post-trip period prediction horizon using the following equation: [00005]ηb.Math.e.Math.y.Math.o.Math.n.Math.d=Eb.Math.e.Math.y.Math.o.Math.n.Math.dDb.Math.e.Math.y.Math.o.Math.n.Math.d.[0063] In some embodiments, the onboard machine learning module 114 can learn energy rater (kWh/s) for accessories, which is a learned number μ.sub.acc. In one or more embodiments, the onboard machine learning module 114 can learn steady state energy rate loss as a result of external factors (e.g., low temperature) (kWh/s) under different outside temperatures. To be sure, these data is stored in a one-dimensional lookup table for μ.sub.lo(T.sub.out). The onboard machine learning module 114 can learn extra transient energy usage loss as a result of external factors (e.g., low temperature), which can be stored in a two-dimensional lookup table for K.sub.lo(T.sub.cab, T.sub.out). In general, these various learned parameters can be determined using the onboard machine learning module 114, which monitors signal output from each of the drivetrain system 105, the climate control system 107, and the individual vehicle accessories 109.
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Adetola et al by providing for calculating the temperature-based energy loss parameter (e.g., energy loss as a result of external factors) as taught by Zeng et al (paragraphs [0045]-, [0055], [0056], [0063]).
One of the ordinary skill in the art would have been motivated to make such a modification so that the energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate) can be calculated as taught by Zeng et al (paragraph [0045]).
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Regarding independent claim 11, Adetola et al (US 2019/0277647 A1) teaches, A method of charging a battery system for a transport refrigeration unit (“TRU”) of a transport (figures 1-6), the method comprising: analyzing a weather pattern for a location and/or a travel route to determine an expected environment for the transport (figures 3, 6, paragraphs [0060], [0062], [0066]-[0068]); determining a maximum state of charge for the battery system as a function of the expected environment (paragraphs [0065], [0070]); and charging the battery system with a power supply to obtain the maximum state of charge (paragraphs [0058], [0065], [0069]).
Adetola et al is silent about determining a thermal loss quotient for the TRU and determining a maximum state of charge for the battery system as a function of the thermal loss quotient and the expected environment.
Zeng et al (US 2020/0294323 A1) teaches, ([0045] The temperature-based energy loss parameter (e.g., energy loss as a result of external factors) can be calculated using E.sub.lo=μ.sub.lo.Math.t+K.sub.lo, where μ.sub.lo is the learned energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate), t is the trip period time, and K.sub.lo is the learned extra energy usage for bringing the current vehicle temperature up to the target temperature (transient energy usage). The value of μ.sub.lo is obtained from the lookup Table 1 which includes learned parameters. The input to the lookup table is the outside temperature T.sub.out. Again, μ.sub.lo is determined according to a relationship μ.sub.lo=μ.sub.lo(T.sub.out), in some embodiments. If the trip is long and the temperature change is significant along the trip due to time and location change, the average efficiency from multiple temperature values can be used. The following equation can be used to calculate the average value [00003]μlo=μlo(Tout,1)+μlo(Tout,2)+.Math.+μlo(Tout,n)n, where the value of K.sub.lo is obtained from a lookup Table 2 of the learned parameters. The inputs to the lookup table are the initial cabin temperature T.sub.cab and the outside temperature T.sub.out. Again, K.sub.lo=K.sub.lo(T.sub.cab, T.sub.out), in some embodiments. [0055] According to some embodiments, the temperature-based energy loss parameter can be calculated using the following equation: E.sub.lo=μ.sub.lo.Math.t+N.Math.K.sub.lo, where μ.sub.lo is the learned energy rate (kWh/s), t (s) is the expected driving time, and K.sub.lo (kWh) is the learned transient extra energy usage. N is the expected number of trips with cold starts in this time window. The value of μ.sub.lo is obtained from a one-dimensional lookup table of the learned parameters. The input to the lookup table is the outside temperature T.sub.out at the time of the time window, which is obtained from a third-party weather data service or system. Thus, μ.sub.lo=μ.sub.lo(T.sub.out), in some embodiments, where the value of t is obtained from the learned weekly operation pattern table. The value of N is obtained from the learned weekly operation pattern table, and the value of K.sub.lo is obtained from a two-dimensional lookup table of the learned parameters (see Table 2 above). The inputs to the lookup table are the initial cabin temperature T.sub.cab and the outside temperature T.sub.out. In various embodiments, K.sub.lo=K.sub.lo(T.sub.cab, T.sub.out), where the cabin temperature is obtained using T.sub.out and the expected temperature difference ΔT is obtained from the learned weekly operation pattern table. In sum, T.sub.cab=T.sub.out+ΔT, in various embodiments. [0056] The energy rate for the post-trip period (e.g., beyond-the-trip horizon) can be calculated using the total energy consumption in the post-trip period prediction horizon divided by the distance of the post-trip period prediction horizon using the following equation: [00005]ηb.Math.e.Math.y.Math.o.Math.n.Math.d=Eb.Math.e.Math.y.Math.o.Math.n.Math.dDb.Math.e.Math.y.Math.o.Math.n.Math.d. [0063] In some embodiments, the onboard machine learning module 114 can learn energy rater (kWh/s) for accessories, which is a learned number μ.sub.acc. In one or more embodiments, the onboard machine learning module 114 can learn steady state energy rate loss as a result of external factors (e.g., low temperature) (kWh/s) under different outside temperatures. To be sure, these data is stored in a one-dimensional lookup table for μ.sub.lo(T.sub.out). The onboard machine learning module 114 can learn extra transient energy usage loss as a result of external factors (e.g., low temperature), which can be stored in a two-dimensional lookup table for K.sub.lo(T.sub.cab, T.sub.out). In general, these various learned parameters can be determined using the onboard machine learning module 114, which monitors signal output from each of the drivetrain system 105, the climate control system 107, and the individual vehicle accessories 109).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Adetola et al by providing for calculating the temperature-based energy loss parameter (e.g., energy loss as a result of external factors) as taught by Zeng et al (paragraphs [0045]-, [0055], [0056], [0063]).
One of the ordinary skill in the art would have been motivated to make such a modification so that the energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate) can be calculated as taught by Zeng et al (paragraph [0045]).
Regarding dependent claim 12, Adetola et al (US 2019/0277647 A1) and Zeng et al (US 2020/0294323 A1) teach the method of Claim 11.
Adetola et al is silent about wherein the thermal loss quotient is determined from heat rise over a period of time and/or energy input required to maintain a steady transport temperature.
Zeng et al further teaches, wherein the thermal loss quotient is determined from heat rise over a period of time and/or energy input required to maintain a steady transport temperature ([0070] In various embodiments, the steady state energy rate loss as a result of external factors (e.g., low temperature) (kWh/s) under different outside temperatures can be updated. During vehicle operation, after the first 10 minutes (again, merely an example), and every 10 minutes, the energy loss is E.sub.lo, and the energy rate for energy loss is [00015]μl.Math.o=El.Math.ot. The outside temperature is T.sub.amb. μ.sub.cl and may be used to update the corresponding value in the one-dimensional lookup table for μ.sub.lo(T.sub.out) using the general parameter updating rules).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Adetola et al by providing for calculating the temperature-based energy loss parameter (e.g., energy loss as a result of external factors) as taught by Zeng et al (paragraphs [0045], [0055], [0056], [0063]).
One of the ordinary skill in the art would have been motivated to make such a modification so that the energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate) can be calculated as taught by Zeng et al (paragraph [0045]).
Regarding dependent claim 13, Adetola et al (US 2019/0277647 A1) and Zeng et al (US 2020/0294323 A1) teach the method of Claim 11.
Adetola et al further teaches, further comprising determining the maximum state of charge as a function of a transport temperature need and estimated operation time ([0033] In various embodiments, the process for calculating the energy usage for a trip period further comprises calculating a first climate control-based energy consumption parameter 322 based on any combination of a learned transient extra energy for climate control 324, which is a function of an initial cabin temperature data 326 and the outdoor temperature data 316, and a learned efficiency for climate control 330 which is a function of the outdoor temperature data 316. In some embodiments, the process for calculating the energy usage for a trip further comprises calculating a first vehicle accessories-based energy consumption parameter 332 based on a learned efficiency for vehicle accessories 334. In various embodiments, the energy consumption parameters are calculated with reference to a trip time 335 (e.g., the length of vehicle operation when in a trip period.[0034] FIG. 3B is a schematic diagram that illustrates energy consumption data calculations performed in a post-trip or beyond the horizon time frame. The plurality of energy consumption parameters can be referred to as “second” parameters when calculated in reference to the post-trip period. In some embodiments, the method includes calculating energy consumption for operating the vehicle after completion of the planned route by calculating a driving-based energy consumption parameter 336 based on an expected energy consumption 338 for driving the vehicle. In various embodiments, the method can include calculating a temperature-based energy loss parameter 340 based on any combination of a learned temperature-based energy loss 342 for the vehicle based on future outdoor temperature data 344, and a learned cold start-based energy loss 346 for the vehicle which is a function of the future outdoor temperature data 344 and a predicted initial vehicle temperature 348. It will be understood that the future outdoor temperature data 344 can be obtained from any third-party platform or service providing weather data.[0035] In various embodiments, the method can include calculating a climate control-based energy consumption parameter 350 based on a learned extra transient energy for climate control 352, which is a function of a predicted initial cabin temperature data 354 and the future outdoor temperature data 344. This calculation can also include a learned efficiency for climate control 356 which is a function of the future outdoor temperature data 344. In some embodiments, the climate control-based energy consumption parameter 350 is further affected by an expected or predicted number of cold starts 358 that may occur. The cold start predictive measures can also be used in the calculation of the temperature-based energy loss parameter 340. According to some embodiments, the method can also comprise utilizing the first vehicle accessories-based energy consumption parameter 360 that is a function of a learned efficiency of vehicle accessories 362. In some embodiments, these values are determined as a function of an expected length of a trip (trip time 364)).
Regarding dependent claim 14, Adetola et al (US 2019/0277647 A1) and Zeng et al (US 2020/0294323 A1) teach the method of Claim 11.
Adetola et al further teaches, (The method 600 may further include: determining a battery remaining discharge time period in response to the predicted energy consumption 406; and determining whether the battery remaining discharge time period is longer than at least one of the one or more potential routes 402 (0069).
Adetola et al is silent about, wherein the maximum state of charge is determined for a 6-16 hour operation period.
Zeng et al (US 20200294323 A1) teaches ([0060] FIG. 3 illustrates a flowchart of a method 300 for optimizing power distribution to one or more vehicles (e.g., the vehicles 280 shown in FIG. 2) and/or one or more electrically powered accessories (e.g., the electrically powered accessories 285 shown in FIG. 2) at a power distribution site (e.g., the power distribution site 200 shown in FIG. 2). For illustrative purposes, the one or more electrically powered accessories described in the method 300 are CCUs. Thus, the term electrically powered accessories and CCUs are used interchangeably. It will be appreciated that in other embodiments, the method 300 can be used with one or more different types of electrically powered accessories. It will also be appreciated that in some embodiments the method 300 can be a rolling or repetitive process that can plan, for example, hours ahead, days ahead, etc. It will also be appreciated that in some embodiments the method 300 can create a model for a plurality of time intervals and can also optionally aggregate all of the time interval models. [0070] In various embodiments, the steady state energy rate loss as a result of external factors (e.g., low temperature) (kWh/s) under different outside temperatures can be updated. During vehicle operation, after the first 10 minutes (again, merely an example), and every 10 minutes, the energy loss is E.sub.lo, and the energy rate for energy loss is [00015]μl.Math.o=El.Math.ot. The outside temperature is T.sub.amb. μ.sub.cl and may be used to update the corresponding value in the one-dimensional lookup table for μ.sub.lo(T.sub.out) using the general parameter updating rules).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Adetola et al by providing for calculating the temperature-based energy loss parameter (e.g., energy loss as a result of external factors) as taught by Zeng et al (paragraphs [0045]-, [0055], [0056], [0063]).
One of the ordinary skill in the art would have been motivated to make such a modification so that the energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate) can be calculated as taught by Zeng et al (paragraph [0045]).
Regarding dependent claim 19, Adetola et al (US 2019/0277647 A1) teaches the system of Claim 15.
Adetola et al (US 2019/0277647 A1) further teaches, determining the maximum state of charge for the battery system as a function of the expected environment (paragraphs [0065], [0070]).
Adetola et al is silent about further wherein the controller comprises a predetermined thermal loss quotient for the transport, and determines the maximum state of charge for the battery system as a function of the expected environment and the thermal loss quotient.
Zeng et al (US 2020/0294323 A1) teaches, ([0045] The temperature-based energy loss parameter (e.g., energy loss as a result of external factors) can be calculated using E.sub.lo=μ.sub.lo.Math.t+K.sub.lo, where μ.sub.lo is the learned energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate), t is the trip period time, and K.sub.lo is the learned extra energy usage for bringing the current vehicle temperature up to the target temperature (transient energy usage). The value of μ.sub.lo is obtained from the lookup Table 1 which includes learned parameters. The input to the lookup table is the outside temperature T.sub.out. Again, μ.sub.lo is determined according to a relationship μ.sub.lo=μ.sub.lo(T.sub.out), in some embodiments. If the trip is long and the temperature change is significant along the trip due to time and location change, the average efficiency from multiple temperature values can be used. The following equation can be used to calculate the average value [00003]μlo=μlo(Tout,1)+μlo(Tout,2)+.Math.+μlo(Tout,n)n, where the value of K.sub.lo is obtained from a lookup Table 2 of the learned parameters. The inputs to the lookup table are the initial cabin temperature T.sub.cab and the outside temperature T.sub.out. Again, K.sub.lo=K.sub.lo(T.sub.cab, T.sub.out), in some embodiments. [0055] According to some embodiments, the temperature-based energy loss parameter can be calculated using the following equation: E.sub.lo=μ.sub.lo.Math.t+N.Math.K.sub.lo, where μ.sub.lo is the learned energy rate (kWh/s), t (s) is the expected driving time, and K.sub.lo (kWh) is the learned transient extra energy usage. N is the expected number of trips with cold starts in this time window. The value of μ.sub.lo is obtained from a one-dimensional lookup table of the learned parameters. The input to the lookup table is the outside temperature T.sub.out at the time of the time window, which is obtained from a third-party weather data service or system. Thus, μ.sub.lo=μ.sub.lo(T.sub.out), in some embodiments, where the value of t is obtained from the learned weekly operation pattern table. The value of N is obtained from the learned weekly operation pattern table, and the value of K.sub.lo is obtained from a two-dimensional lookup table of the learned parameters (see Table 2 above). The inputs to the lookup table are the initial cabin temperature T.sub.cab and the outside temperature T.sub.out. In various embodiments, K.sub.lo=K.sub.lo(T.sub.cab, T.sub.out), where the cabin temperature is obtained using T.sub.out and the expected temperature difference ΔT is obtained from the learned weekly operation pattern table. In sum, T.sub.cab=T.sub.out+ΔT, in various embodiments. [0056] The energy rate for the post-trip period (e.g., beyond-the-trip horizon) can be calculated using the total energy consumption in the post-trip period prediction horizon divided by the distance of the post-trip period prediction horizon using the following equation: [00005]ηb.Math.e.Math.y.Math.o.Math.n.Math.d=Eb.Math.e.Math.y.Math.o.Math.n.Math.dDb.Math.e.Math.y.Math.o.Math.n.Math.d. [0063] In some embodiments, the onboard machine learning module 114 can learn energy rater (kWh/s) for accessories, which is a learned number μ.sub.acc. In one or more embodiments, the onboard machine learning module 114 can learn steady state energy rate loss as a result of external factors (e.g., low temperature) (kWh/s) under different outside temperatures. To be sure, these data is stored in a one-dimensional lookup table for μ.sub.lo(T.sub.out). The onboard machine learning module 114 can learn extra transient energy usage loss as a result of external factors (e.g., low temperature), which can be stored in a two-dimensional lookup table for K.sub.lo(T.sub.cab, T.sub.out). In general, these various learned parameters can be determined using the onboard machine learning module 114, which monitors signal output from each of the drivetrain system 105, the climate control system 107, and the individual vehicle accessories 109).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Adetola et al by providing for calculating the temperature-based energy loss parameter (e.g., energy loss as a result of external factors) as taught by Zeng et al (paragraphs [0045]-, [0055], [0056], [0063]).
One of the ordinary skill in the art would have been motivated to make such a modification so that the energy rate (kWh/s) for maintaining the vehicle temperature under the current temperature condition (steady state energy usage rate) can be calculated as taught by Zeng et al (paragraph [0045]).
6. Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Adetola et al (US 2019/0277647 A1) and in view of Kemmet et al (US 2024/0075816 A1).
Regarding dependent claim 10, Adetola et al (US 2019/0277647 A1) teaches the method of Claim 1.
Adetola et al (US 2019/0277647 A1) further teaches determining a required battery capacity ([0068] At block 610, a TRU predicted operation 404 is determined along each of the one or more potential routes 402 in response to at least one of the route parameters 381, the container parameters 384, the TRU parameters 385, the weather parameters 386, the perishable good parameters 389, and the perishable good requirements 388. At block 612, a predicted energy consumption 406 from the energy storage device 62 by the TRU 26 along each of the one or more potential routes 402 in response to the TRU predicted operation 404 and the energy storage device parameters 382).
Adetola et al is silent about adding or removing a battery in the battery system.
Kemmet et al (US 2024/0075816 A1) teaches, (Referring now to FIG. 8, a diagram of a supplemental battery system (SBS) 800 is shown. The SBS 800 may be connected to an existing battery electric vehicle system (i.e., main battery) 802 via a connector. In applications where extra battery capacity is needed only a portion of the time, the SBS may be added used on an as needed basis. In applications where the main battery 802 has achieved their degraded life (e.g., 80%), for example before the warranty period expires, the SBS 800 may be used to provide extra capacity to effectively create a longer usable life of the product. The SBS 800 may also be used as a quick charge mechanism. By swapping, adding, or replacing the SBS 800 additional energy may be added to a system. The SBS 800 may not replace the main battery 802 in a system(Paragraph [0042]). The SBS 800 may include one or more batteries 806. The one or more batteries 806 may include a battery management system (BMS). The BMS may ensure that the one or more batteries 806 and/or the main battery 802 are protected and any operation out of their safety limit sis prevented. The BMS may monitor a state of charge (SOC) along with a state of health (SOH) of the one or more batteries 806 and/or the main battery 802. The SBS 800 may also include a DC/DC converter 804 between the main battery 802 and the one or more batteries 806. The DC/DC converter 804 may be used to ensure the voltage output of the one or more batteries 806 is compatible with the voltage of the main battery 802. In an example, the DC/DC converter 804 may be separate from the one or more batteries 806 and may be installed (e.g., permanently) elsewhere on the vehicle. In an example, the SBS 800 may be a low voltage unit (e.g., less than approximately 60 VDC) to allow it to be safely transported already charged and swapped (Paragraph [0043]). The SBS 800 may provide one or more of capacity extension and life extension for the main battery 202. In an example, a capacity of the SBS 800 may be additive to the capacity of the main battery 202. In situations where the capacity of the main battery 202 has decreased (e.g., over time, through use, or due to environmental conditions) the SBS 800 may increase a total capacity of the existing battery electric vehicle system by adding additional batteries at a later date from initial manufacture (Paragraph [0044]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Adetola et al by providing for a supplemental battery system as taught by Kemmet et al (paragraph [0032]).
One of the ordinary skill in the art would have been motivated to make such a modification so that the additional battery capacity can be swapped, added or replaced on an as needed basis to provide additional energy may be added to a system as taught by Kemmet et al (paragraph [0032], [0043]).
Regarding dependent claim 20, Adetola et al (US 2019/0277647 A1) teaches the system of Claim 15.
Adetola et al (US 2019/0277647 A1) further teaches, wherein the controller determines a required battery capacity and notifies an operator (figures 4 and 5, paragraphs [0054], [0070]).
Adetola is silent about notifies an operator to add or remove a battery in the battery system as a function of the required battery capacity
Kemmet et al US 2024/0075816 A1 teaches, ([0042] Referring now to FIG. 8, a diagram of a supplemental battery system (SBS) 800 is shown. The SBS 800 may be connected to an existing battery electric vehicle system (i.e., main battery) 802 via a connector. In applications where extra battery capacity is needed only a portion of the time, the SBS may be added used on an as needed basis. In applications where the main battery 802 has achieved their degraded life (e.g., 80%), for example before the warranty period expires, the SBS 800 may be used to provide extra capacity to effectively create a longer usable life of the product. The SBS 800 may also be used as a quick charge mechanism. By swapping, adding, or replacing the SBS 800 additional energy may be added to a system. The SBS 800 may not replace the main battery 802 in a system. [0043] The SBS 800 may include one or more batteries 806. The one or more batteries 806 may include a battery management system (BMS). The BMS may ensure that the one or more batteries 806 and/or the main battery 802 are protected and any operation out of their safety limit sis prevented. The BMS may monitor a state of charge (SOC) along with a state of health (SOH) of the one or more batteries 806 and/or the main battery 802. The SBS 800 may also include a DC/DC converter 804 between the main battery 802 and the one or more batteries 806. The DC/DC converter 804 may be used to ensure the voltage output of the one or more batteries 806 is compatible with the voltage of the main battery 802. In an example, the DC/DC converter 804 may be separate from the one or more batteries 806 and may be installed (e.g., permanently) elsewhere on the vehicle. In an example, the SBS 800 may be a low voltage unit (e.g., less than approximately 60 VDC) to allow it to be safely transported already charged and swapped. [0044] The SBS 800 may provide one or more of capacity extension and life extension for the main battery 202. In an example, a capacity of the SBS 800 may be additive to the capacity of the main battery 202. In situations where the capacity of the main battery 202 has decreased (e.g., over time, through use, or due to environmental conditions) the SBS 800 may increase a total capacity of the existing battery electric vehicle system by adding additional batteries at a later date from initial manufacture).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Adetola et al by providing for a supplemental battery system as taught by Kemmet et al (paragraph [0032]).
One of the ordinary skill in the art would have been motivated to make such a modification so that the additional battery capacity can be swapped, added or replaced on an as needed basis to provide additional energy may be added to a system as taught by Kemmet et al (paragraph [0032], [0043]).
Closest Prior art
7. The following relevant prior art of record is not cited in the office action.
Lavrich et al (US 2022/0080806 A1) teaches, Systems and methods are provided for providing energy consumption feedback for powering a transport climate control system using external data. This can include determining whether an energy level of an energy storage source is greater than an expected energy consumption of a transport climate control system during a route, based on route parameters and route conditions. The route conditions may be obtained from a source such as a remote server, and include data such as weather data, traffic data, or the like. The systems and methods may further compare current energy levels to an updated predictions of energy consumption during transit to determine if the energy level is sufficient to complete the route and alert the user when the energy level is insufficient to complete the route.
Beaufrere et al (US 2024/0034120 A1) teaches, A method of operating a transport refrigeration unit of a vehicle comprises determining a vehicle route from a current location of the vehicle to a final destination of the vehicle; determining whether to operate a refrigeration system of the transport refrigeration unit in a normal mode or an economy mode; and operating the refrigeration system in the economy mode.
Lavrich et al (US 2022/0080803 A1) teaches, Methods and systems for providing feedback for a transport climate control system are disclosed. The transport climate control system provides climate control to a climate controlled space of a transport unit. The method includes determining, by a controller, a first energy level state capable of providing power to the transport climate control system. The method also includes obtaining, by the controller, status data when a predetermined triggering event occurs. The method further includes determining, by the controller, a second energy level state capable of providing power to the transport climate control system after a predetermined time interval. Also the method includes determining energy consumption data based on the first energy level state and the second energy level state. The method further includes combining the status data and the energy consumption data to obtain feedback data. The method also includes displaying, via a display device, the feedback data.
Ananthakrishan et al (US 2019/0242716 A1) teaches, A method for determining potential routes for perishable goods is provided comprising: storing container parameters of a container to store the perishable goods, vehicle parameters of a vehicle to transport the perishable goods within the container, transportation refrigeration unit (TRU) parameters of a transportation refrigeration unit (TRU) to control environment conditions within the container, and perishable good requirements for transporting the perishable goods within the container; receiving route parameters, weather parameters, and perishable good parameters, the route parameters including an origination location of the perishable goods, a destination location of the perishable goods, and a route time completion window; determining one or more potential routes in response to the route parameters; and determining predicted energy source consumption by the TRU for each of the one or more potential routes in response to the vehicle parameters, container parameters, TRU parameters, perishable good requirements, route parameters, weather parameters, and perishable good parameters.
Charoulet et al (US 2024/0149743 A1) teaches, A method for managing power consumption in a BEV includes obtaining real time vehicle data via an ECU of the BEV and receiving information regarding deliveries that are planned by a user for a specific day based on a user input or data acquisition from a remote database. The method further includes determining consumption of energy for completion of the deliveries on the specific day based on the obtained real time vehicle data and the received information and then calculating a distance that can be traveled by the BEV based on the determined consumption of energy. The method further includes determining a temperature difference between an internal temperature of a TRU of the BEV and an external temperature outside the TRU in real time, estimating an impact of the determined temperature difference on the determined consumption of energy, and thereafter displaying the results of the estimation on an HMI.
Srnec et al (US 2020/0141746 A1) teaches, Methods and systems for operating a transport climate control system of a vehicle are provided. The method includes obtaining a state of charge of an energy storage device capable of providing power to the transport climate control system; determining an energy level including the state of charge, receiving a planned route for the vehicle, and receiving route status data associated with the planned route for the vehicle. The route status data includes traffic data, weather data, and/or geographic data identifying areas where the transport climate control system is to be solely powered by the energy storage device. The method further includes determining whether the energy level is sufficient to complete the planned route for the vehicle based on the planned route and the route data, and when the energy level is not sufficient to complete the planned route for the vehicle, providing a notification to a user via a display.
Ducher et al (US 2023/0243314 A1) teaches, A power system for a transport refrigeration unit (200) of a vehicle, and a method of powering a transport refrigeration unit (200) of a vehicle. The power system for a transport refrigeration unit (200) of a vehicle includes: a battery unit (280) configured to supply electrical power to a refrigeration system of the transport refrigeration unit (200); a generator (240) configured to charge the battery unit (280); an engine (250) configured to drive the generator (240); and a control system (220). The control system (220) is configured to: receive or determine a vehicle route from a current location of the vehicle to a destination of the vehicle; predict how a power level of the battery unit (280) will change on the vehicle route; and control an operational state of the engine (250) based on the vehicle route and the predicted power level.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SURESH RAJAPUTRA whose telephone number is (571) 270-0477. The examiner can normally be reached between 8:00 AM - 5:00 PM.
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/SURESH K RAJAPUTRA/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 1/23/2026