DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-24 are presented for examination on the merits.
Claim Rejections - 35 USC § 112
2. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
3. Claim 24 is rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
a) Claim 24 recites a “method claim.” However, the claim is directed to neither a method class of invention/process nor an apparatus class of invention, but rather embraces or overlaps two different classes of invention. The claim should be written either in “method” classes of invention or “apparatus” classes of invention. For prosecution purpose, the claim is treated as system classes of invention. There are no process/method steps to consider the claim as a process/method claim. The claim does not set forth any steps involved in the method/process. A claim is indefinite where it merely recites a use without any active, positive steps delimiting how this use is actually practiced.
Claim Rejections – 35 USC § 101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
5. Claim 24 is rejected under 35 U.S.C. 101 because the claimed recitation of method, without setting forth any steps involved in the process, results in an improper definition of a process, i.e., results in a claim which is not a proper process claim under 35 U.S.C. 101. See for example Ex parte Dunki, 153 USPQ 678 (Bd.App. 1967) and Clinical Products, Ltd. v. Brenner, 255 F. Supp. 131, 149 USPQ 475 (D.D.C. 1966).
Claim Rejections - 35 USC § 103
6. 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 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.
7. 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 of this title, 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.
8. Claims 1-8, 10, 11, 13-20 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Koike JP 2017003349 A) in view of Vuylsteke (US 2022/0024438).
As to claim 1, Koike discloses in battery state estimation device having the claim:
a. a method for estimating current-dependent non-linear equivalent circuit model (ECM) parameters of a battery read on Page 8, Para. 1, (As shown in FIG. 2, the reaction resistance model Mbv is expressed by an equivalent circuit model including a parallel connection body of a resistance component and a capacitance component. As a result, the reaction resistance model Mbv can express the nonlinear characteristic of the impedance by the capacitance component while keeping the resistance component current dependent):
b. measuring a battery voltage across terminals of the battery and a battery current drawn from the battery read on Page 2, Para. 4, (The battery pack 10 includes a voltage sensor 21, a current sensor 22, and a temperature sensor 23. The voltage sensor 21 detects the voltage V between the terminals of each battery cell 20a. The inter terminal voltage V corresponds to a closed circuit voltage (CCV) of the battery cell 20a. The current sensor 22 detects the current I flowing through the battery cell 20a. The temperature sensor 23 detects the temperature of the assembled battery 20, in other words, the temperature T of each battery cell 20a);
c. deriving linear ECM parameters for modeling a linear ECM of the battery with multiple resistive-capacitive elements with different time constants to characterize temporal behaviors of the battery read on Page 2, Para. 7-9, (the battery cell 20a is modeled by an equivalent circuit model M as shown in FIG. As shown in FIG. 2, the battery model M is represented as a series connection body of a DC resistance model Ms, a reaction resistance model Mbv, and a diffusion resistance model Mw. The reaction resistance model Mbv models a reaction resistance representing an electrode interface reaction in the positive electrode and the negative electrode of the battery cell 20a. The reaction resistance model Mbv is expressed by a parallel connection body of a resistance component having a resistance value Rbv and a capacitance component having a capacitance Cbv. That is, the reaction resistance model Mbv is composed of an RC parallel circuit. “Vbv” in the figure indicates a potential difference in reaction resistance);
d. continuously tracking an impedance for each of the multiple resistive-capacitive elements and an open circuit voltage of the battery read on Page 3, Para. 3 & Page 4, Para. 6, (The battery ECU 30 identifies each parameter of the battery model M based on the detected voltage V, the detected current I, and the detected temperature T, thereby obtaining the potential difference Vs of the DC resistance, the potential difference Vbv of the reaction resistance, and the potential difference Vw of the diffusion resistance. Calculate. Here, the open-circuit voltage OCV of the battery cell 20a is calculated based on the following equation (eq1) from the detection voltage V, the DC resistance potential difference Vs, the reaction resistance potential difference Vbv, and the diffusion resistance potential difference Vw. Can do. “T” indicates time). Koike does not explicitly recite wherein continuously monitoring the battery current to detect high current events; continuously tracking a deviation for each of the multiple resistive-capacitive elements during the high current events; deriving non-linear online ECM parameters based on the linear ECM parameters and the deviations for the multiple resistive-capacitive elements during the high current events.
However, Vuylsteke in electric vehicle and control strategy using a battery state cures this deficiency by teaching that it may be beneficial wherein:
e. continuously monitoring the battery current to detect high current events; continuously tracking a deviation for each of the multiple resistive-capacitive elements during the high current events; deriving non-linear online ECM parameters based on the linear ECM parameters
and the deviations for the multiple resistive-capacitive elements during the high current events read on ¶ 0004 & ¶ 0019, (he high-level structure of various embodiments illustrated and described in detail with respect to the Figures includes sensors to measure battery temperature, terminal voltage, and current. These sensors feed current and, when available, an at-rest voltage/OCV (open circuit voltage) measurement to the measurement-based SOC estimation and all three measurements to the Kalman Filter. In at least one embodiment, a Randle’s circuit is used with a single RC pair having a first resistance in series with an RC pair, i.e. a second resistance and a capacitance in parallel with the second resistance. Other implementations may use a higher order equivalent circuit model (e.g. more RC pairs). Again, the Kalman Filter is used to adapt the control model parameters including at least the first resistance, the second resistance, and the capacitance. Other states should capture the voltage across any RC pairs and either SOC or OCV).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of invention to incorporate the estimation of battery equivalent circuit model parameters by decomposition of sense current and terminal voltage into sub-bands of Vuylsteke into Koike in order to provide an estimated present sample of battery voltage for its respective sub-band and a combiner compares the true sample to the predicted present sample to generate a prediction error so that the prediction error be used in the parameter update equation of the adaptive filter to provide a controller which controls an update algorithm to control resistor estimate block to minimize the prediction error.
As to claim 2, Koike further discloses:
a. wherein the deviations at certain current levels are interpolated or extrapolated from an average current drawn from the battery during high current events and an average current drawn from the battery in the absence of high current events read on Page 6, Para. 3-6, (then, by substituting equation (eq14) into equation (eq22) to make a difference equation, the following equation (eq23) can be obtained. In the equation (eq23), the subscript “(k)” indicates the current value. The subscript “(k−1)” indicates the previous value. The subscript “(k−2)” indicates the previous value. Here, the equation (eq23) is arranged as the following equation (eq24). In the equation (eq24), “α” and “γ” are constants measured in advance through experiments or the like. “Ts” is a sampling period. “I (k)” can be obtained from the detected current I. “T (k)” can be obtained from the detected temperature T. Calculating system state transactions continuously at district sampling times based on immediate current step deviations).
As to claim 3, Koike further discloses:
a. calculating impedances of the multiple resistive-capacitive elements and the deviations of the multiple resistive- capacitive elements read on Page 8, Para. 1, (As shown in FIG. 2, the reaction resistance model Mbv is expressed by an equivalent circuit model including a parallel connection body of a resistance component and a capacitance component. As a result, the reaction resistance model Mbv can express the nonlinear characteristic of the impedance by the capacitance component while keeping the resistance component current dependent. That is, the time constant of reaction resistance can be considered. Therefore, by estimating the SOC of the battery cell 20a using the battery model M having the reaction resistance model Mbv, the SOC of the battery cell 20a, that is, the state of the battery cell 20a can be estimated with higher accuracy). Koike does not explicitly recite wherein the space comprises a cargo interior of a vehicle and transmitting the control signal occurs in response to receiving an indication that the asset is next to be delivered along a route of the vehicle.
However, Vuylsteke further teaches:
b. multiple resistive-capacitive elements and the deviations of the multiple resistive- capacitive elements using an adaptive algorithm read on ¶ 0018-0019, (the control model and estimator produce more accurate states, particularly SOC, that can be used to inform additional control algorithms and can replace extensive lookup tables used in various prior art strategies, which would otherwise require significant engineering resources for calibration. The high-level structure of various embodiments illustrated and described in detail with respect to the Figures includes sensors to measure battery temperature, terminal voltage, and current. These sensors feed current and, when available, an at-rest voltage/OCV (open circuit voltage) measurement to the measurement-based SOC estimation and all three measurements to the Kalman Filter. In at least one embodiment, a Randle’s circuit is used with a single RC pair having a first resistance in series with an RC pair, i.e. a second resistance and a capacitance in parallel with the second resistance. Other implementations may use a higher order equivalent circuit model (e.g. more RC pairs). Again, the Kalman Filter is used to adapt the control model parameters including at least the first resistance, the second resistance, and the capacitance. Other states should capture the voltage across any RC pairs and either SOC or OCV
As to claim 4, Vuylsteke further teaches:
a. wherein deriving linear ECM parameters and deriving non-linear online ECM parameters is performed by an adaptive algorithm, and an adaptation rate of the adaptive algorithm is changed during high current events read on ¶ 0004, (a vehicle comprises a traction battery having a plurality of cells, a temperature sensor configured to measure battery temperature of the traction battery, a current senor configured to measure battery current flow to and from the traction battery, a voltage sensor configured to measure output terminal voltage of the traction battery, an electric machine powered by the traction battery and configured to provide propulsive power to the vehicle, and a controller configured to control at least one of the electric machine and the traction battery in response to an estimated battery power capability based on a battery model having a plurality of model parameters reinitialized in response to at least one of the parameters exceeding a corresponding parameter limit. The controller may be further configured to reinitialize the plurality of model parameters in response to a change in battery current (delta battery current) exceeding a corresponding threshold. The plurality of model parameters may include a first resistance, a second resistance, and a capacitance of the battery model, wherein the first resistance is in series with the second resistance and the capacitance is in parallel with the second resistance. The controller may be further configured to adjust the first resistance, the second resistance, and the capacitance during operation of the vehicle using a Kalman filter. The controller may control at least one of the electric machines and the traction battery in response to a state of charge (SOC) of the traction battery, the SOC based on the plurality of model parameters, the battery temperature, the battery current, and the battery terminal voltage. Each of the plurality of model parameters may be reinitialized to a previously stored value in response to a trigger condition, which may include a vehicle key-on, a parameter value crossing a limit, or a delta battery current exceeding a threshold. The vehicle may include a transceiver configured to wirelessly communicate vehicle data to a cloud server, wherein each of the plurality of model parameters is reinitialized to a value received from the cloud server. The vehicle may include an internal combustion engine coupled to the electric machine).
As to claim 5, Vuylsteke further teaches:
a. wherein the high current event comprises an average of instantaneous value of the battery drawn from the battery exceeding a threshold read on ¶ 0004, (a vehicle comprises a traction battery having a plurality of cells, a temperature sensor configured to measure battery temperature of the traction battery, a current senor configured to measure battery current flow to and from the traction battery, a voltage sensor configured to measure output terminal voltage of the traction battery, an electric machine powered by the traction battery and configured to provide propulsive power to the vehicle, and a controller configured to control at least one of the electric machine and the traction battery in response to an estimated battery power capability based on a battery model having a plurality of model parameters reinitialized in response to at least one of the parameters exceeding a corresponding parameter limit. The controller may be further configured to reinitialize the plurality of model parameters in response to a change in battery current (delta battery current) exceeding a corresponding threshold. The plurality of model parameters may include a first resistance, a second resistance, and a capacitance of the battery model, wherein the first resistance is in series with the second resistance and the capacitance is in parallel with the second resistance. The controller may be further configured to adjust the first resistance, the second resistance, and the capacitance during operation of the vehicle using a Kalman filter. The controller may control at least one of the electric machines and the traction battery in response to a state of charge (SOC) of the traction battery, the SOC based on the plurality of model parameters, the battery temperature, the battery current, and the battery terminal voltage. Each of the plurality of model parameters may be reinitialized to a previously stored value in response to a trigger condition, which may include a vehicle key-on, a parameter value crossing a limit, or a delta battery current exceeding a threshold. The vehicle may include a transceiver configured to wirelessly communicate vehicle data to a cloud server, wherein each of the plurality of model parameters is reinitialized to a value received from the cloud server. The vehicle may include an internal combustion engine coupled to the electric machine). .
As to claim 6, Koike further discloses:
a. determining an available power that can be drawn from the battery for a given period of time before the battery voltage decreases to a brown-out voltage based on the linear online ECM parameters read on Page 3, Para. 3, (The battery ECU 30 identifies each parameter of the battery model M based on the detected voltage, the detected current I, and the detected temperature T, thereby obtaining the potential difference Vs of the DC resistance, the potential difference Vbv of the reaction resistance, and the potential difference Vw of the diffusion resistance. Calculate. Here, the open-circuit voltage OCV of the battery cell 20a is calculated based on the following equation (eq1) from the detection voltage V, the DC resistance potential difference Vs, the reaction resistance potential difference Vbv, and the diffusion resistance potential difference Vw. Can do. “T” indicates time. The battery ECU 30 calculates the open end voltage OCV based on the equation (eq1), and calculates the SOC of the battery cell 20a from the open end voltage OCV. Next, a method for calculating the SOC of the battery cell 20a based on the battery model M will be described in detail).
As to claim 7, Koike further discloses:
a. determining a maximum current that can be drawn from the battery for a given period of time before the battery voltage decreases to a brown-out voltage based on the linear online ECM parameters read on Page 6, Para. 3-8, (Then, by substituting equation (eq14) into equation (eq22) to make a difference equation, the following equation (eq23) can be obtained. In the equation (eq23), the subscript “(k)” indicates the current value. The subscript “(k−1)”indicates the previous value. The subscript “(k−2)” indicates the previous value. Here, the equation (eq23) is arranged as the following equation (eq24). In the equation (eq24), “α” and “γ” are constants measured in advance through experiments or the like. “Ts” is a sampling period. “I (k)” can be obtained from the detected current I. “T (k)” can be obtained from the detected temperature T. Further, “ΔVbv (k)” and “ΔVbv (k−1)” can be obtained from the detected voltage V. Details are as follows. First, the time constant of reaction resistance is shorter than the time constant of diffusion resistance. Therefore, the change in the potential difference Vbv of the reaction resistance appears as a fast response of the detection voltage V. Similarly, a change in the potential difference Vs of the DC resistance also appears as a fast response of the detection voltage V. On the other hand, the change in the potential difference Vw of the diffusion resistance appears as a slow response of the detection voltage V. Therefore, if the sampling period of the voltage sensor 21 is set to a period corresponding to the time constant of the reaction resistance, in other words, if it is set to a period shorter than the period corresponding to the time constant of the diffusion resistance).
As to claim 8, Koike further discloses:
a. determining an energy that the battery can sustain before the battery voltage decreases to a brown-out voltage based on the linear online ECM parameters read on Page 9, Para. 9, (constants previously measured by experiments or the like are used as the predetermined values α and γ, but the predetermined values α and γ may be values learned by the battery ECU 30. Note: calculating state of energy capabilities over an integrated time envelope using linear model resistance bounds is a foundational function of vehicular battery management software).
As to claim 10, Koike further discloses:
a. maintaining online updating of a characterization table that sets forth values for the deviation for each of the multiple resistive-capacitive elements at different current amplitudes for a given state of charge and temperature associated with the battery read on Page 9, Para. 1, (The reaction resistance parameter calculation unit 33 uses the reaction resistance potential difference Vbv1 as “Vbv (k)”. The reaction resistance parameter calculation unit 33 calculates differential value of the reaction resistance potential difference Vbv1 and uses the differential value as“dVbv (k) / dt”. The reaction resistance parameter calculation unit 33 calculates the reaction resistance parameter β and the detection current I, the detection temperature T, the reaction resistance potential difference Vbv (k), and the differential value “dVbv (k) / dt” based on the equation (eq31). The capacitance Cbv is identified. Unlike in the first embodiment, “Vbv” and “I” can be separated from the reaction resistance parameter β and the capacitance Cbv. Therefore, as an identification method, a linear optimization method such as a linear least square method is used. Can be used. The reaction resistance parameter calculation unit 33 outputs the identified reaction resistance parameter β to the reaction resistance voltage estimation unit 36
As to claim 11, Koike further discloses:
a. for modelling an equivalent circuit for a battery, comprising: determining equivalent circuit model (ECM) parameters to model the battery wherein the ECM parameters include current-dependent non-linear online ECM parameters; and deriving the current dependent non-linear online ECM parameters by using linear ECM parameters and resistive-capacitive (RC) pair impedance deviation estimates for RC pairs of the ECM multiple amplitudes of the current wherein the RC pair impedance deviation estimates are determined by continuously tracking an impedance and an impedance deviation for each RC pair read on Page 6, Para. 1-7, (The transfer function of the RC parallel circuit is expressed by the following equation (eq15). Here, “s ”is a complex (frequency) number. When the equation (eq15) is discretized by the backward Euler method, that is, discretized by the following equation (eq16), the following equation (eq17) can be derived. Here, “z” is a delay operator, and “Ts” is a sampling period. The following equation (eq22) can be derived by transforming the equation (eq17) based on the following equations (eq18), (eq19), (eq20), and (21). Then, by substituting equation (eq14) into equation (eq22) to make a difference equation, the following equation (eq23) can be obtained. In the equation (eq23), the subscript “(k)” indicates the current value. The subscript “(k−1)”indicates the previous value. The subscript “(k−2)” indicates the previous value. Here, the equation (eq23) is arranged as the following equation (eq24). In the equation (eq24), “α” and “γ” are constants measured in advance through experiments or the like. “Ts” is a sampling period. “I (k)” can be obtained from the detected current I. “T (k)” can be obtained from the detected temperature T. Further, “ΔVbv (k)” and “ΔVbv (k−1)” can be obtained from the detected voltage V).
As to claim 13, the claim is interpreted and rejected as to claim 1.
As to claim 14, the claim is interpreted and rejected as to claim 2.
As to claim 15, the claim is interpreted and rejected as to claim 3.
As to claim 16, the claim is interpreted and rejected as to claim 4.
As to claim 17, the claim is interpreted and rejected as to claim 5.
As to claim 18, the claim is interpreted and rejected as to claim 6.
As to claim 19, the claim is interpreted and rejected as to claim 7.
As to claim 20, the claim is interpreted and rejected as to claim 8.
As to claim 22, the claim is interpreted and rejected as to claim 10.
As to claim 23, the claim is interpreted and rejected as to claim 11.
Allowable Subject Matter
9. a, Claims 9, 12 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. However, an updated search will need to be performed after the next response from Applicant.
b. As to claims 1-15, there is no prior art to reject claims 1-15. However, applicant should resolve the USC 112 rejection and U.S.C. 112 rejection to place the application in condition for allowance. An update search needs to be performed after the next response from applicant.
Citation of pertinent Prior Arts
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i. Namiki (US 12024057 B2) discloses in state estimation system includes a first processor, and the first processor executes: a first state variable measurement process of measuring a first state variable of a monitored object; a second state variable estimation repetition process of repeating a second state variable estimation process of inputting a measurement value of the first state variable into a learned model and acquiring an estimate value of a second state variable outputted from the learned model; and an estimation accuracy decline information output process of outputting estimation accuracy decline information when a predetermined estimate value sudden change determination condition is met with respect to a first estimate value of the second state variable acquired in response to input of a first measurement value of the first state variable, and
ii. Ozkan (US 11977126 B1) discloses in a computer-implemented method may include receiving charge cycle data pertaining to a battery pack. The method may include determining, based on the charge cycle data, whether a noise level of a battery management system exceeds a first threshold. In response to determining the noise level exceeds the first threshold, the method may include determining an initial state of charge of the battery pack using coulomb counting by reversing the charge cycle data. In response to determining the noise level does not exceed the first threshold, the method may include determining whether a rest time before charge cycle exceeds a second threshold. In response to determining the rest time before charge cycle does not exceed the second threshold, the method may include determining the initial state of charge of the battery pack using coulomb counting by reversing the charge cycle data.
Conclusion
10. If the claimed invention is amended, Applicant is respectfully requested to indicate the portion(s) of the specification, which dictate(s) the structure/description relied upon to assist the Examiner in proper interpretation of the amended language and also to verify and ascertain the metes and bounds of the claimed invention. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fekadeselassie Girma whose telephone number is (571) 270-5886. The examiner can normally be reached on Monday thru Friday, 8:30 – 5:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta Goins can be reached on (571) 272-2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Fekadeselassie Girma/
Primary Examiner Art Unit 2689
Prosecution Insights