Predictive model for estimating battery states

Patent metadata

Field	Value
Publication number	US-2023406109-A1
Application number	US-202318319606-A
Country	US
Kind code	A1
Filing date	May 18, 2023
Priority date	Jul 21, 2017
Publication date	Dec 21, 2023
Grant date	—

Abstract

A battery management system (BMS) for a vehicle includes a module for estimating the state of a rechargeable battery, such as its state of charge, in real time. The module includes a learning model for predicting the state of a battery based on the vehicle's usage and related factors unique to the vehicle, in addition to a sensed voltage, current and temperature of a battery.

First claim

1 .- 21 . (canceled)

22 . A management system for electrochemical batteries, comprising
two or more battery cells; one or more sensors configured to provide voltage, current, impedance, pressure, or temperature data of the two or more battery cells; and a prediction module for a battery type, coupled to the one or more sensors, the prediction module comprising:
a trained battery model that includes a plurality of input parameters and at least one target, wherein the plurality of input parameters include at least one of a voltage, current, impedance, pressure, and temperature input parameter, and the target is at least one battery state,
a prediction component that uses the trained battery model and a new set of values for the input parameters to predict a battery state;
wherein the trained battery model includes additional input parameters, the additional input parameters comprising one or more of
(a) the age of the battery,
(b) a last battery, or power train servicing date,
(c) a vehicle identification number (VIN), or make and model,
(d) a driving pattern,
(e) a geographic location or predicted weather forecast,
(f) an age of a registered driver,
(g) number of discharge cycles, or average percentage of battery discharges between charges using an external power source,
number of warning conditions issued on the battery.

23 . The management system of claim 22 , wherein the sensor data is obtained from one or more of, or a combination of, a measured voltage, current, impedance, pressure, or temperature of one or more of
(a) a battery cell, (b) a stack of battery cells, (c) a pouch comprising battery cells, (d) a can comprising battery cells, (e) a module comprising cans, and (f) a battery pack.

24 . The management system of claim 22 , wherein the additional input parameters comprises one or more of
(a) a driving pattern, (b) a predicted weather forecast, (c) an age of a registered driver, (d) number of discharge cycles, or average percentage of battery discharges between charges using an external power source, and (e) number of warning conditions issued on the battery, wherein the prediction model is trained on both the one or more sensor data and the additional parameters.

25 . The management system of claim 22 , the one or more sensors comprising
one or more sensors configured to measure pressure of the series string of battery cells, a module of batteries, and/or individual battery cells, a sensor configured to measure voltage of the two or more battery cells, a sensor configured to measure total charge passed through the two or more battery cells, and/or temperature sensors configured to measure ambient temperature and battery temperature in at least one location.

26 . The management system of claim 25 , wherein the sensor configured to measure voltage of the two or more battery cells, the sensor configured to measure total charge passed through the two or more battery cells, and/or the temperature sensors configured to measure ambient temperature and battery temperature in at least one location is a single sensor.

27 . The management system of claim 25 , wherein the sensor configured to measure voltage of the two or more battery cells, and/or the temperature sensors configured to measure ambient temperature and battery temperature in at least one location is a single sensor.

28 . The management system of claim 26 , wherein the single sensor is integrated into a bus bar.

29 . The management system according to claim 22 , wherein
the at least two or more battery cells and the one or more sensors are located on a vehicle, the vehicle further comprising a processor capable of being coupled to a server accessible over one or more networks, and the prediction module is accessible to the processor through the server.

30 . A vehicle comprising the management system according to claim 22 .

31 . The vehicle of claim 30 , wherein the vehicle further comprises:
a processor; and a computer readable, non-transitory medium comprising the prediction module stored as computer readable code configured for being executed by the processor; wherein the prediction module is coupled to the sensors through the processor.

32 . The vehicle of claim 31 , further including a Battery Management System (BMS), the BMS comprising:
the processor, the computer readable, non-transitory medium, and the one or more sensors.

33 . The vehicle of claim 30 , wherein the vehicle further comprises
an electronic control unit (ECU); a vehicle information storage unit accessible by the ECU and providing vehicle data comprising one or more of
(a) a last battery, or power train servicing date,
(b) a vehicle identification number (VIN), or make and model,
(c) a driving pattern based on, e.g., samples of real-time battery discharge and regenerative charging data corresponding to a vehicle powertrain delivered torques and braking sequences,
wherein the battery model input parameters include the at least one of a voltage, current, impedance, pressure, and temperature input parameter, and at least one of the vehicle data input parameter.

34 . The vehicle of claim 33 , wherein the vehicle information storage unit is a telematics device.

35 . A method, comprising:
using a trained battery model having input parameters including at least one of a voltage, current, impedance, pressure, or temperature input parameter for a battery type, and at least one target for the battery type; using at least one or more sensors located on a vehicle, obtaining data on at least one physical state of two or more battery cells of the vehicle, wherein the data obtained comprises at least one of a measured voltage, current, impedance, pressure, or temperature of the two or more battery cells; wherein the trained battery model includes additional input parameters, the additional input parameters comprising one or more of
(a) the age of the battery,
(b) a last battery, or power train servicing date,
(c) a vehicle identification number (VIN), or make and model,
(d) a driving pattern,
(e) a geographic location or predicted weather forecast,
(f) an age of a registered driver,
(g) number of discharge cycles, or average percentage of battery discharges between charges using an external power source,
(h) number of warning conditions issued on the battery,
wherein the trained battery model is trained on both the one or more sensor data and the additional parameters.

36 . The method of claim 35 , further comprising:
making the trained battery model, including training the battery model using training sets comprising values for each one of the input parameters and a corresponding value for the at least one target using at least the obtained data; receiving new training sets for the battery type over a network; and re-training the battery model on the new training sets.

37 . The method of claim 35 , wherein the vehicle has a processor and a non-transitory memory medium, the battery model residing in the non-transitory memory medium, the method further including
using the vehicle processor to predict a battery state using the trained battery model and a new training set, and producing an output based on the predicted battery state.

38 . The method of claim 37 , wherein the output is displayed on a user interface (UI) such as a vehicle user interface (VUI) or a mobile device.

39 . The method of claim 38 , wherein the UI displays one or more of the following information in the output,
(a) repa

Assignees

• Quantumscape Battery Inc

Inventors

• Holme Timothy

Classifications

• B60L3/12 Primary

  Recording operating variables {; Monitoring of operating variables} · CPC title

• B60L58/12

  responding to state of charge [SoC] · CPC title

• B60L50/66

  Arrangements of batteries · CPC title

• G01R31/3648

  comprising digital calculation means, e.g. for performing an algorithm · CPC title

• G01R31/396

  Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery · CPC title