# US 2026/0091684 A1 · Predictive model for estimating battery states > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0091684 A1 - Document ID: US20260091684A1 - Status: Published application - Kind: A1 - Application number: 19/295,379 - Filed: 2025-08-08 - Published: 2026-04-02 - Earliest claimed: 2017-07-21 - Assignees: QuantumScape Battery, Inc. - Applicants: QuantumScape Battery, Inc. - Inventors: Timothy HOLME - Web page: https://patentgrep.com/patent/US20260091684A1 ## CPC - B60L 3/12: Recording operating variables {; Monitoring of operating variables} - B60L 50/66: {Arrangements of batteries} - B60L 58/10: for monitoring or controlling batteries - B60L 58/12: responding to state of charge [SoC] - B60L 58/16: responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH] - B60L 58/21: having the same nominal voltage - G01R 31/3648: {comprising digital calculation means, e.g. for performing an algorithm} - G01R 31/367: Software therefor, e.g. for battery testing using modelling or look-up tables - G01R 31/382: Arrangements for monitoring battery or accumulator variables, e.g. SoC - G01R 31/396: Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery - H01M 10/486: {for measuring temperature} - B60L 2240/545: Temperature - B60L 2240/547: Voltage - B60L 2240/549: Current - B60L 2260/44: by parameter estimation - B60L 2260/46: by self learning - H01M 50/204: Racks, modules or packs for multiple batteries or multiple cells - Y02E 60/10: Energy storage using batteries - Y02T 10/70: Energy storage systems for electromobility, e.g. batteries - B60L: PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES (arrangements or mounting of electrical propulsion units or of plural diverse prime-movers for mutual or common propulsion in vehicles B60K1/00, B60K6/20; arrangements or mounting of electrical gearing in vehicles B60K17/12, B60K17/14; preventing wheel slip by reducing power in rail vehicles B61C15/08; dynamo-electric machines H02K; control or regulation of electric motors H02P); SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES (electric coupling devices combined with mechanical couplings of vehicles B60D1/64; electric heating for vehicles B60H1/00); ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL (control or regulation of electric motors H02P); MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) - H01M: PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY - Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION - Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION ## 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. ## Claims ### Claim at position 1 -21. (canceled) ### Claim 22 A method, comprising: providing a trained machine learning battery model; inputting, into the trained machine learning battery model, obtained data comprising a measured voltage, current, impedance, pressure, temperature of at least one series string of battery cells, or a combination thereof; inputting at least one battery type; using the trained machine learning battery model to predict a battery state; and producing an output based on the predicted battery state. ### Claim 23 (depends on 22) The method of claim 22, wherein the trained machine learning battery model was trained using training sets comprising values for each one of a series of input parameters and a corresponding value for the at least one target using at least the obtained data, wherein the trained machine learning 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. ### Claim 24 (depends on 23) The method of claim 23, wherein the trained battery model is trained on one or more sensor data and the additional parameters; wherein the obtained data is obtained while a series string of battery cells is (a) in an open circuit condition, (b) under an operating load, (b) during a charging event, (c) during a diagnostic event, (d) during a repair event, or (e) during a safety event. ### Claim 25 (depends on 22) The method of claim 22, further comprising: 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. ### Claim 26 (depends on 22) The method of claim 22, further comprising 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 vehicle has a processor and a non-transitory memory medium, the battery model residing in the non-transitory memory medium; using the vehicle processor to predict the battery state using the trained battery model and a new training set, and producing an output based on the predicted battery state. ### Claim 27 (depends on 26) The method of claim of claim 26, wherein 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 a series string of battery cells, a sensor configured to measure total charge passed through the series string of battery cells, and/or temperature sensors to measure ambient temperature and battery series temperature in at least one location. ### Claim 28 (depends on 26) The method of claim 26, wherein the output is displayed on a user interface (UI) such as a vehicle user interface (VUI) or a mobile device. ### Claim 29 (depends on 28) The method of claim 28, wherein the UI displays one or more of the following information in the output, (a) repair battery when the predicted battery state predicts the SOH is below 50%, (b) warning message when the predicted battery state predicts an unsafe battery condition, (c) warning message when the predicted battery state predicts energy available is insufficient for a predicted event, and (d) warning message when the predicted battery state predicts power available insufficient for the predicted event. ### Claim 30 (depends on 26) The method of claim 26, wherein the output is sent to a vehicle control system, and wherein the vehicle control system performs one or more of the following tasks based on the predicted state of the battery: when SOC is about 80%, allowing the battery to discharge at maximum operating power rate, limiting the power availability when the SOC is below 20% or below 15% or below 10%, notifying a driver when the predicted power availability is below 6 kW or 5 kW or 4 kW or 3 kW or 2 kW or 1 kW, and limiting power to auxiliary systems when the predicted power availability is below 6 kW or 5 kW or 4 kW or 3 kW or 2 kW or 1 kW. ### Claim 31 (depends on 26) The method of claim 26, wherein the vehicle has a processor configured for being coupled to a server located over one or more networks and the battery model is accessible through the server, the method further comprising: uploading to the server the obtained data, and receiving the trained battery model from the server. ### Claim 32 (depends on 26) The method of claim 26, further comprising retraining the battery model on a continuous basis as the sensor data provides the prediction module with time varying values for the at least one of a measured voltage, current, impedance, pressure, or temperature.