EP4055655A1 - Method for predicting an ageing condition of a battery - Google Patents
Method for predicting an ageing condition of a batteryInfo
- Publication number
- EP4055655A1 EP4055655A1 EP20800084.4A EP20800084A EP4055655A1 EP 4055655 A1 EP4055655 A1 EP 4055655A1 EP 20800084 A EP20800084 A EP 20800084A EP 4055655 A1 EP4055655 A1 EP 4055655A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- battery
- data
- aging
- aging condition
- storage device
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000032683 aging Effects 0.000 title claims abstract description 135
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000013473 artificial intelligence Methods 0.000 claims description 8
- 238000013213 extrapolation Methods 0.000 claims description 5
- 230000015572 biosynthetic process Effects 0.000 claims 1
- 230000006399 behavior Effects 0.000 description 13
- 230000004927 fusion Effects 0.000 description 10
- 238000007726 management method Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000007781 pre-processing Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 230000002596 correlated effect Effects 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000003750 conditioning effect Effects 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/4285—Testing apparatus
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/10—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time using counting means or digital clocks
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M2220/00—Batteries for particular applications
- H01M2220/20—Batteries in motive systems, e.g. vehicle, ship, plane
Definitions
- the invention relates to a method for predicting an aging condition of a battery.
- the invention further relates to a vehicle which comprises at least one battery, the aging state of which is predicted according to the method according to the invention and / or whose aging behavior is improved based on the aging state predicted according to the method according to the invention.
- the invention also relates to a prediction system which is set up to carry out the method according to the invention.
- the state of health (SOH) of a battery depends on various influencing factors. These are, for example, current throughput through the battery, number and depth of charging and discharging cycles of the battery, maximum charging and discharging current of the battery, thermal circuits for the battery, operating temperature of the battery, state of charge (SOC) of the battery , etc. Since all these factors determine the SOH value in a generally individually operated battery in different form and weighting, an exact determination of this value is only possible with difficulty.
- the document WO 2019/017991 A1 describes a battery management system for a motor vehicle that contains a module for estimating the states of a rechargeable battery.
- the battery is preferably designed as a lithium-ion battery and comprises a plurality of battery cells that are connected to one another in series or in parallel.
- the data of the battery include, for example, a voltage profile of the battery, a current profile of the battery and an operating temperature profile of the battery.
- the data can also include battery-characterizing variables.
- the battery-characterizing variables of the battery include, for example, temperature, charging currents, energy throughput, state of charge or combinations of states, such as high state of charge at high temperature for a long time.
- the battery-characterizing variables of the battery also include chemical data of the battery.
- the usage profile is understood to mean, in particular, the load and charging profiles of the battery.
- the data from the battery is then transmitted to a storage device.
- data from several comparison batteries are stored in the storage device.
- Aging state values and / or aging state curves of the comparison batteries are also stored in the memory device, preferably as a function of usage profiles of the respective comparison battery.
- the storage device can be assigned to a data-driven fleet model.
- the storage device preferably comprises the data-driven fleet model.
- the data stored in the storage device are processed and analyzed by means of the data-driven fleet model.
- the aging condition values of the battery are determined by means of a battery reference model at defined times and / or at defined events.
- the battery reference model includes relationships between the data of the battery, the aging condition value of the battery and the usage profiles of the battery.
- signals or signal profiles of the battery are processed by means of a preprocessing model before the aging condition values are determined.
- an aging condition value can be calculated directly and precisely by means of the battery reference model. If a label is present, the battery reference model is implemented as an artificial intelligence based model. If there is no label yet, the battery reference model corresponds to the value that is calculated, for example, in a vehicle control unit.
- an aging condition value of the battery can be calculated, for example, by means of a battery management system.
- This aging condition value calculated by the battery management system then serves as a base value for the battery reference model and is correlated with battery-characterizing battery variables, which are an indicator of the degree of aging of the battery.
- An aging condition profile of the battery is then formed on the basis of the aging condition values determined for the battery.
- a predicted aging condition value and / or a predicted aging condition curve of the battery are then determined.
- the predicted aging condition value and / or the predicted aging condition curve of the battery can be determined by means of an extrapolation of the aging condition curve formed.
- the predicted aging condition value and / or the predicted aging condition curve can also be determined by means of an assignment of the aging condition curve formed to an ascertained aging condition curve from the data of the comparison battery stored in the storage device, several aging condition curves being determined from the data of the comparison batteries stored in the storage device.
- the determination can be carried out by means of a clustering, such as a nearest neighbor heuristic or a k-means algorithm, of loads on the battery with regard to the aging status curves of different usage profiles.
- a load prediction of the battery is preferably carried out.
- the load prediction can be carried out, for example, on the basis of predictive route data of a vehicle, navigation data of the vehicle and further information from a control unit of the vehicle.
- the predictive route data can be determined by means of a car-to-x communication, in particular a car-to-car communication.
- Car-to-X / car communication is an exchange of information and data between vehicles and surroundings / vehicles with the aim of reporting critical and dangerous situations to the driver at an early stage.
- the predicted aging condition value and / or the predicted aging condition curve of the battery are preferably compared with an actual aging condition value and / or an actual aging condition curve of the battery from the actual aging condition values determined by the battery reference model.
- the predicted aging condition value and / or the predicted aging condition curve of the battery can also be used with a stored aging condition value and / or a stored aging condition curve of the comparison batteries from the storage device, which on the one hand extrapolates the individual aging condition curve of the respective comparison batteries and on the other hand, using statistical distribution of the comparison batteries, confidence intervals for the aging condition values be predicted, matched. Both comparisons are preferably carried out.
- the battery reference model is preferably designed as a model based on artificial intelligence.
- the battery reference model is designed as a physical model, a meta-model or a data model, which can be a characteristic diagram of the battery.
- several of the above-mentioned models can be used to form the battery reference model.
- the battery reference model is preferably compared using the data of the comparison batteries stored in the storage device.
- the battery reference model can be based on the from Battery management system calculated, serving as a base value for the battery reference model aging condition value of the battery and the data stored in the storage device of the comparison batteries are parameterized.
- the aging condition values of the battery are preferably determined for different prediction horizons.
- a prediction horizon is to be understood as meaning, for example, a certain period of time in connection with a certain minimum number of driving cycles of a vehicle.
- calendar and cyclical aging of the battery with current and temperature load are taken into account equally.
- short-term predicted aging condition values such as 2 weeks and / or 10 driving cycles
- medium-term predicted aging condition values such as 4 weeks and / or 20 driving cycles
- long-term predicted aging condition values such as 8 weeks and / or 40 driving cycles
- the data from the battery are preferably transmitted to the storage device by means of a wireless network.
- the wireless network can be designed as a WLAN network.
- the wireless network is preferably designed as a cellular network, such as a UMTS or LTE network.
- the storage device is preferably designed as a cloud storage facility. However, it is also conceivable that the storage device is designed as a storage medium, such as, for example, a memory of a control device of the battery or an external memory.
- the battery data transmitted to the storage device are preferably always monitored and evaluated.
- the data of the battery and the respective comparison battery stored in the storage device are preferably always verified and evaluated.
- the monitoring, evaluation and verification of the data transmitted to the storage device and the data stored in the storage device are preferably carried out by means of a model based on artificial intelligence.
- the actual value which is determined by the battery reference model, is always adapted and taken into account for the future prediction.
- a possible systematic deviation is included in the further predicted aging condition curve and corrected so that the prediction residuals are normally distributed.
- an operating strategy for the battery based on the predicted aging condition value or the predicted aging condition profile is applied which pursues the aim of counteracting the aging behavior of the battery and thus extending the service life of the battery.
- This can be achieved, for example, by changing the performance limits, charging behavior, operating temperature or the like, putting the battery into a more gentle operating state.
- the battery can leave an originally poor aging condition curve and switch to a better aging condition curve.
- a slow aging condition decrease can be achieved by changing the operating strategy.
- Effective measures both passive and active, relate, for example, to a current reduction, temperature control or a recommendation to avoid rapid charging cycles or the like.
- a strategy adjustment can also serve as a specification for other batteries connected to the storage device with a similar aging condition curve as a specification for their aging condition-optimal behavior.
- the aging condition value can also decrease more moderately as a result of load changes, for example through more journeys in city or overland traffic or as a result of a change in the charging behavior, and can switch to a different aging condition curve without operating strategy intervention.
- a vehicle which comprises at least one battery whose state of aging is predicted according to the method according to the invention and / or whose aging behavior is improved based on the state of aging predicted according to the method according to the invention.
- Further information or parameters of the vehicle can also be transferred to the storage device.
- a prediction system is also proposed which is set up to carry out the method according to the invention for predicting an aging state of a battery.
- the prediction system can, for example, have a battery reference model, a storage device which has a data-driven model, and a fusion model.
- the battery reference model characterizes the underlying battery technology.
- the data-driven model heuristically describes the actual behavior of comparison batteries with regard to battery aging.
- the fusion model combines both approaches to a highly precise aging condition calculation and prediction of the battery.
- the fusion model is preferably designed as a model based on artificial intelligence.
- the prediction system can also be a
- the method according to the invention enables aging to be determined without taking into account the individual aging condition dependencies.
- software in a battery management system does not need to be adapted for determining an aging state and making a prediction.
- the battery reference model can be compared with the data from the storage device and thus the accuracy of the predicted aging condition value is improved.
- the method according to the invention advantageously allows a quick reaction to changed load behavior of the battery by means of short-term prognosis, e.g. with regard to a systematic change in usage behavior or anomaly detection.
- an operating strategy of the battery can be used which leads to an extension of the battery life and / or an increase in conductivity.
- non-obvious contributors to the aging of the battery can be analyzed, modeled and verified across all vehicles in a storage device using, for example, big data methods and artificial intelligence and can then be used directly to predict the aging of the battery.
- the prediction of the aging condition is thus also enriched by additional information from the storage device, which further improves the accuracy of the aging prediction.
- artificial intelligence in the storage device can analyze, model and verify large amounts of data across all vehicles. New product generations and / or software updates can be continuously optimized through the findings from the storage device through newly determined interrelationships.
- the method according to the invention can be used to derive an online verified battery reference model for the unknown batteries from the data stored in the storage device.
- FIG. 1 shows a flow chart of the method according to the invention for predicting an aging state of a battery
- FIG. 2 shows a schematic representation of a prediction system for carrying out the method according to the invention.
- FIG. 1 shows a flow chart 100 of the method according to the invention for predicting an aging state of a battery.
- a first step 101 data of the battery are provided as a function of various usage profiles of the battery.
- the data of the battery include, for example, a voltage profile of the battery, a current profile of the battery and an operating temperature profile of the battery. Of course, the data can also include battery-characterizing variables.
- the usage profile is understood to mean, in particular, the load and charging profiles of the battery.
- the data from the battery are transmitted to a storage device 240 (see FIG. 2).
- data from a plurality of comparison batteries are stored in the storage device 240. Aging state values and / or aging state curves of the comparison batteries are also stored in the storage device 240, preferably as a function of usage profiles of the respective comparison battery.
- the storage device 240 can be assigned to a data-driven fleet model 242 (see FIG. 2).
- the storage device 240 preferably comprises the data-driven fleet model 242.
- the data stored in the storage device 240 are processed and analyzed by means of the data-driven fleet model 242.
- aging state values of the battery are determined by means of a battery reference model 230 (see FIG. 2) at defined times and / or at defined events.
- the battery reference model 230 comprises relationships between the data of the battery, the aging condition value of the battery and the usage profiles of the battery. Signals or signal profiles of the battery are preferably processed by means of a preprocessing model 220 (see FIG. 2) before the aging condition values are determined.
- an aging condition value can be calculated directly and precisely using the battery reference model 230.
- an aging condition value of the battery can be calculated, for example, by means of a battery management system.
- This aging condition value calculated by the battery management system then serves as a base value for the battery reference model 230 and is correlated with battery-characterizing variables of the battery, which are an indicator of a degree of aging of the battery.
- an aging condition profile of the battery is formed on the basis of the determined aging condition values of the battery.
- a predicted aging condition value and / or a predicted aging condition profile of the battery are determined.
- the predicted aging condition value and / or the predicted aging condition curve of the battery can be determined by means of an extrapolation of the aging condition curve formed.
- the predicted aging condition value and / or the predicted aging condition curve can also be determined by means of an assignment of the aging condition curve formed to an ascertained aging condition curve from the data of the comparison battery stored in the storage device 240, with several aging condition curves being determined from the data of the comparison batteries stored in the storage device 240.
- the determination can be carried out by means of clustering, such as a nearest neighbor heuristic or a k-means algorithm for determining the loads on the battery with regard to the aging status curves of different usage profiles.
- the extrapolation and the assignment of the aging condition curve formed can be used together.
- FIG. 2 shows a schematic representation of a prediction system 200 which is set up to carry out the method according to the invention.
- the prediction system 200 comprises a preprocessing model 220, a battery reference model 230, a storage device 240 which has a data-driven fleet model 242, a load prediction model 250 and a fusion model 260.
- data from a battery (not shown) of a vehicle 210 are initially provided as a function of various usage profiles of the battery, as well as all signals from vehicle 210.
- the data of the battery include, for example, a voltage profile of the battery, a current profile of the battery and an operating temperature profile of the battery.
- the data can also include battery-characterizing variables include.
- the usage profile is understood to mean, in particular, the load and charging profiles of the battery.
- the signals from the battery and the vehicle 210 are processed by the preprocessing model 220 for further use.
- the processed signals are then transmitted to the battery reference model 230, the storage device 240 and the load prediction model 250.
- the storage device 240 stores data from a plurality of comparison batteries. Aging state values and / or aging state curves of the comparison batteries are also stored in the storage device 240, preferably as a function of usage profiles of the respective comparison battery. Signals or information from other vehicles are also stored in memory device 240. The data stored in the storage device 240 are processed and analyzed by means of the data-driven fleet model 242.
- the load prediction model 250 can be used, for example, to predict a load on the battery.
- the load prediction can be carried out, for example, on the basis of predictive route data from vehicle 210, navigation data from vehicle 210 and further information from a control unit of vehicle 210.
- the predictive route data can be determined by means of a communication, such as, for example, a car-to-x / car communication, and transmitted between the load prediction model 250 and the storage device 240.
- aging state values of the battery are determined by means of the battery reference model 230 at defined times and / or at defined events.
- the battery reference model 230 includes relationships between the data of the battery, the aging condition value of the battery and the usage profiles of the battery.
- the result of the load prediction model 250 is also taken into account.
- the aging state values determined by the battery reference model 230, the result from the load prediction model 250 and the data stored in the storage device 240 become the fusion model 260 transferred.
- the fusion model 260 is designed as a model based on artificial intelligence. An aging condition of the battery is predicted by means of the fusion model 260.
- the battery reference model 230 is compared with the aid of the data of the comparison batteries stored in the storage device 240.
- the fusion model 260 thus brings together the model-based aging condition calculation and prediction of the battery reference model 230 and the data-driven aging condition calculation and prediction of the data-driven fleet model 242.
- an operating strategy for the battery or vehicle 210 is developed using the fusion model 260, which pursues the aim of counteracting the aging behavior of the battery and thus extending the service life of the battery. This can be achieved, for example, by changing the performance limits, charging behavior, operating temperature or the like, putting the battery into a more gentle operating state.
- the battery can thus leave an originally poor aging condition curve and switch to a better aging condition curve.
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102019217299.7A DE102019217299A1 (en) | 2019-11-08 | 2019-11-08 | Method for predicting an aging condition of a battery |
PCT/EP2020/080344 WO2021089391A1 (en) | 2019-11-08 | 2020-10-29 | Method for predicting an ageing condition of a battery |
Publications (1)
Publication Number | Publication Date |
---|---|
EP4055655A1 true EP4055655A1 (en) | 2022-09-14 |
Family
ID=73040071
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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EP20800084.4A Pending EP4055655A1 (en) | 2019-11-08 | 2020-10-29 | Method for predicting an ageing condition of a battery |
Country Status (5)
Country | Link |
---|---|
US (1) | US20220252671A1 (en) |
EP (1) | EP4055655A1 (en) |
CN (1) | CN114600298A (en) |
DE (1) | DE102019217299A1 (en) |
WO (1) | WO2021089391A1 (en) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102021208020A1 (en) | 2021-07-26 | 2023-01-26 | Robert Bosch Gesellschaft mit beschränkter Haftung | Method and device for determining and improving a confidence in a prediction of an aging state of energy stores |
DE102021208227A1 (en) | 2021-07-29 | 2023-02-02 | Robert Bosch Gesellschaft mit beschränkter Haftung | Method and device for providing an overall trajectory of an aging state from a plurality of aging state profiles of an electrical energy store |
DE102021208340A1 (en) | 2021-08-02 | 2023-02-02 | Robert Bosch Gesellschaft mit beschränkter Haftung | Method and device for providing a calculated and predicted state of health of an electrical energy store with a state of health model determined using machine learning methods and active learning methods |
DE102021209106A1 (en) | 2021-08-19 | 2023-02-23 | Robert Bosch Gesellschaft mit beschränkter Haftung | Method and device for carrying out a charging process for a device battery |
DE102021214161A1 (en) | 2021-12-10 | 2023-06-15 | Robert Bosch Gesellschaft mit beschränkter Haftung | Method and device for providing an aging status for a portable battery with correction of status observations based on systematic status and environmental influences |
Family Cites Families (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6037777A (en) * | 1998-09-11 | 2000-03-14 | Champlin; Keith S. | Method and apparatus for determining battery properties from complex impedance/admittance |
DE102009042656A1 (en) * | 2009-09-23 | 2011-03-24 | Bayerische Motoren Werke Aktiengesellschaft | Method for controlling or regulating at least one operating parameter influencing the aging state of an electrical energy store |
US9146595B2 (en) * | 2011-08-05 | 2015-09-29 | Qualcomm Incorporated | Systems and methods for remotely monitoring or controlling a battery |
DE102011080638A1 (en) * | 2011-08-09 | 2013-02-14 | Robert Bosch Gmbh | Method for determining state of battery mounted in motor vehicle, involves determining operation hours of battery or aging factor of battery based on parameters of temperature and current |
US9058038B2 (en) * | 2012-03-29 | 2015-06-16 | GM Global Technology Operations LLC | Method and system for predicting vehicle battery health using a collaborative vehicle battery health model |
DE102014200645A1 (en) * | 2014-01-16 | 2015-07-16 | Robert Bosch Gmbh | Method for battery management and battery management system |
DE102015001050A1 (en) * | 2015-01-29 | 2016-08-04 | Man Truck & Bus Ag | Method and device for controlling and / or regulating at least one operating parameter of the electrical energy store influencing an aging state of an electrical energy store |
EP3203574A1 (en) * | 2016-02-08 | 2017-08-09 | Siemens Aktiengesellschaft | Life cycle management for an energy store |
DE102016224548A1 (en) * | 2016-12-09 | 2018-06-14 | Robert Bosch Gmbh | Method for operating an electrical energy storage system and corresponding machine-readable storage medium, electronic control unit and electrical energy storage system |
US10598734B2 (en) * | 2016-12-30 | 2020-03-24 | Nio Usa, Inc. | Reporting of vehicle battery state of health and charge |
DE102017103617A1 (en) * | 2017-02-22 | 2018-08-23 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Method for estimating the aging state of a battery system |
WO2019017991A1 (en) | 2017-07-21 | 2019-01-24 | Quantumscape Corporation | Predictive model for estimating battery states |
US20190130332A1 (en) * | 2017-10-31 | 2019-05-02 | General Electric Company | Clinical telemetry patient monitoring battery management system and method |
JPWO2019087018A1 (en) * | 2017-11-02 | 2020-12-24 | 株式会社半導体エネルギー研究所 | Capacity estimation method and capacity estimation system for power storage devices |
DE102018204848A1 (en) * | 2018-03-29 | 2019-10-02 | Robert Bosch Gmbh | Method and device for operating a device, in particular a vehicle in the event of a fault |
SE541804C2 (en) * | 2018-04-09 | 2019-12-17 | Scania Cv Ab | Methods and control units for determining an extended state of health of a component and for control of a component |
US10921383B2 (en) * | 2019-03-07 | 2021-02-16 | Mitsubishi Electric Research Laboratories, Inc. | Battery diagnostic system for estimating capacity degradation of batteries |
-
2019
- 2019-11-08 DE DE102019217299.7A patent/DE102019217299A1/en active Pending
-
2020
- 2020-10-29 US US17/630,087 patent/US20220252671A1/en active Pending
- 2020-10-29 CN CN202080077327.2A patent/CN114600298A/en active Pending
- 2020-10-29 WO PCT/EP2020/080344 patent/WO2021089391A1/en unknown
- 2020-10-29 EP EP20800084.4A patent/EP4055655A1/en active Pending
Also Published As
Publication number | Publication date |
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DE102019217299A1 (en) | 2021-05-12 |
US20220252671A1 (en) | 2022-08-11 |
CN114600298A (en) | 2022-06-07 |
WO2021089391A1 (en) | 2021-05-14 |
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