CN117048344A - Method and system for estimating and/or predicting maximum available capacity of battery in vehicle - Google Patents

Method and system for estimating and/or predicting maximum available capacity of battery in vehicle Download PDF

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Publication number
CN117048344A
CN117048344A CN202311250151.8A CN202311250151A CN117048344A CN 117048344 A CN117048344 A CN 117048344A CN 202311250151 A CN202311250151 A CN 202311250151A CN 117048344 A CN117048344 A CN 117048344A
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China
Prior art keywords
battery
maximum available
vehicle
capacity
data
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Pending
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CN202311250151.8A
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Chinese (zh)
Inventor
S·于贝内尔
梅雪
B·海因泰尔
李美静
常琳
吴昊
霍培琦
王子昊
陈彦超
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Mercedes Benz Group AG
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Mercedes Benz Group AG
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Priority to CN202311250151.8A priority Critical patent/CN117048344A/en
Publication of CN117048344A publication Critical patent/CN117048344A/en
Pending legal-status Critical Current

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
    • B60L3/12—Recording operating variables ; Monitoring of operating variables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B60L2240/547—Voltage
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B60L2240/549—Current
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/44—Control modes by parameter estimation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Abstract

The invention relates to a method for estimating and/or predicting the maximum available capacity of a battery in a vehicle, said method comprising: data collection phase: acquiring vehicle data of the vehicle at a plurality of moments, wherein the vehicle data comprises battery data and mileage data; and a data processing stage: acquiring the maximum available capacity of the capacity parameter of the battery at the plurality of moments based on the battery data; and a data fitting stage: fitting the maximum available capacity of the battery as a function of the range of the vehicle. The invention also relates to a system for estimating and/or predicting the maximum available capacity of a battery in a vehicle. According to the invention, by using the real-time monitoring system, the maximum available capacity of the battery can be estimated and/or predicted without knowing specific parameters of the battery in advance, so that the state of the battery is pre-warned and the recycling of the battery is pre-determined.

Description

Method and system for estimating and/or predicting maximum available capacity of battery in vehicle
Technical Field
The present invention relates to a method for estimating and/or predicting the maximum available capacity of a battery in a vehicle. The invention also relates to a system for estimating and/or predicting the maximum available capacity of a battery in a vehicle.
Background
In order to accelerate the energy structure transformation, new energy automobiles (New Energy Vehicle, NEV) typified by electric automobiles are becoming popular. In a new energy automobile, a battery is a key component and plays a critical role in running performance, particularly running safety.
However, in practical applications, the performance of the battery, for example, the capacity of the battery inevitably decreases with the age of the battery. In case of a battery failure, damage to vehicle components may be caused and even driving safety of the vehicle may be affected. Therefore, it is necessary to estimate the relevant state of the vehicle battery in order to obtain information about the range and performance of the vehicle early and to replace the battery in time when necessary.
As is known from the prior art, the SoC-OCV curve gives corresponding open circuit voltage OCV (Open Circuit Voltage, OCV) values at different SoC (State of Charge) values, whereby the SoC value can be determined by means of the open circuit voltage method by means of the OCV-SoC curve.
However, due to the polarization effect of the cell, it takes 1-2 hours to rest or relax to measure the exact OCV. If SoC of the battery is controlled to be changed from 100% to 0% at 1% intervals by setting the discharge current, at least 100 to 200 hours are required. This is quite time consuming.
Furthermore, this places high demands on the memory of the battery management system (Battery Management System, BMS) because it is difficult to store a history of the entire charging period.
In addition, soC-OCV curves of batteries are also affected by aging degree, ambient temperature, standing time, etc., and SoC-OCV curves of different batteries or SoC-OCV curves of the same battery in different states (charged state and discharged state) are also different. Therefore, estimating the state of the battery based on the SoC-OCV relationship curve is not so accurate. In particular, the estimation becomes less and less accurate with age or age.
Disclosure of Invention
The invention is based on the object of providing an improved solution which enables an accurate estimation and/or prediction of the current and/or the subsequent maximum available capacity of the battery during the charging period and/or during the driving period without requiring specific parameters of the battery, such as knowledge about the open circuit voltage, the resistance etc.
This object is achieved by a method for estimating and/or predicting a maximum available capacity of a battery in a vehicle and a system for estimating and/or predicting a maximum available capacity of a battery in a vehicle.
According to a first aspect of the present invention, a method for estimating and/or predicting a maximum available capacity of a battery in a vehicle is presented, the method comprising:
data collection phase: acquiring vehicle data of the vehicle at a plurality of moments, wherein the vehicle data comprises battery data and mileage data;
and a data processing stage: acquiring the maximum available capacity of the capacity parameter of the battery at the plurality of moments based on the battery data; and
data fitting stage: fitting the maximum available capacity of the battery as a function of the range of the vehicle.
Within the scope of the invention, the capacity parameter of the battery can be understood as a parameter that directly or indirectly characterizes the capacity of the battery and its state, for example a parameter that is related to the state of the battery, such as electrical quantity, energy, power, state of health, etc. The State of the battery may be collectively referred to as SoX in addition to the SoC mentioned above, and the State SoX of the battery includes SoE (State-of-Energy), soP (State-of-Power), soH (State-of-Health), and the like. Here, some concepts describing the state of the battery are explained first.
Soc (State-of-Charge) refers to the State of remaining available Charge of a battery, and is expressed in terms of percentages according to the following formula:
wherein Q is c Refers to the residual available power of the current battery, Q n Refers to the maximum available charge of the new battery or the rated charge capacity of the battery. Here, Q n Which decreases as the battery ages.
Soe (State-of-Energy) refers to the State of the remaining available Energy stored in the battery and is expressed in terms of percentages according to the following formula:
wherein E is c Refers to the remaining available energy of the current battery, E n Refers to the maximum available energy of the new battery or the rated energy capacity of the battery. Here, E n Which decreases as the battery ages.
Soh (State-of-Health) refers to the State that a battery is in during the period from the beginning of life to the end of life, and is expressed in terms of percentages, for example, according to the following formula:
3.1 representing SoH by charge:
wherein Q is aged_max Refers to the maximum charge of the current battery, Q new_max Refers to the maximum charge of the new battery.
3.2 SoH by volume:
wherein E is curr Refers to the maximum energy of the current battery, E rated Refers to the maximum capacity of the new battery.
A preferred embodiment of the method according to the invention proposes that, when the maximum available capacity is obtained:
-determining the capacity parameter of the battery at the starting instant T start Start state and at end time T end An end state of (2);
-determining the amount of change in the capacity parameter of the battery between the start time and the end time; and
-determining the maximum available capacity of the capacity parameter of the battery.
A preferred embodiment of the method according to the invention proposes that the capacity parameter is an electrical quantity, wherein the maximum available capacity of the capacity parameter is as follows:
wherein Q is cap Representing maximum available capacity of electricity, Q c h arged Indicating change in electrical quantityVariable, soC start Representing the initial state of the electrical quantity, soC end Indicating the end state of the electric quantity, I bat Representing the current of the battery, t representing the time interval of data acquisition.
A preferred embodiment of the method according to the invention proposes that the capacity parameter is energy, wherein the maximum available capacity of the capacity parameter is as follows:
wherein E is cap Representing the maximum available capacity of energy, E c h arged Represents the change of energy, soE start Represents the initial state of energy SoE end Indicating the end state of the electric quantity, U bat Representing the voltage of the battery, I bat Representing the current of the battery, t representing the time interval of data acquisition.
A preferred embodiment of the method according to the invention proposes that a linear regression can be used in the fitting:
y＝a*x+b
where y represents the maximum available capacity of the capacity parameter and x represents the driving range of the vehicle.
According to a preferred embodiment of the method according to the invention, an end-of-life threshold is set, and a mileage corresponding to the end-of-life of the battery is determined based on the fitting and the end-of-life threshold.
According to a preferred embodiment of the method according to the invention, an early warning threshold is set, and when a capacity parameter of the battery, such as the state of health SoH, reaches the early warning threshold, corresponding measures are taken, such as outputting early warning information, for battery replacement or for secondary use.
According to a second aspect of the invention, a system for estimating and/or predicting a maximum available capacity of a battery in a vehicle is proposed, the system comprising a control device configured for performing the method according to the invention.
A preferred embodiment of the method according to the invention proposes that the system further comprises a real-time monitoring system fitted in the vehicle.
According to a third aspect of the invention, a computer program product, such as a computer-readable program medium, is proposed, the computer program product comprising or storing computer program instructions which, when being executed by a processor, are capable of performing the method according to the invention
The features and details described in connection with the method according to the invention are also applicable here to the system according to the invention and to the computer program product according to the invention, and vice versa.
Drawings
Further advantages, features and details of the invention emerge from the following description of embodiments of the invention which follows with reference to the drawings. The features mentioned here can be used individually or in any combination. The drawings show:
fig. 1 shows a schematic flow chart of a method according to the invention; and
fig. 2 shows a schematic graph of the result of the method according to the invention.
Detailed Description
Fig. 1 shows a schematic flow chart of a method 1 according to the invention.
The method 1 according to the invention is explained below in connection with an RTM system, taking the quantity Q or the energy E as a capacity parameter.
The real-time monitoring RTM system assembled in the NEV sends real-time vehicle data to the enterprise and government back end according to the national standard. After electrical power is turned on, RTM activates and data is sent at intervals (e.g., every 15 seconds, every 30 seconds, etc., also referred to as a transmit cycle). Exemplary such data include:
complete vehicle data, such as vehicle operating state, operating mode, vehicle speed, accumulated mileage, total voltage, total current, insulation, SOC, DCDC status, etc.;
fuel cell data such as voltage, current, fuel consumption rate, temperature, hydrogen concentration, hydrogen pressure, etc.;
vehicle location data, such as positioning data, longitude, latitude, etc.;
…
-user-defined data.
After the vehicle is started or after the electrical connection, the RTM system is activated, which can trigger the method according to the invention at the same time.
In method step S1, relevant data is collected. Here, the relevant data includes at least the following vehicle data: battery data (e.g., SOC value, voltage value, current value, etc.) and mileage data. It is conceivable that these vehicle data are each associated with a respective time stamp. In other words, the battery data and the mileage data can be assigned and/or ordered to each other by time stamp.
For example, collecting the charge of the battery at a starting time T start Start state SoC of (a) start And at the end time T end End state SoC end . Furthermore, the current I of the battery during this period is collected bat 。
In method step S3, the relevant data are processed in order to obtain the charge of the battery at a certain moment or for a certain period of time (for example, at a starting moment T start And end time T end In a time period in between).
For example, it is determined that the charge of the battery is at the start time T start And the end time T end The variation Q between charged ：
Where t represents the time interval of data acquisition, otherwise known as the sampling period. Here, t may be, for example, just a transmission period, but is not limited thereto.
Subsequently, the maximum available capacity Q of the charge of the battery is determined cap I.e. the current maximum available power.
A similar procedure applies also to the case where the energy of the battery is used as a capacity parameter. Which is different in that it is also necessary to collect the voltage U of the battery bat The following are provided:
wherein E is cap Representing the maximum available capacity of energy, E charged Represents the change of energy, soE start Represents the initial state of energy SoE end Indicating the end state of the electric quantity, I bat Representing the current of the battery, t representing the time interval of data acquisition.
Here, the capacity state of the battery of the vehicle is obtained in real time not only during charging but also during traveling. Thus, the maximum available capacity of the battery can be obtained in real time, and the battery state in the form of a percentage is calculated based on the maximum available capacity, so that the accuracy of estimation (such as the accuracy of estimation of the range in a short period) is improved, and adverse effects of factors such as aging degree and the like are reduced.
In method step S5, the resulting data is fitted. Illustratively, a fit can be made to the course of the capacity of the battery over time. Preferably, the capacity of the battery can be fitted as a function of the driving range of the vehicle, as is shown for example in fig. 2.
In fig. 2 a schematic graph of the fitting result of the method 1 according to the invention is shown. The graph depicts capacity loss with mileage (Capacity Loss over mileage), with the horizontal axis being mileage, in "km", and the vertical axis being capacity (here energy), in "kWh". The measured values (measured) are shown in a scatter diagram, and the fitted values (i.e. the values derived by means of a model) are shown in a dashed line.
For example, linear regression is used here, and for example, the least squares method is used. The fitting function is as follows:
y＝a*x+b
where y represents the maximum available capacity of the capacity parameter and x represents the driving range of the vehicle.
Based on the fit, a determined capacity at the determined mileage can be calculated or predicted. Further, the state of health SoH at the determined mileage can be calculated or predicted, and thus the End-of-Life (EoF) to be reached at the determined mileage can be calculated or predicted. For example, the health state threshold SoH corresponding to the end of life can be set th Set to 70%, 75%, 80%, or any suitable value as an end-of-life threshold, based on the fitting function and the end-of-life threshold, the following equation can be derived:
wherein x is EoF For the driving mileage corresponding to the end of life, x init Is the driving distance near the zero position. Solving the equation to obtain a driving mileage value x EoF Namely the driving mileage corresponding to the end of the service life.
In addition, an early warning threshold value can be set, which can be set slightly greater than the state of health threshold value SoH th When the capacity parameter, such as the state of health SoH, of the battery reaches the early warning threshold, corresponding measures, such as outputting early warning information, are taken to replace the battery or put into secondary use. These warning information can be output to the driver to alertThe driver notes that it may also be output to an after-market organization or uploaded to the background.
In the present invention, in particular by means of a real-time monitoring RTM system fitted in a vehicle (new energy vehicle NEV), relevant state parameters of the battery are collected and obtained not only during charging but also during driving, so that the current maximum available capacity and battery state as well as the predicted future maximum available capacity and battery state can be accurately estimated without knowing relevant specific parameters about the battery. In this way, the adverse effect of the degree of ageing on the estimation or prediction is reduced and the relevant personnel can take corresponding measures early.
Here, some advantages brought by the present invention are listed:
-estimating battery capacity and battery status without knowledge of battery parameters such as OCV curve, resistance;
-calculating or predicting the state of health SoH of the battery, so that a low SoH warning can be issued to e.g. after-market organizations in order to replace the low-capacity battery early; and
-estimating the current available capacity of the battery so as to decide in advance the use of the second life.
Although specific embodiments of the invention have been described in detail herein, they are presented for purposes of illustration only and are not to be construed as limiting the scope of the invention. Various alternatives and modifications can be devised without departing from the spirit and scope of the invention.

Claims (10)

1. A method for estimating and/or predicting a maximum available capacity of a battery in a vehicle, the method comprising:
data collection phase: acquiring vehicle data of the vehicle at a plurality of moments, wherein the vehicle data comprises battery data and mileage data;
and a data processing stage: acquiring the maximum available capacity of the capacity parameter of the battery at the plurality of moments based on the battery data; and
data fitting stage: fitting the maximum available capacity of the battery as a function of the range of the vehicle.
2. The method of claim 1, wherein, upon acquiring the maximum available capacity:
-determining the capacity parameter of the battery at the starting instant T start Start state and at end time T end An end state of (2);
-determining the capacity parameter of the battery at the starting instant T start And the end time T end The amount of change between; and
-determining the maximum available capacity of the capacity parameter of the battery.
3. The method of claim 2, wherein the capacity parameter is an electrical quantity, wherein the maximum available capacity of the capacity parameter is as follows:
wherein Q is cap Representing maximum available capacity of electricity, Q c h arged Representing the amount of change in the electrical quantity, soC start Representing the initial state of the electrical quantity, soC end Indicating the end state of the electric quantity, I bat Representing the current of the battery, t representing the time interval of data acquisition.
4. The method of claim 2, wherein the capacity parameter is energy, wherein the maximum available capacity of the capacity parameter is as follows:
wherein E is cap Representing the maximum available capacity of energy, E c h arged Represents the change of energy, soE start Represents the initial state of energy SoE end Indicating the end state of the electric quantity, U bat Representing the voltage of the battery, I bat Representing the current of the battery, t representing the time interval of data acquisition.
5. The method of any one of claims 1 to 4, wherein linear regression can be used in performing the fitting:
y＝a*x+b
where y represents the maximum available capacity of the capacity parameter and x represents the driving range of the vehicle.
6. The method of any one of claims 1 to 5, wherein an end-of-life threshold is set, and a mileage corresponding to the end-of-life of the battery is determined based on the fit and the end-of-life threshold.
7. The method according to any of claims 1 to 6, wherein an early warning threshold is set, and when a capacity parameter of the battery, such as the state of health SoH, reaches the early warning threshold, corresponding measures are taken, such as outputting early warning information, for battery replacement or for secondary use.
8. A system for estimating and/or predicting a maximum available capacity of a battery in a vehicle, the system comprising a control device configured to perform the method according to any one of claims 1 to 7.
9. The system of claim 8, further comprising a real-time monitoring system fitted in the vehicle.
10. A computer program product, such as a computer readable program medium, comprising or storing computer program instructions which, when executed by a processor, are capable of performing the method according to any one of claims 1 to 7.
CN202311250151.8A 2023-09-26 2023-09-26 Method and system for estimating and/or predicting maximum available capacity of battery in vehicle Pending CN117048344A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117665597A (en) * 2024-01-31 2024-03-08 云储新能源科技有限公司 Lithium battery OCV estimation method, system, electronic equipment and medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117665597A (en) * 2024-01-31 2024-03-08 云储新能源科技有限公司 Lithium battery OCV estimation method, system, electronic equipment and medium
CN117665597B (en) * 2024-01-31 2024-04-12 云储新能源科技有限公司 Lithium battery OCV estimation method, system, electronic equipment and medium

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