CN116699412A - Residual capacity estimation method of energy storage battery module - Google Patents
Residual capacity estimation method of energy storage battery module Download PDFInfo
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Classifications
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- 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
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- 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]
- G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
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- 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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The invention provides a residual capacity estimation method of an energy storage battery module, which comprises the following specific estimation steps: s1: collecting data of the residual capacity of the energy storage battery module; s2: preprocessing data of the residual capacity of the energy storage battery module; s3: extracting the characteristics of the residual capacity of the energy storage battery module; s4: estimating the state of the residual capacity of the energy storage battery module; s5: estimating the state of the residual capacity of the energy storage battery module; s6: the accuracy of the residual capacity of the energy storage battery module is improved; s7: the feedback control of the residual capacity of the energy storage battery module can sequentially perform data acquisition, data preprocessing, feature extraction, state estimation, precision improvement and feedback control on the residual capacity of the energy storage battery module through the seven estimation steps, so that the high-efficiency estimation processing of the residual capacity of the energy storage battery module can be achieved, and in addition, the precision improvement of the designed residual capacity of the energy storage battery module can be improved, and the precision of the high-efficiency estimation of the residual capacity of the energy storage battery module can be improved.
Description
Technical Field
The invention relates to the technical field of energy storage battery modules, in particular to a residual capacity estimation method of an energy storage battery module.
Background
The method for estimating the residual capacity of the energy storage battery module generally uses a technology based on battery state estimation, and BSE is a technology for estimating the current state of the battery by monitoring parameters such as current, voltage and temperature of the battery, and common BSE technologies include kalman filtering, particle filtering, extended kalman filtering and the like. The core idea of the methods is that when estimating the state of the battery, the battery is regarded as a dynamic system, a mathematical model is established, and the estimated value of the state of the battery is continuously updated by observing the input and output signals of the battery, besides the BSE technology, other methods based on machine learning, such as support vector regression, an artificial neural network and the like, can also be used for estimating the residual capacity of the energy storage battery module. These methods may use historical data to train a model and estimate the remaining capacity of the battery by predicting future battery behavior.
However, the existing remaining capacity estimation method of the energy storage battery module has the following problems: the precision is poor: since the estimation of the battery state and the calculation of the residual capacity involve a plurality of parameters and the parameters are coupled with each other, the estimation accuracy is greatly affected, and the current estimation method has difficulty in meeting the high-accuracy requirement. For this purpose, a corresponding technical solution is required to be designed to solve the existing technical problems.
Disclosure of Invention
The invention aims to provide a residual capacity estimation method of an energy storage battery module, which solves the problem that the existing residual capacity of the energy storage battery module has poor precision: because the estimation of the battery state and the calculation of the residual capacity involve a plurality of parameters, and the parameters are mutually coupled, the estimation precision is greatly influenced, and the current estimation method is difficult to meet the high-precision requirement.
In order to achieve the above purpose, the present invention provides the following technical solutions: the residual capacity estimation method of the energy storage battery module comprises the following specific estimation steps:
s1: collecting data of the residual capacity of the energy storage battery module;
s2: preprocessing data of the residual capacity of the energy storage battery module;
s3: extracting the characteristics of the residual capacity of the energy storage battery module;
s4: estimating the state of the residual capacity of the energy storage battery module;
s5: estimating the state of the residual capacity of the energy storage battery module;
s6: the accuracy of the residual capacity of the energy storage battery module is improved;
s7: feedback control of the residual capacity of the energy storage battery module;
through the seven estimation steps, the data acquisition, the data preprocessing, the feature extraction, the state estimation, the accuracy improvement and the feedback control of the residual capacity of the energy storage battery module can be sequentially performed, the efficient estimation processing of the residual capacity of the energy storage battery module can be achieved, and in addition, the accuracy improvement of the designed residual capacity of the energy storage battery module can be achieved, and the accuracy of the efficient estimation of the residual capacity of the energy storage battery module can be improved.
As a preferred embodiment of the present invention, S1: the method comprises the steps of collecting data of the residual capacity of the energy storage battery module, connecting a detection circuit with the energy storage battery module to be detected through a battery detector, collecting real-time data of the battery through monitoring parameters such as current, voltage and temperature of the battery, and uploading the data to a computer end for standby.
As a preferred embodiment of the present invention, S2: the data of the residual capacity of the energy storage battery module is preprocessed, and the collected original data is subjected to filtering, correction and the like, so that noise and nonlinear effects are eliminated, and the data quality is improved.
As a preferred embodiment of the present invention, S3: and extracting the characteristics of the residual capacity of the energy storage battery module, and selecting proper characteristic parameters such as a charge state, internal resistance, capacity attenuation rate and the like according to the physical characteristics and the working state of the battery to establish a battery state model.
As a preferred embodiment of the present invention, S4: the state estimation of the residual capacity of the energy storage battery module utilizes a BSE technology or a machine learning method to estimate the state of the battery and updates the state estimation value of the battery, so that the Kalman filtering and support vector regression method can be selected according to specific conditions.
As a preferred embodiment of the present invention, S5: the state estimation of the residual capacity of the energy storage battery module is realized by using a neural network-based model and a statistical model method according to the current estimated battery state and the historical use condition, calculating the residual capacity of the battery by using a mathematical model and outputting an estimation result.
As a preferred embodiment of the present invention, S6: the accuracy of the residual capacity of the energy storage battery module is improved, and the characteristic extraction method is improved: selecting more accurate and representative characteristic parameters, such as extracting internal resistance parameters by using a method based on spectrum analysis, so as to improve the fitting degree and prediction precision of a model, and optimizing a state estimation algorithm: the state estimation is performed by using a complex BSE algorithm or a machine learning method, so that the estimation accuracy is further improved, for example, the battery state can be estimated by using a modified algorithm of kalman filtering, such as unscented kalman filtering, and temperature compensation is introduced: the working temperature of the battery has a great influence on the capacity of the battery, so that the battery capacity can be corrected in real time by introducing a temperature correction term, the estimation precision is improved, and the data fusion technology is adopted: the information of various data sources is fused, such as the historical use record of the battery and the current real-time data are combined, and relevant factors are analyzed, so that the accuracy of an estimation result is improved, and the method is suitable for different battery types: corresponding models are established for batteries with different brands, different types and different specifications, and estimation is performed according to characteristic parameters of the batteries, so that estimation accuracy is improved, and real-time monitoring and feedback control are performed: the energy storage battery module is subjected to proper feedback control by monitoring parameters such as the battery state, the residual capacity and the like in real time, so that the safety and the performance of the energy storage battery module are ensured.
As a preferred embodiment of the present invention, S7: and (3) carrying out feedback control on the residual capacity of the energy storage battery module, and carrying out proper feedback control on the energy storage battery module according to the estimated residual capacity value so as to ensure the safety and performance of the battery.
Compared with the prior art, the invention has the following beneficial effects:
the invention can achieve the efficient estimation processing of the residual capacity of the energy storage battery module through data acquisition, data preprocessing, feature extraction, state estimation, precision improvement and feedback control of the residual capacity of the energy storage battery module, and optimizes and improves the aspects of data processing, model establishment, state estimation, temperature compensation, data fusion, battery type adaptability, real-time monitoring, feedback control and the like, thereby improving the precision of the residual capacity of the energy storage battery module, effectively solving the problems that the calculation of the residual capacity involves a plurality of parameters, the parameters are mutually coupled, and the whole estimation result is inaccurate due to the existence of an error of one parameter.
Detailed Description
Example 1:
the residual capacity estimation method of the energy storage battery module comprises the following specific estimation steps:
s1: collecting data of the residual capacity of the energy storage battery module;
s2: preprocessing data of the residual capacity of the energy storage battery module;
s3: extracting the characteristics of the residual capacity of the energy storage battery module;
s4: estimating the state of the residual capacity of the energy storage battery module;
s5: estimating the state of the residual capacity of the energy storage battery module;
s6: the accuracy of the residual capacity of the energy storage battery module is improved;
s7: feedback control of the residual capacity of the energy storage battery module;
through the seven estimation steps, the data acquisition, the data preprocessing, the feature extraction, the state estimation, the accuracy improvement and the feedback control of the residual capacity of the energy storage battery module can be sequentially performed, the efficient estimation processing of the residual capacity of the energy storage battery module can be achieved, and in addition, the accuracy improvement of the designed residual capacity of the energy storage battery module can be achieved, and the accuracy of the efficient estimation of the residual capacity of the energy storage battery module can be improved.
S1: the method comprises the steps of collecting data of the residual capacity of the energy storage battery module, connecting a detection circuit with the energy storage battery module to be detected through a battery detector, collecting real-time data of the battery through monitoring parameters such as current, voltage and temperature of the battery, and uploading the data to a computer end for standby.
S2: the data of the residual capacity of the energy storage battery module is preprocessed, and the collected original data is subjected to filtering, correction and the like, so that noise and nonlinear effects are eliminated, and the data quality is improved.
S3: and extracting the characteristics of the residual capacity of the energy storage battery module, and selecting proper characteristic parameters such as a charge state, internal resistance, capacity attenuation rate and the like according to the physical characteristics and the working state of the battery to establish a battery state model.
S4: the state estimation of the residual capacity of the energy storage battery module utilizes a BSE technology or a machine learning method to estimate the state of the battery and updates the state estimation value of the battery, so that the Kalman filtering and support vector regression method can be selected according to specific conditions.
S5: the state estimation of the residual capacity of the energy storage battery module is realized by using a neural network-based model and a statistical model method according to the current estimated battery state and the historical use condition, calculating the residual capacity of the battery by using a mathematical model and outputting an estimation result.
S6: the accuracy of the residual capacity of the energy storage battery module is improved, and the characteristic extraction method is improved: selecting more accurate and representative characteristic parameters, such as extracting internal resistance parameters by using a method based on spectrum analysis, so as to improve the fitting degree and prediction precision of a model, and optimizing a state estimation algorithm: the state estimation is performed by using a complex BSE algorithm or a machine learning method, so that the estimation accuracy is further improved, for example, the battery state can be estimated by using a modified algorithm of kalman filtering, such as unscented kalman filtering, and temperature compensation is introduced: the working temperature of the battery has a great influence on the capacity of the battery, so that the battery capacity can be corrected in real time by introducing a temperature correction term, the estimation precision is improved, and the data fusion technology is adopted: the information of various data sources is fused, such as the historical use record of the battery and the current real-time data are combined, and relevant factors are analyzed, so that the accuracy of an estimation result is improved, and the method is suitable for different battery types: corresponding models are established for batteries with different brands, different types and different specifications, and estimation is performed according to characteristic parameters of the batteries, so that estimation accuracy is improved, and real-time monitoring and feedback control are performed: the energy storage battery module is subjected to proper feedback control by monitoring parameters such as the battery state, the residual capacity and the like in real time, so that the safety and the performance of the energy storage battery module are ensured.
S7: and (3) carrying out feedback control on the residual capacity of the energy storage battery module, and carrying out proper feedback control on the energy storage battery module according to the estimated residual capacity value so as to ensure the safety and performance of the battery.
The invention can achieve the efficient estimation processing of the residual capacity of the energy storage battery module through data acquisition, data preprocessing, feature extraction, state estimation, precision improvement and feedback control of the residual capacity of the energy storage battery module, and optimizes and improves the aspects of data processing, model establishment, state estimation, temperature compensation, data fusion, battery type adaptability, real-time monitoring, feedback control and the like, thereby improving the precision of the residual capacity of the energy storage battery module, effectively solving the problems that the calculation of the residual capacity involves a plurality of parameters, the parameters are mutually coupled, and the whole estimation result is inaccurate due to the existence of an error of one parameter.
Example 2:
the residual capacity estimation method of the energy storage battery module comprises the following specific estimation steps:
s1: collecting data of the residual capacity of the energy storage battery module;
s2: preprocessing data of the residual capacity of the energy storage battery module;
s3: extracting the characteristics of the residual capacity of the energy storage battery module;
s4: estimating the state of the residual capacity of the energy storage battery module;
s5: estimating the state of the residual capacity of the energy storage battery module;
s6: feedback control of the residual capacity of the energy storage battery module;
through the six estimation steps, the data acquisition, the data preprocessing, the feature extraction, the state estimation and the feedback control of the residual capacity of the energy storage battery module can be sequentially performed, and the efficient estimation processing of the residual capacity of the energy storage battery module can be achieved.
S1: the method comprises the steps of collecting data of the residual capacity of the energy storage battery module, connecting a detection circuit with the energy storage battery module to be detected through a battery detector, collecting real-time data of the battery through monitoring parameters such as current, voltage and temperature of the battery, and uploading the data to a computer end for standby.
S2: the data of the residual capacity of the energy storage battery module is preprocessed, and the collected original data is subjected to filtering, correction and the like, so that noise and nonlinear effects are eliminated, and the data quality is improved.
S3: and extracting the characteristics of the residual capacity of the energy storage battery module, and selecting proper characteristic parameters such as a charge state, internal resistance, capacity attenuation rate and the like according to the physical characteristics and the working state of the battery to establish a battery state model.
S4: the state estimation of the residual capacity of the energy storage battery module utilizes a BSE technology or a machine learning method to estimate the state of the battery and updates the state estimation value of the battery, so that the Kalman filtering and support vector regression method can be selected according to specific conditions.
S5: the state estimation of the residual capacity of the energy storage battery module is realized by using a neural network-based model and a statistical model method according to the current estimated battery state and the historical use condition, calculating the residual capacity of the battery by using a mathematical model and outputting an estimation result.
S6: and (3) carrying out feedback control on the residual capacity of the energy storage battery module, and carrying out proper feedback control on the energy storage battery module according to the estimated residual capacity value so as to ensure the safety and performance of the battery.
According to the invention, the high-efficiency estimation processing of the residual capacity of the energy storage battery module can be achieved through data acquisition, data preprocessing, feature extraction, state estimation, accuracy improvement and feedback control of the residual capacity of the energy storage battery module.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A residual capacity estimation method of an energy storage battery module is characterized by comprising the following steps of: the specific estimation steps are as follows:
s1: collecting data of the residual capacity of the energy storage battery module;
s2: preprocessing data of the residual capacity of the energy storage battery module;
s3: extracting the characteristics of the residual capacity of the energy storage battery module;
s4: estimating the state of the residual capacity of the energy storage battery module;
s5: estimating the state of the residual capacity of the energy storage battery module;
s6: the accuracy of the residual capacity of the energy storage battery module is improved;
s7: feedback control of the residual capacity of the energy storage battery module;
through the seven estimation steps, the data acquisition, the data preprocessing, the feature extraction, the state estimation, the accuracy improvement and the feedback control of the residual capacity of the energy storage battery module can be sequentially performed, the efficient estimation processing of the residual capacity of the energy storage battery module can be achieved, and in addition, the accuracy improvement of the designed residual capacity of the energy storage battery module can be achieved, and the accuracy of the efficient estimation of the residual capacity of the energy storage battery module can be improved.
2. The method for estimating remaining capacity of an energy storage battery module according to claim 1, wherein: s1: the method comprises the steps of collecting data of the residual capacity of the energy storage battery module, connecting a detection circuit with the energy storage battery module to be detected through a battery detector, collecting real-time data of the battery through monitoring parameters such as current, voltage and temperature of the battery, and uploading the data to a computer end for standby.
3. The method for estimating remaining capacity of an energy storage battery module according to claim 1, wherein: s2: the data of the residual capacity of the energy storage battery module is preprocessed, and the collected original data is subjected to filtering, correction and the like, so that noise and nonlinear effects are eliminated, and the data quality is improved.
4. The method for estimating remaining capacity of an energy storage battery module according to claim 1, wherein: s3: and extracting the characteristics of the residual capacity of the energy storage battery module, and selecting proper characteristic parameters such as a charge state, internal resistance, capacity attenuation rate and the like according to the physical characteristics and the working state of the battery to establish a battery state model.
5. The method for estimating remaining capacity of an energy storage battery module according to claim 1, wherein: s4: the state estimation of the residual capacity of the energy storage battery module utilizes a BSE technology or a machine learning method to estimate the state of the battery and updates the state estimation value of the battery, so that the Kalman filtering and support vector regression method can be selected according to specific conditions.
6. The method for estimating remaining capacity of an energy storage battery module according to claim 1, wherein: s5: the state estimation of the residual capacity of the energy storage battery module is realized by using a neural network-based model and a statistical model method according to the current estimated battery state and the historical use condition, calculating the residual capacity of the battery by using a mathematical model and outputting an estimation result.
7. The method for estimating remaining capacity of an energy storage battery module according to claim 1, wherein: s6: the accuracy of the residual capacity of the energy storage battery module is improved, and the characteristic extraction method is improved: selecting more accurate and representative characteristic parameters, such as extracting internal resistance parameters by using a method based on spectrum analysis, so as to improve the fitting degree and prediction precision of a model, and optimizing a state estimation algorithm: the state estimation is performed by using a complex BSE algorithm or a machine learning method, so that the estimation accuracy is further improved, for example, the battery state can be estimated by using a modified algorithm of kalman filtering, such as unscented kalman filtering, and temperature compensation is introduced: the working temperature of the battery has a great influence on the capacity of the battery, so that the battery capacity can be corrected in real time by introducing a temperature correction term, the estimation precision is improved, and the data fusion technology is adopted: the information of various data sources is fused, such as the historical use record of the battery and the current real-time data are combined, and relevant factors are analyzed, so that the accuracy of an estimation result is improved, and the method is suitable for different battery types: corresponding models are established for batteries with different brands, different types and different specifications, and estimation is performed according to characteristic parameters of the batteries, so that estimation accuracy is improved, and real-time monitoring and feedback control are performed: the energy storage battery module is subjected to proper feedback control by monitoring parameters such as the battery state, the residual capacity and the like in real time, so that the safety and the performance of the energy storage battery module are ensured.
8. The method for estimating remaining capacity of an energy storage battery module according to claim 1, wherein: s7: and (3) carrying out feedback control on the residual capacity of the energy storage battery module, and carrying out proper feedback control on the energy storage battery module according to the estimated residual capacity value so as to ensure the safety and performance of the battery.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008268161A (en) * | 2007-03-29 | 2008-11-06 | Furukawa Electric Co Ltd:The | Method and device for estimating battery residual capacity and battery power supply system |
| CN103424712A (en) * | 2013-08-16 | 2013-12-04 | 江苏欧力特能源科技有限公司 | Method for measuring residual capacity of battery in online manner on basis of particle swarm optimization |
| CN103616647A (en) * | 2013-12-09 | 2014-03-05 | 天津大学 | Battery remaining capacity estimation method for electric car battery management system |
| CN104777433A (en) * | 2015-05-04 | 2015-07-15 | 中颖电子股份有限公司 | Lithium-ion-battery residual capacity estimating method based on HMM |
| CN107037366A (en) * | 2016-12-02 | 2017-08-11 | 江苏富威能源有限公司 | A kind of electric rail car lithium ion battery control system |
| CN110045288A (en) * | 2019-05-23 | 2019-07-23 | 中山大学 | A kind of capacity of lithium ion battery On-line Estimation method based on support vector regression |
| CN110990781A (en) * | 2019-11-07 | 2020-04-10 | 深圳供电局有限公司 | Electric vehicle charging load prediction method and system and storage medium |
| CN111157899A (en) * | 2020-01-20 | 2020-05-15 | 南京邮电大学 | Method for estimating SOC of battery based on model fusion idea |
| CN111656205A (en) * | 2018-09-13 | 2020-09-11 | 宝马股份公司 | Method for determining the capacity of a battery cell, evaluation device, monitoring device, high-voltage battery, and motor vehicle |
| CN112630659A (en) * | 2020-06-01 | 2021-04-09 | 青岛大学 | Lithium battery SOC estimation method based on improved BP-EKF algorithm |
| CN113514770A (en) * | 2021-05-14 | 2021-10-19 | 江苏欧力特能源科技有限公司 | Lithium battery residual capacity SOC prediction algorithm based on open-circuit voltage and battery temperature drive |
| CN114660464A (en) * | 2022-03-18 | 2022-06-24 | 中国矿业大学 | Lithium ion battery state of charge estimation method |
| CN115327385A (en) * | 2022-07-29 | 2022-11-11 | 武汉理工大学 | Power battery SOC value estimation method and system |
| CN115932598A (en) * | 2022-11-29 | 2023-04-07 | 南昌师范学院 | Lithium battery SOC nonlinear combination estimation method based on equivalent circuit model parameter identification |
-
2023
- 2023-05-17 CN CN202310554414.8A patent/CN116699412A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008268161A (en) * | 2007-03-29 | 2008-11-06 | Furukawa Electric Co Ltd:The | Method and device for estimating battery residual capacity and battery power supply system |
| CN103424712A (en) * | 2013-08-16 | 2013-12-04 | 江苏欧力特能源科技有限公司 | Method for measuring residual capacity of battery in online manner on basis of particle swarm optimization |
| CN103616647A (en) * | 2013-12-09 | 2014-03-05 | 天津大学 | Battery remaining capacity estimation method for electric car battery management system |
| CN104777433A (en) * | 2015-05-04 | 2015-07-15 | 中颖电子股份有限公司 | Lithium-ion-battery residual capacity estimating method based on HMM |
| CN107037366A (en) * | 2016-12-02 | 2017-08-11 | 江苏富威能源有限公司 | A kind of electric rail car lithium ion battery control system |
| CN111656205A (en) * | 2018-09-13 | 2020-09-11 | 宝马股份公司 | Method for determining the capacity of a battery cell, evaluation device, monitoring device, high-voltage battery, and motor vehicle |
| CN110045288A (en) * | 2019-05-23 | 2019-07-23 | 中山大学 | A kind of capacity of lithium ion battery On-line Estimation method based on support vector regression |
| CN110990781A (en) * | 2019-11-07 | 2020-04-10 | 深圳供电局有限公司 | Electric vehicle charging load prediction method and system and storage medium |
| CN111157899A (en) * | 2020-01-20 | 2020-05-15 | 南京邮电大学 | Method for estimating SOC of battery based on model fusion idea |
| CN112630659A (en) * | 2020-06-01 | 2021-04-09 | 青岛大学 | Lithium battery SOC estimation method based on improved BP-EKF algorithm |
| CN113514770A (en) * | 2021-05-14 | 2021-10-19 | 江苏欧力特能源科技有限公司 | Lithium battery residual capacity SOC prediction algorithm based on open-circuit voltage and battery temperature drive |
| CN114660464A (en) * | 2022-03-18 | 2022-06-24 | 中国矿业大学 | Lithium ion battery state of charge estimation method |
| CN115327385A (en) * | 2022-07-29 | 2022-11-11 | 武汉理工大学 | Power battery SOC value estimation method and system |
| CN115932598A (en) * | 2022-11-29 | 2023-04-07 | 南昌师范学院 | Lithium battery SOC nonlinear combination estimation method based on equivalent circuit model parameter identification |
Non-Patent Citations (1)
| Title |
|---|
| 彭思敏 等: "基于无迹卡尔曼滤波的并联型电池系统SOC估计研究", 《中国农机化学报》, pages 291 - 295 * |
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