Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm
The state of charge estimation of a pure electric vehicle power battery pack is one of the important contents of the battery management system. Improving the estimation accuracy of the battery pack’s SOC is conducive to giving full play to its performance and preventing overcharge and discharge of a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2021/4991332 |
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| author | Zhaona Lu Junlong Wang Chuanxing Wang Guoqing Li |
| author_facet | Zhaona Lu Junlong Wang Chuanxing Wang Guoqing Li |
| author_sort | Zhaona Lu |
| collection | DOAJ |
| description | The state of charge estimation of a pure electric vehicle power battery pack is one of the important contents of the battery management system. Improving the estimation accuracy of the battery pack’s SOC is conducive to giving full play to its performance and preventing overcharge and discharge of a single battery. At present, the open-circuit voltage ampere-hour integral method is traditionally used to estimate the SOC value of the battery pack; however, this estimation method is not accurate enough to correct the initial value of SOC and cannot solve the problem of current time integration error between this correction and the next correction. As for the battery performance and characteristics of electric vehicles, it is pointed out that the size of the model value will affect the estimation accuracy of the Kalman signal value. Based on the analysis of the factors to be referred to in the calculation and estimation of SOC by Kalman for pure electric vehicles, the scheme is improved considering the change of battery model value, and the Kalman scheme is proposed. The feasibility and accuracy of the scheme are proved by several battery simulation experiments. |
| format | Article |
| id | doaj-art-9e3095c14d5942e18bd8602d15a8cd31 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-9e3095c14d5942e18bd8602d15a8cd312025-02-03T01:07:06ZengWileyAdvances in Multimedia1687-56992021-01-01202110.1155/2021/4991332Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data AlgorithmZhaona Lu0Junlong Wang1Chuanxing Wang2Guoqing Li3School of Automotive EngineeringSchool of Automotive EngineeringSchool of Automotive EngineeringSchool of Automotive and Traffic EngineeringThe state of charge estimation of a pure electric vehicle power battery pack is one of the important contents of the battery management system. Improving the estimation accuracy of the battery pack’s SOC is conducive to giving full play to its performance and preventing overcharge and discharge of a single battery. At present, the open-circuit voltage ampere-hour integral method is traditionally used to estimate the SOC value of the battery pack; however, this estimation method is not accurate enough to correct the initial value of SOC and cannot solve the problem of current time integration error between this correction and the next correction. As for the battery performance and characteristics of electric vehicles, it is pointed out that the size of the model value will affect the estimation accuracy of the Kalman signal value. Based on the analysis of the factors to be referred to in the calculation and estimation of SOC by Kalman for pure electric vehicles, the scheme is improved considering the change of battery model value, and the Kalman scheme is proposed. The feasibility and accuracy of the scheme are proved by several battery simulation experiments.http://dx.doi.org/10.1155/2021/4991332 |
| spellingShingle | Zhaona Lu Junlong Wang Chuanxing Wang Guoqing Li Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm Advances in Multimedia |
| title | Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm |
| title_full | Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm |
| title_fullStr | Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm |
| title_full_unstemmed | Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm |
| title_short | Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm |
| title_sort | application error analysis of soc estimation of pure electric vehicles based on kalman signal big data algorithm |
| url | http://dx.doi.org/10.1155/2021/4991332 |
| work_keys_str_mv | AT zhaonalu applicationerroranalysisofsocestimationofpureelectricvehiclesbasedonkalmansignalbigdataalgorithm AT junlongwang applicationerroranalysisofsocestimationofpureelectricvehiclesbasedonkalmansignalbigdataalgorithm AT chuanxingwang applicationerroranalysisofsocestimationofpureelectricvehiclesbasedonkalmansignalbigdataalgorithm AT guoqingli applicationerroranalysisofsocestimationofpureelectricvehiclesbasedonkalmansignalbigdataalgorithm |