State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm
Abstract In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow searc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-03-01
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| Series: | Energy Science & Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ese3.1656 |
| _version_ | 1797263031850237952 |
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| author | Juqiang Feng Feng Cai Long Wu Xing Zhang Kaifeng Huang |
| author_facet | Juqiang Feng Feng Cai Long Wu Xing Zhang Kaifeng Huang |
| author_sort | Juqiang Feng |
| collection | DOAJ |
| description | Abstract In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities. |
| first_indexed | 2024-04-25T00:06:33Z |
| format | Article |
| id | doaj.art-0515728415ac43338691068743d5e1c4 |
| institution | Directory Open Access Journal |
| issn | 2050-0505 |
| language | English |
| last_indexed | 2024-04-25T00:06:33Z |
| publishDate | 2024-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj.art-0515728415ac43338691068743d5e1c42024-03-14T05:27:48ZengWileyEnergy Science & Engineering2050-05052024-03-0112389691210.1002/ese3.1656State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithmJuqiang Feng0Feng Cai1Long Wu2Xing Zhang3Kaifeng Huang4State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Huainan ChinaState Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Huainan ChinaSchool of Mechanical and Electrical, Engineering Huainan Normal University Huainan ChinaSchool of Mechanical and Electrical, Engineering Huainan Normal University Huainan ChinaSchool of Mechanical and Electrical, Engineering Huainan Normal University Huainan ChinaAbstract In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities.https://doi.org/10.1002/ese3.1656backpropagation neural networkcross‐mutation strategymining lithium‐ion batteriessparrow search algorithmstate of charge |
| spellingShingle | Juqiang Feng Feng Cai Long Wu Xing Zhang Kaifeng Huang State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm Energy Science & Engineering backpropagation neural network cross‐mutation strategy mining lithium‐ion batteries sparrow search algorithm state of charge |
| title | State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm |
| title_full | State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm |
| title_fullStr | State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm |
| title_full_unstemmed | State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm |
| title_short | State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm |
| title_sort | state of charge estimation for lithium ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm |
| topic | backpropagation neural network cross‐mutation strategy mining lithium‐ion batteries sparrow search algorithm state of charge |
| url | https://doi.org/10.1002/ese3.1656 |
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