Academic Journal
State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm
| العنوان: | State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm |
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| المؤلفون: | Wei, Meng, Ye, Min, Li, Jia Bo, Wang, Qiao, Xu, Xin Xin |
| المساهمون: | National Natural Science Foundation of China, national key research and development program of china, Major scientific and technological projects in Henan Province |
| المصدر: | Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ; volume 236, issue 2-3, page 241-252 ; ISSN 0954-4070 2041-2991 |
| بيانات النشر: | SAGE Publications |
| سنة النشر: | 2021 |
| الوصف: | State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1177/09544070211018038 |
| الاتاحة: | https://doi.org/10.1177/09544070211018038 https://journals.sagepub.com/doi/pdf/10.1177/09544070211018038 https://journals.sagepub.com/doi/full-xml/10.1177/09544070211018038 |
| Rights: | https://journals.sagepub.com/page/policies/text-and-data-mining-license |
| رقم الانضمام: | edsbas.F6A9C620 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1177/09544070211018038 |
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