State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm
| Autor: | Xinxin Xu, Min Ye, Qiao Wang, Jia Bo Li, Meng Wei |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Energy management 020209 energy Mechanical Engineering 020208 electrical & electronic engineering Aerospace Engineering chemistry.chemical_element 02 engineering and technology Battery management systems Ion Sine cosine algorithm State of charge chemistry 0202 electrical engineering electronic engineering information engineering Electronic engineering Key (cryptography) Lithium Dynamic neural network |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 236:241-252 |
| ISSN: | 2041-2991 0954-4070 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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