Improved gray wolf particle filtering and high‐fidelity second‐order autoregressive equivalent modeling for intelligent state of charge prediction of lithium‐ion batteries

2021 ◽  
Author(s):  
Yanxin Xie ◽  
Shunli Wang ◽  
Carlos Fernandez ◽  
Chunmei Yu ◽  
Yongcun Fan ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Particle Filtering ◽  
Lithium Ion ◽  
Second Order ◽  
State Of Charge ◽  
High Fidelity ◽  
Gray Wolf

Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries

2013 ◽  
Vol 60 (11) ◽  
pp. 5260-5269 ◽  
Author(s):  
Marcos E. Orchard ◽  
Pablo Hevia-Koch ◽  
Bin Zhang ◽  
Liang Tang
Keyword(s):  
Lithium Ion Batteries ◽  
Particle Filtering ◽  
Risk Measures ◽  
Lithium Ion ◽  
State Of Charge

scholarly journals State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1012 ◽  
Author(s):  
Yidan Xu ◽  
Minghui Hu ◽  
Chunyun Fu ◽  
Kaibin Cao ◽  
Zhong Su ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Second Order ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Temperature Dependent ◽  
Soc Estimation

Accurate estimation of battery state of charge (SOC) is of great significance for extending battery life, improving battery utilization, and ensuring battery safety. Aiming to improve the accuracy of SOC estimation, in this paper, a temperature-dependent second-order RC equivalent circuit model is established for lithium-ion batteries, based on the battery electrical characteristics at different ambient temperatures. Then, a dual Kalman filter algorithm is proposed to estimate the battery SOC, using the proposed equivalent circuit model. The SOC estimation results are compared with the SOC value obtained from experiments, and the estimation errors under different temperature conditions are found to be within ±0.4%. These results prove that the proposed SOC estimation algorithm, based on a temperature-dependent second-order RC equivalent circuit model, provides accurate SOC estimation performance with high temperature adaptability and robustness.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

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.

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