A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

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.

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A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach

Energy ◽

10.1016/j.energy.2017.07.035 ◽

2017 ◽

Vol 138 ◽

pp. 118-132 ◽

Cited By ~ 35

Author(s):

Qian-Kun Wang ◽

Yi-Jun He ◽

Jia-Ni Shen ◽

Zi-Feng Ma ◽

Guo-Bin Zhong

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Lithium Ion Batteries ◽

Equivalent Circuit ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

Modeling Framework ◽

Unified Modeling ◽

Model Approach

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SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4052274 ◽

2021 ◽

pp. 1-28

Author(s):

Yanbo Che ◽

Yibin Cai ◽

Hongfeng Li ◽

Yushu Liu ◽

Mingda Jiang ◽

...

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Equivalent Circuit ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

Variable Order ◽

Nonlinear Correlation ◽

Nonlinear Autoregressive ◽

Autoregressive Neural Network

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

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