Global parametric sensitivity analysis of equivalent circuit model based on Sobol’ method for lithium-ion batteries in electric vehicles

With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future.

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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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