Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling
Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling
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DOI：
10.1016/j.jpowsour.2020.229108
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发表时间：
2021-01-31
影响因子：
9.2
通讯作者：
Mashayek, Farzad
中科院分区：
文献类型：
--
作者：
Ragone, Marco;Yurkiv, Vitaliy;Mashayek, Farzad
锂离子电池荷电状态（SOC）估计是纯电动汽车（BEV）应用中电池管理系统（BMS）的关键任务。在这项工作中，我们提出了一个建模框架SOC估计使用不同的机器学习（ML）方法，即支持向量回归机（SVR），人工神经网络（ANN）和长短期记忆（LSTM）网络。必要的训练数据已经使用Matlab/Simulink的BEV汽车模拟开发，与电化学Comsol Multiphysics模型的LIB集成。开发的BEV和LIB操作的多物理模型允许研究驾驶条件对电化学和降解的影响（即，固体电解质中间相- SEI -形成和分解）过程发生在Tesla S和Nissan Leaf BEV中采用的不同化学物质的电池内部。我们的研究还指出了在开发信息数据集时考虑BEV不同组成部分的重要性，这些数据集是SOC评估学习算法实施所需的。因此，所提出的工作为基于BEV和LIB动态响应的模拟生成真实的训练数据奠定了基础，这允许基于数据驱动方法的更精确的SOC估计。
State-of-charge (SOC) estimation in a lithium-ion battery (LIB) is a crucial task of the battery management system (BMS) in battery electric vehicle (BEV) applications. In this work, we propose a modeling framework for SOC estimation using different machine learning (ML) methods, i.e. support vector regressor (SVR), artificial neural network (ANN), and long-short term memory (LSTM) network. The necessary training data have been developed using Matlab/Simulink automotive simulations of BEV, integrated with an electrochemical Comsol Multiphysics model of LIBs. The developed multi-physics model of BEV and LIBs operation allows to investigate the effect of driving conditions on the electrochemical and degradation (i.e., the solid electrolyte interphase - SEI - formation and decomposition) processes occurring inside batteries of different chemistries adopted in the Tesla S and Nissan Leaf BEVs. Our study remarks also the importance of taking into account the different components of BEV in the development of informative datasets, which are required for the implementation of learning algorithms for SOC evaluation. Thus, the proposed work establishes a basis for the generation of realistic training data based on simulations of BEV and LIBs dynamic response, which allows a more precise SOC estimation based on data driven approaches.