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.

锂离子电池（LIB）中的充电状态（SOC）估算是电池电动车（BEV）应用中电池管理系统（BMS）的一项关键任务。在这项工作中，我们提出了使用不同机器学习（ML）方法进行SOC估计的建模框架，即支持向量回归（SVR），人工神经网络（ANN）和长期短期记忆（LSTM）网络。使用Matlab / Simulink的BEV汽车仿真技术，并与LIB的电化学Comsol Multiphysics模型集成，已经开发出必要的训练数据。已开发的BEV和LIB操作的多物理场模型可以研究驾驶条件对电化学和降解的影响（即，特斯拉S型和日产Leaf电动汽车采用的不同化学物质的电池内部发生了固体电解质中间相（SEI-形成和分解）过程。我们的研究还指出，在开发信息量数据集时必须考虑BEV的不同组成部分，而这是实施SOC评估学习算法所必需的。因此，拟议的工作为基于BEV和LIBs动态响应的模拟生成现实训练数据奠定了基础，从而可以基于数据驱动的方法进行更精确的SOC估计。这是实施用于SOC评估的学习算法所必需的。因此，拟议的工作为基于BEV和LIBs动态响应的模拟生成现实训练数据奠定了基础，从而可以基于数据驱动的方法进行更精确的SOC估计。这是实施用于SOC评估的学习算法所必需的。因此，拟议的工作为基于BEV和LIBs动态响应的模拟生成现实训练数据奠定了基础，从而可以基于数据驱动的方法进行更精确的SOC估计。