Hannan M A, Lipu M S Hossain, Hussain Aini, Ker Pin Jern, Mahlia T M I, Mansor M, Ayob Afida, Saad Mohamad H, Dong Z Y
Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang, 43000, Malaysia.
Centre for Integrated Systems Engineering and Advanced Technologies, FKAB, Universiti Kebagsaan Malaysia, Bangi, 43600, Malaysia.
Sci Rep. 2020 Mar 13;10(1):4687. doi: 10.1038/s41598-020-61464-7.
State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.
荷电状态（SOC）是评估电动汽车（EV）电池存储系统时使用的一个关键指标。因此，由于锂离子电池具有快速充电、长寿命周期和高能量密度等特性，其SOC估计已得到广泛研究。然而，由于锂离子电池在不同工作环境下特性各异，对其进行精确的SOC评估仍然具有挑战性。机器学习技术已被广泛用于设计一种先进的SOC估计方法，该方法无需电池化学反应、电池模型、内部特性和额外滤波器等信息。在此，针对增强的SOC估计，从学习能力、准确性、泛化性能和收敛速度方面展示了优化机器学习技术的能力。我们通过锂离子电池实验、电动汽车行驶循环、温度、噪声和老化效应来验证所提出的方法。我们表明，在各种运行条件下，所提出的方法在准确性、适应性和鲁棒性方面优于几种现有技术方法。
