State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms

World Electric Vehicle Journal

Aritra Ghosh¹  Alagar Karthick²  Venkatesan Chandran³  Robbi Rahim⁴  ChandrashekharK. Patil⁵  Dharmaraj Ganeshaperumal⁶ 

[1] College of Engineering, Mathematics and Physical Sciences, Renewable Energy, University of Exeter, Cornwall TR10 9FE, UK;Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, AvinashiRoad, Arasur, Coimbatore 641 407, Tamil Nadu, India;Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, AvinashiRoad, Arasur, Coimbatore 641 407, Tamil Nadu, India;Department of Informatics Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Sumatera Utara 20219, Indonesia;Department of Mechanical Engineering, Brahma Valley College of Engineering & Research Institute, Nashik 422 213, Maharashtra, India;School of Electronics and Electrical Technology, Kalasalingam Academy of Research and Education, Krishnankoil 626126, Tamil Nadu, India;

关键词: lithium-ion battery;    battery management;    sustainable energy;    machine learning algorithms;    electric vehicles;    state of charge;   

DOI  :  10.3390/wevj12010038

来源: DOAJ

【 摘 要 】

The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

【 授权许可】

Unknown