A Survey on Battery State of Charge and State of Health Estimation Using Machine Learning and Deep Learning Techniques

Abstract

For long-lasting electric vehicles, accurate health evaluation and lifetime prediction of lithium-ion batteries are critical. Early diagnosis of poor performance allows for prompt battery system maintenance. This lowers operating expenses and lessens the risk of accidents and malfunctions. The rise of “Big Data” analytics and related statistical/computational technologies has sparked interest in data-driven battery health estimates. In this paper, we review several articles to highlight their achievability and also environmentally friendly in production with health of battery in reality. We distinguish how machine learning and deep learning algorithms helpful in estimating SOC and SOH of Li-ion battery that are utilized in durable electric vehicles. In addition, we explained the basics of battery, cells, types of battery along with its characteristics were analyzed. Moreover, we summarized the state-of-art table comprises techniques used, which state of estimation either SOH or SOC, metrics used by various machine learning and deep learning algorithms, and discussed their benefits too.