State of charge, remaining useful life and knee point estimation based on artificial intelligence and Machine learning in lithium-ion EV batteries: A comprehensive review

• Published in: Renewable energy focus Vol. 42; pp. 146 - 164
• Main Authors: Shah, Aryan, Shah, Khushi, Shah, Charmi, Shah, Manan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2022
• Subjects: Artificial intelligence; Battery; Electrical vehicles; Machine learninList of; Electrical vehicles; Artificial intelligence; Machine learninList of; Battery
• ISSN: 1755-0084; 1878-0229
• DOI: 10.1016/j.ref.2022.06.001

•The emerging dependency on Electric vehicles and the role of Machine Learning in bolstering it.•Hybrid techniques tend to outwit different approaches.•Comprehensive review of various battery life estimation techniques about Machine Learning and Deep Learning.•Challenges faced and future scope of Deep Learning and Machine Learning in the State of Charge, knee-point, and Remaining Useful Life prediction of Lithium-ion batteries. Rapid adoption in the usage of li-ion battery-fueled vehicles provides a promising approach to assuage the impact of climate change. The implementation and increasing sales trends in the Electric vehicle industry have created positive results in various regions. The reliability and consistency of the Battery management system in new-age Electric vehicles to predict the remaining battery cycles, State of charge, and knee point prove to be a complex task citing the non-linear behavior of lithium-ion batteries throughout their complete lifecycle. However, to make sure that the batteries work as intended consistently, there arises a need to monitor the battery's health and performance regularly. By examining recent literature, this paper carries out a comparative study of various published research regarding estimating the State of charge for Lithium-ion batteries using multiple methods, classified into three categories: adaptive, data-driven, and hybrid approaches. We also conducted a comparative study on Knee-point and the number of charge-discharge cycles left about electric vehicles. This review intends to furnish a comparative analysis of Machine learning & Artificial intelligence-related estimation techniques and analyze their superiority over traditional data-driven methods. It was observed that hybrid techniques consisting of various Machine learning algorithms yielded good results. This paper aims to perceive the most accurate algorithms and methodologies used, which can be further used in battery management systems to improve battery prognostics drastically. The future works and challenges encountered in various researches have also been mentioned at the end of the review that will hopefully pave the way for increasing efforts towards the development of the advanced SOC, knee point, and RUL methods for future EV uses.