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Review Work on Machine Learning Approaches for Predicting the Remaining Lifespan of Lithium-Ion Batteries
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Author(s): Guruswamy Revana (BVRIT HYDERABAD College of Engineering for Women, India), Nafeesa Khaisar Shaik (BVRIT HYDERABAD College of Engineering for Women, India), Harini Nishtala (BVRIT HYDERABAD College of Engineering for Women, India)and Snehalatha Pasham (BVRIT HYDERABAD College of Engineering for Women, India)
Copyright: 2024
Pages: 12
Source title:
A Sustainable Future with E-Mobility: Concepts, Challenges, and Implementations
Source Author(s)/Editor(s): Lakshmi D. (VIT Bhopal University, India), Neelu Nagpal (Maharaja Agrasen Institute of Technology, India), Neelam Kassarwani (Maharaja Agrasen Institute of Technology, India), Vishnu Varthanan G. (VIT Bhopal University, India)and Pierluigi Siano (University of Salerno, Italy)
DOI: 10.4018/979-8-3693-5247-2.ch006
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Abstract
Lithium-ion batteries play a crucial role in storing energy for electric vehicles, and their reliability is of paramount importance. These batteries are widely used in various appliances for energy storage, catering to specific appliance requirements. Understanding the battery's reliability is essential, given its vital role in energy storage. Even when fully charged to 100%, the battery's capacity undergoes changes as the number of usage cycles increases. Once the capacity surpasses limit of acceptable performance, it leads to a depleted battery incapable of retaining a charge. As a result, the concept of remaining service life (RSL) becomes pivotal in battery management systems (BMS) for both industrial purposes and scholarly investigations. This chapter delves into the appropriate method for predicting RSL, incorporating the implementation of machine learning techniques.
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