Optimizing battery life: a TinyML approach to lithium-ion battery health monitoring

Abstract

Electrical vehicles (EVs) are crucial nowadays due to their reduction in greenhouse gas emissions, decreasing dependence on remnant fuels, and improving air quality. For EVs, the battery is the heart that determines range, performance, and efficiency. Also, it directly impacts the cost and overall vehicle life span. Lithium-ion (Li-ion) batteries are pivotal in powering modern portable electronics and electric vehicles due to their high energy density and durability. Issues with current batteries include slow charging, short cycles, and low energy density. Most of the problems with current batteries are resolved by Li-ion batteries, which also helps explain why EV usage is increasing globally. However, to guarantee maximum performance and safety, estimating the remaining useful life and health state of these batteries remains a major difficulty. To improve battery lifetime of the battery and to overcome the problems of delayed charging, this study introduces a tiny machine learning (TinyML) method. An innovative machine learning approach is put forth that allows for effective learning on devices with limited resources, which enables real-time monitoring of the health status of the Li-ion batteries.