Battery lifetime estimation and optimization in electric vehicles

Abstract:
Lithium-ion (Li-ion) batteries are one of the most commonly used power sources in electric vehicles (EVs) thanks to their characteristics of high energy density, high power density, and low self-discharge rate. However, Li-ion batteries reach their end of life in EVs before their expected lifetime. The performance of Li-ion batteries deteriorates over time, within or without operation, resulting in capacity and power fade, process known as battery aging. The non-ideal battery life restricts the further development and user-acceptance of EVs. Therefore, it is necessary to limit degradation and to extend battery lifetime for EVs. This research tries to obtain aging characteristics of Li-ion batteries in EVs through customized accelerated aging tests, to adopt and improve the machine learning (ML) algorithms for estimating state of health (SOH) and predicting remaining useful life (RUL) of batteries, and to extend the battery life by changing the EV discharging profile, which can be derived from driving profile.

Funding: CSC Scholarship