A Case Study into Impacts of Usage Patterns on Lithium-Ion Battery Aging through Incremental Capacity Analysis Based Health Monitoring

Abstract

Incremental capacity (IC) analysis is an important tool in lithium-ion battery (LIB) health assessment, owing to its flexibility in utilizing data from readily available on-board sensors and its capability of interpreting LIB degradation mechanisms from a physics-based perspective. This paper identifies the optimal health feature (HF) as well as the state-of-charge (SOC) based optimal testing profile through IC analysis, which enables the LIB health monitoring through partial charging data that is practically achievable for plug-in hybrid electric vehicle (PHEV) application. Experimental data from various different aging profiles have been applied to validate the identified optimal HF and SOC-based optimal testing profile. Impacts of diverse usage patterns on LIB aging are analyzed through different combinations of four key characterizing stress factors, namely, depth-of-discharge (DOD), charging C-rate, operating mode and temperature. Results on impact analysis can be closely related with realistic automotive usage patterns, thus providing useful guidance to the design of battery management system (BMS) to achieve a more reliable prediction of LIB remaining useful life (RUL) and the improved lifetime control strategies.