A joint grey relational analysis based state of health estimation for lithium ion batteries considering temperature effects

A reliable and efficient state-of-health (SOH) estimation is essential to prolong service life of lithium-ion batteries (LIBs), optimize power management strategies and reduce cost. In this paper, a joint grey relational analysis (JGRA) based SOH estimation method considering temperature effects is proposed to explore the degradation mechanism of LIBs at different temperature and a generic temperature regressive model is developed. JGRA is used to calculate joint grey correlation degree (JGCD) of the incremental capacity (IC) curves of aged batteries to fresh battery due to their geometric proximity. Experiments including life cycle tests and capacity tests are conducted on three lithium cobalt batteries at different temperatures to verify the performance of the proposed method. The results show that the proposed method can evaluate SOH of LIBs at different temperatures within 2.41% error bound.

Automated summary

In this study, a joint grey relational analysis (JGRA) based state-of-health (SOH) estimation method considering temperature effects was proposed and validated through experiments, showing an accurate evaluation of SOH of lithium-ion batteries at different temperatures.