Methods for State of Health Estimation and Remaining Useful Life Prognostics for EV Lithium-ion Batteries: A Review
2022-01-0850
03/29/2022
- Content
- Lithium-ion batteries (LIBs) power majority of battery electric vehicles due to their relatively better performance and reliability. LIBs offer high energy density, cycle life, power capability when used in their operating zones. However, LIBs experience ageing and performance degradation due to operating conditions and internal factors which if not characterised lead to unexpected battery failures combined with safety hazards. State of health (SOH) metric provides an effective measure on the battery health and be used to calculate the Remaining useful life (RuL) of the battery. Accurate SOH prediction in Electric vehicles is key to improve pack performance, enable preventive maintenance to reduce unexpected failures, enhance battery life and safety. EV battery packs are equipped with Battery Management Systems (BMS). State of art BMSs are equipped with advanced algorithms for state estimation and are IOT enabled to save field performance data over cloud. Currently, algorithms which can accurately battery health and RuL estimation is a challenging issue and a core factor in battery pack/ BMS development. To this end, this paper discusses ageing of LIBs used in electric vehicles and reviews the methods for SOH and RuL prediction. Experimental methods for SOH calibration, adaptive techniques used in onboard estimation, data driven/machine learning based methods which are suitable for cloud based deployment and hybrid methods are summarised. The key advantages and challenges of each of the methods is presented along with their accuracy and precision. Finally, the implementation challenges of real time SOH/ RuL estimation algorithms are discussed along with future research trends.
- Citation
- Aphale, S., "Methods for State of Health Estimation and Remaining Useful Life Prognostics for EV Lithium-ion Batteries: A Review," SAE Technical Paper 2022-01-0850, 2022, .