Battery (electricity)

Quantum mechanics

Algorithm

Engineering

State of health

Lithium (medication)

Reliability engineering

Lithium-ion battery

Endocrinology

Systems engineering

Operating system

Power (physics)

Computer science

Battery capacity

Estimation

Thermodynamics

Physics

Organic chemistry

Fade

Operations management

Medicine

Chemistry

Metric (unit)

State (computer science)

IEEE Transactions on Industry Applications

The implementation of an accurate and low computational demanding state-of-health (SOH) estimation algorithm represents a key challenge for the battery management systems in electric vehicle (EV) applications. In this article, we investigate the suitability of the incremental capacity analysis (ICA) technique for estimating the capacity fade and subsequently the SOH of LMO/NMC-based EV lithium-ion batteries. Based on calendar aging results collected during 11 months of testing, we were able to relate the capacity fade of the studied batteries to the evolution of four metric points, which were obtained using the ICA. Furthermore, the accuracy of the proposed models for capacity fade and SOH estimation was successfully verified considering two different aging conditions.

Lithium-Ion Battery State-of-Health Estimation Using the Incremental Capacity Analysis Technique