Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications
By Ryan Ahmed, McMaster University; Javier Gazzarri, MathWorks; Simona Onori, Clemson University; Saeid Habibi, McMaster University; Robyn Jackey, MathWorks; Kevin Rzemien, MathWorks; Jimi Tjong, Ford Motor Company; and Jonathan LeSage, MathWorks
Batteries are a component of paramount importance for hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs). As such, they require accurate real-time monitoring and control to avoid overcharge or over discharge conditions that shorten their lifespan and affect safety. Effective estimation of critical battery pack parameters such as the state of charge (SOC), state of health (SOH), and remaining capacity, requires a high-fidelity battery model that accounts for all operating conditions throughout the battery's lifespan.
This paper presents a method for offline battery model parameter estimation at various battery states of health.
This paper was presented at SAE World Congress 2015.