Identification of Aging Mechanisms for State of-Health Estimation in Electric Vehicle-based Lithium-ion Batteries




Battery lifetime models based on aging mechanisms can be used by (1) battery management systems (BMSs) to accurately predict the battery life in real-life applications and (2) by researchers to propose new generation of batteries with improved performance. This research mainly aims to propose a new or optimize the current battery lifetime models for state-of-health (SOH) estimation based the degradation mechanism. The degradation in battery performance are caused by both external factors (such as temperature, stress, and charging-discharging method) and internal factors (such as loss of lithium-ion, loss of active materials, and decomposition of electrolyte). Most current SOH estimation method treat battery as a black box for empirical fitting, overlooking the actual chemical meaning of some parameters, which results in deviations from the actual situation. Therefore, an accurate aging evaluate algorithm will be based on comprehensive understanding and consideration of the role of external factors and the corresponding internal aging mechanism of the battery.

Funding: China Scholarship Council
Effektiv start/slut dato01/03/202129/02/2024


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