Degradation analysis and tanks-in-series modeling of lithium-ion batteries with state of health-adaptive charging strategies

Dynamic operating conditions significantly impact the lifetime of lithium-ion batteries in electric vehicles. While battery lifetime can be extended by optimizing charging profiles to reduce degradation, many existing charging optimization approaches are developed based on fixed, idealized full charge-discharge cycles. These differ from the random and partial charge-discharge behavior in real-world operation and do not consider the influence of battery degradation on charging current profile optimization. To address these limitations, this study designs two groups of battery aging tests to study charging optimization in real-world operation: one subjected to four fixed charging scenarios based on typical daily commuting patterns, and the other to dynamically changing charging scenarios based on state of health change. Capacity degradation, internal resistance increase, and charging time of all cells are analyzed and compared. Degradation modes such as loss of active materials and lithium inventory are examined through incremental capacity and differential voltage analyses. A novel tanks-in-series thermal-aging model is proposed to rapidly simulate battery behavior under dynamic charging, enabling rapid exploration of more charging scenarios constrained by experimental channels or costly to perform. Results demonstrate that dynamically switching charging strategies based on state of health can effectively extend battery lifetime while reducing overall charging time. Moreover, the model proves efficient in identifying optimal charging strategies. These findings offer valuable insights into charging optimization considering practical use scenarios, and present a promising tool for charging optimization.