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Model-based fast charging of lithium-ion batteries: Impact of thermal gradients on the degradation of battery systems with cells connected in parallel and series

电池(电) 降级(电信) 电解质 涓流充电 材料科学 电压 电气工程 热的 汽车蓄电池 锂(药物) 光电子学 系列(地层学) 原电池 计算机科学 热阻 接口(物质) 电容器 串并联电路 健康状况 锂离子电池 锂电池 热分析 开路电压 电子工程 电池电压 汽车工程 电极 稳态(化学) 等效串联电阻 高压
作者
Christian Allgäuer,Tobias Brehler,Yijun Liu,Claudius Diez,Kareem Abo Gamra,Michael K. Kick,Raphael D. Urban,Cristina Grosu,Markus Lienkamp
出处
期刊:eTransportation [Elsevier BV]
卷期号:28: 100563-100563
标识
DOI:10.1016/j.etran.2026.100563
摘要

Fast-charging times of less than 15 min are a key enabler for the widespread adoption of battery electric vehicles. Various fast-charging strategies have been developed in recent years to ensure low charging times without shortening the battery lifetime. While these strategies were primarily investigated at the cell level, transferring them to the system level is challenging. This study investigated the aging behavior of a battery module with four cells connected in parallel and four strands connected in series (4s4p topology) using passive cell balancing. To achieve charging times of approximately 15 min while mitigating lithium plating, anode-potential control was employed as a charging strategy. Aging experiments revealed that thermal gradients between the serially connected strands led to diverging degradation. The strand with the highest temperature exhibited increased capacity loss and resistance growth. As a self-reinforcing effect, this strand was exposed to the highest depth of discharge due to its reduced capacity, which exacerbated the degradation. Differential voltage analyses revealed loss of lithium inventory as the dominant aging mode, presumably caused by solid electrolyte interface growth. The presented approach reveals the underlying self-reinforcing degradation loop mechanisms in module-level fast charging, which demonstrates that a holistic view of the thermal management with cells connected in parallel and series is crucial to maximize battery lifetime. • Model-based fast charging profile on system level. • Experimental aging of a battery system until a state of health of 70%. • Investigation of the thermal gradients and cell balancing within the module. • Degradation analysis using differential voltage analysis (DVA) and ECM fitting.
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