锂离子电池全寿命周期个性化退役与评价方法*

Personalized Retiring and Assessing Methods for Lithium-ion Batteries within the Full Lifespan

  • 摘要: 锂离子电池(Lithium-ion batteries, LIBs)广泛应用于储能系统(Energy storage system, ESS)、电动汽车(Electric vehicles, EVs)等领域。然而,电池在运行过程中容量会逐渐下降直至退役。传统方法以80%健康状态(State of health, SOH)作为退役标准,未考虑电池实际衰退速率,不仅不能充分利用健康电池,而且难以有效保障非健康电池的安全性。同时,SOH相等但电池老化特性和衰退速度不一定相同。仅以SOH评价无法准确反映电池老化差异。为此,提出一种锂离子电池全寿命周期个性化退役标准和老化评价方法。以容量衰退梯度和SOH为特征,首次定义全新退役指标(Index of decommissioning, IoD),计算IoD在80%SOH下的分布,获取退役阈值,并以此阈值为标准定义电池退役时刻。提出一种全新的健康状态评价指标—电池容量跳水度(Terminal diving rate, TDR),评价电池在使用过程中出现的非线性老化现象。通过在MIT公开数据集上验证,所提方法计算简单、鲁棒性强,能够实现电池个性化退役,更有效地评估电池老化差异,提高电池利用率,保障使用安全。

     

    Abstract: Lithium-ion batteries(LIBs) have been widely used in energy storage system(ESS), electric vehicles(EVs) and other fields. However, the capacity will gradually decrease during the battery utilization until it is retired. 80% state of health(SOH) is usually taken as the retiring standard in traditional methods, which not considers the actual degradation rate. Not only can not make full use of the healthy batteries, but also make it difficult to efficiently ensure the safety of unhealthy batteries. Meanwhile, SOH is equal, but battery aging characteristics and rate are not necessarily the same. The SOH is used alone to evaluate to make the accurate reflection of aging differences impossible. Therefore, a personalized retiring standard and aging assessment methods are proposed in this paper, which aim to the LIBs within the full lifespan. The new index of decommissioning(IoD) is firstly defined, which is characterized by the capacity degradation gradient and current SOH. The distribution of IoD under 80% SOH is calculated to obtain the retired threshold, which is used as the standard to define the battery retirement. Meanwhile, the brand-new health assessment index - capacity terminal diving rate(TDR) is proposed to evaluate the nonlinear aging phenomena that happens during the battery use. Through the verification on the MIT public dataset, the proposed method has the advantages of simple calculation and strong robustness. It can realize the battery personalized retirement and more effective assessment in aging differences, leading to the higher utilization rate and safer use.

     

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