A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime
A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime
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DOI：
10.1016/j.est.2019.100819
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发表时间：
2019-10
影响因子：
9.4
通讯作者：
M. Varini;P. Campana;G. Lindbergh
中科院分区：
文献类型：
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作者：
M. Varini;P. Campana;G. Lindbergh
预测锂离子电池的全寿命性能对于电动汽车和能源电网等集成能源系统的设计和优化运行是必要的。为了预测的目的，文献中已经提出了几个模型，具有不同程度的复杂性和可预测性。特别是，电化学模型的计算成本很高，而经验模型被剥夺了物理意义。在本工作中，提出了一个半经验模型，该模型保持了经验方法(低拟合数参数、低阶代数方程)的计算效率，同时提供了对电池在运行过程中发生的过程的见解。所提出的模型在实验电池循环上得到了成功的验证：具体地说，在容量衰减20%的条件下，以及在不同温度下的动态循环。在计算时间和相关系数R2方面，都获得了与最新经验模型相当的性能。此外，通过分析装配参数随循环次数的变化，可以确定所考虑的所有方案的电池整体退化过程中的限制过程。因此，建议的模型适合在系统建模中实施，并可用作改进设计和业务战略的信息工具。
Predicting the performance of Li-ion batteries over lifetime is necessary for design and optimal operation of integrated energy systems, as electric vehicles and energy grids. For prediction purposes, several models have been suggested in the literature, with different levels of complexity and predictability. In particular, electrochemical models suffer of high computational costs, while empirical models are deprived of physical meaning. In the present work, a semi-empirical model is suggested, holding the computational efficiency of empirical approaches (low number of fitting parameters, low-order algebraic equations), while providing insights on the processes occurring in the battery during operation. The proposed model is successfully validated on experimental battery cycles: specifically, in conditions of capacity fade >20%, and dynamic cycling at different temperatures. A comparable performance to up-to-date empirical models is achieved both in terms of computational time, and correlation coefficientR2. In addition, analyzing the evolution of fitting parameters as a function of cycle number allows to identify the limiting processes in the overall battery degradation for all the protocols considered. The model suggested is thus suitable for implementation in system modelling, and it can be employed as an informative tool for improved design and operational strategies.