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Adaptability of Li-Ion Single Particle Model for Lifetime Simulation Using LFP and LMO Cells

机译:锂离子单粒子模型对使用LFP和LMO电池进行寿命模拟的适应性

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For battery lifetime simulation and control, often a conflict between accuracy and the long simulation horizon arises. A single particle model (SPM) is therefore introduced, which combines the benefits of electrochemical models, which take internal states and concentrations into account, with time efficient empirical approaches in neglecting or averaging unimportant and CPU-intensive effects. Based on existing LiFePO4 (LFP) cell measurements and models by [1], an average SPM consisting of the basic cell kinematics and potentials, as well as a thermal and aging model is described. The aging model is based on solid electrolyte layer (SEI) growth, which is one of the main aging effects. Although such models already exist, they are often designed for one cell chemistry and with intensive measurements. The presented SPM is validated using LFP cell data and then adapted to fit other cell chemistry as well. It is shown that by merely adapting few parameters of the positive electrode using literature values as well as measured voltage, temperature and aging curves, the aging behavior of a LiMn2O4 (LMO) cell and possibly other Li-ion cell chemistry can also be predicted well. By adding a current and voltage dependent diffusion coefficient, also side effects like Mn-dissolution for LMO cells are implemented to improve the model.
机译:对于电池寿命的仿真和控制,通常会在精度和较长的仿真范围之间产生冲突。因此,引入了单粒子模型(SPM),该模型将电化学模型的优点(考虑到内部状态和浓度)与忽略或平均不重要且占用CPU大量影响的高效时间经验方法相结合。基于现有的LiFePO4(LFP)电池测量和[1]建立的模型,描述了由基本的电池运动学和电势以及热和老化模型组成的平均SPM。老化模型基于固体电解质层(SEI)的生长,这是主要的老化效应之一。尽管已经存在这样的模型,但它们通常是针对一种细胞化学性质而设计的,并且需要进行大量测量。提出的SPM使用LFP细胞数据进行了验证,然后也适用于其他细胞化学。结果表明,仅通过使用文献值以及测得的电压,温度和老化曲线来调整正极的几个参数,就可以很好地预测LiMn2O4(LMO)电池的老化行为以及可能的其他锂离子电池化学性质。通过增加电流和电压相关的扩散系数,还可以实现LMO电池的Mn溶解等副作用,以改善模型。

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