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Dynamical modeling of Li-ion batteries for electric vehicle applications based on hybrid Particle Swarm-Nelder-Mead (PSO-NM) optimization algorithm

机译:基于混合粒子群-Nelder-Mead(PSO-NM)优化算法的电动汽车锂离子电池动力学建模

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摘要

In recent years, Li-ion batteries are widely used in various applications, such as electric and hybrid vehicles application. Their higher specific power and energy density, high cycle lifetime and decreasing costs have made them an attractive and alternative energy storage technology to lead-acid or nickel-metal hydride batteries in embedded power supplies. In the present work, the electric modeling of a Li-ion battery cell in real operation conditions imposed by an electric vehicle application is carried out. A dynamic equivalent circuit model has been used to simulate several electrochemical processes occurring in a commercially available 40 Ah Li-ion battery cell with NMC cathode material and graphitic anode. The model is parameterized with measurement data in time-domain using a hybrid Particle Swarm-Nelder-Mead (PSO-NM) optimization algorithm. This last one is used to solve the parameters identification problem of Li-ion battery model.
机译:近年来,锂离子电池广泛用于各种应用,例如电动和混合动力车辆应用。它们具有更高的比功率和能量密度,高循环寿命以及降低的成本,使其成为嵌入式电源中铅酸或镍金属氢化物电池的诱人替代能源存储技术。在当前工作中,对电动汽车应用施加的实际运行条件下的锂离子电池单元进行了电气建模。动态等效电路模型已用于模拟在具有NMC阴极材料和石墨阳极的可商购40 Ah锂离子电池中发生的几种电化学过程。使用混合粒子群-奈德-米德(PSO-NM)优化算法在时域中用测量数据对模型进行参数化。最后一个用于解决锂离子电池模型的参数识别问题。

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