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Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications

机译:锂离子电池电极密度分析,锂离子电池老化估计应用

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摘要

Abstract Lithium‐ion battery is the commonly used energy storage technology in electric vehicles (EVs) because of its inexpensive manufacturing cost and high energy capacity. For optimal utilization of its capacity and lifetime, reliable state of health (SoH) monitoring solutions have to be included in the battery management system (BMS). SoH of a cell is affected by several reasons such as internal degradation or external damages that need to be estimated. This article analyses the current density in electrode and electrolyte of an EV lithium‐ion cell using a simulation assisted method that leads to improvement in SoH estimation accuracy. The experimental results are analysed through the fusion of the magnetic field images captured by quantum fluxgate magnetometers, installed on the surface of the cell, together with the real‐time simulation of the multi‐physics model of the cell. The magnetic field sensors measure the magnetic field intensity with an accuracy of ±2 mT. The real‐time simulation input data is updated from the measurements of both the magnetic field sensors and the battery cycler. The multi‐physics model of the cell is developed in COMSOL modelling software, and real‐time data fusion process is implemented on dSPACE Microlabbox real‐time simulator. Results confirm that the proposed monitoring solution provides useful insight that can be employed in ageing estimation of EV batteries.
机译:摘要锂离子电池是电动车辆(EVS)中常用的能量存储技术,因为其廉价的制造成本和高能量容量。为了最佳利用其容量和寿命,可靠的健康状况(SOH)监控解决方案必须包含在电池管理系统(BMS)中。细胞的SOH受到几种原因的影响,例如需要估计的内部退化或外部损害。本文利用模拟辅助方法分析了EV锂离子电池电极和电解质的电流密度,导致SOH估计精度的提高。通过由量子磁通磁力计的磁场图像的熔化来分析实验结果,该磁场磁力计捕获在电池表面上,以及电池的多物理模型的实时模拟。磁场传感器测量磁场强度,精度为±2 mt。从磁场传感器和电池循环器的测量更新实时仿真输入数据。单元格的多物理模型在COMSOL建模软件中开发,实时数据融合过程在DSPACE MicroLabbox实时模拟器上实现。结果证实,建议的监控解决方案提供了有用的洞察力,可用于EV电池的老化估计。

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