聚合物锂电池(LiFePO4)电流估计器参数优化

Ja’Far Madani, Bobby Rian Dewangga, A. Cahyadi, S. Herdjunanto
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摘要

由于需要消除电池管理系统(BMS)中的电流传感器组件,电池电流估计会上升。消除电流传感器是为了降低BMS的生产成本。此外,消除电流传感器也可以降低BMS的总功耗。在BMS中安装了电流估计方案,而不是利用电流传感器读取电流值。本文提出了一种基于简单电池模型的电流估计算法,该算法基于充电状态-开路电压(SOC-OCV)关系的多项式函数更新电池内部电容随时间的变化。然后寻求多项式函数的最优阶,以期使电流估计误差最小化。为了验证该方法的有效性,对脉冲负载试验进行了电流估计。然后将当前估计结果与当前传感器读数进行比较。结果表明,电流估计能够跟随当前传感器读数的趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parameter Optimization of Current Estimator for Lithium Polymer Battery (LiFePO4)
Battery current estimation rises as there is a need to eliminate current sensor components in battery management system (BMS). Elimination of current sensors is intended to reduce the cost of BMS production. In addition, the elimination of current sensors can also reduce the total power consumption in the BMS. Instead of utilizing current sensors to read the current values, a current-estimation scheme is installed in the BMS. In this paper a current-estimation algorithm is proposed based on a simple battery model by updating the internal capacitance that changes over time based on a polynomial function of State of Charge - Open Circuit Voltage (SOC-OCV) relationship. The optimal order of the polynomial function is then sought in the hope of minimizing current estimation errors. To demonstrate the effectiveness of the proposed method, current estimation was performed for the pulsed-load test. The current estimation results are then compared to the current sensor readings. The results show that the current estimate is able to follow the trend of the current sensor readings.
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