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Current Estimation of Lithium Polymer Battery Using Recursive Least Square Algorithm

机译：递归最小二乘算法电流估计锂聚合物电池

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Current information on a battery is paramount important in a battery management system (BMS). In many systems, the current sensor is used to get the current value. In spite of its importance, a current sensor is expensive. To overcome this issue, this research aims to design a current estimation algorithm which is based on a current sensorless method where the battery is modeled in a Thevenin equivalent circuit model. The Thevenin model is then formed into autoregressive exogenous (ARX) model and the parameters are extracted by using Recursive Least Square (RLS) algorithm. This research uses lithium polymer battery with a capacity of 2200 mAh and the tests conducted in this research are constant pulse load test and variation load test to learn the performance of the algorithm. The results show that the current estimation has an error of 0.0863A RMSE in pulse load test and 0.6916A RMSE in variation load test.
机译：电池电流信息在电池管理系统（BMS）中是至关重要的。在许多系统中，电流传感器用于获取当前值。尽管重要的是，电流传感器昂贵。为了克服这个问题，本研究旨在设计一种基于电流传感器方法的电流估计算法，其中电池在临时等效电路模型中建模。然后将临时模型形成自回归的外源（ARX）模型，并且通过使用递归最小二乘（RLS）算法提取参数。本研究采用锂聚合物电池容量为2200 mah，本研究中进行的测试是恒定脉冲负荷测试和变化负荷测试，以学习算法的性能。结果表明，电流估计在脉冲负荷试验中具有0.0863A RMSE的误差和变异负载测试中的0.6916A RMSE。

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