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Kalman filter based state-of-charge estimation for lithium-ion batteries in hybrid electric vehicles using pulse charging

机译:基于卡尔曼滤波器的混合动力电动汽车锂离子电池充电状态估计(脉冲充电)

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The battery is one of the most important energy storage components in EV/HEV. Failing to estimate the state of the charge accurately will bring the risk of overcharge or over discharge. The traditional Coulomb counting method will bring accumulated error over time, therefore high deviation occurs between the estimated and real state of charge. Different estimation strategies are compared in this paper, i.e., Coulomb counting method, open-circuit-voltage method and Kalman filter based state of charge estimation. Experimental results validate the effectiveness of Kalman filter during the on-line application.
机译:电池是EV / HEV中最重要的储能组件之一。无法准确估计充电状态会带来过充电或过放电的风险。传统的库仑计数方法会随时间带来累积的误差,因此在估算的荷电状态和实际荷电状态之间会发生高偏差。本文比较了不同的估计策略,即库仑计数法,开路电压法和基于卡尔曼滤波器的电荷状态估计。实验结果验证了在线应用过程中卡尔曼滤波器的有效性。

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