磷酸铁锂电池的偏置补偿型荷电状态与健康状态联合估计
Bias-Compensated State of Charge and State of Health Joint Estimation for Lithium Iron Phosphate Batteries
AI总结：
针对磷酸铁锂电池因开路电压曲线平坦易受电压测量偏置影响、状态估计精度低的问题，提出结合双扩展卡尔曼滤波与偏置补偿的SOC-SOH联合估计算法，在多种工况下均能保持低误差与鲁棒性。
摘要：
准确估计荷电状态（SOC）和健康状态（SOH）对电池的安全可靠运行至关重要。电压测量偏置会严重影响状态估计精度，尤其是磷酸铁锂（LFP）电池，因其平坦的开路电压（OCV）曲线而易受影响。本研究提出一种偏置补偿算法，可在电压测量偏置影响下可靠估计LFP电池的SOC和SOH。具体而言，在电压测量偏置影响较弱的高斜率SOC区间，采用双扩展卡尔曼滤波（DEKF）估计SOC和SOH。此外，在低斜率SOC区域估计得到的电压测量偏置会被用于后续的SOC与SOH联合估计中进行补偿，以进一步提升状态估计精度。实验结果表明，在不同温度和老化条件下，该算法的性能显著优于不考虑偏置的传统方法。此外，即使存在30mV的电压测量偏置，该偏置补偿算法仍能实现低于1.5%的SOC估计误差和低于2%的SOH估计误差。最后，即使电压测量偏置在运行过程中发生变化，所提算法仍能保持鲁棒性，将状态估计误差维持在2%左右。
英文摘要：
Accurate estimation of the state of charge (SOC) and state of health (SOH) is crucial for the safe and reliable operation of batteries. Voltage measurement bias highly affects state estimation accuracy, especially in Lithium Iron Phosphate (LFP) batteries, which are susceptible due to their flat open-circuit voltage (OCV) curves. This work introduces a bias-compensated algorithm to reliably estimate the SOC and SOH of LFP batteries under the influence of voltage measurement bias. Specifically, SOC and SOH are estimated using the Dual Extended Kalman Filter (DEKF) in the high-slope SOC range, where voltage measurement bias effects are weak. Besides, the voltage measurement biases estimated in the low-slope SOC regions are compensated in the following joint estimation of SOC and SOH to enhance the state estimation accuracy further. Experimental results indicate that the proposed algorithm significantly outperforms the traditional method, which does not consider biases under different temperatures and aging conditions. Additionally, the bias-compensated algorithm can achieve low estimation errors of below 1.5% for SOC and 2% for SOH, even with a 30mV voltage measurement bias. Finally, even if the voltage measurement biases change in operation, the proposed algorithm can remain robust and keep the estimated errors of states around 2%.
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