精确估算锂离子电池的荷电状态(State of Charge,SOC)对于克服电动汽车发展的限制并促进其更广泛的商业应用非常关键.针对宽温度全服役周期内动力电池内部状态难以精确估计的难题,提出一种融合多新息最小二乘法和多新息无迹卡尔曼滤波(Multi Innovation Least Squares Method-Multiple Innovation Unscented Kalman Filter,MILS-MIUKF)的锂离子电池 SOC 估算方法,实现了在全寿命周期内宽温度条件下锂离子电池SOC的精确估计.首先,采用二阶RC等效电路作为模型基础,利用多新息理论对传统最小二乘法进行改进,实现了动力电池内部状态的实时监测和模型参数的在线辨识,并且与传统最小二乘法辨识结果进行对比;然后,为了解决传统无迹卡尔曼滤波算法(Unscented Kalman Filter,UKF)对历史数据利用率低导致估算精度低的问题,在UKF框架内融入了多新息理论,提出了多新息UKF的锂离子电池SOC估算方法;最后,基于HPPC和UDDS两种典型的电池测试工况,进行了不同温度、不同老化状态下的试验验证,并与扩展卡尔曼滤波和UKF算法进行对比.结果表明:所提出的MILS-MIUKF方法能够有效改善最小二乘法和UKF对历史数据利用率低的缺点,并且在宽温度全寿命条件下能够准确反映动力电池内部状态和准确估算电池SOC,最大电压误差不超过60 mV,SOC估计误差控制在2％范围内,证明了所提算法的有效性.
Wide Temperature Lifetime SOC Estimation of Lithium-ion Batteries Based on MILS-MIUKF Algorithm
Accurate estimate of the state of charge(SOC)of lithium-ion batteries is critical for overcoming limitations in the development of electric vehicles and promoting their wider commercial applications.To address the challenge of accurately estimating the internal state of power batteries under a wide range of temperature and full-life conditions,in this study,a novel estimation method is proposed for the SOC of lithium-ion batteries.This method integrates the Multi-Innovation Least Squares Method(MILS)with the Multi-Innovation Unscented Kalman Filter(MIUKF),enabling precise SOC estimation for lithium-ion batteries across their entire lifespan under a wide range of temperatures.First,a second-order RC equivalent circuit was adopted as the fundamental model.The traditional least-squares method was enhanced via the integration of multi-innovation theory,thereby facilitating the real-time monitoring of the internal state of the power battery and the online identification of model parameters.The results derived from this approach were compared with those obtained using the traditional least-squares method.To address the problem of low estimation accuracy due to the traditional Unscented Kalman Filter(UKF)algorithm's low utilization of historical data,MIUKF was proposed as a method for estimating the SOC of lithium-ion batteries by integrating the theory of multi-innovation into the UKF framework.Finally,experimental verification was conducted under two typical battery test conditions,HPPC and UDDS,across different temperatures and aging states.The proposed algorithm was also compared with Extended Kalman Filter(EKF)and UKF to demonstrate its effectiveness.The results indicate that the MILS-MIUKF method proposed in this study effectively addresses the limitations of the least squares method and UKF in terms of the low utilization of historical data.Furthermore,it accurately reflects the internal state of the power battery and precisely estimates the battery SOC under a wide range of temperatures throughout the battery lifespan.The maximum voltage error is maintained below 60 mV,while the SOC estimation error is maintained within 2％.The effectiveness of the proposed method is testified.
automotive engineeringstate of chargemulti-innovation theorylithium-ion batteryleast square methodunscented Kalman filter
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