As the unscented Kalman filtering algorithm is sensitive to the battery model and susceptible to the uncertain noise interference, an improved iterate calculation method is proposed to improve the charged state prediction accuracy of the lithium ion battery packs by introducing a novel splice Kalman filtering algorithm with adaptive robust performance. The battery is modeled by composite equivalent modeling and its parameters are identified effectively by investigating the hybrid power pulse test. The sensitivity analysis is carried out for the model parameters to obtain the influence degree on the prediction effect of different factors, providing a basis of the adaptive battery characterization. Subsequently, its implementation process is carried out including model building and adaptive noise correction that are perceived by the iterate charged state calculation. Its experimental results are analyzed and compared with other algorithms through the physical tests. The polarization resistance is obtained as R_p = 16.66 mΩ and capacitance is identified as C_p = 13.71 kF. The ohm internal resistance is calculated as R_o = 68.71 mΩ and the charged state has a prediction error of 1.38% with good robustness effect, providing a foundational basis of the power prediction for the lithium ion battery packs.

由于无味卡尔曼滤波算法对电池模型敏感，易受不确定噪声干扰的影响，提出了一种新型的迭代计算方法，通过引入自适应的新型拼接卡尔曼滤波算法，提高了锂离子电池组的充电状态预测精度。强大的性能。通过复合等效模型对电池进行建模，并通过研究混合动力脉冲测试有效地确定其参数。对模型参数进行敏感性分析，得出对不同因素预测效果的影响程度，为自适应电池表征提供依据。后来，它的执行过程包括通过迭代充电状态计算所感知的模型构建和自适应噪声校正。分析其实验结果，并通过物理测试与其他算法进行比较。极化电阻为R _p  = 16.66mΩ，电容确定为C _p  = 13.71 kF。欧姆内阻计算为R _o  = 68.71mΩ，充电状态的预测误差为1.38％，具有良好的鲁棒性效果，为锂离子电池组的功率预测提供了基础。