The safety assurance is very important for the unmanned aerial vehicle lithium ion batteries, in which the state of charge estimation is the basis of its energy management and safety protection. A new equivalent modeling method is proposed for the mathematical expression of different structural characteristics, and an improved reduce particle‐adaptive Kalman filtering model is designed and built, in which the incorporate multiple featured information is absorbed to explore the optimal representation by abandoning the redundant and abnormal information. And then, the multiple parameter identification is investigated that has the ability of adapting the current varying conditions, according to which the hybrid pulse power characterization test is accommodated. As can be known from the experimental results, the polynomial fitting treatment is carried out by conducting the curve fitting treatment and the maximum estimation error of the closed‐circuit‐voltage is 0.48% and its state of charge estimation error is lower than 0.30% in the hybrid pulse power characterization test, which is also within 2.00% under complex current varying working conditions. The iterate calculation process is conducted for the unmanned aerial vehicle lithium ion batteries together with the compound equivalent modeling, realizing its adaptive power state estimation and safety protection effectively.

中文翻译：

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基于复合等效模型和迭代减少粒子自适应卡尔曼滤波的无人飞行器锂离子电池安全保证新方法

对于无人机锂离子电池来说，安全保证非常重要，其中充电状态估计是其能量管理和安全保护的基础。针对不同结构特征的数学表达式，提出了一种新的等效建模方法，设计并建立了一种经过改进的归约粒子自适应卡尔曼滤波模型，该模型吸收了多个特征信息，通过舍弃多余的和多余的信息来探索最优表示。异常信息。然后，研究了具有适应电流变化条件的能力的多参数识别，据此进行了混合脉冲功率特性测试。从实验结果可以看出，通过进行曲线拟合处理进行多项式拟合处理，在混合脉冲功率特性测试中，闭路电压的最大估计误差为0.48％，其荷电状态估计误差小于0.30％。在复杂的电流变化工作条件下也保持在2.00％以内 结合复合等效模型，对无人机锂离子电池进行了迭代计算，有效地实现了自适应功率状态估计和安全保护。在复杂的电流变化工作条件下为00％。结合复合等效模型，对无人机锂离子电池进行了迭代计算，有效地实现了自适应功率状态估计和安全保护。在复杂的电流变化工作条件下为00％。结合复合等效模型，对无人机锂离子电池进行了迭代计算，有效地实现了自适应功率状态估计和安全保护。