精确预测动力电池的荷电状态（state of charge, SOC）是电池管理系统（battery management system, BMS）的关键技术，对动力电池的性能和安全具有重要意义。但电池在采集数据的环节会产生噪声从而影响动力电池SOC估计精度，所以提出一种基于粒子群优化深度置信网络和自适应扩展卡尔曼／自适应H_∞滤波（particle swarm optimization and deep belief network and adaptive extended Kalman filter/adaptive H-infinite filtering fusion, PSO-DBN-AEKF/AHIFF）相融合的电池SOC估计方法。首先，用粒子群优化算法（particle swarm optimization, PSO）对深度置信网络（deep belief network, DBN）加以改进得到粒子群优化深度置信网络（particle swarm optimization and deep belief network, PSO-DBN）进行SOC估计，以避免局部最优问题同时提高计算速率。然后，针对电池数据噪声的问题，通过自适应扩展卡尔曼滤波（adaptive extended Kalman filter, AEKF）来减少白噪声的影响，使用自适应H_∞滤波（adaptive H-infinite filtering fusion, AHIFF）来减少有色噪声的影响，并使用SOC融合估计器实现不同噪声情况下的最优估计策略。实验结果表明：PSO-DBN-AEKF/AHIFF融合算法相较于传统算法在高斯白噪声、有色噪声干扰下的SOC估计结果具有更高的预测精度和鲁棒性。

Accurately predicting the state of charge(SOC) of power batteries is the key technologies of battery management system(BMS), which is of great significance to the performance and safety of power batteries. However, the battery will produce noise during the process of data collection, which will affect the SOC estimation accuracy of power battery. Therefore, a SOC estimation method based on the particle swarm optimization and deep belief network and adaptive extended Kalman filter/adaptive H-infinite filtering fusion (PSO-DBN-AEKF/AHIFF) was proposed. Firstly, the particle swarm optimization (PSO) was used to improve the deep belief network (DBN) to obtain the particle swarm optimization and deep belief network (PSO-DBN) for the SOC estimation, so as to avoid the local optimal problem and increase the speed of computation. Next, with a focus on the issue of battery data noise, the adaptive extended Kalman filter (AEKF) was used to reduce the impact of white noise and the adaptive H-infinite filtering fusion (AHIFF) was used to reduce the impact of colored noise, and the SOC fusion estimator was used to achieve the optimal estimation strategy under different noise conditions. In comparison to the conventional algorithm, the experimental results show that the PSO-DBN-AEKF/AHIFF algorithm has higher prediction accuracy and robustness of SOC estimation results under the interference of Gaussian white noise and colored noise.