基于PSO-BP-UKF算法的锂电池SOC估计方法研究OA
Research on SOC Estimation of Lithium Battery Based on PSO-BP-UKF Algorithm
锂电池的荷电状态(SOC)是锂电池质量管理的核心之一.基于有效的SOC估计是确保锂电池安全高效工作的必要条件,提出一种利用粒子群算法(PSO)优化反向传播(BP)神经网络,并将优化后的BP神经网络SOC输出值作为无迹卡尔曼滤波(UKF)观测值的锂电池SOC估计方法.使用来自马里兰大学的FUDS工况电池测试数据,将所提的PSO-BP-UKF算法与GA-BP-UKF算法、BP算法进行对比.结果表明,在25℃环境下,PSO-BP-UKF算法的最大偏差<3.17%,平均误差<6.44%,均方根偏差<0.002 5,相比GA-BP-UKF算法和BP方法都有较大幅度的提高,说明所提算法具备有效性与实用性.
The state of charge(SOC)of lithium batteries is one of the core of quality management of lithium batteries.Based on effective SOC estimation is also necessary to ensure the safe and efficient operation of lithium batteries,A method for estimating the SOC of lithium batteries is proposed,which uses particle swarm algorithm(PSO)to optimize the backpropagation(BP)neural network as the observed value of the unscented Kalman filter(UKF).The proposed PSO-BP-UKF algorithm is compared with the GA-BP-UKF algorithm and the BP algorithm using FUDS operating condition battery test data from the University of Maryland.Taking the test results in 25℃environment,the maximum deviation of PSO-BP-UKF algorithm is within 3.17%,the average error is within 6.44%,and the root-mean-square deviation is within 0.002 5,which is significantly improved than both GA-BP-UKF algorithm and BP method,and shows that the proposed algorithm is the effective and practical.
李洋;石振刚
沈阳理工大学 信息科学与工程学院,辽宁 沈阳 110159
动力与电气工程
SOC估计无迹卡尔曼滤波算法锂电池粒子群算法BP神经网络
SOC estimationunscented Kalman filter(UKF)algorithmlithium batteryparticle swarm algorithm(PSO)BP neural network
《电器与能效管理技术》 2024 (006)
42-48 / 7
中国地震局地震科技星火计划公关项目(XH24007A);辽宁省地震局预研项目(LZ202302Y)
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