| 吴文进,吴晶,詹文法,查申龙,苏建徽.基于云端数据的AEKF-LSTM融合算法预测电池SOC[J].电子测量与仪器学报,2026,40(1):43-50 |
| 基于云端数据的AEKF-LSTM融合算法预测电池SOC |
| AEKF-LSTM fusion algorithm based on cloud data predicts battery SOC* |
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| DOI： |
| 中文关键词: 荷电状态SOC AEKF算法 LSTM神经网络 小动力电动车 云端数据 |
| 英文关键词:state of charge (SOC) AEKF algorithm LSTM neural network lower power electric vehicle cloud data |
| 基金项目:国家自然科学基金重大研究计划项目(92573109)、国家自然科学基金面上项目(62474002)、国家自然科学基金青年基金(62205005)、安徽高校协同创新项目(GXXT-2021-025)资助 |
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| Author | Institution |
| Wu Wenjin | School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246133, China |
| Wu Jing | School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246133, China |
| Zhan Wenfa | School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246133, China |
| Zha Shenlong | School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246133, China |
| Su Jianhui | 2.School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;
3.Institute of Energy of Hefei Comprehensive National Science Center (Anhui Energy Laboratory), Hefei 230071, China |
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| 中文摘要: |
| 针对目前电池荷电状态（stage of charge,SOC）估计算法存在稳定性差、误差大等缺点，提出一种基于实车云端放电数据的自适应扩展卡尔曼滤波（adaptive extended
Kalman filter, AEKF）与长短时记忆（long short term memory,LSTM）融合的算法，预测小动力电动车的电池SOC。首先采用自适应遗忘因子最小二乘法（adaptive forgetting factor
recursive least squares,AFFRLS）辨识电池的二阶RC等效电路模型参数。其次，将云端实时采集到的放电数据作为研究目标，通过AEKF-LSTM融合算法对小动力电动车的电池SOC进行预测实验，实验过程中AEKF-LSTM融合算法将当前时刻的端电压、电流、温度以及上一时刻电池的SOC作为输入，以更新的SOC作为输出训练估计模型。最后，将AEKF-LSTM融合算法和单一AEKF算法预测电池SOC的结果与实际SOC值进行比较，实验结果表明，AEKF-LSTM融合算法的均方根误差（root mean square error,RMSE）为0.005 8 V, 平均绝对误差（mean absolute error,MAE）为0.004 1 V，比AEKF算法的RMSE减小0.008 7 V，MAE减小0.116 4 V，且AEKF-LSTM融合算法的RMSE和MAE均在0.6%以内，证明了该融合算法有较高的估计精度和较强的鲁棒性。 |
| 英文摘要: |
| Owing to the shortcomings of current state of charge (SOC) estimation algorithms, such as poor stability and large error, a new algorithm based on the integration of adaptive extended Kalman filter (AEKF) and short term memory (LSTM) based on real vehicle cloud discharge data was proposed to predict SOC of small-power electric vehicles. Adaptive forgetting factor least square method (AFFRLS) was used to identify the second order RC equivalent circuit model parameters of the battery. Secondly, the real-time discharge data collected by the cloud is taken as the research target, and the AEKF-LSTM fusion algorithm is used to predict the battery SOC of small-power electric vehicles. The AEKF-LSTM fusion algorithm takes the terminal voltage, current, temperature at the current moment and the SOC of the battery at the previous moment as inputs, and uses the updated SOC as the output to train the estimation model. Finally, compare the battery SOC prediction results of the AEKF-LSTM fusion algorithm and the single AEKF algorithm with the actual SOC values. The experimental results show that the root mean square error (RMSE) of the AEKF-LSTM fusion algorithm is 0.005 8 V, and the mean absolute error (MAE) is 0.004 1 V. Compared with the AEKF algorithm, its RMSE is reduced by 0.008 7 V and its MAE is reduced by 0.116 4 V, and both RMSE and MAE of AEKF-LSTM fusion algorithm are less than 0.6%. It is proved that the fusion algorithm has high estimation accuracy and strong robustness. |
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