| 引用本文: | 毛 玲,赵建辉,林 涛,等.数据缺失下基于变递推间隔修正辅助模型的电池荷电状态估计[J].电力系统保护与控制,2025,53(22):132-140.[点击复制] |
| MAO Ling,ZHAO Jianhui,LIN Tao ,et al.State of charge estimation of lithium-ion batteries under data loss based on a variable recursive interval correction auxiliary model[J].Power System Protection and Control,2025,53(22):132-140[点击复制] |
|
| 摘要: |
| 在基于模型的锂离子电池荷电状态(state of charge, SOC)估计中，传感器采集的数据对SOC估计至关重要。在实际应用中，由于传感器随机故障，可能导致数据缺失的问题，将严重影响SOC估计的准确性。为了解决这一问题，提出了一种基于变递推间隔修正辅助模型随机梯度的方法。首先，引入了正态分布参数模拟传感器随机故障的情况。然后，在数据缺失下，通过定义已知数据点的整数序列，采用基于变递推间隔的方法得到不可观测的数据，并通过修正辅助模型随机梯度迭代算法补偿未知的信息向量参数。最后，在参数辨识中引入了收敛指数和遗忘因子以提高参数的收敛速度和精度。实验结果表明，所提方法在各种工况和不同缺失率下的平均绝对误差在2.5%以内，表现出较强的鲁棒性。 |
| 关键词: 锂离子电池 荷电状态 数据缺失 随机梯度 辅助模型 |
| DOI：10.19783/j.cnki.pspc.241735 |
| 投稿时间：2024-12-25修订日期：2025-07-04 |
| 基金项目:上海市自然科学基金项目资助(21ZR1425300) |
|
| State of charge estimation of lithium-ion batteries under data loss based on a variable recursive interval correction auxiliary model |
| MAO Ling1,ZHAO Jianhui1，2,LIN Tao 1,ZHAO Jinbin3,ZHAO Shudong4,BI Junhui5 |
| (1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 2. Shanghai Power T&T
Engineering Company Co., Ltd., Shanghai 200235, China; 3. Offshore Wind Power Research Institute, Shanghai University of
Electric Power, Shanghai 200090, China; 4. Chongqing Changan Automobile Company Limited, Chongqing 400023, China;
5. The Fourth Construction Co., Ltd. of China Electronics System Engineering, Shijiazhuang 050081, China) |
| Abstract: |
| In model-based state of charge (SOC) estimation for lithium-ion batteries, data collected by sensors are crucial for accurate SOC estimation. In practical applications, random sensor failures may lead to data loss, which can seriously affect the accuracy of SOC estimation. To address this problem, a method based on a variable recursive interval correction auxiliary model with stochastic gradient is proposed. First, a normal distribution parameter is introduced to simulate random sensor failures. Second, under conditions of data loss, a sequence of integers representing known data points is defined, and the unobservable data are obtained using a variable recursive interval approach. The unknown information vector parameters are then compensated through a correction auxiliary model stochastic gradient iterative algorithm. Finally, a convergence index and forgetting factor are introduced into parameter identification to improve convergence speed and accuracy. Experimental results show that the proposed method achieves a mean absolute error within 2.5% under various operating conditions and different data loss rates, demonstrating strong robustness. |
| Key words: lithium-ion battery SOC data loss stochastic gradient auxiliary model |
