基于RUN

泰然健康网
2024-12-16 06:19

摘要：

实车动力电池的健康状态(state of health，SOH)评估存在数据质量差、工况不统一、数据利用率低等问题，本文面向阶梯倍率充电工况构建多源特征提取及SOH估计模型。首先，通过数据清洗、切割、填充，获取独立的充电片段；其次，基于不同电流阶段计算容量，实现原始数据利用率达96.9%，并与单独限定SOC范围计算容量的方法相比，误差降低48.1%以上；然后，从当前工况、历史累积两个维度提取多个健康因子，对于当前工况特征值，通过灰色关联度及干扰性随机森林重要度分析双重筛选。对于历史累积特征值，利用Spearson相关性分析和核主成分分析方法(kernel principal component analysis，KPCA)降低信息冗余；最后，对门控循环单元网络模型(gated recurrent unit，GRU)引入注意力机制和龙格库塔优化算法(Runge Kutta optimizer，RUN)，建立RUN-GRU-attention模型，基于实车运行数据集与现有5种模型进行对比，实验结果表明，无论是包含单阶段还是多阶段电流的测试样本，优化模型的估计精度更佳，误差不高于0.0086，并且随着充电循环次数增加表现出良好的误差收敛性，可有效预测SOH波动趋势。

关键词: 实车动力电池, 阶梯倍率充电, 健康状态估计, 多源特征提取, 龙格库塔优化算法, 机器学习

Abstract:

The evaluation of the state of health (SOH) for real-vehicle batteries is challenging owing to poor data quality, inconsistent operating conditions, and limited data utilization. This paper presents a multisource feature extraction and SOH estimation model specifically designed for step-rate charging conditions. First, charging segments are obtained through data cleaning, segmenting, and filling processes. Next, capacity is calculated using data from various current stages, achieving a raw data utilization rate of 96.9%. Compared to methods that calculate capacity within a restricted state of charge (SOC) range, this approach reduces error by over 48.1%. Finally, health factors are extracted based on current operating conditions and historical data accumulation. For current operating condition feature values, dual screening is performed using grey correlation analysis and random forest importance analysis to manage interference. For historical cumulative feature values, Spearman correlation analysis and Kernel Principal Component Analysis (KPCA) are employed to reduce information redundancy. Finally, an attention mechanism and Runge-Kutta optimizer (RUN) are integrated into the Gated Recurrent Unit (GRU) network model. The performance of this optimized model is then compared with five existing models using an actual vehicle operation dataset. The experimental results demonstrate that the optimized model achieves superior estimation accuracy, with an error margin of no more than 0.0086, regardless of whether the test samples include single-stage or multi-stage currents. Additionally, the model shows excellent error convergence as the number of charging cycles increases and effectively predicts the trend of SOH fluctuations.

Key words: real-vehicle battery, step rate charging, SOH estimation, multisource features extraction, Runge Kutta optimization algorithm, machine learning

中图分类号:

TM 911