基于额外树回归器的电动汽车电池的有效充电状态估计:基于数据的方法
Sadiqa Jafari1, Yung-Cheol Byun2
1Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, South Korea.
Heliyon
|December 13, 2024
概括
准确的电动汽车充电状态 (SoC) 预测对于可持续的移动性至关重要. 一个额外树回归器模型在预测电动汽车电池SoC方面表现出卓越的表现,超过了LightGBM.
科学领域:
- * 电气工程 电气工程
- * 可持续的交通运输
- * 机器学习用于能源系统.
背景情况:
- *全球变暖需要减少碳排放,推动对电动汽车 (EV) 的需求.
- *精确的电荷状态 (SoC) 估计对于优化电动汽车电池性能和能源管理至关重要.
- * 现有的SoC预测方法需要改进,以便在现实世界中应用.
研究的目的:
- *为电动汽车电池系统开发和验证一个强大的SoC预测模型.
- * 评估机器学习算法的有效性,特别是额外树回归器 (ETR) 和光梯度增强 (LightGBM),用于SoC估计.
- *强调电池容量在SoC预测中的重要性.
主要方法:
- *利用了包括电压,电流,温度和SoC在内的各种驾驶周期下的电动汽车的综合数据集.
- * 实现并比较了额外树回归器 (ETR) 和光梯度增强 (LightGBM) 算法的性能.
- * 专注于代表性驾驶周期,以确保真实世界条件的复制.
主要成果:
- * 额外树回归器 (ETR) 模型在SoC预测中显著优于LightGBM模型.
- * ETR 实现了高的 R 平方值,即 0.9983.
- * ETR表现出卓越的准确性,根平均平方误差 (RMSE) 为0.62,平均绝对误差 (MAE) 为0.085,平均平方误差 (MSE) 为0.39.
结论:
- *开发的ETR模型为电动汽车电池提供了准确可靠的SoC预测.
- *精确的SoC估计提高了电动汽车能源管理和电池利用率.
- * 该研究强调了电池容量在有效预测可持续移动性SoC中的关键作用.
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