采用Tyrannosaurus优化器进行Quaternion生成对抗驱动的SoC估计,以改善混合动力电动汽车可再生能源能源管理
M Sivaramkrishnan1, Jaganathan Subramani2, Mohammad Mukhtar Alam3,4
1Department of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, India. krishbe95@gmail.com.
Scientific reports
|May 9, 2025
概括
这项研究引入了一种新方法,使用由Tyrannosaurus优化算法 (TOA) 优化的四边形生成对抗网络 (QGAN),用于在混合动力电动汽车 (HEV) 中准确估计电池充电状态 (SOC). 这提高了电动汽车的可再生能源管理.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
背景情况:
- 混合动力电动汽车 (HEV) 的日益普及需要高效的能源管理.
- 精确的充电状态 (SOC) 估计对于HEV中的电池管理系统 (BMS) 至关重要.
- 目前的SOC估计方法需要性能改进才能可靠运行.
研究的目的:
- 在可再生能源管理 (REM) 框架内开发一种新的HEV SOC估计方法.
- 通过改进能源管理,提高电动和混合动力汽车的安全性和效率.
- 为了解决现有的SOC估计技术的局限性.
主要方法:
- 实施深度学习 (DL) 方法,使用四边形生成对抗网络 (QGAN).
- 使用Tyrannosaurus优化算法 (TOA) 优化QGAN模型的超参数.
- 验证DLSOC-REM方法用于HEV中的SOC估计.
主要成果:
- 拟议的DLSOC-REM方法在各种驱动周期和温度的SOC估计中显示出高精度.
- 在US06驱动周期中实现了约0.05%的根平均平方误差 (RMSE).
- 达到0.1%的平均绝对误差 (MAE) 和0.0025%的平均平方误差 (MSE).
结论:
- 基于TOA的QGAN模型为电池建模和SOC估计提供了一种简化但准确的方法.
- 开发的DLSOC-REM框架为HEV安全的可再生能源管理提供了强大的解决方案.
- 这项研究为提高电动汽车电池管理系统性能提供了一种新有效的方法.
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