现代车辆CO2排放的自动化决策框架使用多模式发动机遥测和功能可解释性.
Shelesh Krishna Saraswat1, Mustafa Abdullah2, Mohammed Ihsan Habelalmateen3,4
1Department of ECE, GLA University, Mathura, 281406, India. shelesh.saraswat@gla.ac.in.
Scientific reports
|March 9, 2026
概括
预测车辆的二氧化碳排放是复杂的. 本研究介绍了一种使用多层感知器 (MLP) 和元启发式优化进行更准确的二氧化碳排放建模的机器学习框架.
科学领域:
- 环境科学 环境科学
- 汽车工程 汽车工程
- 人工智能的人工智能
背景情况:
- 汽车二氧化碳排放的预测受到复杂的发动机动力学和驾驶条件的阻碍.
- 现有的模型与燃料,机械和操作参数的非线性相互作用作斗争.
研究的目的:
- 为准确的车辆二氧化碳排放建模开发高性能机器学习框架.
- 通过使用先进的人工智能技术,提高预测准确性和趋同稳定性.
主要方法:
- 使用多层感知器 (MLP) 架构与元启发式优化 (角优化算法和巨型鸟优化) 结合.
- 采用多模式发动机遥测数据 (燃料类型,传动,排量等). ) 的情况.
- 应用特征选择 (递归特征消除) 和可解释性技术 (SHAP,类激活映射) 来识别关键排放驱动因素.
主要成果:
- 通过巨型鱼优化增强的MLP实现了优异的预测性能,R2 = 0.9881和RMSE = 6.478.
- 通过先进的解释性方法,确定了影响车辆二氧化碳排放的主要驱动因素.
- 在排放建模中证明了更高的精度和趋同稳定性.
结论:
- 集成可解释的AI模型为车辆排放预测提供了一种强大的方法.
- 该框架可以为低碳汽车设计和城市流动性规划提供信息.
- 调查结果支持对运输部门排放的环保意识的政策制定.
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