用深度学习预测油电混动车能耗，精度高且可扩展。
Deep Learning-Based Analysis of Power Consumption in Gasoline, Electric, and Hybrid Vehicles
- 基于动力系统特征，用机器学习和深度模型预测实时与累计能耗。
- 汽油车瞬时误差低于0.001，累计误差低于3%；电动车和混动车累计误差分别低于4.1%和2.1%。
- 适合研究新能源车能效、车企优化动力系统或做车载能耗评估的人。
准确预测能耗对提升效率和降低环境影响至关重要，但传统依赖专用仪器或固定物理模型的方法难以大规模应用于真实场景。本研究提出一种可扩展的数据驱动方法，利用动力系统动态特征集，结合传统机器学习与深度神经网络，估算内燃机（ICE）、纯电（EV）及混合动力（HEV）车辆的瞬时与累计能耗。ICE模型实现高瞬时精度，平均绝对误差与均方根误差均在10⁻³量级，累计误差低于3%；针对EV与HEV，Transformer与长短期记忆（LSTM）模型表现最佳，累计误差分别低于4.1%与2.1%。结果验证了该方法在多类型车辆与模型中的有效性。不确定性分析显示，EV与HEV数据集变异更大，源于复杂能量管理策略，凸显为先进动力系统构建鲁棒模型的必要性。
原文摘要 · Abstract (English)
Accurate power consumption prediction is crucial for improving efficiency and reducing environmental impact, yet traditional methods relying on specialized instruments or rigid physical models are impractical for large-scale, real-world deployment. This study introduces a scalable data-driven method using powertrain dynamic feature sets and both traditional machine learning and deep neural networks to estimate instantaneous and cumulative power consumption in internal combustion engine (ICE), electric vehicle (EV), and hybrid electric vehicle (HEV) platforms. ICE models achieved high instantaneous accuracy with mean absolute error and root mean squared error on the order of $10^{-3}$, and cumulative errors under 3%. Transformer and long short-term memory models performed best for EVs and HEVs, with cumulative errors below 4.1% and 2.1%, respectively. Results confirm the approach's effectiveness across vehicles and models. Uncertainty analysis revealed greater variability in EV and HEV datasets than ICE, due to complex power management, emphasizing the need for robust models for advanced powertrains.
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