用物理约束神经算子，仅凭速度加速度估算电动车参数与能耗。
A Hybrid Surrogate for Electric Vehicle Parameter Estimation and Power Consumption via Physics-Informed Neural Operators
- 基于傅里叶神经算子构建谱参数算子，融合可微物理模块。
- 特斯拉车均误差0.2kW（约1%高速平均牵引功率），起亚EV9为0.8kW。
- 参数可解释，适配不同采样率，可用于导航优化与车载诊断。
我们提出一种混合代理模型，用于电动汽车参数估计与功率消耗预测。该模型结合了基于傅里叶神经算子骨干网络的新型谱参数算子（Spectral Parameter Operator），以捕捉全局上下文，并在前向传播中集成可微分物理模块。仅需速度和加速度输入，即可输出随时间变化的电机效率、再生制动效率、空气阻力、滚动阻力、有效质量及辅助功率。这些参数驱动嵌入物理规律的电池功率估计，无需额外的物理残差损失。模块化设计使模型收敛至具有物理解释性的参数，反映车辆实时状态与工况。我们在特斯拉Model 3、Model S及起亚EV9的真实行车数据上进行了评估。对于特斯拉车型，平均绝对误差达0.2kW（约为高速巡航时平均牵引功率的1%），起亚EV9约为0.8kW。该框架具备良好可解释性，能泛化至未见工况与采样率，适用于路径优化、节能路线规划、车载诊断及健康状态管理。
原文摘要 · Abstract (English)
We present a hybrid surrogate model for electric vehicle parameter estimation and power consumption. We combine our novel architecture Spectral Parameter Operator built on a Fourier Neural Operator backbone for global context and a differentiable physics module in the forward pass. From speed and acceleration alone, it outputs time-varying motor and regenerative braking efficiencies, as well as aerodynamic drag, rolling resistance, effective mass, and auxiliary power. These parameters drive a physics-embedded estimate of battery power, eliminating any separate physics-residual loss. The modular design lets representations converge to physically meaningful parameters that reflect the current state and condition of the vehicle. We evaluate on real-world logs from a Tesla Model 3, Tesla Model S, and the Kia EV9. The surrogate achieves a mean absolute error of 0.2kW (about 1% of average traction power at highway speeds) for Tesla vehicles and about 0.8kW on the Kia EV9. The framework is interpretable, and it generalizes well to unseen conditions, and sampling rates, making it practical for path optimization, eco-routing, on-board diagnostics, and prognostics health management.
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