A Hybrid Surrogate for Electric Vehicle Parameter Estimation and Power Consumption via Physics-Informed Neural Operators

TL;DR

This paper introduces a physics-informed neural operator-based hybrid surrogate model for electric vehicle parameter estimation and power consumption prediction, achieving high accuracy and interpretability on real-world data.

Contribution

It proposes a novel Fourier Neural Operator-based architecture combined with a physics module for accurate, interpretable EV parameter and power estimation from minimal inputs.

Findings

Achieves about 1% error in power estimation for Tesla vehicles.

Generalizes well to unseen conditions and sampling rates.

Enables applications like eco-routing and diagnostics.

Abstract

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.