用物理约束神经网络精准预测电动车能耗与动态参数。
EV-PINN: A Physics-Informed Neural Network for Predicting Electric Vehicle Dynamics
- 基于物理约束与自动微分，从车速和时间推算动力参数。
- 在特斯拉车型上验证，误差低于0.0023，能耗预测极准。
- 无需额外传感器，适合车载实时能耗估算场景。
车载动态参数（如空气阻力、滚动阻力）的实时预测，有助于实现电动车的精确路径规划。本文提出EV-PINN，一种用于预测巡航期间瞬时电池功率与累计能耗的物理信息神经网络方法，可泛化至电动车非线性动力学。该方法利用基于动力学的自动微分，学习电机效率、再生制动效率、车辆质量、空气阻力系数及滚动阻力系数等真实世界参数，并确保与实测车辆数据一致。模型在特斯拉Model 3长续航版和Model S上分别使用15分钟和35分钟的现场电池日志数据进行验证。仅以车速和时间作为输入，模型在两种车型上分别取得0.002195和0.002292的验证损失，展现出优异的准确性和泛化能力。结果表明，该方法可在无需额外传感器的情况下，有效估计参数并预测实际驾驶条件下的电池使用情况。
原文摘要 · Abstract (English)
An onboard prediction of dynamic parameters (e.g. Aerodynamic drag, rolling resistance) enables accurate path planning for EVs. This paper presents EV-PINN, a Physics-Informed Neural Network approach in predicting instantaneous battery power and cumulative energy consumption during cruising while generalizing to the nonlinear dynamics of an EV. Our method learns real-world parameters such as motor efficiency, regenerative braking efficiency, vehicle mass, coefficient of aerodynamic drag, and coefficient of rolling resistance using automatic differentiation based on dynamics and ensures consistency with ground truth vehicle data. EV-PINN was validated using 15 and 35 minutes of in-situ battery log data from the Tesla Model 3 Long Range and Tesla Model S, respectively. With only vehicle speed and time as inputs, our model achieves high accuracy and generalization to dynamics, with validation losses of 0.002195 and 0.002292, respectively. This demonstrates EV-PINN's effectiveness in estimating parameters and predicting battery usage under actual driving conditions without the need for additional sensors.
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