A Gray-Box Surrogate Vehicle Energy Consumption Model Capable of Real-Time Updating

Vehicle energy consumption model, as a function of its operational environment, plays a significant role in real-time optimization of vehicle route and speed for a given pair of origin and destination with a desired arrival time for minimal energy consumption. In this article, a gray-box vehicle energy consumption model is developed based on a high-fidelity vehicle dynamic model with environmental influence based on the kriging modeling method, which includes rolling resistance, aerodynamics, gravity, and energy consumption of air conditioning and heater (HVAC), along with environmental conditions, such as temperature, wind speed, etc. The data-driven model, trained based on Gaussian process assumption, ensures the accuracy of the resulting model with a modeling error below 2.5%. The real-time model updating is based on recursive least-squares optimization using current driving data so that the model used for route and speed optimization represents the current vehicle status. The proposed gray-box model is validated in computer-in-the-loop simulations using SUMO and MATLAB with less than 2% error of energy consumption, which is a significant improvement over the vehicle dynamic model with up to 35% error in certain cases. A case study also indicates energy consumption reduction for vehicle route-speed optimization.