Hybrid Dynamic Programming and Regression Approach for Fuel-Efficient Eco-Driving Optimization

Eco-driving has emerged as a promising approach to reducing fuel consumption in road vehicles by optimizing driving behavior for enhanced system efficiency. This paper formulates the eco-driving problem within an optimal control framework. Due to the nonlinear dynamics and complex operational constraints, dynamic programming (DP) is employed to solve the optimization problem. To further improve computational efficiency and ensure constraint compliance, we propose a hybrid method that integrates DP with a regression-based algorithm. The proposed approach is validated through co-simulation using Matlab/Simulink and CarSim, demonstrating its effectiveness in achieving fuel-efficient vehicle operation.