Integrated Cost-optimal Convex Optimization for Eco-Driving of Connected Hybrid Electric Vehicles on Sloped and Curved Roads

Sloped and curved roads are inevitable in daily driving, where slope and curvature act as critical external disturbances that directly influence energy-efficient driving performance. Meanwhile, an integrated eco-driving framework combining speed optimization and energy management can enhance energy-saving optimality. However, balancing computing burden and solution optimality for such frameworks under sloped and curved conditions remains challenging. To address this, this paper proposes an integrated convex optimization method for hybrid electric vehicles, aiming to improve driving efficiency, reduce energy consumption, and extend battery lifetime while ensuring high computational efficiency. First, a vehicle driving model incorporating road curvature, powertrain energy-flow model, and battery state-of-health update model, are established, forming an integrated eco-driving optimization problem targeting minimum total cost. Given the problem's high complexity, convex approximations and relaxations are applied to construct an integrated convex optimization problem in second-order cone programming form, balancing optimality and efficiency. The feasibility of the relaxation method is theoretically analyzed. Simulation tests on flat racetrack, viaduct, and hilly roads with slopes and curves show that the proposed method effectively reduces the total cost of energy savings and battery life extension, with negligible memory requirements and significantly higher computational efficiency, where computing time is less than 0.005% of that of dynamic programming.