A general constrained optimization framework for the eco-routing problem: Comparison and analysis of solution strategies for hybrid electric vehicles

Vehicles electrification marks a very important step towards sustainable mobility. However, energy efficiency and driving range of electrified vehicles are nowadays a major concern. From an algorithmic perspective, eco-routing opens up new possibilities regarding the strategies and tools aimed at improving energy efficiency by finding an energy-minimal route under different constraints coming from vehicle characteristics (powertrain, battery capacity, etc.) and user preferences (travel time, etc.). In this work, a powertrain-independent speed prediction model is presented. This model is then used to derive a fast numerical solution of the powertrain energy management for hybrid electric vehicles. Furthermore, a new general formulation is derived for the minimum-energy navigation problem, with a focus on the specific complexity introduced by electrified vehicles. The general constrained optimization problem is reformulated in several alternative ways in order to achieve a solution in limited computation time. The most commonly used approaches nowadays in the literature (integer programming and shortest path algorithms on directed graphs) are compared and benchmarked in terms of solution accuracy and computational effort. The objective is to identify best-practices in accurately and efficiently solving the constrained eco-routing problem.