Optimal Ecodriving Control: Energy-Efficient Driving of Road Vehicles as an Optimal Control Problem

Sciarretta, Antonio; De Nunzio, Giovanni; Ojeda, Luis Leon · 2015 · OpenAlex-citations

DOI: 10.1109/mcs.2015.2449688

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Summary

This paper addresses the challenge of reducing greenhouse gas emissions and energy consumption in the transportation sector, specifically focusing on improving "wheel-to-meters" efficiency for passenger vehicles. While traditional eco-driving relies on heuristic guidelines or reactive feedback systems, the authors propose framing energy-efficient driving as a rigorous optimal control problem. The goal is to determine drive commands that minimize energy consumption for a given trip without requiring structural changes to the vehicle, leveraging predictive information about road topology and traffic conditions. The authors develop a general formulation of the Eco-Driving Optimal Control Problem (ED-OCP) applicable to internal combustion engine vehicles (ICEVs), electric vehicles (EVs), and hybrid-electric vehicles (HEVs). The methodology involves modeling vehicle longitudinal dynamics, including resistance forces from aerodynamics, rolling friction, and road slope, alongside powertrain energy consumption models. The optimization problem is defined by minimizing an objective function representing energy consumption over a specific time or distance horizon, subject to state constraints (such as speed limits and battery state of charge) and control constraints (such as torque limits). The paper reviews three primary offline solution techniques: Pontryagin’s Minimum Principle (PMP), which provides analytical insights but faces numerical challenges; Dynamic Programming (DP), a numerical method that handles complex constraints but suffers from computational complexity; and analytical solutions, which offer closed-form results suitable for real-time implementation. For HEVs, a bi-level optimization approach is described, decoupling speed trajectory optimization from power-split optimization. The findings demonstrate that optimal driving profiles can be systematically derived for various scenarios, including highway driving, urban traffic with traffic lights, and adaptive cruise control. The paper highlights that optimal control strategies often involve "bang-bang" control structures or singular arcs, where the vehicle operates at boundary limits or specific efficient regimes. Specific analytical solutions are presented for EVs and HEVs, showing how optimal torque and speed profiles depend on road slope and traffic constraints. The authors also discuss the integration of route information, such as GIS data for road slope and traffic light status, which are critical for calculating these optimal trajectories. The significance of this work lies in its unification of disparate eco-driving concepts into a single optimal control framework. By providing a general mathematical formulation and reviewing solution methods, the paper bridges the gap between theoretical control systems and practical automotive applications. It suggests that moving from heuristic rules to predictive, optimization-based control can yield substantial energy savings. The inclusion of analytical solutions and bi-level optimization methods points toward feasible real-time implementations, supporting the development of advanced driver assistance systems and autonomous driving features that prioritize energy efficiency.

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• eco driving