Optimal Predictive Eco-Driving Cycles for Conventional, Electric, and Hybrid Electric Cars

International audienceIn this paper, the computation of eco-driving cycles for electric, conventional and hybrid vehicles using receding horizon and optimal control is studied. The problem is formulated as consecutive-optimization problems aiming at minimizing the vehicle energy consumption under traffic and speed constraints. The impact of the look-ahead distance and the optimization frequency on the optimal speed computation is studied to find a trade-off between the optimality and the computation time of the algorithm. For the three architectures considered, simulation results show that in urban driving conditions, a look-ahead distance of 300m to 500m leads to a sub-optimality less than 1% in the energy consumption compared to the global solution. For highway driving conditions, a look-ahead distance of 1km to 1.5km leads to a sub-optimality less than 2% compared to the global solution

Optimal predictive eco-driving cycles for conventional and electric cars

International audienceIn this paper, the computation of eco-driving cycles for electric and conventional vehicles using receding horizon and optimal control is investigated. The problem is formulated as consecutive-optimization problems aiming at minimizing the vehicle energy consumption under traffic and speed constraints. The solving method is based on Dynamic Programming (DP). The impact of the look-ahead distance on the optimal speed computation is studied to find a trade-off between the optimality and the computation time. Simulation results show that in urban driving conditions, a look-ahead distance of 300m to 500m leads to a sub-optimality less than 0.6% in the energy consumption compared to the global solution. For highway driving conditions, a look-ahead distance of 1km to 2km leads to a sub-optimality less than 0.7% compared to the global solution

Computation of eco-driving cycles for Hybrid Electric Vehicles: Comparative analysis

International audienceIn this paper, the calculation of eco-driving cycles for a Hybrid Electric Vehicle (HEV), using Dynamic Programming (DP), is investigated from the solving method complexity viewpoint. The study is based on a comparative analysis of four optimal control problems formulated using distinct levels of modeling. Starting with three state dynamics (vehicle position and speed, battery state-of-charge) and three control variables (engine and electric machine torque, gear-box ratio), the number of state variables is reduced to two in a first simplification. The other two simplifications are based on decou-pling the optimization of the control variables into two steps: an eco-driving cycle is calculated supposing that the vehicle is propelled only by the engine. Then, assuming that the vehicle follows the eco-driving cycle calculated in the first step, an off-line energy management strategy (torque split) for an HEV is calculated to split the requested power at the wheels between the electric source and the engine. As is shown, the decreased complexity and the * Corresponding author. decoupling optimization lead to a sub-optimality in fuel economy while the computation time is noticeably reduced. Quantitative results are provided to assess these observations

Towards an Analytical Eco-Driving Cycle Computation for Conventional Cars

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PROCEDE DE DETERMINATION D’UNE CONSIGNE DE VITESSE POUR MINIMISER LA CONSOMMATION ENERGETIQUE D’UN VEHICULE

L'invention porte sur un procédé de détermination d'une consigne de vitesse pour une chaîne de traction d'un véhicule, ledit procédé comprenant les étapes suivantes - la détermination (E2) d'une distance d'observation, désignée horizon électronique, inférieure à la distance totale, - la mise en oeuvre (E3) d'un premier algorithme de détermination d'un profil de vitesse maximale sur un tronçon considéré, jusqu'à l'horizon électronique, à partir de données issues d'au moins une source, - la mise en oeuvre (E5) d'un deuxième algorithme d'optimisation de la consommation énergétique pour déterminer un profil de vitesse optimale permettant de minimiser la consommation énergétique sur le tronçon considéré, jusqu'à l'horizon électronique, en fonction au moins du profil de vitesse maximale déterminée par le premier algorithme