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

Autor: Guillaume Colin, D. Maamria, Kristan Gillet, Cédric Nouillant, Yann Chamaillard
Přispěvatelé: Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), PSA Peugeot - Citroën (PSA), PSA Peugeot Citroën (PSA)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: IEEE Transactions on Vehicular Technology
IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers, 2019, 68 (7), pp.6320-6330. ⟨10.1109/TVT.2019.2914256⟩
ISSN: 0018-9545
DOI: 10.1109/TVT.2019.2914256⟩
Popis: International audience; In 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.
Databáze: OpenAIRE