TY - GEN
T1 - Multi-objective optimisation of a hybrid electric vehicle
T2 - 4th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2007
AU - Cook, Robert
AU - Molina-Cristobal, Arturo
AU - Parks, Geoff
AU - Correa, Cuitlahuac Osornio
AU - Clarkson, P. John
PY - 2007
Y1 - 2007
N2 - The design of a Hybrid Electric Vehicle (HEV) system is an energy management strategy problem between two sources of power. Traditionally, the drive train has been designed first, and then a driving strategy chosen and sometimes optimised. This paper considers the simultaneous optimisation of both drive train and driving strategy variables of the HEV system through use of a multi-objective evolutionary optimiser. The drive train is well understood. However, the optimal driving strategy to determine efficient and opportune use of each prime mover is subject to the driving cycle (the type of dynamic environment, e.g. urban, highway), and has been shown to depend on the correct selection of the drive train parameters (gear ratios) as well as driving strategy heuristic parameters. In this paper, it is proposed that the overall optimal design problem has to consider multiple objectives, such as fuel consumption, reduction in electrical energy stored, and the 'driveability' of the vehicle. Numerical results shows improvement when considering multiple objectives and simultaneous optimisation of both drive train and driving strategy.
AB - The design of a Hybrid Electric Vehicle (HEV) system is an energy management strategy problem between two sources of power. Traditionally, the drive train has been designed first, and then a driving strategy chosen and sometimes optimised. This paper considers the simultaneous optimisation of both drive train and driving strategy variables of the HEV system through use of a multi-objective evolutionary optimiser. The drive train is well understood. However, the optimal driving strategy to determine efficient and opportune use of each prime mover is subject to the driving cycle (the type of dynamic environment, e.g. urban, highway), and has been shown to depend on the correct selection of the drive train parameters (gear ratios) as well as driving strategy heuristic parameters. In this paper, it is proposed that the overall optimal design problem has to consider multiple objectives, such as fuel consumption, reduction in electrical energy stored, and the 'driveability' of the vehicle. Numerical results shows improvement when considering multiple objectives and simultaneous optimisation of both drive train and driving strategy.
UR - http://www.scopus.com/inward/record.url?scp=37249071400&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-70928-2_27
DO - 10.1007/978-3-540-70928-2_27
M3 - Conference contribution
AN - SCOPUS:37249071400
SN - 9783540709275
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 330
EP - 345
BT - Evolutionary Multi-Criterion Optimization - 4th International Conference, EMO 2007, Proceedings
PB - Springer
Y2 - 5 March 2007 through 8 March 2007
ER -