Hybrid electric vehicles (HEVs) provide large potential to save energy and reduce emission, and smart vehicles bring out great convenience and safety for drivers. By combining these two technologies, vehicles may achi...
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Hybrid electric vehicles (HEVs) provide large potential to save energy and reduce emission, and smart vehicles bring out great convenience and safety for drivers. By combining these two technologies, vehicles may achieve excellent performances in terms of dynamic, economy, environmental friendliness, safety, and comfort. Hence, a smart hybrid electric vehicle (s-HEV) is selected as a platform in this paper to study a car-following process with optimizing the fuel consumption. The whole process is a multi-objective optimal problem, whose optimal solution is not just adding an energy management strategy (EMS) to an adaptive cruise control (ACC), but a deep fusion of these two methods. The problem has more restricted conditions, optimalobjectives, and system states, which may result in larger computing burden. Therefore, a novel fuel consumption optimization algorithm based on model predictive control (MPC) is proposed and some search skills are adopted in receding horizon optimization to reduce computing burden. Simulations are carried out and the results indicate that the fuel consumption of proposed method is lower than that of the ACC+EMS method on the condition of ensuring car-following performances. (C) 2016 Elsevier Ltd. All rights reserved.
Differential Evolution (DE) is a relatively new EA and it has become a major branch of EA. Based on the classical Differential Evolution. we proposed a new multi-objective Differential Evolutionary Algorithm ill this ...
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ISBN:
(纸本)9783540921363
Differential Evolution (DE) is a relatively new EA and it has become a major branch of EA. Based on the classical Differential Evolution. we proposed a new multi-objective Differential Evolutionary Algorithm ill this paper. Compared with NSGA-II and epsilon-MOEA, the experimental results demonstrate that the new algorithm tends to be more effective in obtaining good convergence and can converge to the true Pareto front with comparable efficiency.
multi-objectiveoptimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has so...
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multi-objectiveoptimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time.
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