Discovering driving strategies with a multiobjective optimization algorithm |
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Affiliation: | 1. School of Automotive Engineering, Chongqing University, Chongqing 400044, China;2. School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore;3. State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., No. 9 Jinyu Street, Liangjiang New Area, Chongqing, 400039, China;4. AVL Powertrain UK Ltd, Coventry, CV4 7EZ, UK;5. Mechanical and Mechatronics Engineering, Univ. of Waterloo, ON, N2L 3G1, Canada |
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Abstract: | When driving a vehicle along a given route, several objectives such as the traveling time and the fuel consumption have to be considered. This can be viewed as an optimization problem and solved with the appropriate optimization algorithms. The existing optimization algorithms mostly combine objectives into a weighted-sum cost function and solve the corresponding single-objective problem. Using a multiobjective approach should be, in principle, advantageous, since it enables better exploration of the multiobjective search space, however, no results about the optimization of driving with this approach have been reported yet. To test the multiobjective approach, we designed a two-level Multiobjective Optimization algorithm for discovering Driving Strategies (MODS). It finds a set of nondominated driving strategies with respect to two conflicting objectives: the traveling time and the fuel consumption. The lower-level algorithm is based on a deterministic breadth-first search and nondominated sorting, and searches for nondominated driving strategies. The upper-level algorithm is an evolutionary algorithm that optimizes the input parameters for the lower-level algorithm. The MODS algorithm was tested on data from real-world routes and compared with the existing single-objective algorithms for discovering driving strategies. The results show that the presented algorithm, on average, significantly outperforms the existing algorithms. |
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Keywords: | Driving strategy Traveling time Fuel consumption Driving optimization Multiobjective optimization Black-box approach |
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