Hybrid cars are a promising approach for providing individual mobility with lower CO2-emissions and without compromising on affordability and driving range. In order to reach these targets a highly efficient control (energy management) is required. In mass production vehicles control is often organized using simple, quick, and easy to understand rule-based systems. Such a rule-base typically contains a moderate number of parameters which can be tuned using methods like evolutionary algorithms to improve performance. However, prior work basically targets a minimization of fuel consumption. In this work we present a many-objective evolutionary optimization that considers up to 7 objectives in parallel. We outline the additional optimization challenges that arise due to the large number of objectives and demonstrate that a substantial performance increase, over all objectives, can be achieved.
CITATION STYLE
Rodemann, T., Narukawa, K., Fischer, M., & Awada, M. (2015). Many-objective optimization of a hybrid car controller. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9028, pp. 593–603). Springer Verlag. https://doi.org/10.1007/978-3-319-16549-3_48
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