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Mark Schudeleit, Meng Zhang, Xiaofei Qi, Ferit Küçükay, Andreas Rausch

Enhancement of an Adaptive HEV Operating Strategy Using Machine Learning Algorithms

SSE-SZQRK+16

For vehicle manufacturers as well as for many drivers saving fuelhas been a popular issue. In order to maximize the potential of the consumptionsavings,optimization of operating strategy of vehicles, particularly of HEV(HEV: hybrid electric vehicle.), becomes an increasingly important task.To enhance the current rule-based operating strategy of HEV, an adaptiveheuristic operating strategy has been developed, which identifies driving patternsand chooses the best parameter set for the control strategy from a database.However, this strategy does not fit to the driving behavior of individual drivers.Therefore, a further knowledge-based approach that independently optimizesthe operating strategy has been developed using of multigene symbolicregression by utilizing supervised machine learning.The investigation showed that a knowledge-based approach is able to saveabout 18.3 % CO2 and fuel compared to a basic heuristic strategy.

Journal

Proceedings of the 7th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2016)

Jahr

2016

Ort

Corfu, Greece

Datei

Enhancement of an Adaptive HEV Operating Strategy Using Machine Learning Algorithms

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