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EnglishOn-line optimal control represents a crucial issue in the development of multimode power split hybrid electric vehicles (HEVs). Finding a control strategy that guarantees fuel economy optimality and ease of implementation simultaneously still reveals an open research question. This paper aims at developing an on-line control approach for multimode HEVs based on previously implemented offline control. The two control levels for multimode HEVs are presented: the operating mode selection and the torque split determination. The former is addressed adopting a machine learning approach where artificial neural networks (NNs) are trained in supervised learning using offline control data. The torque split is resolved on-line according to efficiency-based maps extracted offline. Simulation results for a specific multimode HEV design demonstrate the effectiveness of the developed control strategy in minimizing the value of predicted fuel consumption. Furthermore, a sensitivity study is conducted for the NN sizing parameters. The ease of implementation and adaptability suggests the potential application of the developed online control approach in a design methodology for multimode HEVs.
From Off-line to On-line Control of a Multimode Power Split Hybrid Electric Vehicle Powertrain / Anselma, Pier Giuseppe; Huo, Yi; Roeleveld, Joel; Belingardi, Giovanni; Emadi, Ali. - 52(2019), pp. 141-146. ((Intervento presentato al convegno 9th IFAC Symposium on Advances in Automotive Control AAC 2019 tenutosi a Orléans, France nel 23-27 June 2019.
| Titolo: | From Off-line to On-line Control of a Multimode Power Split Hybrid Electric Vehicle Powertrain | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
| Serie: | ||
| Abstract: | On-line optimal control represents a crucial issue in the development of multimode power split hy...brid electric vehicles (HEVs). Finding a control strategy that guarantees fuel economy optimality and ease of implementation simultaneously still reveals an open research question. This paper aims at developing an on-line control approach for multimode HEVs based on previously implemented offline control. The two control levels for multimode HEVs are presented: the operating mode selection and the torque split determination. The former is addressed adopting a machine learning approach where artificial neural networks (NNs) are trained in supervised learning using offline control data. The torque split is resolved on-line according to efficiency-based maps extracted offline. Simulation results for a specific multimode HEV design demonstrate the effectiveness of the developed control strategy in minimizing the value of predicted fuel consumption. Furthermore, a sensitivity study is conducted for the NN sizing parameters. The ease of implementation and adaptability suggests the potential application of the developed online control approach in a design methodology for multimode HEVs. | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2753781
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