On-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) [10.1016/j.ifacol.2019.09.023].

From Off-line to On-line Control of a Multimode Power Split Hybrid Electric Vehicle Powertrain

Anselma, Pier Giuseppe;Belingardi, Giovanni;
2019

Abstract

On-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.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2753781
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