This paper presents a comparison between a Fuzzy Logic and an Equivalent Consumption Minimization Strategy for the energy management of a Hybrid Electric Vehicle in P2 configuration, i.e. with the secondary energy converter located downstream the clutch. The design of the two methods is conducted aiming to minimize the fuel consumption. Although the adopted strategies are not charge sustaining, an additional goal of the techniques is to obtain a net energy extracted from the battery over a driving cycle that is not far from zero. The presented simulation results are obtained in the case of two homologation driving cycles, namely NEDC and WLTP. The objective of the study is to demonstrate that a non-optimal rule-based method can achieve a performance that is equivalent to a model-based optimal analytical approach.

Fuzzy Logic vs Equivalent Consumption Minimization Strategy for Energy Management in P2 Hybrid Electric Vehicles / Rahmeh, Hadi; Bonfitto, Angelo; Ruzimov, Sanjarbek. - ELETTRONICO. - 4:(2020). (Intervento presentato al convegno ASME - 22nd International Conference on Advanced Vehicle Technologies (AVT) tenutosi a Virtual nel 17-19/08/2020) [10.1115/DETC2020-22431].

Fuzzy Logic vs Equivalent Consumption Minimization Strategy for Energy Management in P2 Hybrid Electric Vehicles

Rahmeh, Hadi;Bonfitto, Angelo;Ruzimov, Sanjarbek
2020

Abstract

This paper presents a comparison between a Fuzzy Logic and an Equivalent Consumption Minimization Strategy for the energy management of a Hybrid Electric Vehicle in P2 configuration, i.e. with the secondary energy converter located downstream the clutch. The design of the two methods is conducted aiming to minimize the fuel consumption. Although the adopted strategies are not charge sustaining, an additional goal of the techniques is to obtain a net energy extracted from the battery over a driving cycle that is not far from zero. The presented simulation results are obtained in the case of two homologation driving cycles, namely NEDC and WLTP. The objective of the study is to demonstrate that a non-optimal rule-based method can achieve a performance that is equivalent to a model-based optimal analytical approach.
2020
978-0-7918-8393-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2851464