Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality. To verify the performance of the adaptive strategy, simulation has been conducted under city and highway driving cycles. Optimal solutions of the equivalence factor and the control outputs, i.e., engine speed and torque, are presented. Results show that the adaptive strategy can maintain battery charge sustaining operation, although there is a drawback that engine activation sometimes happens when vehicle is decelerating or braking. A comparative study is also conducted to verify the fuel economy of the proposed strategy. It is shown that with adaptive strategy, fuel consumption is increased by 9.737% in city driving and 2.409% in highway driving.

Adaptive Real-Time Energy Management of a Multi-Mode Hybrid Electric Powertrain / Wang, Yue; Biswas, Atriya; Anselma, Pier Giuseppe; Rathore, Aashit; Toller, Jack; Rane, Omkar; Wasacz, Bryon; Roeleveld, Joel; Keshavarz Motamed, Zahra; Emadi, Ali. - In: SAE TECHNICAL PAPER. - ISSN 0148-7191. - 1:(2022). (Intervento presentato al convegno 2022 SAE World Congress Experience tenutosi a Detroit, MI, USA nel 5-7 Aprile 2022) [10.4271/2022-01-0676].

Adaptive Real-Time Energy Management of a Multi-Mode Hybrid Electric Powertrain

Anselma, Pier Giuseppe;
2022

Abstract

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality. To verify the performance of the adaptive strategy, simulation has been conducted under city and highway driving cycles. Optimal solutions of the equivalence factor and the control outputs, i.e., engine speed and torque, are presented. Results show that the adaptive strategy can maintain battery charge sustaining operation, although there is a drawback that engine activation sometimes happens when vehicle is decelerating or braking. A comparative study is also conducted to verify the fuel economy of the proposed strategy. It is shown that with adaptive strategy, fuel consumption is increased by 9.737% in city driving and 2.409% in highway driving.
2022
File in questo prodotto:
File Dimensione Formato  
2022-01-0676.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.64 MB
Formato Adobe PDF
1.64 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2961600