This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To address this limitation, an improved DDP approach, integrating the actual travelled position of the vehicle into the control law, is proposed. This way, a given DDP-based EMS can be applied to all the driving cycles, yet still measured on the same road. Stochastic dynamic programming (SDP)-based EMSs are also developed and prove to be more adaptive to driving scenarios completely different from the ones used for their computation. Real-world driving cycles are employed in all the presented cases, while a reduced HEV powertrain model is used to alleviate the typical DP computational burden.
A dynamic programming approach for energy management in hybrid electric vehicles under uncertain driving conditions / Deng, Junpeng; Tipaldi, Massimo; Glielmo, Luigi; Massenio, Paolo Roberto; Del Re, Luigi. - In: INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE. - ISSN 0020-7721. - STAMPA. - (2024). [10.1080/00207721.2024.2304666]
A dynamic programming approach for energy management in hybrid electric vehicles under uncertain driving conditions
Massenio, Paolo RobertoMembro del Collaboration Group
;
2024-01-01
Abstract
This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To address this limitation, an improved DDP approach, integrating the actual travelled position of the vehicle into the control law, is proposed. This way, a given DDP-based EMS can be applied to all the driving cycles, yet still measured on the same road. Stochastic dynamic programming (SDP)-based EMSs are also developed and prove to be more adaptive to driving scenarios completely different from the ones used for their computation. Real-world driving cycles are employed in all the presented cases, while a reduced HEV powertrain model is used to alleviate the typical DP computational burden.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.