A hierarchical eco-driving strategy for hybrid electric vehicles via vehicle-to-cloud connectivity

Rui Liu, Hui Liu, Shida Nie, Lijin Han and Ningkang Yang

Energy, 2023, vol. 281, issue C

Abstract: The emergence of the intelligent transportation system and cloud computing technology has brought the available traffic information and increasing computing power, which lead to a significant improvement in driving performance. In order to enhance energy economy and mobility simultaneously, a hierarchical eco-driving strategy is proposed in this paper, which is comprised of the cloud-level controller and the vehicle-level controller. The dynamic programming-based cloud-level controller optimizes the velocity and battery state-of-charge utilizing the global traffic information obtained from the intelligent transportation system. However, the global traffic information suffers from uncertainties, which deteriorates the effectiveness of the cloud-level controller. The vehicle-level controller is constructed on the model predictive control framework, aiming to cope with the uncertainties, improve fuel economy and reduce travel time. Besides, a transfer learning-based particle swarm optimization algorithm is presented for solving the optimization problem in model predictive control, which can achieve great control performance utilizing the knowledge from the cloud-level controller. To validate the effectiveness of the proposed strategy, simulation tests are conducted. The results demonstrate that the proposed strategy can achieve near-global-optimal performance in fuel economy and mobility. Moreover, the real-time performance of the proposed strategy is validated through the hardware-in-loop test.

Keywords: Eco-driving; Vehicle-to-cloud connectivity; Transfer learning-based particle swarm optimization; Model predictive control; Hybrid electric vehicles (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:281:y:2023:i:c:s0360544223016250

DOI: 10.1016/j.energy.2023.128231

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