Research on Real-time Energy Management Strategy for Hybrid Electric Vehicles based on Edge-cloud Collaborative Optimization

doi:10.3901/JME.2025.16.227

Abstract

Abstract: Improving vehicle fuel economy and reducing the occupation of computing resources are crucial measures to elevate the performance of connected hybrid electric vehicles. To achieve this goal, a real-time energy management strategy for hybrid electric vehicles based on edge-cloud collaborative optimization is proposed here. In order to effectively utilize the driving information stored in the cloud, a sub-cycle division method is designed, and the divided sub-cycles are applied to construct the fitting function, alleviating the online optimization burden of the vehicle. A trigger-based edge-cloud collaborative optimization strategy is proposed. The trigger period is derived and a comprehensive event-triggered mechanism is designed accordingly to reduce the computational load and parameter updating frequency during non-trigger moments, and modify the action in combination with the fitting solution at trigger moments to improve the real-time operation performance of the vehicle. Results demonstrate that the proposed trigger-based edge-cloud collaborative optimization strategy significantly improves the fuel economy of the vehicle and effectively reduces the computational cost of outputting control actions in various testing scenarios. The application potential of the proposed strategy is validated on the hybrid test bench.

Key words: connected hybrid electric vehicles, energy management strategy, event-triggered, edge-cloud collaborative optimization

CLC Number:

U469

YANG Chao, DU Xuelong, WANG Weida, YANG Liuquan, XIANG Changle. Research on Real-time Energy Management Strategy for Hybrid Electric Vehicles based on Edge-cloud Collaborative Optimization[J]. Journal of Mechanical Engineering, 2025, 61(16): 227-238.

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