Optimization of Energy Management Strategy for Series Hybrid Electric Vehicle Equipped with Dual-Mode Combustion Engine Under NVH Constraints
Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without co...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/24/12021 |
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| Summary: | Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without considering the drivability, which is affected by noise–vibration–harshness (NVH) constraints at low vehicle speeds. In this paper, a dual-mode combustion engine was implemented in a plug-in series hybrid electric vehiclethat could operate efficiently either at low loads in homogeneous charge compression ignition (HCCI) mode or at high loads in spark ignition (SI) mode. An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. A MATLAB/Simulink simulation was performed using a well-calibrated model of the target HEV to validate the proposed method, and the results showed that it can achieve a reduction in fuel consumption of around 1.3% to 9.9%, depending on the driving cycle. In addition, the operating power of the battery can be significantly reduced, which benefits the health of the battery. Furthermore, the proposed ECMS-PSO is computationally efficient, which guarantees fast offline optimization and enables real-time applications. |
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| ISSN: | 2076-3417 |