Abstract
The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.
Highlights
Hybrid electric vehicles adopt multiple power sources to drive vehicles to improve fuel economy and reduce pollutant emissions
In order toResults verify the effectiveness of the proposed method, simulation tests were conducted under a test driving cycle, which was a random combination of six standard driving cycles: NurembergR36, In order to verify the effectiveness of the proposed method, simulation tests were conducted
Note that for A-equivalent consumption minimization strategy (ECMS), better fuel economy and state of charge (SOC) management can be expected for a smaller nominal equivalent factor
Summary
Hybrid electric vehicles adopt multiple power sources to drive vehicles to improve fuel economy and reduce pollutant emissions. A similar method can be found in [18], in which fuzzy rules were adopted for driving cycle classification, and each driving pattern was represented by the optimal equivalent factor of a typical standard cycle in the driving class These methods have shown promising results for on-line implementation of the ECMS; they ignore the influence of the SOC on the estimation of the equivalent factor. A novel adaptation method of the equivalent factor is proposed by considering the type of driving pattern and the battery SOC to improve fuel economy.
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