Abstract
This paper presents a new energy management system based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to enhance fuel economy and impose state of charge (SoC) charge-sustainability. First, the relationship between the equivalent factor (EF) of ECMS and the co-state of pontryagin’s minimum principle (PMP) is derived. Second, a new method of implementing the adaptation law using fuzzy proportional plus integral (PI) controller is developed to adjust EF for ECMS in real-time. This adaptation law is more robust than one with constant EF due to the variation of EF as well as driving cycle. Finally, simulations for two driving cycles using ECMS are conducted as opposed to the commonly used rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results confirm that ECMS with Fuzzy PI adaptation law is more robust than ECMS with constant EF as well as PI adaptation law and it achieves significant improvements compared with RB in terms of fuel economy, which is enhanced by 4.44% and 14.7% for china city bus cycle and economic commission of Europe (ECE) cycle, respectively.
Highlights
The term hybrid powertrain generally refers to vehicles equipped with an electric motor and an internal combustion engine
Noise2, cycles to demonstrate the effectiveness of the proposed energy management system
This demonstrates that equivalent consumption minimization strategy (ECMS)-Fuzzy-plus integral (PI) is more robust than ECMS-PI and it proposed control strategy can provide a better performance to adjust equivalent factor (EF), especially when the optimal can better guarantee the final constraint of state of charge (SoC) even though velocity noise is added to driving cycle
Summary
The term hybrid powertrain generally refers to vehicles equipped with an electric motor and an internal combustion engine. In [11], optimal EF is selected for different driving cycles to achieve better fuel economy and impose charge-sustainability, but there is a need for more calibration efforts and, using this method, EF cannot be automatically adapted to the driving cycle Another approach is to obtain the optimal EF for a given driving cycle by an iterative method or DP [12], but this is only possible with a prior knowledge of the whole cycle and cannot be applied in a real condition due to the variation in driving cycles. Sezer et al [17] developed a new ECMS for series HEVs considering the efficiency of the engine, battery, and generator to obtain the cost map combining fuel consumption and emissions; this method cannot adapt to different driving cycles.
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