Abstract
Energy management strategy (EMS) is a key issue for hybrid energy storage system (HESS) in electric vehicles. By innovatively introducing the current speed information, the vehicle speed optimized fuzzy energy management strategy (VSO-FEMS) for HESS is proposed in this paper. Firstly, the pruned fuzzy rules are formulated by the SOC change of battery and super-capacitor to preallocate the required power of vehicle. Then, the real-time vehicle speed is used to optimize the pre-allocated results based on the principle of vehicle dynamics, so as to realize the optimal allocation of required power. To validate the proposed VSO-FEMS strategy for HESS, simulations were done and compared with other EMSs under the typical urban cycle in China (CYC-CHINA). Results show that the final SOC of battery and super-capacitor are optimized in varying degrees, and the total energy consumption under the VSO-FEMS strategy is 2.43% less than rule-based strategy and 1.28% less than fuzzy control strategy, which verifies the effectiveness of the VSO-FEMS strategy.
Highlights
System ModelE semiactive hybrid energy storage system (HESS) controls one of the batteries and the super-capacitor, which is controlled and has high energy transfer efficiency
The fuzzy control rules are predesigned and cannot be adjusted according to real-time changes in driving cycle
The vehicle speed optimized fuzzy energy management strategy (VSO-FEMS) for hybrid energy storage system (HESS) is proposed in this paper. e VSO-FEMS analyzes the driving state of electric vehicles, monitors changes of battery and super-capacitor state of charge (SOC) in the driving process, and formulates corresponding fuzzy control rules. e required power is preallocated by the fuzzy controllers. en, based on the principle of vehicle dynamics, the reference value of supercapacitor SOC is calculated according to the real-time vehicle speed, and the error between the reference value of super-capacitor SOC with its actual SOC is obtained. e final allocation result is optimized by the error value to achieve a reasonable power allocation
Summary
E semiactive HESS controls one of the batteries and the super-capacitor, which is controlled and has high energy transfer efficiency. E fully active HESS needs to control the battery and super-capacitor separately, which has high cost and a more complex control strategy design. E state of charge (SOC) is defined as the ratio between the remaining charge to the total charge of the battery or super-capacitor, which can be calculated from equations (3) and (4): Recover power. As a key component of HESS, the DC/DC converter can effectively control the charge and discharge currents of the super-capacitor, and ensure the high efficiency of HESS. Fr mgf cos(θ) + mg sin(θ), where δ represents conversion ratio of vehicle rolling mass, m represents the curb weight, Ca represents the air drag coefficient, A represents the fronted area, g represents the acceleration of the gravity, f represents the rolling resistance coefficient, and θ represents the slope of the road
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