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In this paper, an energy management strategy for electric vehicles equipped with fuel cell (FC), battery (BAT), and supercapacitor (SC) is considered, aiming at improving the whole performance under a framework of vehicle to network application. In detail, based on wavelet transform and equivalent consumption minimization strategy (ECMS), the demand power of vehicles is optimized to enhance the lifespan of fuel cell, fuel economy, and dynamic performance of electric vehicles. The wavelet transform is used to separate the high-frequency power in order to provide a peak power and recycle the braking energy. The equivalent consumption minimization strategy is used to distribute the low-frequency power to fuel cell and battery for minimizing the hydrogen consumption. Obtained results are studied using an advanced vehicle simulator, and its effectiveness of the strategy is confirmed, which provides a fundamental control method for the IOV application.

Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However, serious power fluctuation has effect on the durability of fuel cell, which still remains one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power, respectively. For accurately adjusting membership functions of proposed fuzzy controllers, genetic algorithm (GA) is adopted to optimize them considering multiple constraints on fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by Advisor-Simulink and experiment bench. Simulation and experimental results confirm that the proposed EMS can effectively reduce hydrogen consumption in three typical drive cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan.

In order to extend fuel cell lifespan and improve fuel economy of electrical hybrid vehicle with fuel cell/battery/ultracapacitor (FCHEV), a frequency decoupling-based energy management strategy (EMS) for FCHEV using fuzzy control method is proposed. In detail, firstly, according to different characteristics of energy sources, required power of FCHEV is decomposed into three frequency ranges based on Harr wavelet transform and an adaptive-fuzzy filter. Secondly, based on the proposed frequency decoupling, the obtained three frequency required power is supplied by fuel cell-battery and ultracapacitor, respectively, which can guarantee power performance of vehicle and reduce pressure and power fluctuation on fuel cell and battery. Thirdly, for improving fuel economy, one fuzzy controller is proposed to split the power between fuel cell and battery. Finally, the proposed strategy in this paper is verified by advisor-simulink and experimental bench. Simulation and experimental results show that the proposed EMS can effectively reduce impact of power fluctuations on fuel cell, extend its lifespan and reduce fuel consumption on 7.94% compared to equivalent consumption minimization strategy.

In this paper, an adaptive sliding mode control (ASMC) strategy is developed for boost DC-DC converter with uncertainty of measurement, load and battery resistance. The error of load and battery resistance caused by the change of load and battery resistance is estimated by using the adaptive rule. Considering the uncertainty and disturbance of load and measurement of sensor, an adaptive sliding mode nonlinear surface is proposed to ensure the transient response and robustness of the system. Simulation results show that, compared with PID method, the output voltage trajectory can track the reference voltage, and the output voltage response is good.

A reinforcement-learning-based energy management strategy is proposed in this paper for managing energy system of Fuel Cell Hybrid Electric Vehicles (FCHEV) equipped with three power sources. A hierarchical power splitting structure is employed to shrink large state-action space based on an adaptive fuzzy filter. Then, the reinforcement-learning-based algorithm using Equivalent Consumption Minimization Strategy (ECMS) is proposed for tackling high-dimensional state-action space, and finding a trade-off between global learning and real-time implementation. The power splitting policy based on experimental data is obtained by using reinforcement learning algorithm, which allows for many different driving cycles and traffic conditions. The proposed energy management strategy can achieve low computation cost, optimal fuel cell efficiency and energy consumption economy. Simulation results confirm that, compared with existing learning algorithms and optimization methods, the proposed reinforcement-learning-based energy management strategy using ECMS can achieve high computation efficiency, lower power fluctuation of fuel cell and optimal fuel economy of FCHEV.

We propose an orientation-selective elliptic optical vortex array (OS-EOVA). Using multicoordinate (namely, polar, Cartesian, and elliptic) transformations, three kinds of operations applied on optical vortex elements (including location, rotation, and stretching) were executed to obtain the desired orientation in the observed plane. Then, exploiting the reverse design technique, the above-mentioned operations were mapped onto the initial execution plane via Fourier transform. Based on this, 1D and 2D OS-EOVAs were generated experimentally and the existence of optical vortices was verified. Specific OS-EOVAs were designed, possessing antenna array orientation as well as radial and azimuthal orientation. Compared to existing OVAs, the OS-EOVA provides an additional modulated dimension, i.e., orientation. This technique will open up some potential applications, such as complex manipulation of multiparticle systems and fabrication of micromaterials with orientation.

In this paper, an energy management strategy (EMS) for hybrid batteries and supercapacitor (SCs) electric vehicles (HBSEV) is considered for extending the batteries lifespan and lifting the power performance of HBSEV. In detail, firstly, vehicle dynamics model and hybrid energy storage source (HESS) models including batteries and SCs are adapted and simplified as fundamental of model prediction control (MPC) approach. Secondly, the EMS of HBSEV is designed for reducing batteries power variation and maintaining SCs SOC in a proper value based on MPC method combining adaptive method. That can enhance batteries lifespan and power performance. Finally, simulation results based on the proposed EMS are given, which show batteries lifespan and power performance for HBSEV are improved obviously compared with a real time average (RTA) strategy. Meanwhile, the SOC of batteries and SCs are maintained in a proper range.