蓄电池/超导混合储能系统非线性鲁棒分数阶控制

针对电动汽车(Electric Vehicle, EV)供电端的蓄电池/超导混合储能系统(Battery/SMES Hybrid Energy Storage Systems, BSM-HESS)设计了一种新型非线性鲁棒分数阶控制(Nonlinear Robust Fractional-Order Control, NRFOC)，从而快速精准地跟踪负荷需求变化。首先，基于规则式策略(Rule-Based Strategy, RBS)实现最优的负荷需求分配。然后，通过高增益扰动观测器(High-Gain Perturbation Observer, HGPO)对BSM-HESS的非线性、参数不确定性和未建模动态聚合而成的扰动进行快速估计，最终该扰动通过NRFOC进行在线完全补偿。此外，NRFOC不依赖于精确的系统模型，仅需测量蓄电池电流和直流侧电压两个状态量。通过三种算例进行研究，即重载条件、轻载条件以及参数不确定性，仿真结果验证了NRFOC的有效性和鲁棒性。

Nonlinear robust fractional-order control of battery/SMES hybrid energy storage systems

This paper designs a novel Nonlinear Robust Fractional-Order Control (NRFOC) for Battery/Superconducting Magnetic Energy Storage (SMES) Hybrid Energy Storage Systems (BSM-HESS) used in Electric Vehicles (EVs). It can track the change of load demand quickly and accurately. First, Rule-Based Strategy (RBS) is adopted to assign the optimal power demand. Then the combined effect of nonlinearities, parameter uncertainties and unmodeled dynamics of BSM-HESS are aggregated into a perturbation, which is rapidly estimated by a High Gain Perturbation Observer (HGPO). The perturbation is fully compensated online by NRFOC. NRFOC does not require an accurate system model. It only needs to measure the battery current and DC bus voltage. Finally, three case studies, including heavy load condition, light load condition and parameter uncertainty, are carried out. Simulation results verify the effectiveness and robustness of NRFOC. This work is supported by National Natural Science Foundation of China (No. 61963020).