Masood Usman, Azeem Muhammad Kashif, Ahmad Iftikhar, Jabbar Absaar Ul
Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
ISA Trans. 2023 Aug;139:406-424. doi: 10.1016/j.isatra.2023.03.035. Epub 2023 Apr 1.
Plugin hybrid electric vehicles (PHEVs) can solve the concerns of toxic gases emissions from fossil fuel. The PHEV under consideration consists of an on-board smart charger and a hybrid energy storage system (HESS) composed of the battery as a primary power source and an ultracapacitor (UC) as a secondary power source conjoined with two DC-DC bidirectional buck-boost converters. The on-board charging unit comprises of an AC-DC boost rectifier and DC-DC buck converter. The entire system's state model has been derived. An adaptive supertwisting sliding mode controller (AST-SMC) has been proposed for the unitary power factor correction at the grid side, tight voltage regulation of the charger and the DC bus, adaptation of time varying parameters and tracking of the currents involving the variation in the load profile. A genetic algorithm has been applied for optimizing the cost function of the controller gains. Key results refers reduction of chattering, adaptation of parametric variations, nonlinearities and external disturbances of the dynamical system. HESS results presents negligible convergence time and overshoots/undershoots even at the transients, and no steady state error. For the driving mode, the switching between dynamic and static behaviors and for parking mode, vehicle to grid (V2G) and grid to vehicle (G2V) operations have been proposed. In order to make nonlinear controller intelligent to achieve the V2G and G2V functionality, a state of charge based high-level controller has also been proposed. A standard Lyapunov stability criteria has been used to ensure asymptotic stability of the entire system. The proposed controller has been compared with sliding mode control (SMC) and finite time synergetic control (FTSC) by the simulation results using MATLAB/Simulink. Also, the hardware in loop setup has been used to validate the performance in real-time.
插电式混合动力汽车（PHEV）可以解决化石燃料有毒气体排放的问题。所考虑的插电式混合动力汽车由一个车载智能充电器和一个混合储能系统（HESS）组成，该系统由作为主要电源的电池和作为辅助电源的超级电容器（UC）以及两个DC-DC双向降压-升压转换器连接而成。车载充电单元由一个AC-DC升压整流器和一个DC-DC降压转换器组成。推导了整个系统的状态模型。提出了一种自适应超扭曲滑模控制器（AST-SMC），用于电网侧的单位功率因数校正、充电器和直流母线的严格电压调节、时变参数的自适应以及涉及负载曲线变化的电流跟踪。应用遗传算法优化控制器增益的成本函数。关键结果表明减少了抖振，适应了动态系统的参数变化、非线性和外部干扰。混合储能系统的结果表明，即使在瞬态情况下，收敛时间和超调量/欠调量也可忽略不计，且无稳态误差。对于驱动模式，提出了动态和静态行为之间的切换，对于停车模式，提出了车辆到电网（V2G）和电网到车辆（G2V）的运行方式。为了使非线性控制器智能化以实现V2G和G2V功能，还提出了一种基于荷电状态的高级控制器。使用标准李雅普诺夫稳定性准则来确保整个系统的渐近稳定性。通过MATLAB/Simulink的仿真结果，将所提出的控制器与滑模控制（SMC）和有限时间协同控制（FTSC）进行了比较。此外，还使用了硬件在环设置来实时验证性能。
