Plugin hybrid electric vehicles (PHEVs) are getting the attention of electric transportation market and the end-users. They comprise of smart charging mechanism and a hybrid energy storage system (HESS). In this study, a topology for HESS based on battery/ultracapacitor (UC), coupled with two bi-directional DC-DC buck-boost converters has been considered. For controlled integrated charging and smooth execution of energy management algorithm, a unidirectional DC-DC converter has been used. The mathematical model of the complete HESS with integrated charging unit for PHEV has been designed. A nonlinear controller termed as adaptive terminal sliding mode control (ATSMC) along with adaptive law has been proposed. The controller parameters have been tuned using genetic algorithm. Also an algorithm of high level control has been presented to switch between static and dynamic behaviors of the PHEV. The objective of the proposed control strategy is to adapt the unknown parameters of the system, deliver power for load well in time, output DC bus voltage regulation and smooth tracking of reference currents for the battery and UC with varying demands of the vehicle. The asymptotic stability of the system has been ensured by using Lyapunov stability theory. Finally, the energy management algorithm using the state of charge (SoC) as decisive factor is incorporated to maintain the stability of the system under varying load conditions. The performance of proposed controller has been compared with conventional sliding mode controller (SMC) and finite time synergetic controller (FTSC) using MATLAB/Simulink. The performance of the system is further verified by testing it on real-time hardware-in-loop setup.

插电式混合动力汽车（PHEV）受到电动运输市场和最终用户的关注。它们包括智能充电机制和混合储能系统（HESS）。在这项研究中，已经考虑了基于电池/超级电容器（UC）的HESS拓扑，并结合了两个双向DC-DC降压-升压转换器。为了控制集成充电并顺利执行能量管理算法，已使用单向DC-DC转换器。设计了带有用于PHEV的集成充电装置的完整HESS的数学模型。已经提出了一种称为自适应终端滑模控制（ATSMC）的非线性控制器以及自适应律。控制器参数已使用遗传算法进行了调整。还提出了一种高级控制算法来在PHEV的静态和动态行为之间切换。提出的控制策略的目的是适应系统的未知参数，及时为负载提供功率，调节输出DC总线电压，并随着车辆需求的变化平稳跟踪电池和UC的参考电流。通过使用Lyapunov稳定性理论来确保系统的渐近稳定性。最后，结合了以荷电状态（SoC）作为决定性因素的能量管理算法，以在变化的负载条件下维持系统的稳定性。拟议的控制器的性能已与常规滑模控制器（SMC）和使用MATLAB / Simulink的有限时间协同控制器（FTSC）进行了比较。