4.7 Article

Robust integral backstepping controller for energy management in plugin hybrid electric vehicles

Journal

JOURNAL OF ENERGY STORAGE
Volume 42, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.est.2021.103079

Keywords

Plugin hybrid electric vehicles; Hybrid energy storage system; Battery; Supercapacitor; Robust integral backstepping; Supervisory control; DC-DC converters; State of charge

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Plugin hybrid electric vehicles (PHEVs) integrate batteries and supercapacitors to achieve smooth energy management and asymptotic stability through a series of controllers and rule-based algorithms, ensuring high performance and reduced toxic gases effectively.
Plugin hybrid electric vehicles (PHEVs) are a suitable choice to achieve enhanced performance and reduced toxic gases. The considered PHEV consists of an integrated charging unit and a hybrid energy storage system (HESS). The proposed HESS comprises of a battery with high energy density and a supercapacitor with high power density coupled together to fulfill the load demands of the vehicle. A DC-DC buck converter with an uncontrolled rectifier has been used for an on-board and balanced charging and two bi-directional DC-DC buck-boost converters have been incorporated to ensure a smooth transition of energy. A rule-based algorithm has been employed as a supervisory controller by incorporating total power inflow and state of charge of the power sources to fulfill the load demands. Moreover, a robust integral backstepping-based nonlinear controller has been designed for the smooth execution and energy management of PHEV in terms of output voltage regulation, reference generation, and smooth tracking of current. Lyapunov stability theory has been used to ensure asymptotic stability of the PHEV. The performance of the proposed controller has been validated using simulations on MATLAB/Simulink by comparing it with the Lyapunov redesign and backstepping controllers already proposed in the literature. The robustness of the proposed controller has been verified by introducing uncertainty in the state model. Finally, the real-time applicability and effectiveness of the proposed work have been ensured using controller hardware in loop (C-HIL) test bench.

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