Design and Validation of Adaptive Barrier Function Sliding Mode Controller for a Novel Multisource Hybrid Energy Storage System Based Electric Vehicle

被引:3
作者
Noor, Faiqa [1 ]
Zeb, Kamran [1 ,2 ]
Ullah, Saif [1 ]
Ullah, Zahid [3 ]
Khalid, Muhammad [2 ,4 ]
Al-Durra, Ahmed [5 ]
机构
[1] Natl Univ Sci & Technol NUST, Sch Elect Engn & Comp Sci, Islamabad 44000, Pakistan
[2] King Fahd Univ Petr & Minerals KFUPM, Interdisciplinary Res Ctr Sustainable Energy Syst, Dhahran 31261, Saudi Arabia
[3] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy
[4] KFUPM, Elect Engn Dept, Dhahran 31261, Saudi Arabia
[5] Khalifa Univ, Adv Power & Energy Ctr, EECS Dept, Abu Dhabi, U Arab Emirates
关键词
Energy management; Batteries; Power system measurements; Energy storage; Density measurement; Voltage control; Hybrid electric vehicles; Electrostatic discharges; Costs; Adaptive systems; Hybrid power systems; Sliding mode control; Steady-state; Hybrid energy storage system (HESS); sliding mode controller (SMC); barrier function (BF); electric vehicle (EV); steady-state error;
D O I
10.1109/ACCESS.2024.3471893
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Conventional vehicles emit many pollutants and natural gases, such as carbon dioxide and nitrogen oxides, which reduce air quality and global warming. Due to their extensive consumption, another driving force behind the search for alternatives is the fast depletion of fossil fuels, oil, and natural gas. Consequently, Hybrid Electric Vehicles (HEVs) have recently been the subject of substantial research to tackle the dual problems of harmful emissions and resource depletion. This research attempts to develop a novel barrier function-based adaptive sliding mode controller (BFASMC) for a hybrid energy storage system (HESS) of electric Vehicle (EV). The HESS comprises a fuel cell (FC), battery, supercapacitor (SC), and photovoltaic (PV). The FC serves as a primary source, while the others are auxiliary sources. DC converters are employed to couple these sources to a DC bus. The proposed BFASMC stabilizes and regulates the DC bus voltage. The system's global stability has been assured through Lyapunov criteria and verified through phase plane (error and error differential) analysis. The proposed controller is compared with conventional sliding mode controller (SMC), integral SMC (ISMC), and double integral SMC (DISMC). The simulation (MATLAB/Simulink) and hardware in loop results (dSPACE MicroLabBox RTI1202) authenticate robustness, efficacy, resilience, chattering free operation, and superiority of proposed BFASMC compared with conventional SMC variants.
引用
收藏
页码:145270 / 145285
页数:16
相关论文
共 30 条
[1]   Hybrid method based energy management of electric vehicles using battery-super capacitor energy storage [J].
Alkawak, Omar A. ;
Kumar, Jambi Ratna Raja ;
Daniel, Silas Stephen ;
Reddy, Chinthalacheruvu Venkata Krishna .
JOURNAL OF ENERGY STORAGE, 2024, 77
[2]   Nonlinear Controller Analysis of Fuel Cell-Battery-Ultracapacitor-based Hybrid Energy Storage Systems in Electric Vehicles [J].
Armghan, Hammad ;
Ahmad, Iftikhar ;
Ali, Naghmash ;
Munir, Muhammad Faizan ;
Khan, Saud ;
Armghan, Ammar .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2018, 43 (06) :3123-3133
[3]   Sensorless Fixed-Time Sliding Mode Control of PMSM Based on Barrier Function Adaptive Super-Twisting Observer [J].
Chen, Long ;
Jin, Zhihui ;
Shao, Ke ;
Wang, Hai ;
Wang, Guangyi ;
Iu, Herbert Ho-Ching ;
Fernando, Tyrone .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2024, 39 (03) :3037-3051
[4]   Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles [J].
Chen, Tzu-Chia ;
Ibrahim Alazzawi, Fouad Jameel ;
Grimaldo Guerrero, John William ;
Chetthamrongchai, Paitoon ;
Dorofeev, Aleksei ;
Ismael, Aras Masood ;
Al Ayub Ahmed, Alim ;
Akhmadeev, Ravil ;
Latipah, Asslia Johar ;
Abu Al-Rejal, Hussein Mohammed Esmail .
MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
[5]   Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning [J].
Chen, Zheng ;
Hu, Hengjie ;
Wu, Yitao ;
Zhang, Yuanjian ;
Li, Guang ;
Liu, Yonggang .
ENERGY, 2020, 211
[6]  
Ilyas U, 2022, Eng Proc, V12, P75, DOI [10.3390/engproc2021012075, DOI 10.3390/ENGPROC2021012075]
[7]   Barrier Function-Based Nonsingular Terminal Sliding Mode Control for Path Tracking of Tractor-Like With Experimental Validation [J].
Ji, Xin ;
Ding, Shihong ;
Cui, Bingbo ;
Ding, Chen ;
Wei, Xinhua .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2023, 70 (08) :3024-3028
[8]  
Korada S.R., 2024, Int. J. Electr. Comput. Eng, V14, P1228
[9]   Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles [J].
Li, Weihan ;
Cui, Han ;
Nemeth, Thomas ;
Jansen, Jonathan ;
Uenluebayir, Cem ;
Wei, Zhongbao ;
Zhang, Lei ;
Wang, Zhenpo ;
Ruan, Jiageng ;
Dai, Haifeng ;
Wei, Xuezhe ;
Sauer, Dirk Uwe .
JOURNAL OF ENERGY STORAGE, 2021, 36
[10]   Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle [J].
Lian, Renzong ;
Peng, Jiankun ;
Wu, Yuankai ;
Tan, Huachun ;
Zhang, Hailong .
ENERGY, 2020, 197