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

Huma, Zil e; Azeem, Muhammad Kashif; Ahmad, Iftikhar; Armghan, Hammad; Ahmed, Shahzad; Adil, Hafiz Mian Muhammad

doi:10.1016/j.est.2021.103079

Cited by 24 publications

(3 citation statements)

References 35 publications

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“…On the other hand, if we plot the curve d = f(d) expressed in equation III. 28, we find that the equilibrium point d 1 is asymptotically stable. Thus, in d 1 the neighborhood, Equation (28) becomes:…”

Section: A Step 1: Integral Backstepping Controller Designmentioning

confidence: 77%

“…In [27], an integral backstepping controller for an inverter applied in an islanded micro-grid has been developed. A robust integral backstepping controller has been proposed for the energy management of Plugin hybrid electric vehicles [28], regarding the reference generation, voltage regulation, and smooth current tracking. In [29], an adaptive backstepping controller has been presented for a buck converter.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Nonlinear Integral Backstepping Controller for DC–DC Three-Level Boost Converters

et al. 2023

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Multi-level DC-DC converters have been widely used in automotive and other high-power applications. Thus, the control of these multi-level converters is an emerging thematic in power electronics to ensure their proper functioning. This paper provides a novel nonlinear control of a DC-DC three level boost converter (T-LBC) based on a backstepping (BS) technique with an integral action and is optimized using genetic algorithms (GA). Firstly, the average state model of the T-LBC is described. Then, this model is used to design an integral BS controller; nevertheless, the controller parameters are often determined manually, which may degrade the control quality. A genetic algorithm-based optimization method is applied to establish the best controller gains and improve the proposed controller efficiency. The asymptotic stability converter is verified using the Lyapunov method criteria. In order to validate the introduced controller under different scenarios, the Matlab/Simulink environment is used. In addition, it is compared with different controllers such as conventional backstepping, fuzzy logic, and proportional-integral-derivate (PID) controllers under varying references to highlight its performance further. Finally, the designed controller is verified experimentally by implementing it using a dSPACE 1104 control board. The simulation and experimental results show that the optimized integral BS controller presents the best performances in terms of settling time, overshoot and steady-state error.INDEX TERMS Three level boost DC-DC converter, nonlinear control, integral backstepping, genetic algorithms, tuning.MOHAMED BAGHDADI received the B.S. degree in industrial informatics and electronics from Mohammed First University, Oujda, Morocco, in 2015, and the M.S. degree in electrical engineering from Cadi Ayyad University, Marrakesh, Morocco, in 2017, he is currently pursuing the Ph.D. degree in electrical engineering and energy efficiency with the Electrical Systems, Energy Efficiency and Telecommunications Laboratory. His research interests include semiconductor device models, digital signal processing chips, design, control, modeling and simulation of semiconductor device, and hardware in the loop strategy.

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Section: A Step 1: Integral Backstepping Controller Designmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Optimized Nonlinear Integral Backstepping Controller for DC–DC Three-Level Boost Converters

et al. 2023

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“…They also suffer from problems related to the abrupt transitions between the various operating modes, which presents a real challenge for conventional controllers to keep the desired power quality and system stability. Numerous linear and nonlinear controllers have been proposed to control HPS, such as proportional-integral (PI) control [45], passivity control [46][47][48], flatness-based control [49,50], backstepping-based control [51], sliding mode control [52,53], adaptive sliding mode control strategy [54], sliding mode state and perturbation observer (SMSPO) [55,56], H-infinity control [57], active disturbance rejection control [58,59], and fractional order sliding mode control (FOSMC) [60,61]. Although the previously mentioned control methods are extremely useful to the readers, none of them have the benefits of a control approach with optimizationbased technique.…”

Section: Introductionmentioning

confidence: 99%

Optimal Adaptive Fractional Order Integral Sliding Mode Controller‐Energy Management Strategy for Electric Vehicles Based on Bald Eagle Search Algorithm

Ghadbane,

Barkat,

Houari

et al. 2024

International Journal of Energy Research

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This research presents an optimal energy management system (EMS) for a lithium-ion battery-supercapacitor hybrid storage system used to power an electric vehicle. The storage systems are connected in parallel to the DC bus by bidirectional DC-DC converters and feed a synchronous reluctance motor through an inverter. The proposed energy management strategy is built on the idea to take full benefits of two combined methods: the bald eagle search algorithm and fractional order integral sliding mode control. To evaluate the effectiveness of the suggested optimal energy management strategy, an urban dynamometer driving schedule (UDDS) driving cycle is considered. The obtained results are compared to a classical fractional order integral sliding mode control-based energy management strategy in terms of voltage ripples, overshoots, and battery final state of charge. The ultimate results approve the ability of the proposed energy management system to enhance the power quality and enhance battery power consumption at the same time. Comprehensive processor-in-the-loop (PIL) cosimulations were conducted on the electric vehicle using the C2000 launchxl-f28379d digital signal processing (DSP) board to assess the practicability and effectiveness of the proposed EMS.

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Neuromorphic deep learning frequency regulation in stand-alone microgrids

Yildirim

Razmi

Fathollahi

et al. 2023

Applied Soft Computing

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

Cited by 24 publications

References 35 publications

Optimized Nonlinear Integral Backstepping Controller for DC–DC Three-Level Boost Converters

Optimized Nonlinear Integral Backstepping Controller for DC–DC Three-Level Boost Converters

Optimal Adaptive Fractional Order Integral Sliding Mode Controller‐Energy Management Strategy for Electric Vehicles Based on Bald Eagle Search Algorithm

Neuromorphic deep learning frequency regulation in stand-alone microgrids

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