Bidirectional Buck-boost Converter Research Articles

Heuristic power management of hybrid source electric vehicle based on PV-battery-PEMFC

Renewable energy sources are now being focused on for usage in electric vehicles. Although majority of electric vehicles run solely on batteries, they have eliminated local emissions but not pollution. To tackle this issue, this research looks into the performance and power management aspects of an electric vehicle's hybrid power system, with a particular emphasis on the interactions between fuel cells, batteries, and photovoltaic (PV) cells. Stable operation is ensured via a bi-directional buck-boost converter, which maintains the voltage from all sources around reference 430 V. Initially, the fuel cell supplies electricity under low light conditions. The PV system and battery take over when the irradiance surpasses 100 W/m2, with the battery first draining and then charging in tandem with the increase in irradiance. 1750 RPM is the constant motor speed that is reached after making the required torque adjustments. A model full electric vehicle with multiple sources has been proposed with an efficient power management algorithm. When the battery is in stationary mode and the state of charge (SOC) is less than 100%, extra solar power is used to charge the battery. The model also takes into consideration using the car for entertainment or gadget charging when the ignition is off. The biogas output can be regulated using an inferential control technique using artificial neural network (ANN) based on the fuel demand or consumption. The system also includes anaerobic digestion of organic waste to provide hydrogen for the fuel cell sustainably from biogas. Power, current, SOC profiles, motor performance graphs, and other validations show that the extensive simulations exhibit efficient power management and a strong model for hybrid electric car power systems. The model has been simulated in the MATLAB Simulink environment and has been tested under realistic conditions.

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Bidirectional Charger for Electric Vehicle Applications

The rise in EV mobility has encouraged the growth of V2G technology. The vehicle-to-grid technology allows bidirectional power flow between the battery of the EV and the grid. In the G2V mode of operation, EV batteries are charged from the grid. Bidirectional buck-boost converters allow power flow control, load balancing, and distributed resource integration, contributing to grid stability and resilience. It also helps in the synchronization of renewable energy sources with the grid by stepping up or down the voltage to match the grid requirements. The proposed system consists of a bidirectional DC-DC converter, which is used to control the power flow in both directions. A MATLAB Simulink model for the converter has been simulated with a PI controller to operate in a closed-loop mode. In addition to the simulation, a prototype model of the converter has been developed.

Design of a bidirectional EV charger using unit template-based current-controlled synchronous reference frame hysteresis

Abstract Existing distribution networks were not designed with large-scale electric vehicle (EV) charging infrastructure in mind. Integrating EV charging stations with the distribution grid might lead to power quality (PQ) issues at the point of common coupling (PCC). This work proposes a two-mode, unit template-based synchronous reference frame hysteresis current-controlled (SRF-HCC) three-phase Level 2 EV charger. Mode one focuses on charging the EV battery from the grid (G2V) and utilizes current and voltage control techniques to enhance battery life and performance. Whereas mode two enables the EV’s stored energy to be discharged to the grid (V2G) by the EV user, allowing the sale of power to support the transient effect of the grid voltage and frequency and enhancing the grid’s PQ. The HCC generates switching pulses for both the AC-DC and buck-boost converters. The SRF-based unit template-based control (SRF-UTC) method ensures system stability, voltage, and frequency regulation for power exchange with the grid by combining its efficiency with that of the HCC. The EV charger proposal comprises three primary components: a 3-phase bidirectional AC-DC converter, a bidirectional buck-boost converter, and a filter circuit. The proposed system was modeled using MATLAB/Simulink and evaluated in two case studies to assess its performance.

СИНТЕЗ ДВОКОНТУРНИХ СИСТЕМ КЕРУВАННЯ НАПРУГОЮ РЕВЕРСИВНИХ ПІДВИЩУВАЛЬНИХ DC-DC ПЕРЕТВОРЮВАЧІВ

The paper deals with the design and analysis of cascaded DC-link voltage control systems for bidirectional buck-boost DC-DC converters. Its model is significantly nonlinear and non-minimum-phase, which makes it impossible to achieve high dynamic quality indicators using standard methods of linear control theory. A new method for design and analysis of DC-DC converters control systems based on partial feedback linearization and the subsequent application of linear PI voltage and current controllers is proposed. It provides that the resulting model is composed of the feedback inter-connected linear asymptotically stable subsystems with bilinear properties. The resulting system is linearized in the vicinity of the trajectories corresponding to the power balance equation. Such form allows to apply the theory of cas-caded systems with two time-scale separation of the control loops dynamics. It not only ensures system stability but also allows to specify the process quality indicators using methods available to control engineers. References 14, figures 9.

Fully Soft Switched Bidirectional Buck–Boost Converter With Improved Voltage Conversion Ratio and Single Auxiliary Switch

In this paper, a fully soft-switched bidirectional buck/boost converter with improved voltage conversion ratio (VCR) and minimum semiconductor devices is proposed. An auxiliary circuit which employs only one active switch along with coupled inductors technique is utilized to increase VCR and provide soft switching condition for all semiconductor devices. Soft switching condition is provided at a wide power range while the leakage inductance energy of coupled inductors is absorbed in both power flow directions. The main switches are turned on at zero voltage-zero current switching and turn off at zero voltage switching, while the switches body diodes used as the converter diodes turn off at zero current switching which alleviates the diodes reverse recovery problem. Soft switching operation and low component count especially the active switches have contributed to realize a proper converter efficiency. Moreover, the converter has maintained continuous current at low voltage side, common ground between the high and low voltage sides and switches voltage stress similar to the basic bidirectional buck/boost topology. A converter prototype at 300W and 100kHz is implemented to confirm the converter theoretical operation and analysis.

A Comparison Between the Quality of Two Level and Three Levels Bidirectional Buck-Boost Converter Using the Neural Network Controller

A Comparison Between the Quality of Two Level and Three Levels Bidirectional Buck-Boost Converter Using the Neural Network Controller

A Comparative Study of the Performances of the LQR Regulator versus the PI Regulator for the Control of a Battery Storage System

Background: This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a linear quadratic regulator (LQR) combined with an artificial neural network (ANN) algorithm. A state representation of the Buck-boost converter is performed. Then the ANN-LQR control strategy is compared to a classical control based on the proportional-integral controller combined with an ANN algorithm. The ANN algorithm generates the reference charging or discharging current based on a comparison between the power generated and the power consumed. In order to obtain an accurate comparison, two identical systems are designed, each consisting of a photovoltaic system optimized by an incremental conductance algorithm (INC) that powers a dynamic load and a backup storage system consisting of a lithium-ion battery. A management and protection algorithm is developed to protect the batteries from overcharge and deep discharge and to manage the load availability on the DC bus. The simulation results show an improvement in the performances of the storage system by the ANN-LQR control compared to the ANN-PI method and an increase in the stability, accuracy, efficiency of the system is observed. : Photovoltaic (PV) energy is one of the most promising technologies for combating climate change and meeting the urgent need for green renewable energy and long-term development. PV energy generation has a number of advantages: Solar energy is limitless and available anywhere on the planet. However, photovoltaic energy is intermittent and depends on meteorological conditions; also, the energy consumed is unpredictable. For this reason, a storage system is necessary to overcome these problems. Objective: The objective of this study is to develop an optimal control using a Linear Quadratic Regulator (LQR) combined with a neural network algorithm (ANN) to improve the performance of an electrical energy storage system and compare the results obtained with the classical control based on the PI regulator. Methods: The state representation of the bidirectional Buck-boost converter was performed in order to apply the optimal control and determine the gain K and the ANN algorithm allowed to determine the charge and discharge current according to a comparison between the power produced and consumed. Results: The simulation results obtained by two control methods can be used to compare and select the appropriate control method to achieve optimal efficiency of the storage system. Conclusion: The combined ANN-LQR technique offer better performances and stability of the installation compared to the ANN-PI controller.

Ultracapacitor as selectable energy buffer in electric vehicle application

A method of ultracapacitor integration with a regenerative braking system for use in electric drive trains is presented in this paper. An ultracapacitor (UC) is an intermediary to store and provide energy on the DC bus in certain scenarios, such as during acceleration and regenerative braking. UC is used to avoid direct inrush loading on the battery, which the electric drive system may cause. The energy from UC is injected into the DC bus in compliment to the battery, for which a dedicated power source switch called the “Main Charge Controller” (MCC) is responsible. The charge discharge of UC is done through a bidirectional buck-boost converter (BDBBC). The use of UC to offload high energy demand from the battery and store energy recovered during regenerative braking helps improve the vehicle's life and per-charge range. The stored energy in UC can be obtained from the battery at the start. UC can also be charged at the time of charging. The design and the presented control are ideal for lightweight electric vehicles. The system uses a 39.9 kJ ultracapacitor, formed from market-available 50F 2.7 V units in a 52S configuration, storage, and a 6kWh battery. The simulated vehicle in battery only case provides a minimum of 154 km range in rough conditions, in best case scenario it provides a 200 km range. The proposed design provides an increased extended range compared to simple battery-based regenerative braking, in the case of this work the hybrid solution provides an addition of more than 30 km of additional range compared to a battery-only solution which is simulated for all the same conditions except without the addition of the UC and its control and handling system, resulting in a total range of 230 km. The speed control response achieved by the user is also very fast. Electric Vehicles have range limitations due to storage technology which is a high entry barrier for consumers looking for reliable vehicles without range anxiety, as storage technology progresses adoption will become easier. This work is an attempt at using hybrid storage and selective use of it, for improved range and sharper speed response. However, this work does not explore the aging of the storage components due to increased operational load and thermal stresses or application to alternate battery chemistries. This work is primarily focused on the vehicles that use lithium-based batteries.

MPPT DC-DC Buck-Boost Converter for Off Grid Hybrid Solar-Wind-Battery System in Ikuza Island, Tanzania

Ikuza Island in Tanzania faces lack of electricity. Therefore, this paper undertakes to design hybrid renewable energy sources for the island by specifically focusing on the buck-boost converter for the energy conversion from these renewable resources. The design of the bidirectional buck-boost converter for maximum power point tracking in off-grid hybrid renewable energy systems is multifaceted due to the inhomogeneity nature of the renewable energy sources. The bidirectional buck-boost converter, solar PV, wind-based generator, and energy storage system were designed and simulated in MATLAB/Simulink software. The designed system was tested with varying solar irradiance (750 to 1000 W/m2), temperature (20 to 25°C) and wind speed (150 to 157.5 radians/s) at constant load of 260 A while load variation involved varying the load current from 0 to 260 A at solar irradiance, temperature, and wind speed of 1000 W/m2, 25 °C and 157.5 radians/s, respectively. The variation of DC link bus voltage at different load conditions was reported. The simulation results show that the designed converter is able to maintain DC link voltage at 600 V. Moreover, the DC link voltage showed a maximum drop of approximately 0.67% during the constant load condition. Contrarily, a significant improvement is observed when the designed converter operates with the hybrid system of solar PV, PMSG-based wind generator and with energy storage system.

A Grid Connected PV Array and Battery Energy Storage Interfaced EV Charging Station

In this work, a charging station for EV (Electrical Vehicle) integrated with a BES (Battery Energy Storage) is presented with enhanced grid power quality. The positive sequence components of the three phase grid voltages are evaluated for the estimation of the unit templates and the reference grid currents. The EV and battery energy storage are connected at DC link using a bidirectional buck-boost converter. During the daytime, EV takes power from the solar array while in its absence it consumes the power from the grid. Additionally, when the system is connected to the grid, the point of common coupling voltages synchronizes with the voltages of the grid. Tests are conducted on a hardware prototype developed in the laboratory for the validation of the satisfactory response under different dynamics conditions.

Modelling of Bi-Directional Buck-Boost Converter for Electric Vehicle

Abstract: The rapid growth of electric vehicles (EVs) has necessitated the development of efficient and versatile energy storage systems. In this study, we present the modelling and implementation of a switching bi-directional buck-boost converter based on electric vehicle hybrid energy storage. The proposed converter offers a flexible and reliable solution for managing energy flow between different storage elements in an EV, such as batteries and ultra-capacitors. First, a comprehensive mathematical model of the converter is developed, taking into account the dynamic behaviour of the energy storage components. This model enables us to analyse and optimize the converter's performance in terms of efficiency, voltage regulation, and power delivery capabilities. Furthermore, simulation studies are conducted to validate the accuracy of the model and assess the converter's performance under various operating conditions. Based on the modelling and simulation results, a practical implementation of the converter is carried out using high-quality electronic components. The design considerations, including component selection, circuit layout, and control strategy, are discussed in detail. The implemented converter is then evaluated experimentally to validate its performance and verify the effectiveness of the proposed modelling approach. The results demonstrate that the switching bidirectional buck-boost converter effectively manages the energy flow between the different storage elements in the hybrid energy storage system. It achieves high conversion efficiency, voltage regulation, and power transfer capabilities, enhancing the overall performance and range of electric vehicles. The developed model and implementation provide valuable insights for the design and optimization of similar converter topologies for electric vehicle applications. Overall, this study contributes to the advancement of energy storage systems in electric vehicles, facilitating the adoption of sustainable and efficient transportation solutions in the future

Development and Application of an Energy Management System for Electric Vehicles Integrated with Multi-input DC-DC Bidirectional Buck-Boost Converter

The rise in environmental pollution, demand for fossil fuels, and higher fuel economy vehicles has raised concerns about the creation of new and efficient transportation vehicles in recent days. These days, most developments in electric vehicles concentrate on making the vehicles more pleasant to ride in. Nonetheless, the emphasis now should be on energy and its most efficient use. To do this, you must give your attention to the origin of the automobile. The answer to this problem may be found in hybrid energy storage systems (HESS). This work is concerned with the design and implementation of an effective energy management system in electric vehicles (EVs) equipped with an active HESS consisting of a battery and a super capacitor via the incorporation of load sharing into this hybridization under a variety of load demand scenarios. To address the demands of high fuel efficiency vehicles, automotive firms are focusing on the development of diesel-engine operated vehicles, electric vehicles, fuel-cell vehicles, plug-in electric vehicles, and hybrid electric vehicles. A Multi-input Bidirectional Buck-Boost (MIB3) DC-DC converter is proposed in this dissertation to provide a greater conversion ratio to the input DC voltage. The multi-input converter recommended has fewer components and a simpler control method, making it more trustworthy and cost-effective. This converter also has bidirectional power flow functionality, making it suitable for charging the battery during regenerative braking in an electric or hybrid vehicle. Three different energy sources are used in the suggested topology: a photovoltaic (PV) panel, a battery, and an ultra-capacitor

A Single-Magnetic Bidirectional Integrated Equalizer Using Multi-Winding Transformer and Voltage Multiplier for Hybrid Energy Storage System

A single-magnetic bidirectional integrated equalizer using the multi-winding transformer and voltage multiplier for the hybrid energy storage system is proposed. The multi-winding transformer and voltage multiplier, driven by the current ripple of the inductor in the bidirectional buck-boost converter, are used for the battery string and supercapacitor string voltage balance, respectively. Compared to the previous balance circuits, for the schedule of the proposed equalizer, only one winding and one diode are used for each battery. Meanwhile, one capacitor and two diodes are used for each supercapacitor, simplifying the balance circuit. This method has the advantages of low cost, suitable for cells with different voltage levels, multiplexed converter switches, and extremely simple structure. It does not have any active switch for the voltage balancing function. A prototype for four supercapacitor cells and four batteries is set up. The results show that the proposed circuit balances the HESS when the DC-DC converter works, which verifies the theoretical analysis.

Analysis and Control of an Input-Parallel Output-Series Connected Buck-Boost DC–DC Converter for Electric Vehicle Powertrains

Many electric vehicle powertrains use a boost type dc-dc converter to step-up the battery voltage for optimization and improving vehicle performance. The converter allows variation of inverter dc-link voltage to further reduce losses. By focusing on boost operation only, previously proposed drivetrains have not fully utilized the benefits of variable dc-link. This paper describes a new non-isolated bidirectional dc-dc converter capable of voltage step-down as well as high-gain voltage step-up operation. Such wide voltage range operation can lead to improved efficiency of electric vehicle drivetrains. The proposed converter comprises two non-inverting buck-boost modules, configured to operate in various optimized modes depending on the load requirement. Compared to existing bidirectional buck-boost converters, the proposed converter offers lower switch stress, reduced converter size and better efficiency. The operation of the converter in different modes is described in detail, followed by small-signal modeling and an outline of the control scheme. Experimental results obtained in an all-SiC 5 kW prototype show stable operation while the converter emulates the behaviour of the dc-dc stage in a variable dc-link EV powertrain under the ECE 15 drive cycle. The prototype demonstrated peak efficiencies of more than 98.5% and 98% for voltage step-up and step-down operations, respectively.

Design and Modeling of Hybrid Electric Vehicle Powered by Solar and Fuel Cell Energy with Quadratic Buck/Boost Converter

Considering technological advancements and international standards mandating reduced greenhouse gas emissions, automobile manufacturers have shifted their focus toward innovative technologies related to fuel-cell electric vehicles. Despite having an all-electric powertrain, fuel cell electric vehicles (FCEVs) utilize a fuel cell stack as their energy source, which runs on hydrogen and results in the emission of only water and heat. Due to the absence of tailpipe pollutants, fuel-cell electric vehicles are considered zero-emission vehicles. Among fuel cell types, low-temperature and low-pressure fuel cells, such as Proton Exchange Membrane Fuel Cells (PEMFCs), are well-suited for vehicular applications as they exhibit high power density, operate at lower temperatures (60-80°C), and are less prone to corrosion than other types of fuel cells. The main objective of this paper is to investigate solar-assisted electric fuel cell vehicles that efficiently integrate the fuel cell system with an electrolyzer and solar power to fulfill the fluctuating power demands of the electric motor and auxiliary systems. A novel EV configuration with a fuel cell, electrolyzer and onboard PV cell is proposed. An onboard PV cell can assist the fuel cell when the irradiation is enough to generate the power. During the idle conditions of vehicles, PV-generated power can be converted into chemical energy using an electrolyzer and generated hydrogen can be stored in a hydrogen tank. To match the voltage required by the motor and sources a quadratic bidirectional buck-boost converter is employed. The proposed configuration is examined by considering variable irradiance and variable speed values. To obtain the maximum power output from the photovoltaic (PV) panel, a Maximum Power Point Tracking (MPPT) algorithm is employed to regulate the PV system. To enhance the efficiency and cost-effectiveness of PV systems, an enhanced version of the incremental conductance algorithm is utilized as the MPPT control strategy. Outer voltage and inner current control are adopted to regulate the DC output voltage of the QBBC converter. An indirect vector-controlled induction motor is used as vehicle drive. Simulations are performed to investigate the proposed EV configuration in MATLAB/SIMULINK.

A Proposed Supercapacitor Integrated with an Active Power Filter for Improving the Performance of a Wind Generation System under Nonlinear Unbalanced Loading and Faults

This study proposes the integration of a supercapacitor (SC) with the DC link of a three-phase four-wire active power filter (APF) by using an interfaced three-level bidirectional buck-boost converter controlled by the fuzzy control approach. APF is a flexible alternating current transmission system (FACTS) device that enhances power quality in the electrical network by reducing the load current harmonics and compensating the reactive power. Regulating the DC voltage of the APF’s DC link and absorbing fluctuations in compensated reactive power during disturbances are the major objectives of the integration of an SC circuit to APF. The studied model is a wind farm of Gabal El-Zeit with a total capacity of 580 MW, connected to unbalanced nonlinear loads. The Gabal El-Zeit wind farm is divided into three projects with a capacity of 240 MW, 220 MW, and 120 MW. The model is simulated by the MATLAB/SIMULINK program, and the effectiveness of the proposed methodology is proved by applying different types of faults such as single-line-to-ground, double-line-to-ground, and three-line-to-ground faults. In addition, an additional SC circuit with a two-level converter is connected to the generators coupled to the wind turbines to enhance the performance of the wind farm during disturbances. The results show that SC-integrated APF can reduce the harmonic distortion and compensate the reactive power for high or low inductive loads. Also, it can regulate the DC voltage and absorb the fluctuations in the reactive power during faults. Finally, the performance and the stability of the overall electric system are improved.

Implementation of artificial neural network based control for power quality enhancement of proton exchange membrane fuel cell powered distributed generation system

In this paper, a fuel cell and battery-based distributed generation system is demonstrated. A proton exchange membrane fuel cell is the primary power generator in this decentralized system, with a lead-acid battery providing backup power. A boost converter regulates the fuel cell’s output power to ensure its optimal performance. A bidirectional buck-boost converter has been employed to integrate battery using slope compensated current control. 5-level cascaded H-Bridge inverter is used to convert available DC power into AC power while integrating DG systems with the grid and improving power quality. The artificial neural network has been trained and used to generate the estimated reference current for sinusoidal pulse width modulation techniques. The artificial neural network has been fine-tuned using the scaled conjugate gradient with the performance value 0.094056. The THD of grid current has been reduced by 12.5% and 8.14% approximately for 30 Ω resistive, 30 mH inductive load and 20 Ω resistive, 15 mH inductive load respectively. The system has been simulated in Matlab/Simulink, and the findings have been validated. The results demonstrate the effectiveness of the methodology.

Hybrid Control and Energy Management of a Residential System Integrating Vehicle-to-Home Technology

Electric vehicles (EV) and photovoltaic (PV) systems are increasingly becoming environmentally friendly and more affordable solutions for consumers. This article discusses the integration of PV and EV in a residential system to meet the requirements of residential loads taking into account the PV supplied power, availability and the state of charge (SOC) of EVs. A hybrid control model has been proposed to control the residential system. The combined PI-Fuzzy logic controller is employed to control the buck-boost bi-directional converter. The DC-AC grid-side converter is controlled by the ADRC controller. The effectiveness of PI-Fuzzy logic controller in reducing voltage and current ripples and ADRC controller in rejecting disturbances is demonstrated in each case. A rule-based energy management strategy has been proposed to control the flow of energy between the components of the residential system. The suggested energy management system (EMS) covers every scenario that might occur. Whether the EV is linked to the home or not, and also takes into account the owner using the EV in an emergency situation. The EV operates in two modes, Home-to-Vehicle (H2V) mode and Vehicle-to-Home (V2H) mode, depending on the power produced by the PV and the conditions related to the EV. All possible scenarios are tested and validated. The simulation results show that the proposed EMS is a reliable solution that can reduce the power grid intervention.

Robust adaptive nonlinear control of plugin hybrid electric vehicles for vehicle to grid and grid to vehicle power flow with hybrid energy storage system

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.