Optimal performance assessment of intelligent controllers used in solar-powered electric vehicle

Introduction. Increasing vehicle numbers, coupled with their increased consumption of fossil fuels, have drawn great concern about their detrimental environmental impacts. Alternative energy sources have been the subject of extensive research and development. Due to its high energy density, zero emissions, and use of sustainable fuels, the battery is widely considered one of the most promising solutions for automobile applications. A major obstacle to its commercialization is the battery's high cost and low power density. Purpose. Implementing a control system is the primary objective of this work, which is employed to change the energy sources in hybrid energy storage system about the load applied to the drive. Novelty. To meet the control objective, a speed condition-based controller is designed by considering four separate math functions and is programmed based on different speed ranges. On the other hand, the conventional/intelligent controller is also considered to develop the switching signals related to the DC-DC converter’s output and applied the actual value. Methods. According to the proposed control strategy, the adopted speed condition based controller is a combined conventional/intelligent controller to meet the control object. Practical value. In this work, three different hybrid controllers adopted speed condition based controller with artificial neural network controller, adopted speed condition based controller with fuzzy logic controller, and adopted speed condition based controller with proportional-integral derivative controller are designed and applied separately and obtain the results at different load conditions in MATLAB/Simulink environment. Three hybrid controller’s execution is assessed based on time-domain specifications.Вступ. Збільшення кількості транспортних засобів у поєднанні із збільшенням споживання ними викопного палива викликало серйозну заклопотаність з приводу їх згубного впливу на навколишнє середовище. Альтернативні джерела енергії були предметом інтенсивних досліджень та розробок. Завдяки високій щільності енергії, нульовим викидам та використанню екологічно чистих видів палива акумулятор широко вважається одним із найперспективніших рішень для застосувань у автомобілях. Основною перешкодою для його комерціалізації є висока вартість батареї та низька питома потужність. Мета. Впровадження системи управління, яка використовується для зміни джерел енергії в системі гібридної накопичення енергії в залежності від навантаження, прикладеного до приводу, є основною метою цієї роботи. Новизна. Для досягнення мети управління контролер на основі умов швидкості розроблено з урахуванням чотирьох окремих математичних функцій та запрограмовано на основі різних діапазонів швидкостей. З іншого боку, вважається, що звичайний/інтелектуальний контролер виробляє сигнали перемикання, пов'язані з вихідним сигналом перетворювача постійного струму, та застосовує фактичне значення. Методи. Відповідно до запропонованої стратегії управління прийнятий контролер на основі умов швидкості є комбінованим традиційним/інтелектуальним контролером для задоволення об'єкта управління. Практична цінність. У цій роботі три різних гібридних контролери, що використовують контролер на основі умов швидкості з контролером штучної нейронної мережі, контролер на основі адаптованих умов швидкості з контролером з нечіткою логікою та контролер на основі прийнятих умов швидкості з пропорційно-інтегрально-диференціальним контролером, розроблені та застосовуються окремо та отримують результати за різних умов навантаження у середовищі MATLAB/Simulink. Робота трьох гібридних контролерів оцінюється з урахуванням специфікацій у часовій області

A Portable Implementation on Industrial Devices of a Predictive Controller Using Graphical Programming

This paper presents an approach for developing an Extended Prediction Self-Adaptive Controller employing graphical programming of industrial standard devices, for controlling fast processes. For comparison purposes, the algorithm has been implemented on three different FPGA (Field Programmable Gate Arrays) chips. The paper presents research aspects regarding graphical programming controller design, showing that a single advanced control application can run on different targets without requiring significant program modifications. Based on the time needed for processing the control signal and on the application, one can efficiently and easily select the most appropriate device. To exemplify the procedure, a conclusive case study is presented

Hybrid fuel cell-supercapacitor system: modeling and energy management using Proteus

The increasing adoption of electric vehicles (EVs) presents a promising solution for achieving sustainable transportation and reducing carbon emissions. To keep pace with technological advancements in the vehicular industry, this paper proposes the development of a hybrid energy storage system (HESS) and an energy management strategy (EMS) for EVs, implemented using Proteus Spice Ver 8. The HESS consists of a proton exchange membrane fuel cell (PEMFC) as the primary source and a supercapacitor (SC) as the secondary source. The EMS, integrated into an electronic board based on the STM32, utilizes a low-pass filter algorithm to distribute energy between the sources. The accuracy of the proposed PEMFC and SC models is validated by comparing Proteus simulation results with experimental tests conducted on the Bahia didactic bench and Maxwell SC bench, respectively. To optimize energy efficiency, simulations of the HESS system involve adjusting the hybridization rate through changes in the cutoff frequency. The analysis compares the state-of-charge (SOC) of the SC and the voltage efficiency of the fuel cell (FC), across different frequencies to optimize overall system performance. The results highlight that the chosen strategy satisfies the energy demand while preserving the FC’s dynamic performance and optimizing its utilization to the maximum

An adaptive power split strategy with a load disturbance compensator for fuel cell/supercapacitor powertrains

Electric vehicles powered by fuel cell and supercapacitor hybrid power sources are of great interest. However, the power allocation between each power source is challenging and the DC bus voltage fluctuation is relatively significant in cascaded PI control schemes. This paper develops a power control strategy with an adjustable cut-off frequency, using an artificial potential field, to adaptively split the load current between the fuel cell and the supercapacitor under various load conditions. The adaptive cut-off frequency is calculated by cutting the load frequency spectrum with an allocation ratio that changes with the supercapacitor state of charge. Therefore, the relatively lower frequency portion of the load current is provided by the fuel cell and the supercapacitor handles the higher frequency portion. To enhance the control performance of the DC bus voltage regulation against the load disturbance, a load disturbance compensator is introduced to suppress the DC bus voltage fluctuation when the load variation occurs, which is implemented by a feed-forward controller that can compensate the load current variation in advance. The effectiveness of the proposed strategy is validated by extensive experiments

Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles

Concerns about climate change, air pollution, and the depletion of oil resources have prompted authorities to enforce increasingly strict rules in the automotive sector. There are several benefits to implementing fuel cell hybrid vehicles (FCHV) in the transportation sector, including the ability to assist in reducing greenhouse gas emissions by replacing fossil fuels with hydrogen as energy carriers. This paper examines different control strategies for optimizing the power split between the battery and PEM fuel cell in order to maximize the PEM fuel cell system efficiency and reduce fuel consumption. First, the vehicle and fuel cell system models are described. A forward approach is considered to model the vehicle dynamics, while a semi-empirical and quasi-static model is used for the PEM fuel cell. Then, different rule-based control strategies are analyzed with the aim of maximizing fuel cell system efficiency while ensuring a constant battery state of charge (SOC). The different methods are evaluated while the FCHV is performing both low-load and high-load drive cycles. The hydrogen consumption and the overall fuel cell system efficiency are considered for all testing conditions. The results highlight that in both low-load cycles and high-load cycles, the best control strategies achieve a fuel cell system efficiency equal or greater to 33%, while achieving a fuel consumption 30% less with respect to the baseline control strategy in low-load drive cycles

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

Energy management strategies for fuel cell vehicles: A comprehensive review of the latest progress in modeling, strategies, and future prospects

Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains

Energy Management of Fuel Cell/Battery/Supercapacitor Hybrid Power Sources Using Model Predictive Control

International audienceWell known as an efficient and eco-friendly power source, fuel cell, unfortunately, offers slow dynamics. When attached as primary energy source in a vehicle, fuel cell would not be able to respond to abrupt load variations. Supplementing battery and/or supercapacitor to the system will provide a solution to this shortcoming. On the other hand, a current regulation that is vital for lengthening time span of the energy storage system is needed. This can be accomplished by keeping fuel cell's and batteries' current slope in reference to certain values, as well as attaining a stable dc output voltage. For that purpose, a feedback control system for regulating the hybrid of fuel cell, batteries, and supercapacitor was constructed for this study. Output voltage of the studied hybrid power sources (HPS) was administered by assembling three dc-dc converters comprising two bidirectional converters and one boost converter. Current/voltage output of fuel cell was regulated by boost converter, whereas the bidirectional converters regulated battery and supercapacitor. Reference current for each converter was produced using Model Predictive Control (MPC) and subsequently tracked using hysteresis control. These functions were done on a controller board of a dSPACE DS1104. Subsequently, on a test bench made up from 6 V, 4.5 Ah battery and 7.5 V, 120 F supercapacitor together with a fuel cell of 50 W, 10 A, experiment was conducted. Results show that constructing a control system to restrict fuel cell's and batteries' current slope and maintaining dc bus voltage in accordance with the reference values using MPC was feasible and effectively done

Model Predictive Control Methods for Photovoltaic Electrical Energy Conversion Systems

Solar photovoltaic energy systems (PV) have had a consistently increasing market penetration over the past seven years, with a total global installed capacity of over 500 GW. A PV installation must harvest the maximum possible electrical energy at the lowest cost to be economically justifiable. This presents many engineering challenges and opportunities within power electronics amongst which include low-cost power converter implementation, high reliability, grid-friendly integration, fast dynamic response to track the stochastic nature of the solar resource, and disturbance rejection to grid transient and partial shading. This dissertation investigates the controls of the power electronic interface with the objective to reduce cost, increase reliability, and increase efficiency of PV energy conversion systems. The overall theme of this dissertation involves exploring the theory of model predictive control (MPC) within a range of applications for PV systems. The applications within PV energy conversion systems are explored, ranging from cell to grid integration. MPC-based maximum power point tracking (MPPT) algorithm is investigated for the power electronics interface to maximize the energy harvest of the PV module. Within the developed MPC based MPPT framework, sensorless current mode and adaptive perturbation are proposed. The MPC framework is expanded further to include inverter control. The control of a single-phase H-bridge inverter and sub-multilevel inverter are presented in this dissertation to control grid current injection. The multi-objective optimization of MPC is investigated to control the dc-link voltage in microinverters along with grid current control. The developed MPC based MPPT controller is shown to operate with a single-stage impedance source three-phase inverter with PID based grid-side control