Design Of Hybrid Electric Vehicle Research Articles

Design and performance analysis of Hybrid electric vehicle

Design and performance analysis of Hybrid electric vehicle

Research on the configuration design and energy management of a novel plug-in hybrid electric vehicle based on the double-rotor motor and hybrid energy storage system

Research on the configuration design and energy management of a novel plug-in hybrid electric vehicle based on the double-rotor motor and hybrid energy storage system

New coordinated drive mode switching strategy for distributed drive electric vehicles with energy storage system

High performance and comfort are key features recommended in hybrid electric vehicle (HEV) design. In this paper, a new coordination strategy is proposed to solve the issue of undesired torque jerks and large power ripples noticed respectively during drive mode commutations and power sources switching. The proposed coordinated switching strategy uses stair-based transition function to perform drive mode commutations and power source switching’s within defined transition periods fitting the transient dynamics of power sources and traction machines. The proposed technique is applied on a battery/ supercapacitor electric vehicle whose traction is ensured by two permanent magnet synchronous machines controlled using direct torque control and linked to HEV front and rear wheels. Simulation results highlight that the proposed coordinated switching strategy has a noteworthy positive impact on enhancing HEV transient performance as DC bus fluctuations were reduced to a narrow band of 6 V and transient torque ripples were almost suppressed.

Design and Control of Hybrid Electric Light Rail Vehicles With Open Ended Winding Machines

Design and Control of Hybrid Electric Light Rail Vehicles With Open Ended Winding Machines

Energy management strategies of hybrid electric vehicles: A comparative review

AbstractWith increasing environmental problems, including air pollution, rising greenhouse gases and global warming, the need to use clean energy resources seems essential. Therefore, the usage of clean and renewable energy sources is suggested as a suitable solution to overcome the mentioned concerns. The transportation section contributes to a huge percentage of energy consumption. Hybrid electric vehicles (HEVs), by combining several energy resources, are considered as a crucial solution to decrease fossil fuel consumption and improve the environmental challenges. The existence of an alternative energy resource and the internal combustion engine together provides optimal power distribution among them to maximise power usage and minimise fuel consumption. Energy management strategy (EMS) is an essential challenge in HEV's design procedure to deal with the power distribution in multiple power source systems to improve the performance of the HEVs. A review of various EMSs for HEVs, followed by an analysis of each type, including its benefits and drawbacks, is presented by the authors. In addition to this, the major challenges in EMSs for HEVs are described and a comprehensive review is presented for strategies addressing these issues.

Design and Performance Analysis of Hybrid Electric Vehicles using Matlab/Simulink

In this paper introduces an integrated method for the design and performance analysis of hybrid electric vehicles. This method considers a set of parameters that influence the system's performance. This project presents an approach for modelling electric vehicles considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The performance of the hybrid electric vehicle is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new fuzzy logic controller is required to set the rules for better performance. Therefore, in this project fuzzy logic-based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicles with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were conducted, and results were observed

Incorporating the best sizing and a new energy management approach into the fuel cell hybrid electric vehicle design

Under the banner of sustainable transportation, fuel cell hybrid electric vehicles (FC-HEVs) have become a matter of fascination in today's ever-growing need to save fossil fuels and reduce greenhouse gas emissions, addressing the 3Es challenges (efficiency, economy, and environment). However, one of the most pressing issues at the moment is determining how to incorporate the optimal sizing and energy management strategy (EMS) into FC-HEVs. In this regard, this article presents an integrated approach for optimal sizing as well as a new energy strategy for the design of FC-HEVs. To minimize the decision variables, including operating cost, fuel consumption, and component weight, an in-house optimization MATLAB code with a Multi-Objective Particle Swarm Optimization algorithm was developed. Four energy cases were tested under ARTEMIS and NEDC driving cycles for simulation, and their influence on the key decision variables was also investigated. The results show that using a battery pack with a supercapacitor reduces fuel consumption by 19% and 30.3% in both driving cycles, while decreasing the maximum output power of the fuel cell. It should be noted that hybrid energy sources have the potential to improve vehicle performance.

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.

Co-Optimization of Design and Control of Energy Efficient Hybrid Electric Vehicles Using Coordination Schemes

AbstractDesign and control co-optimization studies for hybrid vehicles have been proposed in the past. However, such works suffer from difficulties arising due to (a) diverse real- and integer-valued variables, (b) complex nonlinear powertrain dynamics and design interconnections, (c) conflicting objective functions with path constraints, and (d) high computational resources requirements. To meet these challenges, this study presents an efficient co-optimization framework for hybrid electric vehicles (HEVs) which is built using existing algorithms and coordination schemes. Particular emphasis is given to the simultaneous scheme and the decomposition-based scheme. The decomposition-based scheme with the problem decomposition proposed in this work can efficiently handle multitime scale state variables and both integer- and real-valued design and control optimization variables. This is demonstrated by solving the mixed-integer optimal design and control problem of a series hybrid vehicle over a 1-h long drive cycle with time discretization of 1 s. The problem complexity is elevated by using an increasing number of state variables (including battery state of charge, battery energy, and after-treatment system temperature), control variables (such as the engine power and engine on/off), and design parameters (such as the number of battery cells and the type and size of the engine). In addition, a multi-objective cost function is used to find a tradeoff solution between fuel consumption and emissions minimization. The results show that in terms of optimality of the solution, the decomposition-based scheme is comparable with the simultaneous but can give a 14% improvement in computational performance. The effectiveness of the proposed framework is demonstrated by comparing the co-optimization results against a baseline case in which only the optimal control problem is solved. The co-optimized solution yields up to 3.7% average genset efficiency improvement and a fuel consumption reduction to 1.6 kg from 2.5 kg, which is further reduced to 1.5 kg by adding the engine on-off control. Finally, a decision matrix is developed to provide guidance on the selection of the optimization algorithm and coordination scheme for any problem at hand.

A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles. While fuel economy is a key factor in the design of plug-in hybrid electric vehicles, a fuel economy label can educate customers about the economic advantage of purchasing a particular car. The fuel economy label of a PHEV consists of parameters like driving range, electrical energy consumption, fuel economy for city, highway, and combined use, battery recharge time, and fuel consumption rates. The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs. Firstly, the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model. The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model. The output variables simulated were engine size in litres; estimated driving range when the battery is fully charged in km, battery recharged time in hours, city fuel consumption (L/100 ​km), highway fuel consumption (L/100 ​km), combined fuel consumption (L/100 ​km), estimated driving range when the tank is full, carbon dioxide (CO2) emission in grams/km, electric motor power in kW, number of cylinders, and electrical charges consumed in kWh/100 ​km. Different cases of input variables were considered for the inverse function model. The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

Integrating continuous fuzzy Kano model and fuzzy quality function deployment to the sustainable design of hybrid electric vehicle

Integrating continuous fuzzy Kano model and fuzzy quality function deployment to the sustainable design of hybrid electric vehicle

Hybrid Electric Vehicle Design and Simulation Performance Using Isolated DC/DC Converter Based MFO Algorithm

The accuracy and effectiveness of the vehicle’s motor, DC-DC converter, controller, and electrical system are crucial for Hybrid Electric Vehicles (HEV). The study is given with a DC-DC converter architecture and motor based on the MFO control algorithm for smooth driving and quick charging of hybrid electric vehicles. The front-end AC-DC converter’s output is translated by the suggested DC-DC converter architecture, which then provides a controlled supply to the EV propulsion battery. The suggested system consists of a drive train model created in MATLAB using the SIMULINK tool, a motor drive, a DC-DC converter, an inverter, and a drive train. A Simulink model with motor drive model was constructed and successfully simulated in order to operate and evaluate the performance of hybrid electric vehicles utilizing an MFO control method. The simulation findings are examined its performance of EV system and results clearly stated the hybrid car’s fuel efficiency advantages over the conventional vehicle model.

Predictive energy management strategy of dual-mode hybrid electric vehicles combining dynamic coordination control and simultaneous power distribution

For the energy management, the energy conversion usually attracts focus of the researches in the control strategy design of hybrid electric vehicle (HEV), but the computational efficiency and dynamic coordination problem are often ignored, especially for the multi-mode HEV. Thus, this paper proposes a model predictive control (MPC)-based predictive energy management strategy for dual-mode HEV. In this strategy, the future vehicle speed is predicted in the given horizon, and then, an improved sequence quadratic programming algorithm (ISQP) that combines the deep Q-learning is designed to solve MPC problem, which effectively improves the computational efficiency and optimality of original SQP in iterative optimization. Meanwhile, a dynamic process coordination control algorithm is developed to take the torque coordination problem and balance relationship of mode shift dynamic process into the energy management problem. Eventually, the DP, SQP-MPC and rule-based energy management strategy are designed as the benchmark strategies to compare with the proposed method, and they are conducted in the three different test cycles. The results verify that the proposed strategy presents the desirable performance in fuel saving, real-time capability and robustness.

Adaptive design and implementation of fractional order PI controller for a multi-source (Battery/UC/FC) hybrid electric vehicle

ABSTRACT In the modern era, where the massive utilization of fossils fuels is a serious threat to the global environment, the trend of clean and green technological innovations is highly necessitated. The hybrid electric vehicle (HEV) uses more than one means of propulsion, so it consumes less fuel and emits fewer hydrocarbons as compared to internal combustion engine (ICE) vehicles. Since, the overall design of HEV possesses non-linearities and the driving condition sometimes gets very challenging. This paper aims at the modeling and development of an adaptive fractional order PI (AFOPI) controller for speed tracking of a motor according to an Extra Urban Driving Cycle (EUDC) and Japanese 10–15 mode speed cycles. Whereas, conventional controllers lack the ability to track the driving cycles and show oscillation, chattering, and distortion. The AFOPI controller efficiently tracks the driving cycles with the help of a dragonfly searching algorithm (DSA), which optimizes its parameters based on the errors input. The proposed controller has shown an efficiency of 98.8% and its tests carried out at load, speed, and torque variations are then scrutinized and compared with primarily used proportional integer (PI) and field-oriented control (FOC)/vector control (VC) to show the competency and validity of the proposed method.

Integrated fuzzy linguistic preference relations approach and fuzzy Quality Function Deployment to the sustainable design of hybrid electric vehicles

Through the prevalence of sustainable ideas, automobiles are increasingly pursuing environmental protection strategies for green design, the non-traditional hybrid electric vehicles (HEV) are promoted continuously. If the company can add emotional value to the modeling of HEV, it will be helpful to its sustainable design and sales promotion of it. Therefore, an innovative model combining fuzzy linguistic preference relations (FLPR) and fuzzy quality function deployment (QFD) is proposed here to explore the connection between customer sentiment and the front view of the HEV. Compared with the previous methods, FLPR has the advantages of fewer comparison times and high consistency. First, find out the customer’s emotional expectations and attribute weight ranking for HEV through FLPR, and import customer requirements (CRs) on the left side of fuzzy QFD. Second, the grey prediction model was used to screen out the key engineering features (ECs) and the initial weight of HEV. Finally, based on human subjective imprecise natural semantics, fuzzy QFD established a matrix association between CRs and key ECs, then finally obtained the optimal combination of ECs' final weight and morphological design. The results can assist designers to shorten product development cycles and improve customers' emotional satisfaction, which provides a theoretical reference for the sustainable design and marketing of environmentally friendly cars in the future.

Model of Hybrid Electric Vehicle with Two Energy Sources

The paper proposes a Hybrid Electric Vehicle (HEV) design based on the installation of a fuel cell (FC) module in the existing Daewoo Tico electric vehicle to increase its range in urban areas. Installing an FC module supplied by a 2 kg hydrogen tank would not significantly increase the mass of the electric vehicle, and the charging time of the hydrogen tank is lower than the battery charging time. For design analysis, a model was created in the MATLAB/Simulink software package. The model simulates vehicle range at different HEV speeds for Absorbent Glass Mat (AGM) and Proton Exchange Membrane Fuel Cell (PEMFC) power sources. The greatest anticipated benefit derived from the model analysis relates to velocities ranging from 20 km/h to 30 km/h, although the optimal HEV velocity in an urban area is in the range of 30 km/h to 40 km/h. The results indicate that this conversion of Electric Vehicle (EV) to HEV would bring a benefit of 87.4% in terms of vehicle range in urban areas. Therefore, the result of the conversion in this case is a vehicle with sub-optimal characteristics, which are nevertheless very close to optimal.

Improved wild horse optimizer with deep learning enabled battery management system for internet of things based hybrid electric vehicles

Internet of Things (IoT) become an emergent platform in wireless technologies in design of electric vehicles (EVs) and hybrid electric vehicles (HEVs). Dynamic energy storage systems, batteries can be damaged due to overcharging/discharging and their mass penetration deeply affects the grid. For circumventing the likelihood of damage, the EVs and HEVs require an accurate state of charge (SOC) estimation approach for improving the lifetime and safety. An efficient battery management system (BMS) remains a challenging problem in HEVs, commonly utilized to indicate the battery state-of-charge (SOC). As over-charge and over-discharge outcome from predictable damage to the batteries, precise SOC estimation model is needed for HEVs. In this aspect, this study presents an improved wild horse optimizer with deep learning enabled battery management system (IWHODL-BMS) for IoT based HEVs. The presented IWHODL-BMS employs attention based bidirectional long short-term memory (ABiGRU) approach to accurately estimate SOC in HEVs. For enhancing SOC estimation performance of the ABiGRU technique, the IWHO algorithm is utilized as hyperparameter optimizer. The application of the ABiGRU model results in simpler and accurate representation of the input. A comprehensive simulation result portrayed the enhanced outcomes of the IWHODL-BMS model over the other methods under varying measures.

Systematic Modeling and Analysis of On-Board Vehicle Integrated Novel Hybrid Renewable Energy System with Storage for Electric Vehicles

The automobile industry and technology are putting a great significance in improving vehicles to become more fuel economical, but with incremental costs relative to conventional vehicle technologies; these new vehicles are electric vehicles (EV), plug-in hybrid electric vehicles (PHEV), and hybrid electric vehicles (HEV). However, their significant capabilities to reduce petroleum consumption and achieve efficiency over their life cycles offer economic benefits for customers, industry, carmakers, and policymakers. In this paper, an HEV concept based on renewable energy resources (RERs) is proposed. The proposed HEV design utilizes solar PV energy, wind energy, fuel cell, and a supercapacitor (PV + WE + FC + SC) which generates electrical energy via a proton exchange membrane (PEM) and an SC to cater for strong torque requirements. The vehicle incorporates a battery pack in conjunction with an SC for the power demands and an FC as the backup energy supply. An alternator connected to turbine blades runs by wind energy while the car is moving forward, which produces electricity through the alternator to charge the battery. The design aims to ensure zero carbon emission and improved energy efficiency, is lightweight, and incorporates in-wheel motors to eliminate the mechanical transmissions. Modeling and simulation were carried out for each subsystem using MATLAB® and Simulink® packages. ANSYS Fluent simulation was used to analyze wind energy. The standard analysis, e.g., pressure, velocity, and vector contour, were also considered while designing the final model. To regulate the power supply and demand, the selection of energy sources was controlled by a rule-based supervisory controller following a logical sequence that prioritizes energy sources with the SC as a source in-vehicle stop-and-go situations while the battery acts as the primary source, FC as a backup supply, and wind and solar power to recharge the battery. Solar charging is switched on automatically once the vehicle is parked, and the controller controls the energy flow from the alternator during that period.

Motor and Transmission Multiobjective Optimum Design for Tracked Hybrid Electric Vehicles Considering Equivalent Inertia of Track System

In the development of hybrid electric vehicles (HEVs), a series hybrid powertrain is mainly utilized in tracked vehicles to reduce energy consumption. In order to achieve high energy efficiency while maintaining the required driving performance, key design parameters of traction systems, such as transmission ratio and motor torque and power, need to be optimized. With the aim of effectively analyzing a complex track system, this article proposes an equivalent inertia model, which collectively represents the motion of each component of the tracked vehicle. The equivalent inertia model showed that the inertial effect was 34.8% higher than when the total mass of the vehicle was considered exclusively. Based on this inertia model, design objectives, such as energy efficiency and driving performance, were defined as quantified functions. Because of the balanced relationships between the objective functions, this study formulated a multiobjective optimization problem that includes motor stack length and transmission gear ratio as design variables. Based on the multiobjective optimization results, a Pareto front was obtained, which illustrates the balanced relationships between the objective functions. Comparing the initial HEV design, the optimum designs can improve energy efficiency and driving performance as a maximum of 13.0% and 2.9%, respectively.

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

The transportation sector consumes a large amount of fossil fuels consequently exacerbating the global environmental and energy crisis. Fuel-cell hybrid electric vehicles (FCHEVs) are promising alternatives in the continuous transition to clean energy. This article summarizes the recent advances pertaining to the optimization and cutting-edge design of fuel-cell hybrid electric vehicles, especially the fuel cell + battery hybrid topology, and discusses current technological bottlenecks hindering the commercialization of FCHEVs. The development of HEVs, markets, environmental and economic benefits, components, topologies, energy management strategies, degradation mechanisms, and safety standards of FCHEVs are reviewed. Proton exchange membrane fuel cells constitute the mainstream and most mature fuel cell technology for automobile applications. Battery hybridization is currently favored among the available FCHEV topological designs to improve the dynamic response and recover the braking energy. Energy management strategies encompassing logic rule-based simple methods, intelligent control methods, global optimization strategies, and local optimization strategies are described, and issues and challenges encountering FCHEVs are discussed. In addition to promoting the construction of hydrogen supply facilities, future efforts are expected to focus on solving problems such as the high cost, durability of fuel cells, cold start, lifetime of batteries, security and comfort, system optimization, energy management systems, integration, and diagnosis of faults. This review serves as a reference and guide for future technological development and commercialization of FCHEVs. Highlights Advances of the optimization and cutting-edge design of FCHEVs are reviewed. Battery hybridization is currently favored among the available topological designs. Benefits, components, topologies, and energy management strategies are described. Markets, degradation mechanisms, and safety standards of FCHEVs are introduced. Technological bottlenecks hindering the commercialization of FCHEVs are discussed.