Design Of Hybrid Electric Vehicle Research Articles

Research on machine learning methods for energy consumption optimization in plug-in hybrid electric vehicles

Problem. The growing demand for sustainable and energy-efficient transportation has intensified interest in plug-in hybrid electric vehicles as an effective alternative to conventional internal combustion engine vehicles. However, the efficiency and flexibility of these systems are significantly constrained by traditional energy management strategies, which lack adaptability to real-world dynamic driving conditions, road scenarios, and individual driving styles. The need for real-time, predictive, and intelligent control algorithms has become critical to optimize energy use, reduce fuel consumption, and ensure the stability of hybrid propulsion systems. Goal. The objective of this study is to analyze and systematize state-of-the-art machine learning methods for optimizing energy management systems in plug-in hybrid electric vehicles, to identify the key technical challenges and to outline the prospects for the development of intelligent, adaptive energy management systems capable of real-time decision-making in uncertain and variable environments. Methodology. This research is based on a comprehensive review of existing literature and technical implementations, focusing on the use of deep learning, reinforcement learning, and predictive control approaches within energy management systems for plug-in hybrid electric vehicles. The study investigates architectural configurations of plug-in hybrid electric vehicles powertrains, their operational modes, functional requirements of energy management systems, and evaluates machine learning algorithms for power distribution optimization, battery health monitoring, predictive energy control, and personalized driving strategies. Results. The analysis confirms that machine learning-enhanced energy management systems can significantly improve fuel economy, adaptability, and operational reliability under varying road and climatic conditions. Reinforcement learning methods enable continuous policy improvement, predictive models allow proactive energy flow planning, and modular energy management systems architectures enhance system scalability and fault tolerance. The integration of machine learning also facilitates fault detection, battery degradation prediction, and utilization of alternative energy sources such as solar or vibrational energy. These solutions collectively contribute to more intelligent and efficient energy management in modern hybrid vehicles. Originality. This study offers a structured classification of machine learning applications in energy management systems for plug-in hybrid electric vehicles, substantiates the advantages of modular control system architectures, and introduces a novel perspective on incorporating behavioral analysis of driver style into energy management systems strategy personalization. It also highlights underexplored areas such as the use of environmental energy inputs (solar, piezoelectric) and outlines methodological considerations for their integration using predictive analytics. Practical value. The findings provide a foundation for the design of next-generation energy management systems in hybrid vehicles, contributing to enhanced fuel efficiency, extended battery life, and improved environmental performance. The proposed insights can be used by researchers and developers to implement intelligent control systems, reduce development time, and align plug-in hybrid electric vehicle design with smart mobility and sustainability goals.

Sustainable design of products: Balancing quality, life cycle impact, and social responsibility

The shift towards sustainable mobility has increased the demand for energy-efficient and environmentally friendly vehicles, such as Hybrid Electric Vehicles (HEVs). However, designing HEVs that simultaneously meet high product quality, minimize environmental impact, and adhere to social responsibility standards remains a complex challenge. This study presents a decision-making model aimed at integrating these key sustainability criteria into the design and improvement of HEVs. The model combines three indices: the Aggregated Quality Index (AQI), Environmental Impact Index (EII) based on Life Cycle Assessment (LCA), and Social Responsibility Index (SRI), to assess and compare different HEV prototypes. By processing customer expectations, environmental impacts, and social responsibility considerations, the model predicts the optimal prototype that balances quality, environmental sustainability, and social standards. The findings demonstrate that applying this model can significantly enhance decision-making in sustainable vehicle development and support the creation of HEVs that better align with global sustainability goals. This approach has practical implications for automotive manufacturers aiming to innovate responsibly in the green mobility sector.

An optimal energy consumption calculation method of hybrid electric vehicles with frequency distribution characteristics of driving conditions

An optimal energy consumption calculation method of hybrid electric vehicles with frequency distribution characteristics of driving conditions

Design of Plug-in Hybrid Electric Vehicle with State of Charge Estimation using MATLAB

The use of fossil fuels in conventional vehicles has resulted in significant emissions from the transportation sector, which has contributed significantly to climate change. Using electrical vehicle to reduce transportation-related emissions is a better option than using conventional vehicles because the well-designed hybrid electric vehicle can outstrip them. This article looks at various hybrid electric powertrain arrangements and components to develop a hybrid powertrain that meets the EcoCAR mobility challenge competition's performance requirements. There are several variables to consider when deciding together with braking, acceleration, range, fuel economy and emissions. To attain the required performance metrics, MATLAB/Simulink simulations were used to examine five different designs. There was only one design of the powertrain that met all the performance criteria. In this article a P4-hybrid-powertrain, persisting of a 2.6L GM engine, 160 KW American axle manufacturing electric motor drive with an EDU and a 143kW hybrid design services battery packs. The novel quasi model delivers better torque and speed characteristics.

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

Design and performance analysis of Hybrid electric vehicle

Design and performance analysis of Hybrid electric vehicle

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

Light railways are a low carbon emission form of transport, but it is often difficult to electrify tracks in central urban areas. This limitation can be avoided by integrating an on-board battery storage connected to the dc bus of the traction inverter with a boost dc/dc converter. However, the boost converter requires a bulky inductor and introduces additional power losses that are undesirable. This paper proposes to replace the standard induction motor with an open ended winding induction motor connected at one end to the overhead line with the traction inverter and to the other end to a battery with a second inverter in order to reduce the power losses of the traction drive. The paper addresses design and control aspects for light rail vehicles with open ended winding induction machines when the power supply is partly from the overhead line and partly from the on board battery. Moreover, the paper studies in detail the hybrid operations of the traction system i.e. when the overhead line charges the battery during coasting or at the stops. Finally, numerical simulations for a real use case are presented to quantify the reduction of power losses in comparison to the standard solution.

Energy management strategies of hybrid electric vehicles: A comparative review

Abstract With 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

Abstract Design 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

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

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.