Hybrid Electric Vehicle Configuration Research Articles

A switched model predictive control with parametric weights-based mode transition strategy for a novel parallel hybrid electric vehicle

In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 m/s3. It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.

Modeling and Simulation of Hybrid Electric Vehicles for Sustainable Transportation: Insights into Fuel Savings and Emissions Reduction

Most motor vehicles have historically utilized Internal Combustion Engines powered by a fossil fuel. Despite the technological advancements in fuel-efficient engines, further improvements are required to reduce the effect of fuel costs and global warming. This study models and simulates Hybrid Electric Vehicles (HEVs) to evaluate their potential for fuel cost savings and emissions reduction compared to traditional vehicles. Using MATLAB® version R2023a Update 5 (9.14.0.2337262) Simulink version 10.7 (R2023a) and the Advanced Vehicle Simulator (ADVISOR) version 2003-00-r0116, the research examines Series and Parallel HEV configurations. The simulation explores various battery configurations, engine capacities, and power unit models to analyze their impact on energy utilization. The data, collected from the simulations, show significant fuel savings and emissions reduction with HEVs. The Parallel HEV configuration consistently saves fuel with fewer battery modules, while the Series HEV configuration performs better but requires more modules to maintain the system’s State of Charge.

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..

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated.

Collaborative Optimization of Energy Management Strategy and Adaptive Cruise Control Based on Deep Reinforcement Learning

Hybrid electric vehicles (HEVs) have great prospects in reducing fossil fuel consumption, and adaptive cruise control (ACC) technology provides safe and convenient travel for drivers. The fusion of the two technologies can theoretically improve the safety, comfort, and fuel economy of vehicles. Hence, the energy management strategy (EMS) of Prius, a typical HEV configuration, is studied under the car-following scenario. This optimization problem involves complex systems, inconsistent objectives, and stringent constraints, which may be challenging to conventional algorithms. Therefore, a novel deep deterministic policy gradient (DDPG)-based ecological driving strategy (DDPG-ECO) is proposed and the weights of multiple objectives are analyzed to optimize the training results. The extensive simulation experiment compares the effects of Ornstein-Uhlenbeck action noise (OUAN) and soft-max action noise (SAN), which act on the acceleration action. Simulations under different driving cycles show that the fuel economy of DDPG-ECO can achieve more than 90% of dynamic programming (DP)-based methods on the conditions of ensuring car-following performances.

ADVISOR-Based Performance Analysis of a Hybrid Electric Vehicle and Comparison with a Conventional Vehicle

Low efficiency of internal combustion engine (ICE)-based vehicles and their adverse effect on environment have compelled a lot of research on its green counterpart i.e. electric vehicles. But the limited range of electric vehicles coupled with longer recharging times has given rise to the advent of hybrid electric vehicles (HEVs). HEVs use two drive trains i.e. a conventional ICE-based drive train and an electric drive train, thus giving it a better efficiency and reliable operating modes. Based on the topological configuration of drive train, HEVs can be categorized into series HEV or parallel HEV. This paper presents a comparative performance analysis of a small HEV in pure ICE configuration, series HEV (SHEV) configuration and parallel HEV (PHEV) configuration. Advanced Vehicle Simulator (ADVISOR) software is used with Matlab/Simulink platform for simulation purpose. The acceleration performance and gradability test have also been carried out and the result is obtained by investigating vehicle emissions.

Novel Reduced Switch Multi-Level Inverter for Range Extension of Novel EV Configuration

This project gives the novel idea of electric vehicle configuration for heavy vehicles which are most used in up and downstream. This configuration help to improve the efficiency of the system by twice when it is operated in the downstream. so, the concept is modifying the hybrid electric vehicle configuration by changing the internal combustion engine with a generator in addition to minimize the loss and increase the battery performance using a multilevel inverter. Multilevel Inverters are a new group of converters for DC-AC conversion in the medium and high voltage and power applications. In this paper, a new topology for the stepped wave output voltage generations have been proposed with a smaller number of switches. The topology needs 3 DC voltage sources and 7 switches to produce 15 levels at the load, beyond having a smaller number of switches has the advantages in terms of low voltage stresses across the switches. As an add on, a detailed comparative study of different topologies has been discussed in the paper to illustrate the advantages and features of the proposed topologies. Finally, experimental results have been included in the paper to validate the performances of the proposed topologies with a resistive load, and the prototype model is done for the multilevel inverter and the output displayed.

Performance Evaluation of Hybrid Electric Vehicles for Sustainable Transport System

The emissions from the internal combustion (IC) engine vehicle causes pollution which increases the carbon footprints in the environment which causes global warming. In ICE vehicle only 20 % of the energy produced by it is used to run the vehicle and rest 80 % of it get wasted. The emerging technology of Hybrid Electric vehicle (HEV) has become the feasible solution for the modern world as it lessens the carbon emission and augments the fuel performance of vehicle. The role of power electronic converters is very crucial in designing the configuration of HEVs. The performance of the converter is employed for realizing the features of electric traction motor drive. The paper analyses the performance of a small car powered by gasoline based internal combustion engine, series hybrid electric vehicle (SHEV) and parallel hybrid electric vehicle (PHEV) drive train. The simulation has been performed on Advanced Vehicle Simulator (ADVISOR) platform. Different types of HEVs configuration has been analyzed by considering three different driving schedules such as CYC_UDDS, CYC_NEDC and CYC_URBAN_INDIA. The gradability and acceleration test has also been carried out in all category of test vehicles and result is demonstrated by examining vehicle emission at each driving cycle.

Micro gas and steam turbines power generation system for hybrid electric vehicles

Hybrid electric vehicles (HEV) are currently considered a viable solution for fossil fuel saving and pollutant emissions reduction in transportation sector. The typical HEV configuration contains a reciprocating internal combustion engine – gasoline or diesel. Aiming to increase performance and reduce pollution, a study on performance, size and mass of a HEV configuration with micro gas and steam turbine was developed. The proposed system generates 100 kW output power and operates with compressed natural gas (CNG hybrid technology), which is a fuel more environmentally friendly than gasoline or diesel. The study revealed that analysed system performs better than most advanced equivalent ICE engines while its size and mass fit into the bodywork of a car. A solution for arrangement of the system components in the case of a car is also presented.

Estimation of the ECMS Equivalent Factor Bounds for Hybrid Electric Vehicles

The strategy for energy management (EM) of a hybrid electric vehicle (HEV) has a considerable impact on the vehicle fuel economy. One well-known EM strategy is the equivalent consumption minimization strategy (ECMS) that is a form of Pontryagin’s minimum principle (PMP). PMP proves under certain conditions that ECMS yields the maximum fuel economy. However, even if the required conditions are met, the optimal value of the costate still has to be estimated. Many approaches have been suggested for estimating the optimal value of the costate, or the equivalent factor for using battery power in the ECMS cost function. Instead of direct estimation of ECMS optimal equivalent factor, this brief derives estimations for the upper and lower bounds of the optimal equivalent factor. The derived bounds are functions of the HEV configuration and independent of the drivecycle, verified by simulation results. The knowledge about these bounds can be employed in designing new types of adaptive ECMSs (A-ECMSs). To demonstrate the application of the bounds, this brief introduces a new A-ECMS. Finally, the simulation results are presented comparing the fuel economy of the introduced A-ECMS with the fuel economies of an existing A-ECMS and global optimal controller.

Improved Fuel Economy of Through-the-Road Hybrid Electric Vehicle with Fuzzy Logic-Based Energy Management Strategy

Hybrid electric vehicle (HEV) provides drivers with uncompromised drivability while significantly reducing hazardous emissions. This is achieved through optimal power flow solution via energy management strategy (EMS) which efficiently handles energy distribution from the different energy sources of a HEV. In this paper, a through-the-road (TtR) HEV configuration with fuzzy logic-based EMS is proposed. Fuzzy logic is applied in the main control block of the vehicle with a pair of membership functions assisting the power flow controller to select the appropriate power distribution by the hybrid drivetrain based on available resources in real time. The EMS operates in hybrid mode blended control strategy to achieve minimum fuel consumption for the desired trip by prioritising the electrical drivetrain over the internal combustion engine (ICE) for power distribution to the wheels. A Simulink model was constructed in MATLAB® to represent the TtR HEV equipped with in-wheel motors (IWM) in the rear wheels. A fuzzy logic-based EMS controller has been synthesised. The power flow in the TtR HEV is decided based on current vehicle speed and the global discharge rate (GDR) value derived from the current state-of-charge (SOC) of the battery and remaining trip distance. The proposed controller performs well on standard drive cycles and offers up to 62% improvement in fuel consumption compared to the reference model which uses rule-based EMS. Comparisons against other published models are equally encouraging, especially on high average speed drive cycles with up to 19.8% improvements in fuel consumption.

Exergo-technological explicit methodology for gas-turbine system optimization of series hybrid electric vehicles

Significant research efforts have been invested in the automotive industry on hybrid electrified powertrains in order to reduce the dependence of passenger cars on oil. Electrification of powertrains resulted in a wide range of hybrid vehicle architectures. The fuel consumption of these powertrains strongly relies on the energy converter performance, as well as on the energy management strategy deployed on board. This paper investigates the potential of fuel consumption savings of a series hybrid electric vehicle using a gas turbine as an energy converter instead of the conventional internal-combustion engine. An exergo-technological explicit analysis is conducted to identify the best configuration of the gas-turbine system. An intercooled regenerative reheat cycle is prioritized, offering higher efficiency and higher power density than those of other investigated gas-turbine systems. A series hybrid electric vehicle model is developed and powertrain components are sized by considering the vehicle performance criteria. Energy consumption simulations are performed over the Worldwide Harmonized Light Vehicles Test Procedure driving cycle using dynamic programming as the global optimal energy management strategy. A sensitivity analysis is also carried out in order to evaluate the impact of the battery size on the fuel consumption, for self-sustaining and plug-in series hybrid electric vehicle configurations. The results show an improvement in the fuel consumption of 22–25% with the gas turbine as the auxiliary power unit in comparison with that of the internal-combustion engine. Consequently, the studied auxiliary power unit for the gas turbine presents a potential for implementation on series hybrid electric vehicles.

Comparative Analysis on the Rule-based Control Strategy of Two Typical Hybrid Electric Vehicle Powertrain

Abstract Series-Parallel Hybrid ElectricVehicle (HEV) has well applied in practice due to its significant advantages, this paper aimed at two different kind of Series-Parallel HEV configurations: Power Split and Mode Switching configuration, comparing their control strategies, set model and simulate to verify, obtain features and applications of two configurations. The simulation results show that the power split configuration does better in protecting battery and reducing urban fuel consumption, while mode switching configuration has simpler structure.

An optimal energy management development for various configuration of plug-in and hybrid electric vehicle

Due to soaring fuel prices and environmental concerns, hybrid electric vehicle (HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV’s fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.

Study on Power-Train Configuration of Hybrid Electric Vehicle Equipped with Dual Clutch Transmission

In this paper, the advantage of hybrid electric vehicle (HEV) equipped with dual clutch transmission (DCT) was presented. Five HEV simulation models equipped with DCT were built on the basis of AVL CRUISE software. According to different power-train configurations, different control strategies were built by MATLAB/SIMULINK. The advantage and disadvantage of each power-train configurations was analyzed through simulation. The results supplied the foundation for the next step vehicle development.

System design and control strategy of the vehicles using hydrogen energy

This paper presented a system design review of fuel cell hybrid vehicle. Fuel supply, hydrogen storage, DC/DC converters, fuel cell system and fuel cell hybrid electric vehicle configurations were also reviewed. We explained the difference of fuel supply requirement between hydrogen vehicle and conventional vehicles. Three different types of hydrogen storage system for fuel supply are briefly introduced: high pressure, liquid storage and metal oxides storage. Considering of the potential risk of explosion, a security hydrogen storage system is designed to restrict gas pressure in the safe range. Due to the poor dynamic performance of fuel cells, DC/DC converters were added in hybrid vehicle system to improve response to the changes of power demand. Requirements that in order to select a suitable DC/DC converter for fuel-cell vehicles design were listed. We also discussed three different configurations of fuel-cell hybrid vehicles: “FC + B”, “FC + C”, and “FC + B + C”, describing both disadvantages and advantages. “FC + B + C” structure has a better performance among three structures because it could provide or absorb peak current during acceleration and emergency braking. Finally, the energy management strategies of fuel cell and were proposed and the automotive energy power requirement of an application example was calculated.

A New Differential Configuration of Hybrid Electric Vehicle with Torque Regulationloop

The most significant weakness of hybrid electric vehicles(HEV) is the high construction cost and the complicated control strategies due to multiple power sources[1],[2] .In this paper a new developed configuration of hybrid vehicle is presented.The new configuration is simple,cost-effective and easy to implement .The differential unit, which acts as mechanical torque-overflow keeps engine torque within a predefined value .This is achieved by the aid of torque loop attached to one terminal of the differential.The new configuration proved to act continuously varying transmission(CVT)and also can perform efficient control strategies .Minimum emissions can be assured by running the IC-engine within best engine performance zone which is characterized by engine torque and engine speed.

An Economic and In-Service Emissions Analysis of Conventional, Hybrid and Electric Vehicles for Australian Driving Conditions

Hybrid and fully electric vehicles are becoming more common as a response to rising fuel prices and greenhouse considerations. While the benefits of electrification on urban air quality have been studied quite widely, financial assessments of the various alternative vehicle forms are less common, particularly for Australian driving conditions. The aim of this paper is therefore to identify the scenarios under which different vehicle configurations are attractive to the vehicle owner. A Class-E conventional vehicle is compared with full-electric, plug-in hybrid, parallel hybrid, series hybrid and mild hybrid electric vehicle configurations. A simulation model of a conventional internal combustion engine based large sized car is developed and validated against experimental data. The conventional vehicle model is then systematically altered to obtain its increasingly electric variants. The fuel consumption and greenhouse gas emissions are simulated on the legislative NEDC drive cycle and the more representative Australian Urban Drive Cycle (AUDC). The outcomes of these tests are used to estimate the total cost of ownership and in-service emissions, thus allowing the cost of emissions mitigation to be approximated for the different vehicles. Different scenarios are considered for the pricing of energy and major powertrain components. This provides a baseline assessment based on current prices and projections, as well as ‘electrification favorable’ and ‘electrification unfavorable’ scenarios. The impact on vehicle emissions of significant penetration of renewable energy into the Australian electricity grid is also considered.

A new series-parallel hybrid electric vehicle configuration based on an induction motor coupled to a DC machine

With emphasis on a cleaner environment and efficient operation, vehicles today rely more and more heavily on electrical power generation for success. In this paper, to improve the speed performance of the drive against uncertainties caused by load disturbances, an integral switching surface couple mode speed controller was proposed. As such, mathematical modeling of the HEV components as the three phase induction motor coupled to the DC motor in hybrid electric vehicle was introduced. The controller of the ‘induction motor’ (IM) was designed based on input-output feedback linearization technique. It allowed greater electrical generation capacity and the fuel economy and emissions benefits of hybrid electric automotive propulsion. Hybrids transient torque allows the use of start/stop operation as well as brake energy regeneration. Thus, the results of Matlab-Simulink simulations and a comparison between the control schemes are presented. Key words: Hybrid electrical vehicle, induction motor control, D.C motor.

Automated Modelling of Power-Split Hybrid Vehicles

Hybrid electric vehicles (HEV) represent a promising technology to improve the fuel economy of ground vehicles in the near-term. Among the HEV configurations, the power-split configuration offers superior design and control flexibility and achieves highest overall efficiency. In this paper, a methodology to generate dynamic equations automatically for the power-split hybrid power-train is proposed. The designer only needs to specify the topology of the transmission: how the power sources, vehicle, the planetary gears and clutches are connected to each other. The dynamic model can then be generated automatically through a set of rules. This automated generation process makes it possible for a designer to explore different split hybrid configurations quickly and efficiently.