scholarly journals Series-Parallel Hybrid Electric Vehicle Parameter Analysis using MATLAB

2021 ◽  
Vol 9 (10) ◽  
pp. 421-428
Author(s):  
Richik Ray
Keyword(s):  
Internal Combustion Engine ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Matlab Simulink ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric

Abstract: In this paper, a MATLAB based Simulink model of a Series-Parallel Hybrid Electric Vehicle is presented. With the advent of Industry 4.0, the usage of Big Data, Machine Learning, Internet of Things, Artificial Intelligence, and similar groundbreaking domains of technology have usurped manual supervision in industrial as well as personal scenarios. This is aided by the drastic shift from orthodox and conventional Internal Combustion Engine based vehicles fuelled by fossil fuels in the order of petrol, diesel, etc., to fully functional electric vehicles developed by renowned companies, for example Tesla. Alongside 100% electric vehicles are hybrid vehicles that function on a system based on the integration of the conventional ICE and the modern Electric Propulsion System, which is referred to as the Hybrid Vehicle Drivetrain. Designs for modern HEVs and EVs are developed on computer software where simulations are run and all the essential parameters for the vehicle’s performance and sustainability are run and observed. This paper is articulated to discuss the parameters of a series-parallel HEV through an indepth MATLAB Simulink design, and further the observations are presented. Keywords: ICE (Internal Combustion Engine), HEV (Hybrid Electric Vehicle), Drivetrain, MATLAB, Simulink, PSD (Power Split Device), Vehicle Dynamics, SOC (State-of-Charge)

Research on the Dynamic Performance and Parameter Design of Parallel Hybrid Electric Vehicle

2010 ◽  
Vol 108-111 ◽  
pp. 613-618
Author(s):  
Wei Zheng ◽  
Qian Fan Zhang ◽  
Shu Mei Cui
Keyword(s):  
Electric Vehicle ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Dynamic Models ◽  
Parametric Design ◽  
Combustion Engine ◽  
Dynamic Performance ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric

According to the Parallel Hybrid Electric Vehicle (PHEV) demands on powertrain systems, the dynamic models of PHEV are built in this paper. Base on the analysis of dynamical characteristics of both internal combustion engine (ICE) and electric machine (EM), the dynamic ability and fuel economy performance of PHEV is presented. The paper focuses on the parametric design of powertrain on vehicle performance, which provided the theoretical foundation for PHEV design. The paper also puts forward the control strategy of PHEV during the operating modes switching, which aims to solve the problem of the power distribution between the ICE and electric motor, which can effectively resolve process control problems of the complex PHEV system. By employing the dynamic model and performing MATLAB simulation, the results of simulation are given, which demonstrate that the PHEV improve performance well.

Development of a Novel Torque Management Strategy for Parallel Hybrid Electric Vehicle

2013 ◽  
Vol 278-280 ◽  
pp. 1729-1736 ◽  
Author(s):  
Xian Wu Gong ◽  
Chuang Gao ◽  
Pei Wang
Keyword(s):  
Fuzzy Logic ◽  
Electric Vehicle ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
The State ◽  
State Of Charge ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric

In this paper, a torque management strategy for parallel hybrid electric vehicle(PHEV) is developed. The proposed strategy is responsible for vehicle's torque distribution during driving，between the Internal Combustion Engine(ICE) and the electric motor(EM) by Fuzzy Logic Control(FLC). This has been investigated through two main aspects. The first is the optimum torque split between the ICE and the EM. The second is sustaining the State of Charge(SOC) of the battery.These goals have been accomplished by developing two fuzzy logic(FL) controllers. The FL controllers are designed based on the state of charge of the battery, the ICE speed, the vehicle's requested torque and the ICE target torque. The strategy is validated by ADVISOR2002 simulation model based on the software Matlab/Simulink. The performance of the vehicle have been analyzed throughout a combined driving cycle that represents the normal and the worst operating conditions.Compared to the electric assistant control strategy(EACS), The simulation results show that the proposed torque management strategy is effective to control the engine's operating points within the highest efficiency as well as sustain the SOC of the battery. Thereby, improving the efficiency of the ICE and the EM, enhancing the battery’s life, reducing fuel consumption and decreasing emission.

scholarly journals Experimental Evaluation and Prediction Algorithm Suggestion for Determining SOC of Lithium Polymer Battery in a Parallel Hybrid Electric Vehicle

2018 ◽  
Vol 8 (9) ◽  
pp. 1641 ◽  
Author(s):  
Insu Cho ◽  
Jongwon Bae ◽  
Junha Park ◽  
Jinwook Lee
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Lithium Ion ◽  
Prediction Algorithm ◽  
Computational Time ◽  
Battery Management System ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric

The necessity of hybrid vehicles and electric vehicles is widely known for reasons such as fossil fuel depletion, climate change, emission norms mandated by regulations, and so on. Expansion of the hybrid vehicle market is a realistic way to respond to fuel efficiency regulations. Hybrid electric vehicles are continuously challenged to meet cross-attribute performance while minimizing energy usage and component cost in a highly competitive automotive market. Current optimization strategy for a parallel hybrid requires much computational time and relies heavily on the drive cycle to accurately represent driving conditions in the future. With increasing application of the lithium-ion battery technology in the automotive industry, development processes and validation methods for the battery management system (BMS) have attracted attention. The purpose of this study is to propose an algorithm to analyze charging characteristics and improve accuracy for determining state of charge (SOC), the equivalent of a fuel gauge for the battery pack, during the regenerative braking period of a TMED type parallel hybrid electric vehicle.

Impact of Motor Size & Efficiency on Acceleration, Fuel Consumption & Emissions of Split-Axle Through-the-Road Parallel Hybrid Electric Vehicle

2014 ◽  
Vol 663 ◽  
pp. 498-503 ◽  
Author(s):  
Saiful A. Zulkifli ◽  
Syaifuddin Mohd ◽  
Nordin B. Saad ◽  
A. Rashid A. Aziz
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Hybrid Vehicles ◽  
The Road ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Improved Performance

A split-axle parallel hybrid drive-train with in-wheel motors allows for existing combustion-engine-driven vehicles to be converted into a hybrid vehicle with minor mechanical modification, resulting in a retrofit-conversion hybrid electric vehicle (HEV). This is achieved by placing electric motors in the hub of the otherwise non-driven wheels. Due to the wheel hub’s size constraint, the allowable size and power of the electric in-wheel motor that can be installed is severely restricted to less than 10 kW per wheel, which raises the concern of lack of improved performance compared to the original vehicle. This work analyzes the influence of motor sizing and efficiency on acceleration performance, fuel consumption and emission levels of three different converted hybrid vehicles, through simulation. Results provide insight into sensitivity of different-sized vehicles with varying-size engines, to the size and efficiency of the retrofitted electric motor.

scholarly journals Modeling of Working Machines Synergy in the Process of the Hybrid Electric Vehicle Acceleration

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5818
Author(s):  
Konrad Prajwowski ◽  
Wawrzyniec Golebiewski ◽  
Maciej Lisowski ◽  
Karol F. Abramek ◽  
Dominik Galdynski
Keyword(s):  
Internal Combustion Engine ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Power Split ◽  
Equivalent Fuel ◽  
Vehicle Acceleration ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

There are many different mathematical models that can be used to describe relations between energy machines in the power-split hybrid drive system. Usually, they are created based on simulations or measurements in bench (laboratory) conditions. In that sense, however, these are the idealized conditions. It is not known how the internal combustion engine and electrical machines work in real road conditions, especially during acceleration. This motivated the authors to set the goal of solving this research problem. The solution was to implement and develop the model predictive control (MPC) method for driving modes (electric, normal) of a hybrid electric vehicle equipped with a power-split drive system. According to the adopted mathematical model, after determining the type of model and its structure, the measurements were performed. There were carried out as road tests in two driving modes of the hybrid electric vehicle: electric and normal. The measurements focused on the internal combustion engine and electrical machines parameters (torque, rotational speed and power), state of charge of electrochemical accumulator system and equivalent fuel consumption (expressed as a cost function). The operating parameters of the internal combustion engine and electric machines during hybrid electric vehicle acceleration assume the maximum values in the entire range (corresponding to the set vehicle speeds). The process of the hybrid electric vehicle acceleration from 0 to 47 km/h in the electric mode lasted for 12 s and was transferred into the equivalent fuel consumption value of 5.03 g. The acceleration of the hybrid electric vehicle from 0 to 47 km/h in the normal mode lasted 4.5 s and was transferred to the value of 4.23 g. The hybrid electric vehicle acceleration from 0 to 90 km/h in the normal mode lasted 11 s and corresponded to the cost function value of 26.43 g. The presented results show how the fundamental importance of the hybrid electric vehicle acceleration process with a fully depressed gas pedal is (in these conditions the selected driving mode is a little importance).

MPC-Based Energy Management of a Parallel Hybrid Electric Vehicle Using Terrain Information

2015 ◽  
Author(s):  
Mohamed Wahba ◽  
Sean Brennan
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Simulation Software ◽  
Electrical Machine ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Power Split ◽  
Hybrid Electric

A parallel hybrid electric vehicle (HEV) combines the power produced by electric machines and a combustion engine to enable improved fuel economy. Optimization of the power-split algorithm managing both torque sources can be readily achieved offline, but online implementation results often show great deviation from expected fuel economy due to traffic, hills, and similar effects that are not easily modeled. Of these external influences, the road grade for a travel route is potentially known a priori given a set destination choice from the driver. To examine whether grade information can improve the performance of a hybrid powertrain controller, we first formulate the vehicle model as a low-order dynamic model, recognizing that the primary dynamics of the energy system are slow. A model predictive control (MPC) strategy utilizing the terrain data is then developed to obtain a time-varying power split between the combustion engine and the electrical machine. Simulation results of the HEV model over multiple standard drive cycles, with different terrain profiles and different cost functions, are presented. Testing of the MPC performance compared to Argonne National Lab’s powertrain simulation software Autonomie shows that the MPC strategy utilizing terrain data gives an improvement of up to 2.2% in fuel economy with respect to the same controller without terrain information, on the same route.

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