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).

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)

A Hybrid Internal Combustion Engine/Battery Electric Passenger Car for Petroleum Displacement

1988 ◽  
Vol 202 (1) ◽  
pp. 51-64 ◽  
Author(s):  
I Foster ◽  
J R Bumby
Keyword(s):  
Internal Combustion Engine ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Electrical Energy ◽  
Internal Combustion ◽  
Hybrid Car ◽  
Environmentally Sensitive Areas ◽  
Environmentally Sensitive ◽  
Internal Combustion Engine Vehicle

This paper examines the potential of the hybrid electric vehicle in substituting petroleum fuel by broad-based electrical energy. In particular a hybrid car is considered. The way in which the powertrain can be controlled and the effect component ratings have on achieving the petroleum substitution objective are described. It is shown that a hybrid vehicle can be designed that can achieve a petroleum substitution of between 20 and 70 per cent of the equivalent internal combustion engine vehicle, be capable of entering environmentally sensitive areas and yet be capable of a range at high and intermediate speeds that is limited only by the size of its fuel tank.

Equivalent fuel consumption optimal control of a series hybrid electric vehicle

2009 ◽  
Vol 223 (8) ◽  
pp. 1003-1018 ◽  
Author(s):  
J-P Gao ◽  
G-M G Zhu ◽  
E G Strangas ◽  
F-C Sun
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Supervisory Control ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Optimal Control Strategy ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

Improvements in hybrid electric vehicle fuel economy with reduced emissions strongly depend on their supervisory control strategy. In order to develop an efficient real-time supervisory control strategy for a series hybrid electric bus, the proposed equivalent fuel consumption optimal control strategy is compared with two popular strategies, thermostat and power follower, using backward simulations in ADVISOR. For given driving cycles, global optimal solutions were also obtained using dynamic programming to provide an optimization target for comparison purposes. Comparison simulations showed that the thermostat control strategy optimizes the operation of the internal combustion engine and the power follower control strategy minimizes the battery charging and discharging operations which, hence, reduces battery power loss and extends the battery life. The equivalent fuel consumption optimal control strategy proposed in this paper provides an overall system optimization between the internal combustion engine and battery efficiencies, leading to the best fuel economy.

scholarly journals Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Haitao Yan ◽  
Yongzhi Xu
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Control ◽  
Time Performance ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

Energy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control strategy based on terminal neural network is proposed. Firstly, based on the definition of instantaneous control strategy, the equivalent fuel consumption of power battery was calculated, and the objective function of the minimum instantaneous equivalent fuel consumption control strategy was established. Then, for solving the time-varying nonlinear equations used to control the torque output, a terminal recursive neural network calculation method using BARRIER functions is designed. The convergence characteristic is analyzed according to the activation function graph, and then the stability of the model is analyzed and the time efficiency of the error converging to zero is deduced. Using ADVISOR software, the hybrid power system model is simulated under two typical operating conditions. Simulation results show that the hybrid electric vehicle using the proposed instantaneous energy control strategy can not only ensure fuel economy but also shorten the control reaction time and effectively improve the real-time performance.

scholarly journals Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881102
Author(s):  
QIN Shi ◽  
Duoyang Qiu ◽  
Lin He ◽  
Bing Wu ◽  
Yiming Li
Keyword(s):  
Support Vector Machine ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Support Vector ◽  
Equivalent Fuel ◽  
Hybrid Electric ◽  
Equivalent Fuel Consumption

For a great influence on the fuel economy and exhaust, driving cycle recognition is becoming more and more widely used in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle in real time with better accuracy and apply the driving cycle recognition to minimize the fuel consumption with dynamic equivalent fuel consumption minimization strategy. The support vector machine optimized by the particle swarm algorithm is created for building driving cycle recognition model. Furthermore,the influence of the two parameters of window width and window moving velocity on the accuracy is also analyzed in online application. A case study of driving cycle in a medium-sized city is introduced based on collecting four typical driving cycle data in real vehicle test. A series of characteristic parameters are defined and principal component analysis is used for data processing. Finally, the driving cycle recognition model is used for equivalent fuel consumption minimization strategy with a parallel hybrid electric vehicle. Simulation results show that the fuel economy can improve by 9.914% based on optimized support vector machine, and the fluctuations of battery state of charge are more stable so that system efficiency and batter life are substantially improved.

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