Research on Modeling and Simulation of Engine-Generator in the Electric Drive Vehicle

2012 ◽  
Vol 512-515 ◽  
pp. 2615-2619
Author(s):  
Qiu Li Liu ◽  
Chun Guang Liu ◽  
Jian Qiang Su ◽  
Wei Wei
Keyword(s):  
Neural Network ◽  
Internal Combustion Engine ◽  
Modeling And Simulation ◽  
Electric Drive ◽  
Bp Neural Network ◽  
Combustion Engine ◽  
Testing Data ◽  
Simulation And Experiment ◽  
Nonlinear Nature ◽  
Complicated Configuration

Since the complicated configuration and nonlinear nature of the internal combustion engine (ICE/engine), it’s difficult to modeling with dynamic characteristic. Aimed to this problem, a method which associates testing data with control theory for engine is presented. For the engine output is always link up generator in the hybrid electric drive vehicle, the work looks them as a whole and establishes the simulation model of engine-generator based on BP neural network and inertia element. The approach of how to computer parameters are introduced detailed. The simulation and experiment results indicated that the model’s performance data is consistent with the actual engine-generator very well.

scholarly journals Analysis of the Total Unit Energy Consumption of a Car with a Hybrid Drive System in Real Operating Conditions

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3966
Author(s):  
Jarosław Mamala ◽  
Michał Śmieja ◽  
Krzysztof Prażnowski
Keyword(s):  
Energy Consumption ◽  
Internal Combustion Engine ◽  
Electric Drive ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Operating Conditions ◽  
Hybrid Drive ◽  
Total Unit ◽  
Unit Energy ◽  
Unit Energy Consumption

The market demand for vehicles with reduced energy consumption, as well as increasingly stringent standards limiting CO2 emissions, are the focus of a large number of research works undertaken in the analysis of the energy consumption of cars in real operating conditions. Taking into account the growing share of hybrid drive units on the automotive market, the aim of the article is to analyse the total unit energy consumption of a car operating in real road conditions, equipped with an advanced hybrid drive system of the PHEV (plug-in hybrid electric vehicles) type. In this paper, special attention has been paid to the total unit energy consumption of a car resulting from the cooperation of the two independent power units, internal combustion and electric. The results obtained for the individual drive units were presented in the form of a new unit index of the car, which allows us to compare the consumption of energy obtained from fuel with the use of electricity supported from the car’s batteries, during journeys in real road conditions. The presented research results indicate a several-fold increase in the total unit energy consumption of a car powered by an internal combustion engine compared to an electric car. The values of the total unit energy consumption of the car in real road conditions for the internal combustion drive are within the range 1.25–2.95 (J/(kg · m)) in relation to the electric drive 0.27–1.1 (J/(kg · m)) in terms of instantaneous values. In terms of average values, the appropriate values for only the combustion engine are 1.54 (J/(kg · m)) and for the electric drive only are 0.45 (J/(kg · m)) which results in the internal combustion engine values being 3.4 times higher than the electric values. It is the combustion of fuel that causes the greatest increase in energy supplied from the drive unit to the car’s propulsion system in the TTW (tank to wheels) system. At the same time this component is responsible for energy losses and CO2 emissions to the environment. The results were analysed to identify the differences between the actual life cycle energy consumption of the hybrid powertrain and the WLTP (Worldwide Harmonized Light-Duty Test Procedure) homologation cycle.

Unified multidisciplinary modeling and simulation analysis of internal power system

2021 ◽  
pp. 1-21
Author(s):  
Guojin Chen ◽  
Chang Chen ◽  
Yiming Yuan ◽  
Yishuai Yue
Keyword(s):  
Internal Combustion Engine ◽  
Modeling And Simulation ◽  
Physical System ◽  
Cooling System ◽  
Combustion Engine ◽  
Control Unit ◽  
Internal Combustion ◽  
Power Equipment ◽  
Supply Rate ◽  
Multidisciplinary Modeling

The internal combustion power equipment is a typical cyber-physical system (CPS). The traditional design method is to separate the information system from the physical system, and then to simulate and optimize separately every system. That can not achieve the best performance. Aiming at the internal combustion power equipment with multi-disciplinary deep integration, this paper establishes the multi-disciplinary model of the whole and key components based on Dymola software. There are mainly mechanical system, combustion system, cooling system, control system and other simulation models, including deceleration and fuel cut-off control unit modeling, start-stop control unit modeling and speed limit control unit modeling. The performance of each model is simulated and analyzed. The mathematical models of engine characteristic curve and fuel supply rate curve are established through experimental study. Finally, taking the simulation model of automobile power system as an example, the simulation calculation and experimental verification are carried out, and the relationship among fuel supply rate, torque, speed and valve of internal combustion engine is obtained, as well as the cooling capacity of the cooling system is studied. The experimental results show that the maximum error between the simulation speed curve and the actual speed curve is within ± 2 km/h. The research results of this paper can provide theoretical basis for multidisciplinary modeling and simulation of internal combustion power equipment, and also provide technical support for performance analysis of internal combustion engine.

Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

2015 ◽  
Vol 779 ◽  
pp. 226-232 ◽  
Author(s):  
Shi Xing Zhu ◽  
Yue Han ◽  
Bo Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Experimental Testing ◽  
Computation Time ◽  
Landing Gear ◽  
Time Varying ◽  
Control Effect ◽  
Simulation And Experiment ◽  
Neural Network Pid

For characteristics of nonlinearity and time-varying volatility of landing gear based on MR damper, a BP neural network PID controller with a momentum was designed on basis of established dynamic mathematical model. BP neural network would adjust three parameters of PID online in time. The controller was inputted the energy which was combined by the feedback of acceleration and displacement of the control system, which greatly reduced the computation time of controller and the control effect was more obvious. After compared with PID, the simulation and experiment have showed that BP neural network PID has a better effect. The arithmetic can be put into practice through experimental testing.

Structural Damage Localization Based on AR Model and BP Neural Network

2011 ◽  
Vol 94-96 ◽  
pp. 1211-1215
Author(s):  
Yan Song Diao ◽  
Fei Yu ◽  
Dong Mei Meng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Ar Model ◽  
Model Parameters ◽  
Damage Localization ◽  
Modal Parameter ◽  
Damage Location ◽  
Simulation And Experiment ◽  
Damage Characteristic

When the AR model is used to identify the structural damage, one problem is often met, that is the method can only make a decision whether the structure is damaged, however, the damage location can not be identified exactly. A structural damage localization method based on AR model in combination with BP neural network is proposed in this paper. The AR time series models are used to describe the acceleration responses. The changes of the first 3-order AR model parameters are extracted and composed as damage characteristic vectors which are put into BP neural network to identify the damage location. The effectiveness of the method is validated by the results of numerical simulation and experiment for a four-layer offshore platform. Only the acceleration responses can be used adequately to localize the structural damage, without the usage of modal parameter and excitation force. Thus the dependence on the modal parameter and excitation can be avoided in this method.

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