Study on the Parametric Optimization for a Parallel Hybrid Electric Vehicle Power Train

Liang, Chao; You-de, LI; Wang, Qingnian

doi:10.4271/2000-01-3109

Cited by 27 publications

(6 citation statements)

References 2 publications

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“…e driving mileage depends on battery technology (to improve battery power density and energy density); also, it is closely related to other factors, such as the configuration, parameter matching, and control method of the power transmission system [1][2][3]. erefore, the key to improving the driving mileage and overall performance of pure electric vehicles lie in the efficient transmission system and its design method and the perfect energy control method [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation

et al. 2020

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Equipped with two power sources, the dual-driving powertrain system for pure electric vehicles has a driving mode different from traditional electric vehicles. Under the premise that the structural form of the transmission system remains unchanged, the following transmission schemes can be adopted for double drive electric vehicles according to the demand power: the main and auxiliary electric transmission scheme (two motors are driven separately with dual-motor coupling drive), the transmission scheme in which the two motors always maintain coupling drive, and the speed-regulating type electric transmission scheme (the main motor is always responsible for driving, and the auxiliary motor is responsible for speed regulation). Therefore, a significant difference exists in the design methods of the power transmission system of double drive electric vehicles and existing vehicles. As for such differences, this paper adopts intelligent algorithm to design the parameters of the transmission system and introduces the genetic algorithm into the optimization design of parameters to obtain the optimal vital parameters of the power transmission system based on computer simulation. The prototype car used in this paper is a self-owned brand car; MATLAB/Simulink platform is used to build the vehicle simulation model, which is used for the computer simulation analysis of the vehicle dynamic performance and economy. It can be seen from the analysis result that the system parameters obtained by using the global optimization method proposed in this study can improve the vehicle dynamic performance and economic performance to varying degrees, which proves the efficiency and feasibility of the optimization method.

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Section: Introductionmentioning

confidence: 99%

Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation

et al. 2020

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“…Simulation analysis indicates that the fuel consumption of the vehicle was reduced by 6.56% and 3.15% under FTP72 and HWFET cycle conditions, respectively [13]. Chu et al conducted a parallel hybrid powertrain parameter optimization study and performed simulation analysis under urban driving cycle conditions in China; they achieved a higher fuel economy with the same state of charge (SOC) before and after optimization [14]. Sheng et al used the GA to optimize the gear ratio of a two-speed AMT in a pure electric vehicle.…”

Section: Optimization Of Key Parametersmentioning

confidence: 99%

Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks

Zhou,

Yang,

Zhang

et al. 2023

Energies

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Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks.

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“…Because the pure electric vehicle is powered by energy power batteries, as long as the energy capacity needed by the vehicle is satisfied, the power of the electric system needed by the vehicle can also be satisfied. So the battery capacity is determined mainly according to its driving range (Chu et al, 2000). The driving range can be extended by increasing the number of batteries, but it will increase the quality, affect the performance and reduce the economy of the vehicle.…”

Section: Determination Of the Battery Capacitymentioning

confidence: 99%

Parameter matching and sensitivity analysis for the powertrain system of micro-electric vehicle

Jin

2019

IJEHV

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Parameter matching for power system with micro-electric vehicle is carried out based on the prototype. The performance of micro-electric vehicle is verified in the ADVISOR software. The mathematical model and calculation method of sensitivity analysis for the complete vehicle curb mass to maximum acceleration of evaluating indicator are established as the example. The maximum acceleration and SOC are selected as the evaluation indexes of the micro-electric vehicle performance, the typical vehicle structural parameters are selected as the influence factors based on many simulation comparisons and comprehensive analysis. The non-parametric statistical method is proposed to analyse the sensitivity of typical structural factors affecting its performance by drawing on one-at-a-time sensitivity measures. Analysis of the results shows that the windward area, the air resistance coefficient, and the centroid height are the most important factors affecting the two evaluation indexes. The final conclusion is significant for the design and application of micro-electric vehicles.

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Study on the Parametric Optimization for a Parallel Hybrid Electric Vehicle Power Train

Cited by 27 publications

References 2 publications

Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation

Study on Global Parameters Optimization of Dual-Drive Powertrain System of Pure Electric Vehicle Based on Multiple Condition Computer Simulation

Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks

Parameter matching and sensitivity analysis for the powertrain system of micro-electric vehicle

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