Electric Assist Control Strategy Research Articles

Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles

This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV). The HEV control strategy is the algorithm according to which energy is produced, used, and saved. Therefore, optimal management of the energy components is a key element for the success of a HEV. In this study, based on an electric assist control strategy (EACS), the fitness function is defined so as to minimize the vehicle engine fuel consumption (FC) and emissions. The driving performance requirements are then considered as constraints. In addition, in order to reduce the number of the decision variables, a new approach is used for the battery control parameters. Finally, the optimization process is performed over three different driving cycles including ECE-EUDC, FTP and TEH-CAR. The results from the computer simulation show the effectiveness of the approach and reduction in FC and emissions while ensuring that the vehicle performance is not sacrificed.

Application of genetic algorithm for simultaneous optimisation of HEV component sizing and control strategy

This paper describes a methodological approach for the simultaneous optimisation of Hybrid Electric Vehicle (HEV) component sizing and control strategy using Genetic Algorithm (GA). In this approach, using a parallel HEV configuration and an Electric Assist Control Strategy (EACS), the optimisation problem is formulated. The whole set of sizing and control variables is then encoded as the chromosomes. The multi-objective target is also defined to minimise the Fuel Consumption (FC) and emissions. In addition, the PNGV performance requirements are considered as constraints. Finally, to evaluate the objective function, three driving cycles, ECE-EUDC, FTP and TEH-CAR, are employed. The simulation results obtained in this study show that the approach is effective, resulting in improvement of the objective value and FC.

An Analysis of Electric Assist Control Strategy for Hybrid Electric Vehicles and Simulation

The paper presents an analysis of electric assist control strategy for hybrid electric vehicles and makes an analysis of simulation on advisor platform.

99/00439 A block-pulse domain technique of harmonics elimination in multilevel pulse-width modulated inverters

This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV). The HEV control strategy is the algorithm according to which energy is produced, used, and saved. Therefore, optimal management of the energy components is a key element for the success of a HEV. In this study, based on an electric assist control strategy (EACS), the fitness function is defined so as to minimize the vehicle engine fuel consumption (FC) and emissions. The driving performance requirements are then considered as constraints. In addition, in order to reduce the number of the decision variables, a new approach is used for the battery control parameters. Finally, the optimization process is performed over three different driving cycles including ECE-EUDC, FTP and TEH-CAR. The results from the computer simulation show the effectiveness of the approach and reduction in FC and emissions while ensuring that the vehicle performance is not sacrificed.