Hybrid Electric Car Fuel Consumption Optimization Research Based on Improved Genetic Algorithm

2016 ◽  
Author(s):  
Yuanpeng Zhu
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Improved Genetic Algorithm ◽  
Electric Car ◽  
Hybrid Electric

scholarly journals A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Pei Li ◽  
Jun Yan ◽  
Qunzhang Tu ◽  
Ming Pan ◽  
Jinhong Xue
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Optimization Method ◽  
Energy Management Strategy ◽  
Quantum Genetic Algorithm ◽  
Hybrid Electric

The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.

scholarly journals Optimal Energy Management for Series-Parallel Hybrid Electric City Bus Based on Improved Genetic Algorithm

Mechanika ◽  
2020 ◽  
Vol 26 (3) ◽  
pp. 252-259
Author(s):  
Bingzhan ZHANG ◽  
Guodong ZHAO ◽  
Yong HUANG ◽  
Yaoyao NI ◽  
Mingming QIU
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Management Strategy ◽  
Dynamic Performance ◽  
Driving Cycle ◽  
Improved Genetic Algorithm ◽  
Energy Management Strategy ◽  
City Bus ◽  
Parallel Hybrid ◽  
Hybrid Electric

This paper aims at proposing an efficient energy management strategy of the series-parallel hybrid electric bus (SPHEB) by using improved genetic algorithm. Firstly, the energy management strategy based on the logical threshold value is developed. The simulation model considering the vehicle dynamic performance is established by the combination of Matlab and Cruise software. Then, an improved genetic algorithm based on adaptive crossover probability and mutation probability is proposed to solve local convergence and premature convergence. Eventually, Chinese typical city bus driving cycle and the composite driving cycle are considered to show the effectiveness of the proposed energy management strategy in terms of the fuel economy. The results indicate that the fuel consumption are improved by 5.85% and 5.01% respectively, and the parameters obtained by optimizing for the composite driving cycle are more adaptable to the driving conditions and have better economic performance in all driving scenarios.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Stirling system optimization for series hybrid electric vehicles

2021 ◽  
pp. 095440702110180
Author(s):  
Charbel R Ghanem ◽  
Elio N Gereige ◽  
Wissam S Bou Nader ◽  
Charbel J Mansour
Keyword(s):  
Fuel Consumption ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Optimization Technique ◽  
System Optimization ◽  
Energy Management Strategy ◽  
Fuel Savings ◽  
Auxiliary Power ◽  
Hybrid Electric ◽  
External Combustion

There have been many studies conducted to replace the conventional internal combustion engine (ICE) with a more efficient engine, due to increasing regulations over vehicles’ emissions. Throughout the years, several external combustion engines were considered as alternatives to these traditional ICEs for their intrinsic benefits, among which are Stirling machines. These were formerly utilized in conventional powertrains; however, they were not implemented in hybrid vehicles. The purpose of this study is to investigate the possibility of implementing a Stirling engine in a series hybrid electric vehicle (SHEV) to substitute the ICE. Exergy analysis was conducted on a mathematical model, which was developed based on a real simple Stirling, to pinpoint the room for improvements. Then, based on this analysis, other configurations were retrieved to reduce exergy losses. Consequently, a Stirling-SHEV was modeled, to be integrated as auxiliary power unit (APU). Hereafter, through an exergo-technological detailed selection, the best configuration was found to be the Regenerative Reheat two stages serial Stirling (RRe-n2-S), offering the best efficiency and power combination. Then, this configuration was compared with the Regenerative Stirling (R-S) and the ICE in terms of fuel consumption, in the developed SHEV on the WLTC. This was performed using an Energy Management Strategy (EMS) consisting of a bi-level optimization technique, combining the Non-dominated Sorting Genetic Algorithm (NSGA) with the Dynamic Programming (DP). This arrangement is used to diminish the fuel consumption, while considering the reduction of the APU’s ON/OFF switching times, avoiding technical issues. Results prioritized the RRe-n2-S presenting 12.1% fuel savings compared to the ICE and 14.1% savings compared to the R-S.

Sign in / Sign up

Export Citation Format

Share Document