Multi-Objective Optimization for Parameters of Energy Management Strategy of HEV Based on Improved NSGA-II

2010 ◽  
Vol 29-32 ◽  
pp. 912-917
Author(s):  
Fei Hu ◽  
Zhi Guo Zhao
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Pareto Optimum ◽  
Multi Objective Optimization ◽  
Energy Management Strategy ◽  
Nsga Ii ◽  
Multi Objective ◽  
Nondominated Sorting

Hybrid Electric Vehicle (HEV) provides fairly high fuel economy with lower emissions compared to conventional vehicles. To enhance HEV performance in terms of fuel economy and emissions, subject to the satisfaction of driving performance, multi-objective optimization for parameters of energy management strategy is inevitable. Considering the defect of the method which transfers multi-objective optimization problem into that of single-objective and the shortage of the Pareto-optimum based nondominated sorting genetic algorithm II (NSGA-II), the NSGA-II has been improved and then applied to the optimization in this paper. The simulation results show that each run of the algorithm can produce many Pareto-optimal solutions and the satisfactory solution can be selected by decision-maker according to the requirement. The results also demonstrate the effectiveness of the approach.

scholarly journals Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Multi Objective

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.

Adaptive parameter optimal energy management strategy based on multi-objective optimization for range extended electric vehicle

2021 ◽  
pp. 095440702110464
Author(s):  
Hanwu Liu ◽  
Yulong Lei ◽  
Yao Fu ◽  
Xingzhong Li
Keyword(s):  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Loss Rate ◽  
Multi Objective Optimization ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Adaptive Parameter ◽  
Point Control ◽  
Optimal Energy Management

With the aim of economy improvement, emission reduction and prolonging the battery service life, an adaptive parameter optimal energy management strategy is proposed for range extended electric vehicle and a method of multi-objective optimization (MOO) is proposed. Firstly, two strategies based on different threshold parameter types, namely velocity-switch-based multi-operation-point control strategy (MCS v–b) and power-switch-based multi-operation-point control strategy (MCS p–b) are designed. Then, the oil-electric conversion loss rate, comprehensive exhaust emission, and battery capacity loss rate are selected as the optimization objectives. The barebones multi-objective particle swarm optimization is applied in MCS v–b and MCS p–b for solving the MOO problem. The simulation results show a clear conflict that three optimization objectives cannot be optimal under the same solution. And then, the individual with optimal comprehensive objective is taken as the final optimization solution to evaluate the performance of the proposed methodology. As expected, the proposed MCS p–b has a positive effect on prolonging the battery service life while ensuring high fuel economy and low emission. Experimental test results thoroughly validate the proposed approach and this result can be used to improve comprehensive performance levels.

scholarly journals Optimization of the fuel economy and emissions for plug in hybrid electric recreational boat energy management strategy using genetic algorithm

2019 ◽  
Vol 10 (2) ◽  
pp. 792
Author(s):  
Jabar Siti Norbakyah ◽  
Abdul Rahman Salisa
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Conventional Vehicle ◽  
Cycle Simulation ◽  
Water Transportation ◽  
Hybrid Electric

<span>Today, the transportation sector has undergone a change from conventional vehicle to hybrid electric vehicle especially land-based with the aim to reduce fuel consumption and emissions. However, water transportation is also one of the contributors of excessive use of fuel and emissions. Therefore, water transport needs changes as it has been done on land transport, especially cars. In this paper, plug in hybrid electric recreational boat (PHERB) is introduced. PHERB is a special model because in PHERB powertrain configuration, it only needed one EM compared to existing configuration with energy management strategy (EMS).  In this work, the optimal EMS for PHERB are presented via genetic algorithm (GA). To estimate the fuel economy and emissions, the model of PHERB is employed numerically in the MATLAB/SIMULINK environment with a special EMS using Kuala Terengganu (KT) river driving cycle. Simulation result of PHERB optimization using GA improve to 15% for KT river driving cycles without violating the PHERB performance.</span>

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