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.

Multi-objective real-time optimization energy management strategy for plug-in hybrid electric vehicle

2018 ◽  
Vol 233 (5) ◽  
pp. 1067-1080 ◽  
Author(s):  
Siyu Du ◽  
Yiyong Yang ◽  
Congzhi Liu ◽  
Fahad Muhammad
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Improve Fuel Economy ◽  
Hybrid Electric ◽  
Multiple Operation ◽  
Real Time Optimization

Plug-in hybrid electric vehicle provides remarkable results for emission reduction and fuel improvement in the current driving cycles. With the appropriate energy management strategy, the torque can be split by switching of multiple operation modes to improve fuel economy. However, in the process, not only the noticeable jerk or torque fluctuation, which may result in vibration of the drivetrain and unpleasant driving sensation, but also the frequent motor-start-engine process would be triggered, which is accompanied by extra fuel consumption and abrasion of the clutch. Therefore, high attention should be paid to reduce the excess operating times of the motor-start-engine process and take advantage of multiple operation modes to improve fuel economy in plug-in hybrid electric vehicle. To solve this problem, a multi-objective real-time optimization energy management strategy is proposed. First, the motor-start-engine dynamic model of 2-degree-of-freedom is established. Then, the motor-start-engine process is analyzed based on a large number of real-world data, and the cost of the motor-start-engine process is quantified for optimization. What’s more, the optimal torque distribution is realized through the powertrain system. Finally, the proposed strategy is verified by the simulation and experiment platform. Results show that the proposed strategy can greatly improve fuel economy, thereby reducing the excess operating times of the motor-start-engine process.

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.

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