scholarly journals Multi-Objective Energy Management Strategy Based on PSO Optimization for Power-Split Hybrid Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2438
Author(s):  
Aimin Du ◽  
Yaoyi Chen ◽  
Dongxu Zhang ◽  
Yeyang Han
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Control Strategy ◽  
Management Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Test Cycle ◽  
Energy Management Strategy ◽  
Swarm Optimization ◽  
Power Split

The hybrid electric vehicle is equipped with an internal combustion engine and motor as the driving source, which can solve the problems of short driving range and slow charging of the electric vehicle. Making an effective energy management control strategy can reasonably distribute the output power of the engine and motor, improve engine efficiency, and reduce battery damage. To reduce vehicle energy consumption and excessive battery discharge at the same time, a multi-objective energy management strategy based on a particle swarm optimization algorithm is proposed. First, a simulation platform was built based on a compound power-split vehicle model. Then, the ECMS (Equivalent Consumption Minimization Strategy) was used to realize the real-time control of the model, and the penalty function was added to modify the objective function based on the current SOC (State of Charge) to maintain the SOC balance. Finally, the key parameters of ECMS were optimized by using a particle swarm optimization algorithm, and the effectiveness of the control strategy was verified under the WLTC (Worldwide Light-Duty Test Cycle) and the NEDC (New European Driving Cycle). The results show that under the WLTC test cycle, the overall fuel consumption of the whole vehicle was 6.88 L/100 km, which was 7.7% lower than that before optimization; under the NEDC test cycle, the fuel consumption of the whole vehicle was 5.88 L/100 km, which was 9.8% lower than that before optimization.

scholarly journals A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1380 ◽  
Author(s):  
Rui Yang ◽  
Yupeng Yuan ◽  
Rushun Ying ◽  
Boyang Shen ◽  
Teng Long
Keyword(s):  
Solar Energy ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Management Strategy ◽  
Energy System ◽  
Generation System ◽  
Energy Management Strategy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New Energy

Due to the pressures caused by the energy crisis, environmental pollution, and international regulations, the largest ship-producing nations are exploring renewable resources, such as wind power, solar energy, and fuel cells to save energy and develop more environmentally-friendly ships. Solar energy has recently attracted a great deal of attention from both academics and practitioners; furthermore, the optimization of energy management has become a research topic of great interest. This paper takes a solar-diesel hybrid ship with 5000 car spaces as its research object. Then, following testing on this ship, experimental data were obtained, a multi-objective optimization model related to the ship’s fuel economy and diesel generator’s efficiency was established, and a partial swarm optimization algorithm was used to solve a multi-objective problem. The results show that the optimized energy management strategy for a hybrid energy system should be tested under different electrical loads. Moreover, the hybrid system’s economy should be taken into account when the ship’s power load is high, and the output power from the new energy generation system should be increased as much as possible. Finally, the diesel generators’ efficiency should be taken into consideration when the ship’s electrical load is low, and the injection power of the new energy system should be reduced appropriately.

Research on control method of Maglev vehicle-guideway coupling vibration system based on particle swarm optimization algorithm

2019 ◽  
Vol 25 (16) ◽  
pp. 2237-2245
Author(s):  
Qin Li ◽  
Hui Wang ◽  
Gang Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Control Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Test Rig ◽  
Swarm Optimization ◽  
Coupling Vibration ◽  
Maglev Vehicle ◽  
Track Stiffness

To solve the problem of vehicle-guideway coupling vibration, a new control approach for the Maglev vehicle-guideway coupled system was investigated. A simplified model of the system was built and a control strategy based on full state feedback and particle swarm optimization algorithm was designed. The robustness of the system considering different track stiffness and the maximum voltage of the magnet were considered when the cost function of the particle swarm algorithm was designed. A real-time test rig using dSPACE was built to test the control strategy. The result from the test rig shows that the new designed control strategy can keep the system stable and has a better response than the traditional linear quadratic optimal method, the control voltage is much lower, the settling time of step response is decreased and the maximum overshoot of the air gap is decreased more than 88%. The robustness of the system in different track stiffness conditions is also much better; that is, when the magnet and the track move relative to each other, the maximum amplitude of vibration of both the track and the magnet is 40–70% lower, and the oscillation caused by the shifting of the track beam converges much more quickly.

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