scholarly journals Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm

Energies ◽  
2015 ◽  
Vol 8 (5) ◽  
pp. 3661-3678 ◽  
Author(s):  
Zeyu Chen ◽  
Rui Xiong ◽  
Kunyu Wang ◽  
Bin Jiao
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Vehicle ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Particle Swarm Optimization Algorithm ◽  
Energy Management Strategy ◽  
Swarm Optimization ◽  
Optimal Energy Management

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.

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