Study on Fuzzy Energy Management Strategy of Parallel Hybrid Vehicle Based on Quantum PSO Algorithm

2016 ◽  
Vol 11 (5) ◽  
pp. 147-158 ◽  
Author(s):  
Yang Lihao ◽  
Wang Youjun ◽  
Zhu Congmin
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Pso Algorithm ◽  
Hybrid Vehicle ◽  
Energy Management Strategy ◽  
Parallel Hybrid ◽  
Fuzzy Energy ◽  
Quantum Pso

scholarly journals Optimization of Energy Management Strategy for the EPS with Hybrid Power Supply Based on PSO Algorithm

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 428
Author(s):  
Bin Tang ◽  
Di Zhang ◽  
Haobin Jiang ◽  
Yinqiu Huang
Keyword(s):  
Energy Management ◽  
Power Supply ◽  
Power Distribution ◽  
Management Strategy ◽  
Pso Algorithm ◽  
Output Current ◽  
Energy Management Strategy ◽  
Power Requirement ◽  
Rule Based ◽  
Hybrid Power

The traditional vehicle power supply is unable to meet the power requirement of electric power steering system (EPS) in heavy-duty vehicles at low speeds. A novel EPS with hybrid power supply (HP-EPS) is constructed in this paper, and a new optimized rule-based energy management strategy of hybrid power supply system is designed. The strategy determines the power distribution of the vehicle power supply (VPS) and super capacitor (SC), as well as the charging or discharging of SC. Furthermore, to minimize the output current fluctuation of the VPS, the optimization model of parameters in the strategy is established and the particle swarm optimization algorithm (PSO) algorithm is applied to optimize the rules in the energy management strategy. The verification for the designed energy management strategy is carried out in MATLAB/Simulink and results show that the output current peak of VPS decreases by 33% and its fluctuation depresses significantly. In addition, the SC is charged timely and fast, which is beneficial to guarantee enough state of charge (SOC) of SC. In conclusion, the optimized rule-based energy management strategy used for the HP-EPS system can meet the current requirement of EPS and effectively reduce the peak and fluctuation of the VPS output current.

scholarly journals A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 696
Author(s):  
Qi Zhang ◽  
Xiaoling Fu
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Membership Function ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Fuzzy Energy

Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.

Sign in / Sign up

Export Citation Format

Share Document