A swarm intelligence-based predictive regenerative braking control strategy for hybrid electric vehicle

Zhang, Yuanbo; Wang, Weida; Chen, Xiang; Yang, Chao; Peng, Haonan; Wei, Chao

doi:10.1080/00423114.2020.1845387

Cited by 28 publications

(13 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When all μ B ̃(f , c) = 1 then B ̃ is an interval type 2 FS (IT2 FS). Despite the fact that the general type 2 fuzzy set's third dimension is no longer necessary because it doesn't transmit any new information about the Interval type 2 Fuzzy (IT2F), [23] It is still possible to describe the interval type 2 fuzzy (IT2F) as a specific case of the general type 2 fuzzy.…”

Section: The Interval Type 2 Fuzzy Snpid Controllermentioning

confidence: 99%

Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

Ghany

Shamseldin

2023

IJPEDS

View full text Add to dashboard Cite

<span lang="EN-US">This paper presents an efficient interval type 2 fuzzy (IT2F) based on a single neuron proportional–integral–derivative (PID), also known as IT2FSNPID controller. The main purpose of the proposed control technique is to track the motion profile of the brushless DC (BLDC) motor. Also, a comparative study was investigated fuzzy type 1 (FT1) and IT2F. IT2F can treat the uncertainty and nonlinearity of the BLDC motor drive electric system in contrast to FT1. The parameters of each control technique were obtained using a new COVID-19 optimization algorithm according to an objective function. Moreover, several tests had been performed to ensure the ability of fuzzy type to absorb the system uncertainty and nonlinearity. All controllers were utilized to operate the BLDC motor sudden change in load and continuous load. The simulation results show that the IT2FSNPID can improve the dynamic response of linear and nonlinear of the same BLDC motor and accommodate the system uncertainty significantly.</span>

show abstract

Section: The Interval Type 2 Fuzzy Snpid Controllermentioning

confidence: 99%

Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

Ghany

Shamseldin

2023

IJPEDS

View full text Add to dashboard Cite

show abstract

“…PSO is on the foundation of observation of the animal cluster activities and behaviors. It uses the data sharing of individuals from the team in a group to make the movement of the entire team and generate one evolution course between disorder and order inside the issue managing space to gain the best solution [7] . Compared with other optimization algorithms, PSO not only requires less code and parameters but also is less affected by the dimension of the problem.…”

Section: Solution Of Brake Energy Efficiency Model Based On Psomentioning

confidence: 99%

Research on the Braking Control Strategy of Automobile Driven Independently by Front and Rear Dual Motors

Li²

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Regenerative braking is one of the most important methods to solve the difficulty of energy loss in city driving. The dual-motor pure electric vehicle is considered to be the object of study with the goal of energy utilization of the reproductive system of braking. The PSO (particle swarm optimization) algorithm is adopted to solve the problems under these constraints of ECE-R13 braking regulations and I-curve. The results of the simulation indicate that compared to the conventional braking tactic of AVL CRUISE, the energy renewal effect of the steering strategy is raised by almost 19.24% and the driving distance is increased by as much as 23.8%.

show abstract

“…The detailed values is listed in Table 2 and the parameters of tire model are also used in our previous work. 11,21 Motor model…”

Section: Longitudinal Dynamic Model Of Vehiclementioning

confidence: 99%

A multi-objective regenerative braking control strategy combining with velocity optimization for connected vehicles

Liu

Han

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

View full text Add to dashboard Cite

Deceleration is unavoidable owing to the traffic lights or obstacles during driving, which lead to great energy dissipation. To promote energy utilizing efficiency, regenerative braking is applied to convert kinetic energy into electricity. Previous researches about regenerative braking concentrate on the braking torque allocation optimization, which is undoubtedly important. Moreover, the control performance can be further improved combining with velocity optimization. With the emerging Connected Vehicle Technology, environmental information is available for vehicles. Therefore, the terminal braking distance and terminal velocity can be derived utilizing the information. Then a multi-objective regenerative braking control strategy based on the pesudospectral method is proposed with the terminal constraints. The control strategy optimizes both the velocity and braking torque allocation to reduce the energy dissipation and battery capacity loss simultaneously. Simulations are carried out under a speed bump scenario. Pareto front is obtained with different weights of the objectives and then analyzed to reveal the tradeoff between energy recovery and battery health, which assists in finding a desired balanced solution. Two representative Pareto optimal solutions are selected for comparison: the energy recovery priory strategy and the energy-life tradeoff strategy. Compared with the energy priory strategy, the energy-life tradeoff strategy decreases the battery capacity loss by 60.65%, but it leads to a 36.07% reduction in the energy recovery.

show abstract

A swarm intelligence-based predictive regenerative braking control strategy for hybrid electric vehicle

Cited by 28 publications

References 41 publications

Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

Research on the Braking Control Strategy of Automobile Driven Independently by Front and Rear Dual Motors

A multi-objective regenerative braking control strategy combining with velocity optimization for connected vehicles

Contact Info

Product

Resources

About