Neural Network Controller to Manage the Power Flow of a Hybrid Source for Electric Vehicles

Zgheib, Rawad; Al‐Haddad, Kamal

doi:10.1109/vppc.2015.7352983

Cited by 16 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12 shows that the power flow characteristic is inversed at the specific V2 values. Due to the anti-power phenomenon, additional concerns must be taken in DAB power regulation, especially when on-line calculations [13]- [15] are used. …”

Section: Resultsmentioning

confidence: 99%

Power plateau and anti-power phenomenon of dual active bridge converter with phase-shift modulation

Xiao

Zhang

Mao

et al. 2018

2018 IEEE Applied Power Electronics Conference and Exposition (APEC)

View full text Add to dashboard Cite

Abstract-In this paper, an improved power flow model for dual active bridge (DAB) converters with phase-shift modulation is introduced. Based on the analysis and the accordingly derived equations, a power plateau phenomenon, in which the phase shift loses its power-regulating capability, is investigated. Moreover, it is found that this power plateau phenomenon leads to an inversed power flow characteristic in some specific regions compared to the models reported in previous literature. The characteristics of the power plateau and its occurring conditions are derived and analyzed in depth. The calculations, simulations and analyses have been verified by experiments.

show abstract

Section: Resultsmentioning

confidence: 99%

Power plateau and anti-power phenomenon of dual active bridge converter with phase-shift modulation

Xiao

Zhang

Mao

et al. 2018

2018 IEEE Applied Power Electronics Conference and Exposition (APEC)

View full text Add to dashboard Cite

show abstract

“…The vehicle uses a drive motor with a rated voltage of 48 V. The specific parameters of the HESS are presented in Table 2. In the simulation models, the battery and the supercapacitor were modelled as a firstorder buffer [35]. The Rint internal resistance model was adopted for the battery, and RC model was selected to simulate the supercapacitor.…”

Section: Simulation Model Parametersmentioning

confidence: 99%

Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information

Zhi³

et al. 2021

Energies

View full text Add to dashboard Cite

To maximize the performance of power batteries and supercapacitors in a hybrid energy storage system (HESS) and to resolve the conflict between the high power demands of electric vehicles and the limitations of high-current charging and discharging of the power battery, a vehicle power demand model incorporating road slope information has been constructed. This paper takes a HESS composed of power battery and supercapacitor as the object, and a rule-based energy management strategy (EMS) based on road slope information is proposed to realize the reasonable distribution and management of energy under the slope condition. According to the slope information of the road ahead, the energy consumption in the next period was predicted, and the supercapacitor is charged and discharged in advance to meet the energy demand of uphill and the energy recovery capacity of downhill to avoid the high current charge and discharge of the battery. Subsequently, the improved EMS performance was simulated under the New York City Cycle (NYCC) driving conditions with additional slope driving conditions. The simulated results indicate that compared to the existing EMS, the proposed EMS based on slope information can effectively distribute the power demand between the power battery and the supercapacitor, can reduce the discharge current and the duration of high-power discharge, and has a 20.4% higher energy recovery efficiency, effectively increasing the cruising range.

show abstract

“…A hybrid controller based on ant clone and BP neural network method has been reported in [15]. Similarly in [22], [28], [29], a fixed weights neural controller is reported to control the power flow between a source and a hybrid vehicle. The reported controller in [15] is a type of fixed controller and will take more resources when implemented on processor.…”

Section: A Literature Backgroundmentioning

confidence: 99%

“…In (24), W i represent the weights and x i is the state vector. Using (22) and 23, (24) is modified as following.…”

Section: A Aann-pimentioning

confidence: 99%

Artificial Neural Network Based Adaptive Control of Single Phase Dual Active Bridge With Finite Time Disturbance Compensation

et al. 2019

View full text Add to dashboard Cite

Single phase Dual Active Bridge (DAB) has found numerous applications in modern energy architectures such as direct current (DC) microgrid, electrical vehicle charging and high voltage direct current (HVDC) system. Due to the model complexities of DAB, this work proposes a model free adaptive control method based on artificial neural network (AANN) which is capable of adjusting the weights online in finite time. The finite time learning property of the proposed controller makes it perfectly robust for the compensation of the disturbances due to source and load side variations. A proportional integral (PI) controller is used to stabilize the nominal dynamics of the system along with the AANN controller. The structure of the proposed controller is as simple as PID controller and as robust as any nonlinear control method. The AANN-PI controller is implemented on TI Launchpad (TMS320F28379D) with a 50 Watts laboratory scale DAB test bench. Finally, the performance of the AANN-PI method is compared experimentally with classical PI and sliding mode controllers. INDEX TERMS Artificial neural network, control system, dual active bridge, dc-dc converters.

show abstract

Neural Network Controller to Manage the Power Flow of a Hybrid Source for Electric Vehicles

Cited by 16 publications

References 8 publications

Power plateau and anti-power phenomenon of dual active bridge converter with phase-shift modulation

Power plateau and anti-power phenomenon of dual active bridge converter with phase-shift modulation

Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information

Artificial Neural Network Based Adaptive Control of Single Phase Dual Active Bridge With Finite Time Disturbance Compensation

Contact Info

Product

Resources

About