A SurveyOn Hybrid Energy Storage System For EV With Regenerative Braking

AnkithaAjith, Thayath; Dhas, G. Justin Sunil

doi:10.1109/iccpcct.2018.8574283

Cited by 5 publications

(1 citation statement)

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“…Several developments to enhance the battery technology for EVs have been realized in recent years. One possible solution is to utilize a hybrid energy storage system (HESS) [4]. HESS is a combination between a lithium-ion battery and a supercapacitor (SC) for an EV's energy source in order to reduce battery stress level and fluctuation, which leads to longer battery life and better EV performance [5,6].…”

Section: Introductionmentioning

confidence: 99%

Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network

2021

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In recent years, the electric vehicles (EVs) power management strategy has been developed in order to reduce battery discharging power and fluctuation when an EV requires high and rapid discharging power due to frequent stop-and-go driving operations. A combination of lithium-ion batteries and a supercapacitor (SC) as the EV’s energy sources is known as a hybrid energy storage system (HESS) and is a promising solution for fast discharging conditions. Effective power management to extensively utilize HESS can be developed if future power demand is accessible. A vehicular network as a typical form of the currently developed internet of things (IoT) has made future information obtainable by collecting information on surrounding data. This paper proposes a power management strategy for the HESS with the support of IoT. Since the obtained information from vehicular network could not directly be used to improve HESS, a two levels control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in the level one control structure to manage a HESS power split based on future information. Since FLC requires future information as a reference input, the future information is obtained by using an artificial neural network (ANN) in a level two control structure. The ANN prediction is direct, which could approximate the future power demand prediction with the assumption that the vehicular network scenario that is used to obtain surrounding information is deployed. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3, respectively, when compared to the battery-only case.

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Section: Introductionmentioning

confidence: 99%