Energy Management of Plug-in Hybrid Electric Vehicle Based on Fuzzy Logic Control

Yao, Fuxing

doi:10.12783/dtetr/icvmee2017/14649

Cited by 1 publication

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“…20,21 Based on charging requirement, characteristics and other factors are used to model, charge, and discharge EV charging. 22,23 Based on mobile energy storage and grid technology (V2G) with the vehicle's renewable energy absorption, charging control system, higher control, and frequency modulation have been enhanced. 24 To solve these challenges, research has been carried out and certain solutions have been proposed to diminish the issues of EVs on distribution network.…”

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confidence: 99%

Energy management approach in electric vehicle with optimizing electricity consumption cost using hybrid method

Vijayaragavan¹,

Krishnakumar

Prabhu

2022

Energy & Environment

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This paper proposes a hybrid approach for the optimal design of electric vehicle (EV) home energy management. The proposed hybrid system combines the execution of the Lichtenberg optimization algorithm and the heap-based optimizer; hence, it is named as LAHBO method. The main purpose of the proposed system is the reduction of costs and improvement of the power factor. Thus, two phases of optimization, such as Lichtenberg optimization algorithm–based cost minimization and heap-based optimizer–based power factor improvement. At initial phase, power conversation, and operating time of the smart home components are decided using the Lichtenberg optimization algorithm method. It is categorized into four groups, such as interruptible, uninterruptible, thermostatically controlled, and non-programmable loads. In second phase, the residential power factor at grid connection point is improved using the heap-based optimizer approach. Finally, the proposed system is carried out on MATLAB platform related to several existing approaches. The proposed method enhances the power factor and diminishes the cost than the existing method. The cost of proposed method is 0.16$ and existing approaches such as CGO, SMO, and SOA cost become 0.2, 0.3, and 0.35$, respectively.

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