scholarly journals Smart Electric Vehicle Charging via Adjustable Real-Time Charging Rates

2021 ◽  
Vol 11 (22) ◽  
pp. 10962
Author(s):  
Theron Smith ◽  
Joseph Garcia ◽  
Gregory Washington
Keyword(s):  
Fuzzy Logic ◽  
Adverse Effects ◽  
Real Time ◽  
Electric Vehicle ◽  
Control Algorithm ◽  
Optimization Techniques ◽  
Electric Vehicle Charging ◽  
Real Time Scale ◽  
Charging Control ◽  
Real World Applications

This paper presents a plug-in electric vehicle (PEV) charging control algorithm, Adjustable Real-Time Valley Filling (ARVF), to improve PEV charging and minimize adverse effects from uncontrolled PEV charging on the grid. ARVF operates in real time, adjusts to sudden deviations between forecasted and actual baseloads, and uses fuzzy logic to deliver variable charging rates between 1.9 and 7.2 kW. Fuzzy logic is selected for this application because it can optimize nonlinear systems, operate in real time, scale efficiently, and be computationally fast, making ARVF a robust algorithm for real-world applications. In addition, this study proves that when the forecasted and actual baseload vary by more than 20%, its real-time capability is more advantageous than algorithms that use optimization techniques on predicted baseload data.

scholarly journals A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 307
Author(s):  
Zhaoxiong Huang ◽  
Zhenhao Li ◽  
Chun Sing Lai ◽  
Zhuoli Zhao ◽  
Xiaomei Wu ◽  
...  
Keyword(s):  
Social Welfare ◽  
Real Time ◽  
Electric Vehicle ◽  
Peak Load ◽  
Market Mechanism ◽  
Double Auction ◽  
Energy Trading ◽  
Charging System ◽  
Electric Vehicle Charging ◽  
System Operator

This work presents a novel blockchain-based energy trading mechanism for electric vehicles consisting of day-ahead and real-time markets. In the day-ahead market, electric vehicle users submit their bidding price to participate in the double auction mechanism. Subsequently, the smart match mechanism will be conducted by the charging system operator, to meet both personal interests and social benefits. After clearing the trading result, the charging system operator uploads the trading contract made in the day-ahead market to the blockchain. In the real-time market, the charging system operator checks the trading status and submits the updated trading results to the blockchain. This mechanism encourages participants in the double auction to pursue higher interests, in addition to rationally utilize the energy unmatched in the auction and to achieve the improvement of social welfare. Case studies are used to demonstrate the effectiveness of the proposed model. For buyers and sellers who successfully participate in the day-ahead market, the total profit increase for buyer and seller are 22.79% and 53.54%, respectively, as compared to without energy trading. With consideration of social welfare in the smart match mechanism, the peak load reduces from 182 to 146.5 kW, which is a 19.5% improvement.

Real-time multi-objective optimisation for electric vehicle charging management

2021 ◽  
Vol 292 ◽  
pp. 126066
Author(s):  
Ridoy Das ◽  
Yue Wang ◽  
Krishna Busawon ◽  
Ghanim Putrus ◽  
Myriam Neaimeh
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Multi Objective ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

scholarly journals An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station

2021 ◽  
Vol 12 (3) ◽  
pp. 107
Author(s):  
Tao Chen ◽  
Peng Fu ◽  
Xiaojiao Chen ◽  
Sheng Dou ◽  
Liansheng Huang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Algorithm ◽  
Current Fluctuation ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Three Phase ◽  
Optimization Control ◽  
Dc Bus ◽  
Vehicle Charging

This paper presents a systematic structure and a control strategy for the electric vehicle charging station. The system uses a three-phase three-level neutral point clamped (NPC) rectifier to drive multiple three-phase three-level NPC converters to provide electric energy for electric vehicles. This topology can realize the single-phase AC mode, three-phase AC mode, and DC mode by adding some switches to meet different charging requirements. In the case of multiple electric vehicles charging simultaneously, a system optimization control algorithm is adopted to minimize DC-bus current fluctuation by analyzing and reconstructing the DC-bus current in various charging modes. This algorithm uses the genetic algorithm (ga) as the core of computing and reduces the number of change parameter variables within a limited range. The DC-bus current fluctuation is still minimal. The charging station system structure and the proposed system-level optimization control algorithm can improve the DC-side current stability through model calculation and simulation verification.

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