Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes

Mao, Tian; Zhang, Xin; Zhou, Baorong

doi:10.3390/en12020265

Cited by 45 publications

(22 citation statements)

References 38 publications

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“…Moreover, we used flexible charging rates for EVs in this paper given that the day-ahead pricing tariff is also variable. This mode of flexible charging is known as C-F mode [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique

Jawad¹,

Qureshi

Ali

et al. 2020

Sensors

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Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC.

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“…Moreover, we used flexible charging rates for EVs in this paper given that the day-ahead pricing tariff is also variable. This mode of flexible charging is known as C-F mode [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique

Jawad¹,

Qureshi

Ali

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The conclusion that follows is that users of electric vehicles should maintain a larger energy reserve in order to be sure that they reach the destination. The author of certain papers made an assumption that the minimum state of charge should be kept at 20% [32]. However, depending on how long the travel was to be, the need for onroute recharging was observed starting from 30% (B-segment vehicles) and 38% (C-segment vehicles).…”

Section: Discussionmentioning

confidence: 99%

Decision Making Support for Local Authorities Choosing the Method for Siting of In-City EV Charging Stations

2020

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Development of electromobility in urban areas requires an appropriate level of vehicle charging infrastructure. Numerous methods for siting of charging stations have been developed to date, and they appear to be delivering diverse outcomes for the same area, which is why local authorities face the problem of choosing the right station layout. The solution proposed in this article is to use a travel planner to evaluate the distribution of charging stations over the area of a metropolis. The decision making support is achieved by determining optimal travel routes for electric vehicles according to their initial state of charge for the three selected station siting methods. The evaluation focused on the following three aspects: (1) number of travels that cannot be made (due to the lack of a charging station at a certain distance around the start point), (2) extension of the travel caused by the need to recharge the vehicle on-route, and (3) additional energy consumption by electric vehicles required to reach the charging station (necessity of departing from the optimal route). An analysis of the results has made it possible to determine a solution which is superior to others. For the case study analysed in the paper, i.e., the territory of the Metropolis of Upper Silesia and Dabrową Basin (Górnośląsko-Zagłębiowska Metropolia, GZM), the distribution of charging stations established in line with method I has returned the best results. What the method in question also makes possible is to indicate a safe minimum energy reserve to complete the travel by eliminating situations of unexpected vehicle immobilisation due to on-route energy depletion and by minimising the phenomenon referred to as range anxiety.

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“…(2) The line segment SP is parallel to the Y-axis. Then distance L is extended along the Y-axis to get the extended points S X s , Y s , P X P , Y P , as shown in Equations (10) and (11).…”

Section: Dynamic-area Constructionmentioning

confidence: 99%

“…The existing charging guidance strategies of EVs have been mainly studied from the grid system and the users. The research from the grid system mainly focuses on the aspects of the power grid load balance [6][7][8] and efficient energy flow management [9,10] when vehicles connect to the power grid system. With the goals of optimizing the power grid load and energy balance, the users' charging requests are reasonably scheduled to achieve peak load shifting and load balance of the power grid.…”

Section: Introductionmentioning

confidence: 99%

Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles

et al. 2020

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With the increasing popularization and competition of electric vehicles (EVs), EV users often have anxiety on their trip to find better charging stations with less travel distance. An intelligent charging guidance strategy and two algorithms were proposed to alleviate this problem. First, based on the next destination of EV users’ trip, the strategy established a dynamic-area model to match charging stations with users’ travel demand intelligently. In the dynamic area, the Dijkstra algorithm is used to find the charging station with the shortest trip. Then, the area extension algorithm and the charging station attribution algorithm were developed to improve the robustness of the dynamic area. The two algorithms can automatically adjust the area size according to the number of charging stations in the dynamic area to reduce the number of nodes traversed by the Dijkstra algorithm. Finally, simulation examples were used to verify the effectiveness of the proposed model and algorithms. The results showed that the proposed intelligent charging guidance strategy can meet the travel demand of users. It is a promising technique in smart cities to find better travel trips with less travel distance and less computed time.

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Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes

Cited by 45 publications

References 38 publications

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique

Decision Making Support for Local Authorities Choosing the Method for Siting of In-City EV Charging Stations

Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles

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