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Electric vehicle charging station constant volume method based on big data

A technology for electric vehicles and charging stations, applied in network data retrieval, neural learning methods, data processing applications, etc., can solve problems such as insufficient charging stations, avoid over-construction and insufficient construction coverage, accurate urban demand estimation, and field Accurate Effect of Station Demand Estimation

Pending Publication Date: 2020-09-25
思极星能科技(四川)有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the site selection of this method only considers the convenience of vehicle charging, and does not plan the station and the number of charging guns according to the actual charging demand, which will cause insufficient charging stations.

Method used

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  • Electric vehicle charging station constant volume method based on big data
  • Electric vehicle charging station constant volume method based on big data
  • Electric vehicle charging station constant volume method based on big data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] A method for constant capacity of electric vehicle charging stations based on big data, such as Figure 6 As shown, obtain the number of vehicles used by the charging gun at the surrounding stations, the demand forecast data of the station, and the land information of the station, and calculate the maximum charging capacity max_count of the station according to the land information of the station; calculate the average number of times the charging gun is used according to the number of vehicles used by the charging gun at the surrounding stations , and combined with the station demand forecast data to calculate the number of charging guns need_count required to meet the demand, and then combined with the station’s maximum charging capacity to obtain the station’s constant capacity min(max_count, need_count). The maximum charging capacity max_count of the station is equal to (area of ​​the parking lot of the station - the area of ​​the aisle of the station) / average parkin...

Embodiment 2

[0032] This embodiment is optimized on the basis of implementation 1, such as figure 1 As shown, the number of stations to be built and their regional distribution are calculated through the charging station distribution model to form a candidate station library; if the surrounding charging stations meet the charging demand, the site selection is not given up, otherwise the charging station is estimated by the station demand forecasting model Station demand, if the estimated charging station demand is greater than or equal to the station construction threshold, determine the location, and then estimate the station demand forecast data through the station demand forecast model. The station building threshold is the minimum order quantity required to achieve the station's break-even within the planned operation period. If the demand is lower than the threshold, the total income covers the construction cost and operating cost, so the construction of the station is not considered....

Embodiment 3

[0035] This embodiment is optimized on the basis of Embodiment 2. The charging station distribution model is based on the charging forecast demand, urban POI distribution and urban road network data, and establishes a constraint function. The distance is used as the objective function and is obtained based on the MOPSO algorithm.

[0036] The MOPSO (Multi-Objective Particle Swarm Optimization Algorithm) used in the present invention is a variant of the PSO (Particle Swarm Optimization Algorithm).

[0037] 1. Distribution model of urban charging stations based on MOPSO optimization algorithm

[0038] Based on urban charging forecast demand (see (8) 2, urban charging demand forecast model), urban POI distribution and urban road network data, a constraint function is established, and the total distance from the station to the road network main road is used as the objective function. Establish the MOPSO optimization algorithm model and calculate the distribution results of urban ...

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PUM

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Abstract

The invention discloses an electric vehicle charging station constant volume method based on big data, and the method comprises the steps: obtaining the number of vehicles used by charging guns of peripheral stations, station demand prediction data and station land information, and calculating the maximum charging capacity max _ count of the stations according to the station land information; calculating the average number of use of charging guns according to the number of use vehicles of charging guns of peripheral stations, calculating the number need _ count of charging guns required to meet the requirements by combining station demand prediction data, and then obtaining the station constant volume min (max _ count, need _ count) by combining the maximum charging capacity of the stations. According to the method, the maximum charging gun capacity of the station is calculated on the basis of accurate station demand prediction data, so that urban demand estimation is more accurate, and urban charging station planning is more reasonable on the basis.

Description

technical field [0001] The invention belongs to the technical field of capacity stabilization of charging stations, and in particular relates to a method for capacity stabilization of electric vehicle charging stations based on big data. Background technique [0002] At present, the site selection and capacity determination methods of urban charging stations are based on the urban road network structure, traffic flow, and residential gathering points. Data such as truck ownership and new energy vehicle policies are included in the site selection method. The location selection will not only directly affect the improper capacity of the charging station, but also may affect the planning and layout of the urban transportation network, the travel convenience of electric vehicle users, and further affect the wide application of electric vehicles. It may also lead to a significant increase in power consumption, making the Some node voltage drops significantly. The current method ...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q10/04G06Q30/02G06Q50/06G06Q50/26G06N3/00G06N3/04G06N3/08G06F16/951G06F16/29
CPCG06Q10/06315G06Q10/067G06Q10/04G06Q30/0202G06Q50/06G06Q50/26G06N3/006G06N3/084G06F16/951G06F16/29G06N3/045
Inventor 谢士明唐强安飞虎岳毫傅世勇李念念潘勇李小山
Owner 思极星能科技(四川)有限公司