TWI822018B - Optimizing method for deployment of charging stations and planning system for charging stations - Google Patents

Abstract

An optimizing method for deployment of charging stations is disclosed and includes the following steps: obtaining physical parameters of multiple electronic vehicles in an operating area; obtaining a specification of a charging pile being used; obtaining an expected operation-plan being adapted in the operating area; computing a total energy-consumption of the operating area in accordance with the physical parameters and the operation-plan; computing a station amount of the charging station needed in the operating area in accordance with the total energy-consumption and the specification of the charging pile. By pre-deciding the station amount, multiple charging stations may be effectively decided and deployed from all the potential stations in the operating area.

Description

Optimization method for charging station deployment and charging station planning system

The present invention relates to charging stations for electric vehicles, and in particular to an optimization method used when deploying charging stations for electric vehicles, and a planning system for implementing the optimization method.

With the advancement of science and technology and the rise of environmental awareness, various electric vehicles have gradually become popular.

For electric vehicles, the most important issue is the convenience of energy services (such as charging). Therefore, how to deploy energy service devices (such as charging stations) in the environment is really crucial. For site deployers, how to select the most appropriate location to deploy an appropriate number of charging stations at a limited cost is a big knowledge.

However, the site evaluation methods and systems currently available on the market do not consider comprehensive parameters when evaluating sites. For example, most evaluation systems obtain a large number of operating models and historical data of electric vehicles and charging stations from operating operators for analysis, thereby evaluating the proposed deployment locations of new charging stations based on actual operating conditions. . However, the above method cannot be used for companies that do not yet have an operating model or have not yet started operations.

Furthermore, the aforementioned charging stations are mainly actually deployed in cities for use by electric vehicles existing in the city. However, different cities have different traffic characteristics (such as road conditions, number of traffic signals, etc.), so operators also need to provide different operating scenarios for different cities. The above-mentioned evaluation system generally does not conduct separate evaluations for different cities, and does not consider the traffic characteristics of the city. Therefore, the evaluation results may not meet the actual needs of electric vehicles in each city.

The main purpose of the present invention is to provide an optimization method for the deployment of charging stations and a planning system for charging stations. The traffic characteristics in the operating area are used as one of the evaluation parameters, thereby calculating the actual needs of electric vehicles. Number of charging stations.

In order to achieve the above goals, the optimization method for charging station deployment of the present invention includes the following steps: a) Obtaining a vehicle physical parameter of a plurality of electric vehicles in an operating area; b) Obtaining a charging station used in the operating area Specifications of a charging pile for the pile; c) Obtain an expected operating situation in the operating area; d) Calculate a total energy consumption in the operating area based on the physical parameters of the vehicle and the operating situation; e) Based on the total energy consumption And the specification of the charging pile determines the number of charging stations required in the operating area; and f) determines the charging station in the operating area based on all potential sites in the operating area and the number of sites.

In order to achieve the above objectives, the charging station planning system of the present invention includes: An input unit is used to designate an operation area and obtain a vehicle physical parameter of a plurality of electric vehicles in the operation area, a charging pile specification of a charging pile in the operation area and a charging pile used in the operation area. The expected operating scenario; the processing unit is connected to the input unit and includes: a total energy consumption assessment module that calculates a total energy consumption in the operating area based on the physical parameters of the vehicle and the operating scenario; and a site quantity assessment module A module determines the number of charging stations required in the operating area based on the total energy consumption and the specifications of the charging pile; and an output unit is connected to the processing unit and outputs the number of stations, where the station The quantity is used together with all potential sites within the operating area to determine the plurality of charging stations within the operating area.

This invention considers the vehicle physical parameters of the electric vehicle in the operation area and the expected operation situation, thereby determining the number of charging stations required in the operation area. Taking into account the unique traffic characteristics within the operating area, it is ensured that the number of stations determined can meet the actual charging needs of electric vehicles in the operating area.

1: Planning system

11: Processing unit

111:Data preprocessing module

112:Total energy consumption assessment module

113: Site quantity evaluation module

114: Service scope group generation module

115:Site selection module

12:Input unit

13:Output unit

2:Data source server

3: Site configuration information

4: Operation area

41:Road

42: Traffic Signal

5: Charging station

6: Service scope group

7: Electric vehicles

8:Potential sites

80:Service scope

91: First service scope group

91: Second service scope group

93:Third service scope group

94: The fourth service scope group

95: The fifth service scope group

R1: path

S20~S30: Calculation steps

S40~S46: Calculation steps

S50~S64: Deployment steps

Figure 1 is a block diagram of a specific embodiment of the planning system of the present invention.

Figure 2 is a specific embodiment of the flow chart of the optimization method of the present invention.

Figure 3 is a first specific embodiment of the charging station deployment schematic diagram of the present invention.

Figure 4 is a specific embodiment of the traffic signal schematic diagram of the present invention.

Figure 5 is a specific embodiment of the traffic delay cost calculation flow chart of the present invention.

Figure 6 is a specific embodiment of the flow chart of the deployment method of the present invention.

Figure 7 is a specific embodiment of a schematic diagram of the service scope of the present invention.

Figure 8 is a specific embodiment of a schematic diagram of a service scope group of the present invention.

Figure 9 is a second specific embodiment of the charging station deployment schematic diagram of the present invention.

A preferred embodiment of the present invention is described in detail below with reference to the drawings.

The present invention discloses an optimization method for the deployment of charging stations (hereinafter referred to as the optimization method in the specification). The optimization method of the present invention is mainly used to optimize the deployment method of charging stations, so that the deployment results of charging stations can better meet the actual needs of specific areas.

Specifically, the aforementioned deployment method is performed by analyzing and evaluating multiple potential sites that are known to be usable in a designated operating area, thereby selecting the most appropriate sites among them for deployment in actual operations. Charging station.

Generally speaking, when selecting charging stations through the above deployment methods, the deployer can only preset the number of stations to be deployed based on cost considerations or historical data collected from past operations. However, each operating area has different characteristics, and the number of sites set by the deployer based on his subjective wishes may not meet the actual needs of the operating area.

The optimization method of the present invention is to use the above deployment method to analyze and evaluate multiple potential sites in the operation area, first based on the characteristics of the operation area (such as traffic load, electric vehicle parameters, charging pile parameters, operation scenarios, etc. ) to determine an appropriate number of sites. During actual selection and deployment, the deployer can select multiple charging stations from all known potential sites in the operation area, and make the number of charging stations comply with the number of sites determined by the optimization method. In this way, in line with the deployer’s Under the premise of this consideration, the number of charging stations actually built can meet the actual needs of electric vehicles in the operating area.

For example, the aforementioned operating area may be a specific city. After the deployer actually explores the operation area, he may find a thousand potential sites. These potential sites refer to locations that meet the geographical environment (such as being able to distribute electricity), the holder is willing to cooperate with the deployer, and can be rented or purchased, etc. . Moreover, through evaluation using the optimization method of the present invention, it can be concluded that at least one hundred charging stations need to be deployed in the operating area to meet the charging needs of all electric vehicles in the operating area.

Following the above, the deployer uses the aforementioned deployment method to select a hundred charging stations that can constitute the greatest charging efficiency among a thousand potential sites, so that the daily charging capacity provided by these charging stations can meet the operational needs. The daily replenishment of electric energy required by all electric vehicles in the area (i.e., the daily consumption of electric energy).

It is worth mentioning that the technical solution of the present invention is that before the deployer actually deploys charging stations, he first determines the number of charging stations that should be deployed in the operating area through an optimization method, and then selects the deployment locations of these charging stations through a deployment method. In other words, the optimization method and deployment method of the present invention do not require the use of real-time data and historical data collected after the charging station starts operating.

First, please refer to FIG. 1 , which is a block diagram of a specific embodiment of the planning system of the present invention. The optimization method of the present invention can mainly be realized through a planning system 1. Specifically, the planning system 1 can be configured to simultaneously execute the optimization method of the present invention and the deployment method described later (described in detail later).

As shown in FIG. 1 , the planning system 1 may mainly have a processing unit 11 , and an input unit 12 and an output unit 13 connected to the processing unit 11 . Based on the functions to be implemented by the planning system 1, the processing unit 11 at least includes a data preprocessing module 111, a total energy consumption evaluation module 112, a site quantity evaluation module 113, a service range group generation module 114 and a site selection module. Group 115, but not limited to this.

The input unit 12 may be a human-machine interface (such as a keyboard, a mouse, a touch pad, etc.), a wired transmission interface (such as a USB transmission port), or a wireless transmission interface (such as a Wi-Fi transmission unit, a Bluetooth transmission unit, etc.). The planning system 1 receives various data required for executing the optimization method and the deployment method through the input unit 12 .

In one embodiment, the planning system 1 can accept the deployer's settings through the input unit 12 . For example, the deployer can use the input unit 12 to specify the operation area (such as a specific city) to be deployed, set the number of electric vehicles to be used in the operation area, the vehicle physical parameters of the electric vehicles, and the expected placement at charging stations. The specifications of the charging piles, the operating scenarios that will be adopted in the operating area, etc.

In another embodiment, the planning system 1 can connect to the data source server 2 through the input unit 12 and receive the data required for the optimization method and the deployment method from the data source server 2 . For example, the planning system 1 can receive traffic network data, traffic signal data, target object distribution data, etc. in the operating area from the data source server 2, but this is not limited.

In one embodiment, the plurality of modules 111 - 115 in the processing unit 11 may be hardware modules. For example, each module 111-115 may be a processor, a micro control unit (Micro Control Unit, MCU), a field programmable gate array (Field Programmable Gate Array, FPGA), or a system on chip (SoC). Wait to be implemented separately.

In another embodiment, the processing unit 11 may be a processor, a central processing unit (CPU), a graphics processing unit (GPU) or a micro control unit, and the plurality of modules in the processing unit 11 Groups 111-115 may be software modules. In this embodiment, the computer executable program code is recorded in the processing unit 11. After the processing unit 11 executes the computer executable program code, it can virtually simulate the computer executable program code into the aforementioned multiple functions according to the functions that can be realized. mold Groups 111-115 (for example, each module 111-115 corresponds to a plurality of subroutines in the computer executable program code). However, the above are only some implementation examples of the present invention, but are not limited thereto.

The output unit 13 can be a wired transmission interface, a wireless transmission interface or a display device, without limitation. After the planning system 1 uses the optimization method of the present invention to determine the number of sites that should be deployed in the operation area, it can selectively output through the output unit 13 . Thereby, the planning system 1 or the deployer can determine a plurality of charging stations in the operating area based on all potential sites in the operating area and the determined and output number of sites (detailed later). Moreover, after the planning system 1 selects multiple charging stations that meet the needs through the aforementioned deployment method, it can also selectively output the site configuration information 3 through the output unit 13 . The site configuration information 3 may include text information or image information of multiple charging stations. Deployers can actually build charging stations in the operation area based on the site configuration information 3.

Please refer to Figures 1 to 2 at the same time, where Figure 2 is a specific embodiment of the flow chart of the optimization method of the present invention. The optimization method of the present invention is used to determine the number of charging stations that should be deployed in the operation area based on the characteristics of the operation area after the deployer designates an operation area. In this way, the problem in the deployment method that the settings of the sites to be deployed cannot meet the needs of the operating area can be optimized. Therefore, the method in Figure 2 is an optimization method for the subsequent deployment method.

The optimization method shown in Figure 2 can be applied to the planning system 1 shown in Figure 1. To use the optimization method of the present invention to determine the number of charging stations that should be deployed in an operation area, the planning system 1 first accepts the deployer's operation through the input unit 12 to specify an operation area (such as a specific city). Furthermore, the planning system 1 obtains the vehicle physical parameters of the plurality of electric vehicles in the operation area (step S20), obtains the charging pile specifications of the charging piles to be used in the operation area (step S22), and obtains the deployment schedule of the electric vehicles that will be operated in the operation area. The expected operating scenario implemented in the region (step S24). Specifically, planning system 1 The above data can be received through the data preprocessing module 111 of the processing unit 11, and the above data can be converted into a data format that can be used and processed by each module 112-115 in the processing unit 11.

In one embodiment, the aforementioned vehicle physical parameters, charging pile specifications and expected operating scenarios include known information, and the planning system 1 can read these known information from the data source server 2 through the input unit 12 . In one embodiment, the aforementioned vehicle physical parameters, charging pile specifications and expected operating scenarios include the deployer's input data, and the planning system 1 can accept the deployer's settings through the input unit 12 .

For example, the aforementioned vehicle physical parameters may include the total number of vehicles in the operating area, as well as the average driving distance (daily), average driving time (daily), average energy consumption, battery capacity, and battery voltage of electric vehicles. and other information. The aforementioned charging pile specifications may include information such as charging efficiency and the number of charging piles installed at each charging station. The aforementioned expected operating scenarios may include the operating hours (daily) of each charging station and the load capacity of additional charging piles in the operating area. Number of vehicles (daily). However, the above is only a specific implementation example of the present invention, but is not limited to the above.

In one embodiment, the planning system 1 mainly determines the number of charging stations for electric vehicles used for business. Therefore, the electric vehicles in the operation area refer to electric vehicles used for business (such as leasing or logistics, etc.) vehicle. In this embodiment, parameters such as the total number of vehicles, daily average driving distance and daily average driving time of electric vehicles, as well as the daily operating hours of each charging station, and the daily operating hours of additional charging piles in the operating area The number of vehicles that can be loaded can be used as a known input parameter based on the business plan. In other words, the vehicle physical parameters and expected operating scenarios used in the optimization method of the present invention are parameters planned by the deployer before the charging station is actually deployed, rather than real-time data collected after the charging station is deployed and activated. Information and historical information.

In addition, parameters such as the average energy consumption, battery capacity and battery voltage of electric vehicles, as well as the charging efficiency of charging piles, are information that is known when the electric vehicles and charging piles are manufactured.

After step S24, the planning system 1 obtains the physical parameters of the vehicle and the operating situation through the total energy consumption assessment module 112 of the processing unit 11, and calculates the total energy consumption in the operating area based on the physical parameters of the vehicle and the operating situation (step S26) . Specifically, what the total energy consumption assessment module 112 calculates in step S26 is the total amount of energy consumption that needs to be filled by charging stations for the electric vehicles in the operation area every day. Next, the processing unit 11 uses the site quantity evaluation module 113 to determine the number of charging stations required in the operation area based on the total energy consumption and charging pile specifications (step S28).

After step S28, the planning system 1 can output the determined number of sites. In this way, the planning system 1 can start the service range group generation module 114 and the site selection module 115 in the processing unit 11 to base on the number of sites within the operation area. All potential sites and the determined number of sites are used to determine and deploy charging stations (step S30).

Please refer to FIGS. 1 to 3 at the same time, where FIG. 3 is a first specific embodiment of the charging station deployment diagram of the present invention. Figure 3 reveals an operating area 4. In the embodiment of FIG. 3 , the planning system 1 analyzes all potential sites in the operation area 4 through the service scope group generation module 114 in the processing unit 11 to plan all potential sites into multiple Service scope group 6 (detailed later). And the planning system 1 selects one or more appropriate charging stations 5 from each service area group 6 through the station selection module 115 in the processing unit 11, and makes the total number of all charging stations 5 equal to the number of stations. The number of sites determined by the evaluation module 113.

Back to Figure 2. The total energy consumption calculated in step S26 refers to the sum of the daily energy consumption of all electric vehicles in the operation area 4, and this energy consumption is filled by the charging stations in the operation area. It is worth mentioning that the charging piles in operation area 4 can include general charging piles and additional charging piles.

In one embodiment, the general charging pile refers to a fast charging charging pile, and the additional charging pile refers to a slow charging charging pile, where the charging efficiency of the slow charging charging pile is lower than the charging efficiency of the fast charging charging pile. In another embodiment, the general charging piles refer to the charging piles installed in each charging station 5, and the additional charging piles refer to household charging piles.

In the present invention, the main purpose of the additional charging pile is to provide electric vehicles for charging outside of operating hours (e.g., 8 hours a day). For example, if the additional charging pile is a home charging pile, the driver can charge the electric vehicle after get off work. Use household charging piles at home to charge electric vehicles. It can be seen from the above description that the charging situation of additional charging piles is different from that of ordinary charging piles (for example, it needs to be charged all night). The optimization method of the present invention is particularly applicable to electric vehicles and charging stations used for business purposes. Therefore, the total energy consumption calculated above is preferably supplied by general charging piles, and the charging provided by the aforementioned additional charging piles Performance is excluded from the calculation basis of the total energy consumption mentioned above.

Specifically, the above-mentioned step S26 calculates the daily total energy consumption of all electric vehicles in the operation area 4. Since some electric vehicles can be charged through additional charging piles, the total energy consumption assessment module 112 can calculate the total energy consumption of the vehicles. After deducting the number of vehicles that can be loaded by additional charging piles from the total, the total energy consumption of the remaining electric vehicles is calculated.

Specifically, in step S26, the total energy consumption assessment module 112 can calculate the daily total energy consumption in the operation area 4 according to the following first formula: EC _total = EC _EV × d × ( nEV - nEV _sc ); where, EC _total is the total daily energy consumption in operation area 4, EC _EV is the average energy consumption of electric vehicles, d is the average daily driving distance of electric vehicles, nEV is the number of electric vehicles in operation area 4 The total amount, nEV _sc is the number of vehicles that can be loaded by additional charging piles. Through the above first formula, the total energy consumption assessment module 112 can calculate the daily total energy consumption in the operation area 4. In other words, when deployers deploy charging stations 5 in the operation area 4, they need to ensure that these charging stations 5 can meet the total energy consumption.

；其中，nCP為營運區域4內所需的充電樁總數，EC_total為營運區域4內的總能耗，h_s為充電站5的營運時間(小時)，h_r為電動載具的平均行駛時間，S_cp為充電樁的充電效率。其中，若一個充電站5內僅配置一個充電樁，則營運區域4內所需的充電樁的充電樁總數，即相等於營運區域4內所需的充電站5的站點數量。 Following the above, after the total energy consumption assessment module 112 calculates the aforementioned total energy consumption, the site number assessment module 113 can determine the number of charging stations 5 required to meet the aforementioned total energy consumption. Specifically, in step S28, the site number evaluation module 113 can calculate the number of charging stations 5 required in the operating area 4 according to the following second formula: nCP

; Among them, nCP is the total number of charging piles required in the operating area 4, EC _total is the total energy consumption in the operating area 4, h _s is the operating time (hours) of the charging station 5, and h _r is the average travel of the electric vehicle time, S _cp is the charging efficiency of the charging pile. Among them, if only one charging pile is configured in one charging station 5, the total number of charging piles required in the operating area 4 is equal to the number of charging stations 5 required in the operating area 4.

Specifically, since the electric vehicle cannot perform charging operations during driving, the above-mentioned second formula subtracts the average driving time ( _hr ) of the electric vehicle from the operating time (h _s ) of the charging station 5 to calculate the operating area 4 The amount of energy required per hour during daily operating hours. This energy is divided by the charging efficiency of the charging pile (S _cp ), which is the minimum number of charging piles (nCP) required for operation area 4.

其中，nCS為營運區域4內所需的充電站5的站點數量，nCP為營運區域4內所需的充電樁的充電樁總數，nCPcs為充電站5的充電樁設置數量。 It is worth mentioning that in some cases, each charging station 5 can be equipped with more than one charging pile. In this case, the site number evaluation module 113 may combine the second formula and the following third formula to calculate the number of charging stations 5 required in the operation area 4 in step S28:

Among them, nCS is the number of charging stations 5 required in the operation area 4, nCP is the total number of charging piles required in the operation area 4, and nCPcs is the number of charging piles in the charging station 5.

Through the second formula and the third formula, the planning system 1 can convert the total daily energy consumption in the operation area 4 into the number of charging stations that can provide this energy. In this way, the deployer can further select and actually deploy the charging stations 5 in the operation area 4 based on the number of stations determined by the planning system 1 .

As mentioned above, the optimization method of the present invention is mainly used to determine the number of charging stations that provide charging services for electric vehicles for commercial use. Therefore, the deployer can set the above parameters before actual commercial use. . The above-mentioned first formula, second formula and third formula will be further described in detail below with an embodiment.

This embodiment is used to determine the number of charging stations 5 for business use. Therefore, the operating hours of the charging stations 5 are set to 8 hours a day to comply with the average working hours. Moreover, this embodiment sets the total number of vehicles in the operation area 4 to 1,000 units based on the rationality of business use (for example, the total number of logistics personnel in the operation area 4), and based on the characteristics of the operation area 4 and the business use characteristics , the average daily driving distance of electric vehicles is set to 100 kilometers, and the average daily driving time is 2.5 hours. For example, logistics personnel work 8 hours a day, of which 2.5 hours are spent actually driving electric vehicles, and the remaining 5.5 hours are spent on tallying, moving goods, administration, resting, and charging electric vehicles.

It is worth mentioning that the unit of the above charging efficiency is C-rate, where the charging efficiency of 1C refers to the 50 Ah/h that can be charged per hour corresponding to a specific battery capacity (for example, corresponding to the battery with the above capacity of 50 Ah/h) , and the charging efficiency of 0.7C means that it can charge 35 Ah/h per hour. C-rate is a commonly used calculation unit in this technical field and will not be described again here.

Based on the above parameters used in this embodiment, the total energy consumption evaluation module 112 can calculate the total energy consumption in the operation area 4 to be 24960 (Ah) through the first formula: EC _total = EC _EV (0.416) × d (100 )×( nEV (1000)- nEV _sc (400))=24960.

It is worth mentioning that the additional charging piles in the present invention may be, for example, slow charging charging piles that the deployer expects to install in the charging station 5, or household charging piles that are expected to be sold to users for use at home. Therefore, the number of vehicles that can be loaded by additional charging piles can be set based on the expected deployment/sales target and used as a known parameter.

Then, the site quantity evaluation module 113 can calculate the total number of charging piles required in the operation area 4 to be at least 130 through the second formula:

Since the total number of charging piles must be greater than the estimated value and must be an integer, planning system 1 can calculate that the total number of charging piles must be at least 130.

Then, the site number evaluation module 113 can calculate the required number of charging stations 5 in the operation area 4 to be at least 130 stations through the third formula:

Through the optimization method of the present invention, the planning system 1 can calculate that in order to meet the daily energy consumption needs of all electric vehicles in the operation area 4 (excluding some electric vehicles that can be met through additional charging piles), the At least 130 charging stations need to be installed5. After completing the above evaluation actions, the planning system 1 then determines 130 charging stations 5 with more suitable locations and benefits among all known potential sites in the operation area 4, and conducts actual deployment.

It is worth mentioning that the above embodiment only takes into account some of the traffic characteristics in the operating area 4, but does not consider the traffic signs on the road (such as red lights) and the traffic delays caused by electric vehicles due to traffic signs. dead cost.

Specifically, since drivers cannot charge electric vehicles while waiting for a red light, when traffic signals are included as evaluation parameters, the average driving time of electric vehicles should be added to the traffic delay cost. By using traffic signals in operation area 4 as strengthening factors, the evaluation results of each step shown in Figure 2 can be further optimized.

Please refer to FIGS. 1 to 4 simultaneously, where FIG. 4 is a schematic diagram of a traffic signal according to a specific embodiment of the present invention. In the embodiment of FIG. 4 , the aforementioned data source server 2 may be, for example, an Open Street Map (OSM) database. The Open Street Map database records traffic data of the operation area 4 . Through the data obtained from the data source server 2, the planning system 1 can construct a map of the operation area 4, and the map depicts the locations of the roads 41 and traffic signals 42 of the operation area 4.

When drivers drive electric vehicles 7 in the operation area 4, they may encounter different numbers of traffic signals 42 on different roads 41, and each traffic signal 42 operates during peak hours (such as commuting time) and off-peak hours. The waiting time may be different (for example, during non-commuting and off-duty hours). In the embodiment of FIG. 4 , when the electric vehicle 7 travels along a specific path R1 and goes to a target charging station 5 , it encounters a total of four traffic signals 42 .

Specifically, the optimization method of the present invention can use the same calculation parameters when calculating the number of charging stations 5 required in different operating areas 4 . However, different operating areas 4 have different traffic conditions. For example, the number of traffic signals in some operating areas 4 is small, and the average waiting time of the traffic signals in some operating areas 4 is relatively long.

Please refer to FIGS. 1 to 5 at the same time, where FIG. 5 is a specific embodiment of the traffic delay cost calculation flow chart of the present invention. To calculate the traffic delay cost in the operation area 4, first, the processing unit 11 of the planning system 1 obtains the traffic signal data of the operation area 4 from the data source server 2 (such as the above-mentioned open street map database) (step S40). This obtains the estimated waiting time corresponding to the traffic signal (step S42).

The aforementioned estimated waiting time may be, for example, the average of the waiting times of all traffic signals in the operation area 4 during peak hours and off-peak hours. Moreover, when encountering a red light on the road 41, you usually do not wait from beginning to end. Therefore, in step S42, the processing unit 11 can fine-tune the parameters of the estimated waiting time (for example, divide the above average value by 2), Estimated waiting time calculated by order More realistic. In one embodiment, the aforementioned estimated waiting time can be directly obtained from government gazettes and actual measurement data in the operation area 4, but it is not limited to this.

Next, the processing unit 11 calculates the average number of waiting signals of the plurality of critical paths in the operation area 4 (step S44). Specifically, the aforementioned critical path refers to a path in the operation area 4 that can satisfy the preset evaluation conditions. For example, a path that starts from a charging station 5 and can travel for more than 2 kilometers, or a path that can travel for more than 3 kilometers at a speed of 40 kilometers per hour, is defined as the above-mentioned critical path. In the present invention, in step S44, the processing unit 11 can perform calculations based on actual statistics by the deployer or by combining the obtained traffic signal data with the map information to obtain the traffic signals on each critical path in the operation area 4. number of logs, and then calculate the above average number of waiting logs.

After step S44, the processing unit 11 can calculate the traffic delay cost based on the estimated waiting time and the average number of waiting signals (step S46). As mentioned above, the traffic delay cost caused by stopping at red lights will prolong the average driving time of electric vehicles. Therefore, when calculating the number of charging stations 5 through the second formula, the station number evaluation module 113 needs to add the traffic delay cost to the average driving time. In other words, h _r in the second formula is the sum of the average travel time and traffic delay cost.

Taking the estimated waiting time of each traffic signal in operation area 4 as an example of 77 seconds, if the processing unit 11 fine-tunes the estimated waiting time with parameter 0.5, and the average number of waiting signals on the critical path is 20 , then the aforementioned traffic delay cost can be calculated as 77×0.5×20=770( s )=0.214( h ). Thereby, the station number evaluation module 113 can recalculate the total number of charging piles through the second formula:

It can be seen from the second formula above that after taking into account the traffic delay cost caused by traffic signals, the total number of charging piles required in operation area 4 increases from a minimum of 130 to a minimum of 135. Thereby, the number of charging stations 5 determined by the optimization method of the present invention will be more in line with the actual needs of the electric vehicles 7 in the operation area 4 .

As mentioned above, the planning system 1 of the present invention can further execute the deployment method based on the number of stations determined by the optimization method, thereby actually deploying the required charging stations 5 into the operation area 4 .

Please continue to refer to Figures 1 to 7. Figure 6 is a specific embodiment of the flow chart of the deployment method of the present invention, and Figure 7 is a specific embodiment of the service scope schematic diagram of the present invention. After the processing unit 11 completes the calculation of the number of charging stations 5 required in the operation area 4 through the site quantity evaluation module 113, it then obtains all potential charging stations 5 in the operation area 4 through the service range group generation module 114. The site information of site 8 is obtained (step S50), and the target object distribution data in the operation area 4 is obtained (step S52).

As mentioned above, the evaluation object of the optimization method of the present invention is the charging station 5 used for business (such as rental, delivery, logistics, etc.), that is to say, the target object of the service provided by the electric vehicle 7 is people. In this embodiment, the target object distribution data may be, for example, population distribution data. The data source server 2 may include the official Census and Statistics Department database. The service area group generation module 114 may obtain data from the data source through the input unit 12 Server 2 receives population distribution data.

Next, the service range group generation module 114 calculates the shortest path between each potential site 8 in the operation area 4 (step S54). It is worth mentioning that the service area group generation module 114 can mainly find the shortest path between each potential site 8 based on the transportation network data of the operating area 4, so the shortest path will correspond to the actual recorded in the geographical information. A road, not necessarily a straight line. in, Transportation network data can be obtained from data source server 2. In addition to the shortest path, the service range group generation module 114 also calculates the service range 80 of each potential site 8 according to preset evaluation conditions (step S56).

In one embodiment, in step S56, the service range group generation module 114 calculates multiple destinations that arrive after starting from a potential site 8 and moving along multiple drivable paths for a predetermined moving distance (for example, 2 kilometers). An extreme position, or a plurality of extreme positions reached after traveling along multiple drivable paths at a predetermined speed (for example, 40 kilometers per hour) for a predetermined moving time (for example, 3 minutes). Furthermore, the service range group generation module 114 performs convex hull calculation (Convex hull) based on these extreme positions to generate the service range 80 of the potential site 8 .

After calculating the respective service areas 80 of all potential sites 8 , the service area group generation module 114 performs operations based on the plural shortest paths and the plural service areas 80 to plan all potential sites 8 in the operation area 4 into plural service range group (for example, service range group 6 shown in Figure 8) (step S58).

Please refer to FIGS. 1 to 8 at the same time, where FIG. 8 is a specific embodiment of a schematic diagram of a service scope group of the present invention. As shown in FIG. 8 , the plurality of service scope groups 6 planned by the service scope group generation module 114 respectively include one or more different potential sites 8 in the operation area 4 .

In one embodiment, in step S58, the service range group generation module 114 mainly executes a clustering algorithm (or unsupervised learning algorithm) based on the shortest path and the service range 80. The plurality of potential sites 8 are grouped into groups, and the plurality of potential sites 8 are planned into multiple service range groups 6 .

It is worth mentioning that the service range group generation module 114 can mainly use the average overlap rate (%) of the service range 80 of each potential site 8 as the grouping target of the grouping algorithm.

Taking the hierarchical clustering algorithm to perform clustering as an example, in one embodiment, the service range group generation module 114 determines the service ranges of multiple potential sites 8 in each service range group 6 after clustering. 80 has the highest average overlap rate. In another embodiment, the service area group generation module 114 makes the average overlap rate of the service areas 80 of the multiple potential sites 8 in each service area group 6 after grouping be greater than a preset threshold.

The service range group generation module 114 continuously performs calculations, and when a specific number of groups (for example, 70) is confirmed, the average overlap rate of these service ranges 80 is the highest, or can exceed a preset threshold (for example, 70%) At this time, the service scope group generation module 114 can output the grouping result as the aforementioned plurality of service scope groups 6 .

In the embodiment of FIG. 8 , the planning system 1 plans all potential sites 8 in the operation area 4 into five service area groups 6 , and the average overlap rate of the service areas 80 of these five service area groups 6 can be reaches the highest level, or can reach the threshold set by the deployer.

The above-mentioned hierarchical grouping algorithm is a common grouping method in the technical field, and will not be described again here. In addition to the hierarchical grouping algorithm, in other embodiments, the K-means algorithm, Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, etc. can also be used to plan the plurality of potential sites 8 into multiple groups. group, but not limited to this.

The main purpose of the deployment method of the present invention is to select a plurality of charging stations 5 from a plurality of potential stations 8 that meet the number of stations determined by the optimization method and have the highest use efficiency. The number of charging stations 5 must be less than that of the potential stations. The number of points is 8. The deployment method of the present invention uses the above step S58 to serve Multiple potential sites 8 with a high overlap rate in the service scope 80 are planned in the same service scope group 6 , thereby summarizing multiple potential sites 8 that are complementary or competing with each other in each scope. When the planning system 1 selects charging stations 5 from each service range group 6, it can ensure that the selected multiple charging stations 5 can provide better service benefits.

Return to Figure 6. After step S58, the service scope group generation module 114 further calculates the number of target objects covered by each service scope group 6 based on the aforementioned target object distribution data (step S60). Specifically, the service scope group generation module 114 calculates the service scopes 80 of all potential sites 8 in each service scope group 6 in step S60, and calculates the total number of target objects covered by these service scopes 80. The greater the number of target objects covered, the greater the number of charging stations 5 that should be deployed in this service scope group 6 .

Take the target object distribution data as population distribution data as an example. When the population that can be covered in a service area group 6 is larger, it means that the number of charging stations 5 that should be deployed in this service area group 6 is more.

After step S60 , the planning system 1 further calculates the number of stations that should be deployed in each service scope group 6 through the station selection module 115 of the processing unit 11 .

Specifically, the station selection module 115 mainly sets the number of charging stations 5 in each service range group 6 based on the number of sites output in the aforementioned step S30 and the number of target objects covered by each service range group 6. The number of stations to be deployed (step S62). Among them, the sum of the number of sites that should be deployed in all service scope groups 6 should be the same as the number of sites determined by the site quantity evaluation module 113 .

It is worth mentioning that the number of stations that should be deployed in each service range group 6 is an integer greater than or equal to zero. The deployment method determines the station that should be deployed based on the number of target objects. If the number of target objects covered by a service scope group 6 is small, the number of stations that should be deployed calculated by the station selection module 115 may be zero.

Please refer to FIGS. 1 to 9 at the same time, where FIG. 9 is a second specific embodiment of the charging station deployment schematic diagram of the present invention. In step S62, the site selection module 115 calculates the number of sites that should be deployed based on the number of target objects covered by each service scope group 6. In other words, the number of stations that should be deployed in each service area group 6 is positively related to the number of target objects covered, but not directly related to the area size covered by each service area group 6 .

In the embodiment of FIG. 9 , the number of sites that should be deployed in the first service range group 91 is four sites, the number of sites that should be deployed in the second service range group 92 is zero sites, and the number of sites that should be deployed in the third service range group 93 The number of stations is two stations, the number of stations that should be deployed in the fourth service range group 94 is one station, and the number of stations that should be deployed in the fifth service range group 95 is one station. Among them, the number of target objects covered by the second service scope group 92 is too small, so the number of stations that should be deployed is zero.

In the present invention, the station selection module 115 needs to select the number of charging stations 5 from a plurality of potential stations 8, which is the number of stations determined by the station quantity evaluation module 113. Therefore, the station selection module 115 can respectively calculate the number of stations that should be deployed in each service range group 6 based on the following fourth formula:

As mentioned above, when the total number of target objects in the operation area 4 is known, and the total number of charging stations 5 required in the operation area 4 has also been determined, the number of target objects covered by the service scope group 6 The more there are, the more stations it should be deployed on.

After step S62, the station selection module 115 selects one or more charging stations to be deployed from a plurality of potential stations 8 included in each service area group 6 according to the number of stations that should be deployed. 5 (step S64). The number of selected charging stations 5 in each service range group 6 is the same as the number of stations that should be deployed calculated by the station selection module 115 in step S62.

In one embodiment, the station selection module 115 performs iterative calculations on each service range group 6 in step S64. When the number of stations that should be deployed is non-zero, the station selection module 115 performs iterative calculations on multiple potential stations 8 in the service range group 6 so that the service range 80 of the selected one or more charging stations 5 can Covers the largest number of target audiences.

For example, if a service scope group 6 includes ten potential sites 8 and the number of sites that should be deployed is one, the site selection module 115 directly selects the potential sites 8 that cover the largest number of target objects in step S64. Charging stations 5 of area group 6 are served for this purpose. If the number of sites that should be deployed in a service scope group 6 is two, the site selection module 115 performs iterative calculations on the ten potential sites 8 in step S64 to select the one that can cover the most after the total service scope 80 is added. Two potential sites 8 of the target number are used as the charging stations 5 of this service area group 6. In the present invention, the station selection module 115 performs the above-mentioned iterative calculation once for each service area group 6 in step S64 to select one or more charging stations 5 in each service area group 6 respectively.

After step S64, the planning system 1 can output the site configuration information 3 through the output unit 13. The aforementioned site configuration information 3 records the total number of charging stations 5 that should be deployed in the operation area 4, and the deployment location of each charging station 5. Thereby, the deployer can actually deploy the charging station 5 in the operation area 4 based on the evaluation results of the planning system 1 .

The optimization method of the present invention considers the traffic characteristics of different areas (such as cities), and shunts fast charging and slow charging charging piles. At the same time, the required charging station sites are determined based on the expected behavior and expected routes of business use. quantity. In this way, the calculation results of the charging station deployment method can be more in line with the actual needs of electric vehicles in the designated area.

The above descriptions are only preferred specific examples of the present invention, which do not limit the patent scope of the present invention. Therefore, all equivalent changes made by applying the content of the present invention are equally included in the scope of the present invention, and are hereby stated. bright.

S20~S30: Calculation steps

Claims (16)

An optimization method for the deployment of charging stations, applied to a planning system of charging stations, including: step a) obtaining a vehicle physical parameter of a plurality of electric vehicles in an operating area from an input unit of the planning system, wherein the The vehicle physical parameters include a total number of vehicles of the plurality of electric vehicles and an average driving distance, an average energy consumption and an average driving time of each electric vehicle; step b) obtains from the input unit the operating area A charging pile specification of a charging pile used; step c) obtains an expected operating situation in the operating area from the input unit, wherein the expected operating situation includes a loadable carrier of an additional charging pile in the operating area The number of charging piles and an operating time of the charging pile in the operating area; step d) uses a total energy consumption assessment module of a processing unit of the planning system to calculate the operating time of the charging pile in the operating area based on the physical parameters of the vehicle and the operating scenario. A total energy consumption; step e) uses a station quantity evaluation module of the processing unit to determine the number of a charging station required in the operating area based on the total energy consumption and the charging pile specifications, where The charging pile specifications include a charging efficiency of the charging pile and a number of charging piles in the charging station; and step f) the processing unit determines the operation based on all potential sites in the operation area and the number of sites Multiple charging stations in the area. The optimization method as described in claim 1, wherein step d) calculates the total energy consumption according to a first formula: EC _total = EC _EV × d × ( nEV - nEV _sc ), where EC _total is the total energy consumption, EC _EV is the average energy consumption, d is the average driving distance, nEV is the total number of vehicles, and nEV _sc is the number of loadable vehicles. The optimization method as described in claim 2, wherein the additional charging pile is a slow charging charging pile or a household charging pile, and a charging efficiency of the slow charging charging pile is smaller than the charging efficiency of the charging pile. The optimization method as described in claim 2, wherein the step e) calculates the total number of charging piles required in the operation area according to a second formula: nCP

, where nCP is the total number of charging piles, EC _total is the total energy consumption, h _s is the operating time, h _r is the average driving time, S _cp is the charging efficiency, where the number of stations is equal to the total number of charging piles . The optimization method as described in claim 4, wherein step e) calculates the number of sites required in the operation area based on a third formula:

, where nCS is the number of stations, nCP is the total number of charging piles, and nCP _cs is the set number of charging piles. The optimization method as described in claim 4, further comprising the following steps: g) obtaining a traffic signal data of the operation area, wherein the traffic signal data includes an estimated waiting time; h) calculating the time in the operation area An average number of waiting signs for multiple critical routes; and i) Calculate a traffic delay cost based on the estimated waiting time and the average number of waiting signs, where in the second formula, h _r is the average travel time and the sum of the traffic delay costs. The optimization method as described in request item 6, wherein the step f) includes: f1) obtaining site information of all potential sites; f2) obtaining a target object distribution data in the operation area; f3) calculating each potential site A shortest path between sites; f4) Calculate a service range for each potential site; f5) Plan all potential sites into multiple service scope groups based on the plurality of shortest paths and the plurality of service scopes; f6) Calculate the number of target objects covered by each service scope group based on the target object distribution data; f7) Calculate the number of stations that should be deployed for each service scope group based on the number of sites and the number of plural target objects, where the sum of the number of stations that should be deployed for all service scope groups is the same as the number of sites; and f8) Select one or more charging stations from a plurality of potential stations included in each service area group according to the number of stations that should be deployed. The optimization method as described in claim 7, wherein the step f4) is to calculate the arrival after starting from each potential site and moving a predetermined moving distance along multiple drivable paths or moving according to a predetermined speed for a predetermined moving time. The complex limit position is a complex number, and a convex hull calculation (Convex hull) is performed based on the complex limit position to generate the service range of each potential site. The optimization method as described in claim 7, wherein the step f5) is to execute a clustering algorithm based on the plurality of shortest paths and the plurality of service ranges to plan all the potential sites into the plurality of service range groups, And the average overlap rate of the service scope of the plurality of potential sites covered in each of the service scope groups is the highest, or the average overlap rate is greater than a preset threshold. The optimization method as described in claim 7, wherein step f7) uses a fourth formula to calculate the number of stations that should be deployed for each service scope group: the number of stations that should be deployed =

A planning system for charging stations, including: An input unit is configured to specify an operation area, and obtain a vehicle physical parameter of a plurality of electric vehicles in the operation area, a charging pile specification of a charging pile in the operation area, and a charging pile used in the operation area. An expected operating scenario, in which the vehicle physical parameters include a total number of vehicles of the plurality of electric vehicles and an average travel distance, an average energy consumption and an average travel time of each electric vehicle. The expected operating scenario includes A loadable number of carriers of an additional charging pile in the operating area and an operating time of the charging pile in the operating area; the processing unit is connected to the input unit and includes: a total energy consumption assessment module, configured to calculate a total energy consumption in the operating area based on the physical parameters of the vehicle and the operating scenario; and a site quantity evaluation module configured to determine the total energy consumption in the operating area based on the total energy consumption and the charging pile specifications. A required number of sites of a charging station, where the charging pile specifications include a charging efficiency of the charging pile and a number of charging piles in the charging station; and an output unit, connected to the processing unit, configured to Output the number of stations, where the number of stations is used together with all potential stations in the operation area to determine the plurality of charging stations in the operation area. The planning system as described in claim 11, wherein the total energy consumption assessment module is configured to calculate the total energy consumption according to a first formula: EC _total = EC _EV × d × ( nEV - nEV _sc ), where EC _total is the total energy consumption, EC _EV is the average energy consumption, d is the average driving distance, nEV is the total number of vehicles, and nEV _sc is the number of loadable vehicles. The planning system of claim 12, wherein the additional charging pile is a slow charging charging pile or a household charging pile, and a charging efficiency of the slow charging charging pile is smaller than the charging efficiency of the charging pile. The planning system as described in claim 12, wherein the site quantity evaluation module is configured to calculate a total number of charging piles required in the operation area according to a second formula: nCP

, where nCP is the total number of charging piles, EC _total is the total energy consumption, h _s is the operating time, h _r is the average driving time, S _cp is the charging efficiency, where the number of stations is equal to the total number of charging piles . The planning system as claimed in claim 14, wherein the site quantity evaluation module is configured to calculate the number of sites required in the operation area according to a third formula:

, where nCS is the number of stations, nCP is the total number of charging piles, and nCP _cs is the set number of charging piles. The planning system as described in claim 14, wherein the input unit is configured to connect to a data source server and obtain a traffic signal data of the operation area from the data source server, wherein the traffic signal data includes a Estimated waiting time, the processing unit is configured to calculate an average number of waiting signals for a plurality of critical routes in the operation area, and calculate a traffic delay cost based on the estimated waiting time and the average number of waiting signals , where in the second formula, h _r is the sum of the average travel time and the traffic delay cost.

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