CN118281872A - A demand forecasting method for multi-mode public charging networks for electric vehicles - Google Patents

A demand forecasting method for multi-mode public charging networks for electric vehicles Download PDF

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CN118281872A
CN118281872A CN202410693260.5A CN202410693260A CN118281872A CN 118281872 A CN118281872 A CN 118281872A CN 202410693260 A CN202410693260 A CN 202410693260A CN 118281872 A CN118281872 A CN 118281872A
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陈炳坤
陈卓
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Monash Science And Technology Research Institute Of Suzhou Industrial Park
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Abstract

The invention relates to a demand prediction method for an electric automobile multimode public charging network, which utilizes a modeling framework based on an agent to construct a user and multimode public charging network, and uses a utility score and a co-evolution algorithm to simulate the daily travel of the user and the interaction between charging behaviors and the public charging network, thereby providing reliable and comprehensive estimation for the potential demands of different service modes so as to reasonably plan the deployment of various novel charging services in the urban public charging network. The method well compensates for the lack of the field data of the emerging charging service and can provide feasibility information for service providers and other stakeholders for integrating the novel charging service as a part of the urban public charging network, thereby being beneficial to designing, deploying and operating the urban public charging system with high efficiency and sustainability.

Description

Demand prediction method for multimode public charging network of electric automobile
Technical Field
The invention relates to the technical field of new energy vehicle charging management, in particular to a demand prediction method for an electric vehicle multimode public charging network.
Background
Electric Vehicles (EVs) are strongly supported and widely popularized in terms of their advantages of being able to alleviate energy crisis, environmental pollution, and promote the development of traffic sustainability. However, the expanding electric car market also puts tremendous pressure on urban public charging networks. Due to construction costs and limitations of land resources, the current main service mode of public charging networks, namely public Fixed Charging Stations (FCS), has not been able to meet the increasing charging demands of electric car users. In order to further improve the service capability and the coverage of the requirements of the existing public charging network, many researches have been proposed to provide more efficient public charging service modes, such as Mobile Charging Service (MCS), power conversion service, private pile sharing service, etc. However, due to the lack of support of relevant field data, the charging service providers cannot understand the potential demand patterns of users for these new charging services, which makes the deployment and integration of different charging services in a public charging network still face great difficulties and challenges.
Disclosure of Invention
The invention aims to overcome the problems in the prior art, and provides a demand prediction method for a multimode public charging network of an electric automobile, which utilizes an Agent-based Modeling framework (ABM) to construct EV users and the multimode public charging network, and uses Utility Scores (utilities Scores) and a co-evolution algorithm (Coevolutionary Algorithm) to simulate daily traveling of the users and interaction between charging behaviors and the public charging network, so as to provide reliable and comprehensive estimation for potential demands of different service modes and reasonably plan deployment of various novel charging services in the urban public charging network.
In order to achieve the technical purpose and the technical effect, the invention is realized by the following technical scheme:
A demand prediction method for an electric automobile multimode public charging network comprises the following steps:
step S1: defining and constructing EV user behaviors and public charging network configuration in an ABM simulation environment;
Step S2: performing interactive simulation on the constructed EV users and a public charging network to generate a whole-day charging plan, and scoring the generated charging plan by using utility scores to judge whether the generated charging plan accords with daily traveling and charging behaviors of the EV users or not;
Step S3: and (3) re-planning the charging plan of the EV user to generate a new charging plan, and iterating with the step (S2) by utilizing a co-evolution algorithm to finally obtain the optimal charging plan of the EV user formed by the current charging network for predicting the demand modes of different service modes.
Further, in the step S1, the construction process of the EV user and the public charging network in the ABM simulation environment is as follows:
Step S1.1: extracting psychological preference characteristics of the individual EV users by using related population survey data so as to define travel and charging behaviors of the individual EV users in the ABM simulation environment;
Step S1.2: in conjunction with the actual operating mechanism of the selected service mode, pricing policies and optional deployment areas, a public charging network configuration in the ABM simulation environment is built.
Further, in the step S2, the utility scoring of the generated charging schedule is achieved by the following steps:
Step S2.1: when the EV user reaches a daily activity place and makes a charging decision, according to the real-time vehicle electric quantity and the daily activity condition, the utility scoring is carried out on the charging service mode by combining the individual mileage anxiety level and the willingness to pay preference of the EV user, and if the selected charging service mode is more matched with the charging behavior and the current activity condition of the EV user, the utility scoring is higher;
Step S2.2: when the charging event is completed, utility grading is further carried out according to the electric quantity obtained by the EV user in the charging event and the charging cost, and if the completed charging event can enable the EV user to obtain more electric quantity or pay less cost, the utility grading is higher;
step S2.3: the utility scoring is carried out on the charging arrangement of the whole day when the commute of the EV user is finished, the residual electric quantity after the commute is finished and the daily commute distance of the EV user are preferentially considered, and if the residual electric quantity of the EV user can better meet the commute of the next day, the utility scoring is higher;
Step S2.4: and accumulating the utility scores of the parts to obtain the final utility score of the charging plan.
Further, in the step S3, a specific flow of the re-planning of the charging plan is as follows:
Step S3.1: grouping EV user groups when utility scoring of a charging plan is finished, firstly grouping EV users which cannot complete commute tasks in the simulation into key groups, and then grouping the rest EV users into non-key groups;
step S3.2: generating a new charging plan for the EV users of the key group;
Step S3.3: the charging plan with the highest utility score is selected from the historical charging plans that have been implemented by the non-critical group EV users for the next round of simulation iteration.
Further, in the step S3.2, a specific generation policy of the new charging schedule is as follows:
step S3.21: retrieving the implemented historical charging schedule and adding a new charging event during a daily activity period in which no over-charging event has been scheduled;
step S3.22: retrieving a daily activity time period in which a historical charging schedule is arranged with charging events, and then randomly removing the charging events corresponding to the time period according to a certain probability;
step S3.23: the remaining daily activity time period without adding or removing the charging event is further searched, and the service mode selection of the EV user in the corresponding charging event is adjusted so that the EV user selects unused charging service for charging in the time period in the next round of simulation iteration.
Further, in the step S3.1, some EV users capable of completing the commuting task are randomly classified into the key group according to a certain probability.
The beneficial effects of the invention are as follows:
The method simulates daily traveling and charging behaviors of EV users in the multimode public charging network by using a modeling framework based on agents so as to generate complete and reliable charging demand estimation. Therefore, the method well compensates for the lack of the field data of the emerging charging service and can provide feasibility information for the service provider and other stakeholders for integrating the novel charging service as a part of the urban public charging network, thereby being beneficial to designing, deploying and operating the efficient and sustainable urban public charging system.
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FIG. 1 is a schematic flow chart of the steps of the method of the present invention;
FIG. 2 is a flow chart of a utility scoring process for a charging plan according to the method of the present invention;
FIG. 3 is a flow chart of a re-planning of a charging schedule according to the method of the present invention;
FIG. 4 is a schematic diagram of a spatial distribution of an example verified Fixed Charging Station (FCS) charging demand;
Fig. 5 is a schematic diagram of a spatial distribution of Mobile Charging Service (MCS) charging requirements for example verification.
Detailed Description
The invention will be described in detail below with reference to the drawings in combination with embodiments.
As shown in fig. 1, a demand prediction method for an electric automobile multimode public charging network includes the following steps:
step S1: defining and constructing EV user behaviors and public charging network configuration in an ABM simulation environment;
Step S2: performing interactive simulation on the constructed EV users and a public charging network to generate a whole-day charging plan, and scoring the generated charging plan by using utility scores to judge whether the generated charging plan accords with daily traveling and charging behaviors of the EV users or not;
Step S3: and (3) re-planning the charging plan of the EV user to generate a new charging plan, and iterating with the step (S2) by utilizing a co-evolution algorithm to finally obtain the optimal charging plan of the EV user formed by the current charging network for predicting the demand modes of different service modes.
In the step S1, the construction process of the EV user and the public charging network in the ABM simulation environment is as follows:
Step S1.1: extracting psychological preference characteristics (such as willingness-to-pay preference, mileage anxiety level) of the individual EV user by using relevant population survey data (such as income level, commute distance, daily activity arrangement) so as to define travel and charging behaviors of the individual EV user in the ABM simulation environment;
Step S1.2: in conjunction with the actual operating mechanism of the selected service mode, pricing policies and optional deployment areas, a public charging network configuration in the ABM simulation environment is built.
As shown in fig. 2, in the step S2, the utility scoring of the generated charging schedule is achieved by:
Step S2.1: when the EV user reaches a daily activity place and makes a charging decision, according to the real-time vehicle electric quantity and the daily activity condition (activity duration and position), the utility scoring is carried out on the charging service mode by combining the individual mileage anxiety level and willingness-to-pay preference of the EV user, and if the selected charging service mode is more matched with the charging behavior and the current activity condition of the EV user, the utility scoring is higher;
Step S2.2: when the charging event is completed, utility grading is further carried out according to the electric quantity obtained by the EV user in the charging event and the charging cost, and if the completed charging event can enable the EV user to obtain more electric quantity or pay less cost, the utility grading is higher;
Step S2.3: utility scoring is carried out on the charging schedule (such as charging time length, charging event times and the like) of the whole day when the commute of the EV user is finished, the residual electric quantity after the commute is finished and the daily commute distance of the EV user are preferentially considered, and if the residual electric quantity of the EV user can better meet the commute of the next day, the utility score is higher;
Step S2.4: and accumulating the utility scores of the parts to obtain the final utility score of the charging plan.
As shown in fig. 3, in the step S3, a specific flow of the re-planning of the charging plan is as follows:
Step S3.1: grouping EV user groups when utility scoring of a charging plan is finished, firstly, grouping EV users (namely, the residual electric quantity of a vehicle is 0) which cannot complete a commuting task in the simulation into a key group (critical group), and then classifying the rest EV users into non-critical groups (non-critical groups);
step S3.2: generating a new charging plan for the EV users of the key group;
Step S3.3: the charging plan with the highest utility score is selected from the historical charging plans that have been implemented by the non-critical group EV users for the next round of simulation iteration.
In the step S3.2, a specific generation strategy of the new charging schedule is as follows:
step S3.21: retrieving the implemented historical charging schedule and adding a new charging event during a daily activity period in which no over-charging event has been scheduled;
step S3.22: retrieving a daily activity time period in which a historical charging schedule is arranged with charging events, and then randomly removing the charging events corresponding to the time period according to a certain probability;
step S3.23: the remaining daily activity time period without adding or removing the charging event is further searched, and the service mode selection of the EV user in the corresponding charging event is adjusted so that the EV user selects unused charging service for charging in the time period in the next round of simulation iteration.
In the step S3.1, EV users (i.e., the remaining power of the vehicle is greater than 0) that can partially complete the commute task are also simultaneously classified into the key group according to a certain probability.
To verify the feasibility and effectiveness of the method of the present invention, which is implemented in the united states of america in the salt lake city area (SLC), the method of the present invention is used to explore the mode selection and demand distribution of EV users in the integrated fixed charging pile (FCS) and Mobile Charging Service (MCS) public charging network, and the specific implementation steps and results are as follows:
1) Data acquisition and simulation environment configuration: first, U.S. census records and time-of-use survey data published in 2019 were collected for the synthesis of EV users in the salt lake city area. And then, using a Google map (Google Place API) to climb the fixed charging facility position of the area and the optional deployment area (and ground parking lot) of the mobile charging service, and further constructing interaction logic of the charging network and EV users in the simulation environment according to the actual operation and pricing strategy of the local charging service. The present example verifies that EV users 249599 bits are generated altogether and that 109 fixed charging stations and a mobile charging service area at 510 are configured in the charging network (note that the present example does not limit the service capacity of the charging resources). Finally, the method is implemented in the selected research area to further obtain the requirement estimation of the EV user on the FCS and the MCS.
2) Charge selection mode analysis: the newly introduced Mobile Charging Service (MCS) in the public charging network expands the charging selection range of EV users, but may also change their charging decision behavior. Knowing the situation of different types of user groups in the multi-mode charging network after the integration of the MCS for various charging service requirements is important to the determination of the target user group of the MCS and to promote the effective popularization and planning of such emerging charging modes. Thus, based on the predicted charge demand estimate, the example further identifies the demand duty cycle of the FCS and MCS for different user classes. As shown in table one, EV users with comfort tendencies and low mileage anxiety levels can be found to more select MCS for charging, while EV users with charge cost tendencies and high mileage anxiety are more willing to use FCS:
Table one:
3) Demand distribution analysis: the method can generate the charge demand estimation with high space-time resolution, thereby being beneficial to reasonably and efficiently planning the deployment of newly introduced charge service in the multimode charge network. Fig. 4 and 5 are spatial distributions of FCS and MCS requirements for EV users in a Salt Lake City (SLC) region, respectively, and it can be found that the distribution of charging requirements exhibits strong spatial heterogeneity, particularly MCS, in the research region, and many deployed MCSs are not fully used, for example, in an SLC city center region (black rectangular highlighting), and even if there are enough parking spaces and MCS deployments, the EV users still rarely consider MCSs and use FCS for vehicle charging. The result emphasizes the necessity of flexible deployment of the MCS, and by using the method of the invention, a charging service provider can strategically deploy the newly introduced charging service in a region with higher demand, thereby improving the utilization rate of the charging service and reducing the waste of charging resources.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1.一种针对电动汽车多模公共充电网络的需求预测方法，其特征在于，该方法包括以下步骤：1. A method for predicting demand for a multi-mode public charging network for electric vehicles, characterized in that the method comprises the following steps: 步骤S1：定义并构建ABM模拟环境中的EV用户行为和公共充电网络配置；Step S1: Define and construct EV user behavior and public charging network configuration in the ABM simulation environment; 步骤S2：对所构建的EV用户和公共充电网络进行交互模拟以产生整日充电计划，并利用效用分数对所生成的充电计划进行效用评分以判断其是否符合EV用户的日常出行及充电行为；Step S2: Interactive simulation is performed on the constructed EV users and the public charging network to generate a full-day charging plan, and the utility score is used to score the generated charging plan to determine whether it is consistent with the daily travel and charging behavior of the EV user; 步骤S3：对EV用户充电计划进行重规划以生成新的充电计划，并利用协同进化算法与步骤S2进行迭代，最终得到当前充电网络组成EV用户的最佳充电计划用以预测不同服务模式的需求模式。Step S3: Replan the charging plan of EV users to generate a new charging plan, and use the co-evolutionary algorithm to iterate with step S2, and finally obtain the optimal charging plan for EV users in the current charging network to predict the demand patterns of different service modes. 2.根据权利要求1所述的针对电动汽车多模公共充电网络的需求预测方法，其特征在于，在所述步骤S1中，EV用户与公共充电网络在ABM模拟环境中的构建过程如下：2. The demand forecasting method for a multi-mode public charging network for electric vehicles according to claim 1 is characterized in that, in step S1, the construction process of EV users and the public charging network in the ABM simulation environment is as follows: 步骤S1.1：利用相关的人口调查数据来提取个人EV用户的心理偏好特征，从而定义其在ABM模拟环境中的出行和充电行为；Step S1.1: Use relevant demographic survey data to extract the psychological preference characteristics of individual EV users to define their travel and charging behaviors in the ABM simulation environment; 步骤S1.2：结合所选服务模式实际的运营机制，定价策略以及可选部署区域来构建ABM模拟环境中的公共充电网络配置。Step S1.2: Construct the public charging network configuration in the ABM simulation environment by combining the actual operating mechanism, pricing strategy, and optional deployment areas of the selected service model. 3.根据权利要求1所述的针对电动汽车多模公共充电网络的需求预测方法，其特征在于，在所述步骤S2中，对所生成充电计划的效用评分通过如下步骤实现：3. The method for demand forecasting of a multi-mode public charging network for electric vehicles according to claim 1, characterized in that, in step S2, the utility scoring of the generated charging plan is achieved by the following steps: 步骤S2.1：当EV用户达到日常活动地点并发生充电决策时，根据实时车辆电量及日常活动情况，结合EV用户个人的里程焦虑等级与支付意愿偏好来对充电服务模式进行效用评分，若所选择的充电服务模式更贴合EV用户的充电行为以及当前活动情况，则该项效用评分越高；Step S2.1: When the EV user reaches the daily activity location and makes a charging decision, the utility score of the charging service mode is calculated based on the real-time vehicle power and daily activity status, combined with the EV user's personal range anxiety level and willingness to pay preference. If the selected charging service mode is more in line with the EV user's charging behavior and current activity status, the higher the utility score; 步骤S2.2：当充电事件完成时，进一步依据EV用户在此次充电事件中所获得的电量以及充电花费进行效用评分，若已完成的充电事件能使得EV用户获得更多的电量或支付更少的费用，则该项效用评分越高；Step S2.2: When the charging event is completed, a utility score is further calculated based on the amount of electricity obtained by the EV user in the charging event and the charging cost. If the completed charging event enables the EV user to obtain more electricity or pay less, the utility score is higher. 步骤S2.3：在EV用户的通勤结束时对整日的充电安排进行效用评分，优先考虑通勤结束后的残余电量以及EV用户每日的通勤距离，若EV用户的残余电量能够更好的满足次日的通勤，则该项效用评分越高；Step S2.3: At the end of the EV user's commute, the utility score of the entire day's charging arrangement is evaluated, with priority given to the remaining power after the commute and the daily commuting distance of the EV user. If the remaining power of the EV user can better meet the next day's commute, the higher the utility score; 步骤S2.4：将上述各个部分的效用分数累加并得到该充电计划的最终效用分数。Step S2.4: Add up the utility scores of the above parts and obtain the final utility score of the charging plan. 4.根据权利要求1或3所述的针对电动汽车多模公共充电网络的需求预测方法，其特征在于，在所述步骤S3中，充电计划重规划的具体流程如下：4. The method for predicting demand for a multi-mode public charging network for electric vehicles according to claim 1 or 3, characterized in that, in step S3, the specific process of re-planning the charging plan is as follows: 步骤S3.1：在充电计划的效用评分结束时对EV用户群体进行分组，首先将本次模拟中未能完成通勤任务的EV用户划入关键组,然后将余下的EV用户归为非关键组；Step S3.1: At the end of the utility scoring of the charging plan, the EV user group is grouped. First, the EV users who failed to complete the commuting task in this simulation are classified into the critical group, and then the remaining EV users are classified into the non-critical group; 步骤S3.2：为关键组的EV用户生成新的充电计划；Step S3.2: Generate a new charging plan for the key group of EV users; 步骤S3.3：从非关键组EV用户已实施过的历史充电计划中选择效用评分最高的充电计划用于下一轮的模拟迭代。Step S3.3: Select the charging plan with the highest utility score from the historical charging plans that have been implemented by non-critical group EV users for the next round of simulation iteration. 5.根据权利要求4所述的针对电动汽车多模公共充电网络的需求预测方法，其特征在于，所述步骤S3.2中，新充电计划的具体生成策略如下：5. The method for predicting demand for a multi-mode public charging network for electric vehicles according to claim 4, characterized in that in step S3.2, a specific generation strategy of a new charging plan is as follows: 步骤S3.21：检索已实施的历史充电计划，并在尚未安排过充电事件的日常活动时间段增添新的充电事件；Step S3.21: Retrieve the implemented historical charging plan and add a new charging event in the daily activity time period where no charging event has been scheduled; 步骤S3.22：检索历史充电计划已安排充电事件的日常活动时间段，然后根据一定概率随机移除该时间段对应的充电事件；Step S3.22: Retrieve the daily activity time period of the charging events that have been scheduled in the historical charging plan, and then randomly remove the charging events corresponding to the time period according to a certain probability; 步骤S3.23：进一步检索剩余未被增添或移除充电事件的日常活动时间段，调整EV用户在对应充电事件中的服务模式选择，使其在下一轮模拟迭代中的该时间段内选择未使用过的充电服务进行充电。Step S3.23: Further retrieve the remaining daily activity time periods in which no charging events have been added or removed, and adjust the service mode selection of the EV user in the corresponding charging event so that the EV user selects the unused charging service for charging during the time period in the next round of simulation iteration. 6.根据权利要求5所述的针对电动汽车多模公共充电网络的需求预测方法，其特征在于，所述步骤S3.1中，根据一定概率随机将部分能够完成通勤任务的EV用户也同时划入关键组中。6. The demand forecasting method for a multi-mode public charging network for electric vehicles according to claim 5 is characterized in that, in step S3.1, some EV users who can complete commuting tasks are also randomly classified into the key group according to a certain probability.
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