Electric vehicle charging path planning method and device

Abstract

The invention discloses an electric vehicle charging path planning method and device, and the method comprises the steps: constructing a constant current-constant voltage segmented charging model through integrating the vehicle state, user habit preference and historical trajectory data, precisely calculating the charging time cost, and employing an analytic hierarchy process to quantify the user charging preference weight. And a reachability charging path set is generated based on the remaining mileage, the paths are graded and sorted in combination with a TOPSIS multi-criterion decision model, and personalized recommendation is realized. The device integrates a BMS electric quantity monitoring module, a CAN bus communication module and a cloud computing module, and supports real-time road condition feedback and personalized path pushing. The system dynamically corrects charging curve parameters and a preference model by recording user selection behaviors to form a closed-loop optimization mechanism. Compared with a traditional scheme, the method gives consideration to the charging efficiency and the user convenience, reduces the decision complexity, and is suitable for intelligent charging decision support in a dynamic traffic environment.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging navigation, and particularly to a method and device for planning an electric vehicle charging path. Background Art

[0002] In China in the past three years, the number of consumer users of electric vehicles has continued to rise, and the ownership of electric vehicles has increased steadily. The increase in electric vehicles has led to long charging queuing times, and due to the inaccurate grasp of the battery power information by some users, there are often problems of critical battery power during driving, increasing the "range anxiety" of users during driving and seriously affecting the user experience. Moreover, the current "range anxiety" is mainly reflected in time. People hope to meet the needs of people's play and daily commuting in terms of time, and relatively reduce the concern weight of the economic cost of charging. A reasonable electric vehicle charging path planning assistance method and its device can provide a more personalized charging plan for users according to their usage habits, reduce the "range anxiety" of users, and at the same time reduce the time cost of users, making a great contribution to improving the user experience.

[0003] The existing charging planning schemes do not use fitting curves to predict the charging time, do not integrate the preferences of users' charging habits, cannot accurately calculate the charging time cost, and lack personalized charging planning for users.

[0004] The present invention constructs a dynamic time prediction mechanism based on a constant current-constant voltage segmented charging model, realizes accurate calculation of the charging duration in different power ranges by fitting the battery charging curve, integrates the user's historical trajectory and charging behavior data, establishes a dynamic preference weight model using the analytic hierarchy process, quantifies implicit requirements such as the utilization rate of leisure time periods and charging pile usage habits, and generates a hierarchical recommendation scheme that takes into account time efficiency and personal convenience by combining multi-objective parameters such as real-time road conditions, charging curve delay, and user preferences using the TOPSIS method. While reducing the user's decision-making burden, it improves the rationality of path planning, effectively balancing the charging efficiency, time cost, and user convenience requirements. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method for planning an electric vehicle charging path, which can combine the charging habits of users, reasonably infer the driving behavior and future behavior of users, actively push a charging assistance plan, and provide corresponding path planning for users, reducing the range anxiety of users using electric vehicles.

[0006] To achieve the above objectives, the present invention proposes the following solutions:

[0007] An electric vehicle charging path planning method includes a real-time power monitoring module based on a single-chip microcomputer, an information aggregation module connected to the whole vehicle module via a CAN bus, a vehicle-mounted ECU module based on the AUTOSAR protocol and RH850 chip, a human-machine interaction module that supports the active push of charging assistance plans, and a cloud server that supports information storage and algorithm analysis.

[0008] The real-time power monitoring module based on a single-chip microcomputer supports being embedded in the vehicle's BMS to obtain the current real-time status of the vehicle and supports obtaining battery charging characteristic data.

[0009] The information aggregation module connected to the whole vehicle module via a CAN bus includes obtaining real-time vehicle information and reporting it to the server, and receiving the planning scheme returned by the server side. The real-time vehicle information acquisition part communicates with in-vehicle sensors through the AUTOSAR protocol and can obtain information such as the remaining battery power SOC, location information, and energy consumption of the vehicle; the part reporting to the server first processes the data into a specified MQTT message form in the ECU and sends the processed data through the in-vehicle WI-FI module; the part receiving the server's downlink data receives the message through the in-vehicle WI-FI module and sends the message to the ECU through the in-vehicle CAN bus for processing into recognizable data.

[0010] The vehicle-mounted ECU module based on the AUTOSAR protocol and RH850 chip communicates with the information aggregation module through the RTE standardized communication mechanism of AUTOSAR and the in-vehicle CAN bus to complete the reporting of data and the reception of the planning scheme, parses the received data, and then feeds back the received planning scheme to the user through the vehicle-mounted display human-machine interaction module.

[0011] The human-machine interaction module that supports the active push of charging assistance plans supports retrieving the destination, and after subsequent calculations and navigation methods, feeds back the visual route and reasonable charging plan to the user. It supports pre-retrieving the destination based on the user's habits when the user does not actively retrieve the destination, and at the same time, gives voice feedback regularly during the interaction process and also gives voice broadcasts for key information. It can also give a forced reminder when the battery power is lower than the threshold. It supports visual comparison of different planned paths.

[0012] The cloud server that supports information storage and algorithm analysis supports automatic connection of in-vehicle WIFI, exchanges data with the vehicle and updates real-time data, collects multi-dimensional data on users' charging habits, stores a unique user convenience model for each user, supports collecting battery-related data during charging, and uses algorithms to establish a battery charging characteristic fitting curve as a characteristic weight for calculating the charging time cost.

[0013] The electric vehicle charging path planning method includes the following steps:

[0014] Step S1: Obtain the vehicle battery voltage, current, SOC data, and the real-time vehicle position, discard the outliers, call the multi-dimensional user convenience data and the user historical trajectory data stored in the cloud. The multi-dimensional user convenience data includes at least charging period preference, holiday route habit, and charging warning generation habit. Integrate and clean the obtained multi-source data to form a structured data set.

[0015] Step S2: Based on the charging characteristic data of the vehicle battery, establish a constant current-constant voltage segmented charging model to fit the battery charging characteristic curve. According to the position of the target power in the fitted curve, calculate the charging time cost of the constant current stage and the charging time cost of the constant voltage stage required to reach the target power from the starting power respectively.

[0016] Step S3: Based on the multi-dimensional user convenience data, use the analytic hierarchy process in the subjective weighting method to determine the user charging habit preference weight, and construct a user convenience model.

[0017] Step S4: Generate an accessible charging path set according to the remaining vehicle mileage and the charging pile location information; combine the user convenience model and the user historical trajectory data to screen the accessible charging path set, generate a pre-screened charging path set, and actively push it to the user terminal.

[0018] Particularly, when the user does not set a destination and there is no significant historical pattern (such as random travel on weekends, according to the user's holiday selection habit), start the exploration mode, and recommend popular areas with the remaining mileage as the radius. If the battery power is lower than the set threshold, trigger the second case.

[0019] Step S5: Traverse the pre-screened charging path set, use the TOPSIS method to score each path; adjust the score in combination with the real-time traffic condition information, sort the paths according to the score from high to low to generate a scored path set, and feedback it to the human-computer interaction module together with the battery charging cost.

[0020] Step S6: The cloud records the path finally selected by the user, updates the preference weight in the user convenience model based on the user's selection, and updates the parameters of the segmented charging model based on the actual charging data for optimizing the subsequent charging path planning.

[0021] Furthermore, the step S1 includes:

[0022] Step S11: Obtain the vehicle battery voltage, current, SOC data, and the real-time vehicle position through the vehicle information aggregation module.

[0023] Step S12: Invoke the multi-dimensional user convenience data and user historical trajectory data stored in the cloud using an encrypted communication protocol. The multi-source data integration includes collecting the user's charging time period preference, holiday route habit, charging warning generation habit, and historical trajectory data.

[0024] After forming the structured data set, encrypt and upload the desensitized data to the cloud through the MQTT protocol.

[0025] Further, the step S2 includes:

[0026] Step S21: Filter the charging characteristic data, and use the least squares method to fit to obtain the segmented charging curve in the constant current stage and the constant voltage stage, and form a constant current-constant voltage segmented charging model based on the charging characteristic data of the vehicle battery.

[0027] Step S22: Calculate the charging time cost in the constant current stage and the constant voltage stage. Specifically, determine the position of the target power on the fitted segmented charging curve. If it is in the constant current segment, calculate the time from the starting power to the constant current to constant voltage point. If it is in the constant voltage segment, calculate the total time of the constant current segment plus the time from the constant current to constant voltage point to the target power.

[0028] Further, step S3 includes:

[0029] Step S31: Build a hierarchical structure model based on the multi-dimensional user convenience data and construct a judgment matrix between the elements of each layer.

[0030] Step S32: Conduct a consistency test on the judgment matrix.

[0031] Step S33: Calculate the weight vector of each layer element, and finally obtain the user charging habit preference weight vector to construct the user convenience model.

[0032] Further, step S4 includes:

[0033] Step S41: By collecting the remaining power, combining the road conditions information and the energy consumption situation of the vehicle itself, obtain the remaining mileage, and form an accessibility path set based on the remaining mileage. The accessibility charging path set refers to the path set corresponding to all the charging pile positions that the vehicle can reach within the remaining mileage.

[0034] Step S42: Conduct a preliminary scoring or screening on the accessible paths according to the preference weights in the user convenience model, and further screen in combination with the route preferences or frequently used locations reflected in the user historical trajectory data to form a pre-screened charging path set. Actively push it to the user through the human-computer interaction module and wait for the user to select. If the user selects the above solution, normal navigation will be carried out. If the user does not select the above solution, execute step S5.

[0035] Further, step S5 includes:

[0036] Step S51: Using the determined charging habit preference weights, combined with the TOPSIS method, score and rank different charging paths. The TOPSIS method for scoring and ranking different charging paths includes defining multiple evaluation attributes for each path in the pre-screened charging path set, constructing a standardized decision matrix, determining the positive ideal solution and the negative ideal solution, calculating the distances of each path to the positive ideal solution and the negative ideal solution, and taking the normalized score as the score.

[0037] Step S52: Obtain the scored path set and feedback it to the human-computer interaction module together with the battery charging cost for the user to make an autonomous choice.

[0038] To achieve the above object, the present invention also provides a device for an electric vehicle charging path planning method, including:

[0039] A memory for storing a computer program; a processor for implementing an electric vehicle charging path planning method when executing the computer program;

[0040] An electric vehicle charging path planning system includes a real-time power monitoring module based on a single-chip microcomputer, an information aggregation module connected to the whole vehicle module based on the CAN bus, an in-vehicle ECU module based on the AUTOSAR protocol and the RH850 chip, a human-computer interaction module supporting the active push of charging assistance plans, a cloud server supporting information storage and algorithm analysis, etc.

[0041] The beneficial effects of the present invention are:

[0042] The present invention fully considers the role of user charging habits in reasonable charging path planning, explores the charging habits of users, establishes a user convenience model, constructs a dynamic time prediction mechanism based on a constant current-constant voltage segmented charging model, realizes accurate calculation of charging duration in different power ranges by fitting the battery charging curve, integrates user historical trajectories and charging behavior data, establishes a dynamic preference weight model using the analytic hierarchy process, quantifies implicit demands such as the utilization rate of leisure time and charging pile usage habits, and generates a hierarchical recommendation scheme that takes into account both time efficiency and personal convenience by combining multi-objective parameters such as real-time road conditions, charging curve delay, and user preferences using the TOPSIS method. Description of the Drawings

[0043] Figure 1 Is a flowchart of an electric vehicle charging path planning system provided by the present invention; Figure 2 Is a flowchart for the formation and execution of the battery charging characteristic fitting curve. Detailed Embodiments

[0044] The technical solution of the present invention will be described completely and in detail below in conjunction with the accompanying drawings of this article. The specific embodiments are only for more vividly describing the patent implementation steps and are not all embodiments. Those skilled in the art can obtain other embodiments of the present invention without any creative work, and all belong to the protection scope of the present invention.

[0045] The present invention provides a method for planning an electric vehicle charging path, and the specific implementation steps are as follows:

[0046] Step S1: Obtain the vehicle battery voltage, current, SOC data, and vehicle real-time position, discard the outliers, and call the user convenience multi-dimensional data and user historical trajectory data stored in the cloud to integrate and clean the obtained multi-source data to form a structured data set.

[0047] The user convenience multi-dimensional data at least includes charging time period preference, holiday route habit, and charging warning generation habit. The user convenience multi-dimensional data at least includes charging time period preference, holiday route habit, and charging warning generation habit.

[0048] Further, the step S1 includes:

[0049] Step S11: Obtain the vehicle battery voltage, current, SOC data, and vehicle real-time position through the vehicle information aggregation module. Process the obvious abnormal data during the hardware data collection process. If it is detected that the remaining battery power SOC is greater than 100% and other situations, trigger the data verification mechanism, discard the outliers, and record the log.

[0050] The charging characteristic data includes collecting the total battery capacity using the power real-time monitoring module , initial remaining power , constant current stage current , constant voltage stage voltage threshold , constant voltage stage time parameter , conversion time from constant current stage to constant voltage stage etc.

[0051] Step S12: Call the user convenience multi-dimensional data and user historical trajectory data stored in the cloud using the encrypted communication protocol. Among them, the integration of multi-source data includes collecting the user's charging time period preference, holiday route habit, charging warning generation habit, and historical trajectory data.

[0052] The user convenience multi-dimensional data mainly includes holiday route selection habit , charging warning generation habit and other multi-dimensional data.

[0053] Further, the step S12 includes:

[0054] Step S121: The charging habit preference parameters for the weight evaluation of charging habit preference can be obtained by collecting basic data of the users of the vehicle. The users can be preliminarily evaluated through questionnaires or other forms.

[0055] In a specific embodiment, the following multi-dimensional user convenience data is collected:

[0056] User basic information : including age, gender, occupation, etc., which are obtained through the form filled in by the user during registration. These information help to initially understand the possible travel and charging patterns of the user. For example, there are differences in the working hours and travel rules of users in different occupations.

[0057] Holiday route selection habit , in order to avoid diluting the daily travel route selection habit, it is necessary to pay attention to identifying whether the user has a preference for long-distance travel during holidays when selecting the holiday route. During holidays, the large flow of people is often accompanied by the situation of charging time peak shifting, and the coincidence degree with the daily travel route selection is often low.

[0058] Charging warning generation habit : Extract the scene features of the user's high-incidence low battery, combine the calendar data to mark the user's working days, rest days, and holidays, and associate the date rules of the low battery events. Specifically, for example, the battery is insufficient before getting off work every Friday, and the user forgets to charge after commuting at the end of each month.

[0059] Daily travel route selection habit : Collect the user's daily travel time data through the in-vehicle system or mobile application to determine the travel frequency and rules of the user at different time periods. Use the information aggregation module to record the routes, departure places, and destination information that the user often travels, as well as the distribution of charging piles on these routes, understand the charging needs of the user when traveling at different distances, and upload them to the cloud database. Travel purpose: When the user uses the vehicle, prompt the user to select the travel purpose through the application interface, such as commuting, shopping, etc. Travels with different purposes may lead to different charging behaviors.

[0060] Charging time selection habit : Statistically analyze at which time period of the day the user usually chooses to charge. By analyzing the charging records of the user at different time periods, determine the user's charging time preference.

[0061] Charging time cost selection habit : As an indispensable part of the charging path planning, the time cost is generally divided into driving time, waiting time, and charging time. Here, the time cost caused during the charging process is emphasized. The time cost is solved from the battery charging characteristic fitting curve in step S2.

[0062] Step S122: Clean and preprocess the user's historical trajectory data to form a set of user's historical trajectory paths.

[0063] Step S123: Preprocess the user convenience data, focusing on recording the charging time period, recording and analyzing the travel trajectory, and analyzing data such as the environment around the travel destination. It is mainly used to feedback the busy and idle time periods in the user's daily life, make reasonable inferences about the user's behavior, record the average time for the user to complete the behavior, and judge whether charging at the corresponding time can save the overall charging time and other user convenience information.

[0064] Step S13: After forming the structured data set, encrypt and upload the desensitized data to the cloud through the MQTT protocol for establishing the user convenience model in Step S2.

[0065] Step S2: Based on the charging characteristic data of the vehicle battery, establish a constant current-constant voltage segmented charging model to fit the battery charging characteristic curve, and calculate the charging time cost of the constant current stage and the charging time cost of the constant voltage stage required to reach the target power from the starting power according to the position of the target power in the fitted curve.

[0066] Since battery characteristics such as the battery itself, energy utilization rate, and battery health will affect the charging process, and the charging characteristics of different batteries will be affected by conditions such as usage time, compared with the traditional method of calculating charging time through power, solving through the battery charging characteristic fitting curve is more accurate.

[0067] Further, the Step S2 includes:

[0068] Step S21: Filter the charging characteristic data, and use the least squares method to fit to obtain the segmented charging curves of the constant current stage and the constant voltage stage, and form a constant current-constant voltage segmented charging model based on the charging characteristic data of the vehicle battery.

[0069] Among them, the constant current model : .

[0070] Among them is the function of the remaining power in the constant current stage changing with time, is the initial remaining power, is the constant current, is the total battery capacity, is the time when the constant current stage switches to the constant voltage stage. <^

[0071] The constant voltage model : .

[0072] where is a function of the remaining power in the constant voltage stage changing with time, is the remaining power at the time of switching, is the constant current, is the total battery capacity, is the time when the constant current stage switches to the constant voltage stage, is the time constant of the constant voltage stage.

[0073] Let , to ensure the continuity from the constant current state to the constant voltage state.

[0074] Step S211: Sample the time t and the remaining power SOC during the charging process, and filter the sampled data to eliminate outliers.

[0075] where the sampling sequence is , , use moving average filtering, take the window function length as 5s to eliminate high-frequency noise, and mark the data with the difference in adjacent SOC exceeding 3% as outliers and eliminate them.

[0076] Step S212: Use the least squares method to fit the charging process curve to minimize the sum of the squares of the distances between the model predicted value and the measured value .

[0077] .

[0078] where the constraint parameter is .

[0079] Step S22: Calculate the charging time cost of the constant current stage and the constant voltage stage. Specifically, determine the position of the target power on the fitted segmented charging curve. If it is in the constant current segment, calculate the time from the starting power to the target power. If it is in the constant voltage segment, calculate the total time of the constant current segment plus the time from the constant current to constant voltage point to the target power.

[0080] Furthermore, the step S22 includes:

[0081] Step S221: Use the real-time power monitoring module based on the single-chip microcomputer to monitor the remaining power at the start of charging in real time, and the end charging power . If the user does not input, it is default to be fully charged, is set to 100%, and in the actual application scenario, the user can manually input how much power they hope to charge to.

[0082] Step S222: Judge the position of the end charging power on the fitted curve. Specifically, it is divided into three cases:

[0083] In the first case, if , it is in a constant current state throughout;

[0084] In the second case, if , the charging process includes a conversion process from constant current to constant voltage;

[0085] In the third case, if , it is in a constant voltage state throughout.

[0086] Step S223: Substitute into the constant current and constant voltage model of charging to obtain the time cost of the constant current process and the time cost of the constant voltage process .

[0087] Where , .

[0088] Step S224: Based on the judgment condition in step S42, obtain the corresponding charging time cost.

[0089] .

[0090] Where is the charging time cost.

[0091] Step S3: Based on the multi-dimensional data of user convenience, use the analytic hierarchy process in the subjective weighting method to determine the user charging habit preference weights and construct a user convenience model.

[0092] Furthermore, step S3 includes:

[0093] Step S31: Based on the multi-dimensional data of user convenience, construct a hierarchical structure model and construct a judgment matrix between elements of each layer.

[0094] Specifically, according to six different sets of charging habit preference parameters as , the following judgment matrix can be established: .

[0095] Where represents the degree of importance of compared with the charging habit preference parameter . Therefore, it satisfies and . When , the two indicators are the same and must satisfy equal importance. Therefore, the main diagonal elements are all 1. After investigation and data integration, it is found that compared with the user's basic information For other influencing factors, they have a greater impact on the charging habit portrait of users. Therefore, when actually verifying the authenticity and reliability of the matrix, the elements in the first column can be used as one of the bases for preliminary judgment, that is To meet the actual situation.

[0096] Step S32: Conduct a consistency test on the judgment matrix: Calculate the consistency index , where is the number of charging habit preference parameters; Find the corresponding average random consistency index :

[0097] Calculate the consistency ratio .

[0098] If , it can be considered that the consistency of the judgment matrix is acceptable; otherwise, the judgment matrix needs to be corrected and analyzed.

[0099] Step S33: Then use the first column of the judgment matrix and utilize to calculate the weight vector of each layer of elements, and finally obtain the weight vector of the user's charging habit preference to construct the user convenience model.

[0100] At the same time, the calculation of the charging habit preference weight here is mainly for the habits of different users. Therefore, the subjective analytic hierarchy process has good applicability and accuracy. At the same time, the entropy weight method can also be used to calculate the objective charging habit preference weight of each index. Determine the charging habit preference weight according to the degree of dispersion of each index data. The greater the degree of dispersion of the data, the stronger the discrimination ability of the index for the scheme, and the greater the corresponding charging habit preference weight.

[0101] Step S4: Generate an accessible charging path set according to the remaining mileage of the vehicle and the charging pile location information; combine the user convenience model and the user historical trajectory data to screen the accessible charging path set, generate a pre-screened charging path set, and actively push it to the user terminal.

[0102] Step S41: By collecting the remaining power, combining the road conditions information and the energy consumption situation of the vehicle itself, obtain the remaining mileage number, and form an accessible path set based on the remaining mileage number. The accessible charging path set refers to the path set corresponding to all the charging pile positions that the vehicle can reach within the remaining mileage.

[0103] Furthermore, step S41 includes:

[0104] In a specific embodiment, the recommendation scheme based on the remaining mileage is mainly divided into the following situations.

[0105] In the first case, the user actively enters the destination. Compare the remaining mileage and the required mileage. If the remaining mileage is greater than the mileage required to reach the destination. That is At this time, where represents the mileage required to reach the destination, represents the mileage required to reach the destination, k represents the safety factor, which is defaulted to 1.2 (reserving 20% redundant power), and can be preset by the vehicle owner according to the actual situation for route planning without prompting for charging. If the remaining mileage is less than the required mileage, that is At this time, it means that there may be risks when arriving, and multi-stop route planning is carried out.

[0106] For the multi-stop route planning mentioned above, with as the principle, based on the principle of charging when the remaining mileage is less than the required mileage, insert N charging stations into the total route to ensure that the power can be replenished on the way to reach the final destination. When reaching the last charging station before the foreseeable destination, the principle can be executed, where is the mileage corresponding to the power threshold, and reach the destination on the premise of not being lower than the mileage corresponding to the power threshold.

[0107] In the second case, if the remaining mileage is less than the set threshold, that is At this time, route planning is carried out based on the shortest required mileage. Trigger the charging decision. Whether the destination is known or not, the system will first look for the nearest charging station and re-plan the route to ensure that the vehicle will not stop midway due to power exhaustion. In this case, even if the user has a clear destination, the power problem should be solved first.

[0108] In the third case, the user does not set the destination, but the system detects that the current time / location highly matches the user's historical trajectory path set. If the remaining power can cover the predicted path and there are available charging facilities near this area, the system will put forward a charging suggestion based on intelligent judgment, suggesting charging in advance.

[0109] In the fourth case, the user does not set the destination and there is no obvious historical pattern (such as random travel on weekends, according to the user's holiday selection habits). Start the exploration mode, and recommend popular areas with the remaining mileage as the radius. If the power is lower than the set threshold, trigger the second case.

[0110] Step S42: Perform a preliminary scoring or screening on the reachable paths according to the preference weights in the user convenience model, and further screen in combination with the route preferences or common locations reflected in the user's historical trajectory data to form a pre-screened charging path set. Actively push it to the user through the human-computer interaction module and wait for the user to select. If the user selects the above solution, normal navigation is performed. If the user does not select the above solution, step S5 is executed.

[0111] Step S5: Traverse the pre-screened charging path set, score each path using the TOPSIS method; adjust the score in combination with the real-time traffic conditions information, sort the scores from high to low to generate a scored path set, and feedback it to the human-computer interaction module together with the battery charging cost.

[0112] Further, step S5 includes:

[0113] Step S51: Use the determined charging habit preference weights, and combine the TOPSIS method to score and sort different charging paths. According to the performance of each path on different indicators, calculate its distances from the ideal charging path solution and the negative ideal charging path solution, and obtain the relative closeness of each path as the basis for evaluating the quality of the path, and provide the user with the optimal charging path recommendation.

[0114] In a specific embodiment, after normalizing the scores, .

[0115] Among them, the unnormalized score of the th route is .

[0116] Among them, the distance between the th route and the negative ideal solution is .

[0117] The distance between the th route and the positive ideal solution is . Among them, is the proportion of each charging habit preference parameter in step S3.

[0118] The negative ideal solution is .

[0119] The positive ideal solution is .

[0120] Among them, the set of all routes that meet the user's current travel purpose is , establish a positive matrix composed of all possible path solutions and 6 influencing factors: .

[0121] Then the standardized decision matrix is denoted as , Each element in is

[0122] Step S52: Evaluate all path schemes under various factors that may affect the charging path scoring coefficient during the user's current trip, and obtain path optimization schemes that meet different user habits from largest to smallest in the form of substantial scores, resulting in a scored path set, which is fed back to the human-computer interaction module together with the battery charging cost for the user to make an independent choice.

[0123] Step S6: The cloud records the path finally selected by the user, updates the preference weights in the user convenience model based on the user's selection, and updates the parameters of the segmented charging model based on the actual charging data for optimizing subsequent charging path planning.

[0124] To achieve the above object, the present invention also provides a device for an electric vehicle charging path planning method, including: a memory for storing a computer program; a processor for implementing an electric vehicle charging path planning method when executing the computer program. A system for an electric vehicle charging path planning method includes a real-time power monitoring module based on a single-chip microcomputer, an information aggregation module connected to the whole vehicle module based on the CAN bus, an in-vehicle ECU module based on the AUTOSAR protocol and the RH850 chip, a human-computer interaction module supporting the active push of charging assistance plans, a cloud server supporting information storage and algorithm analysis, etc.

[0125] The specific embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for planning an electric vehicle charging path, characterized in that, It includes the following steps: S1. Obtain the vehicle battery voltage, current, SOC data and vehicle real-time location, discard outliers, call the user convenience multi-dimensional data and user historical trajectory data stored in the cloud. The user convenience multi-dimensional data includes at least charging period preference, holiday route habit and charging warning generation habit. Integrate and clean the obtained multi-source data to form a structured data set; S2. Based on the charging characteristic data of the vehicle battery, establish a constant current-constant voltage segmented charging model to fit the battery charging characteristic curve. According to the position of the target power in the fitted curve, calculate the charging time cost of the constant current stage and the charging time cost of the constant voltage stage required to reach the target power from the starting power respectively; S3. Based on the user convenience multi-dimensional data, use the analytic hierarchy process in the subjective weighting method to determine the user charging habit preference weight and construct a user convenience model; S4. Generate an accessible charging route set according to the remaining mileage of the vehicle and the charging pile location information; Combine the user convenience model and the user historical trajectory data to screen the accessible charging route set, generate a pre-screened charging route set, and actively push it to the user terminal; S6. Traverse the pre-screened charging route set, use the TOPSIS method to score each path; adjust the score in combination with the real-time traffic condition information, sort the score paths according to the score from high to low, and feedback to the human-computer interaction module; S7. The cloud records the path finally selected by the user, updates the preference weight in the user convenience model based on the user selection, and updates the parameters of the segmented charging model based on the actual charging data for optimizing the subsequent charging route planning.

2. The method for planning an electric vehicle charging path according to claim 1, wherein, The step S1: Obtain the vehicle battery voltage, current, SOC data and vehicle real-time location through the vehicle information aggregation module; call the user convenience multi-dimensional data and user historical trajectory data stored in the cloud using the encrypted communication protocol; the multi-source data integration includes collecting the user charging period preference, holiday route habit, charging warning generation habit and historical trajectory data; after forming the structured data set, encrypt and upload the desensitized data to the cloud through the MQTT protocol.

3. The method for planning an electric vehicle charging path according to claim 1, wherein The step S2: Filter the charging characteristic data, and use the least square method to fit to obtain the segmented charging curves of the constant current stage and the constant voltage stage, and establish a constant current-constant voltage segmented charging model based on the charging characteristic data of the vehicle battery; calculate the charging time cost of the constant current stage and the constant voltage stage, specifically determine the position of the target power on the fitted segmented charging curve. If it is in the constant current section, calculate the time from the starting power to the target power. If it is in the constant voltage section, calculate the total time of the constant current section plus the time from the constant current to constant voltage point to the target power.

4. The method for planning an electric vehicle charging path according to claim 1, wherein The use of the analytic hierarchy process in the subjective weighting method to determine the user charging habit preference weight includes: Construct a hierarchical structure model based on the user convenience multi-dimensional data, construct a judgment matrix between elements of each layer, conduct a consistency test on the judgment matrix. After passing the consistency test, calculate the weight vector of each layer element, and finally obtain the user charging habit preference weight vector to construct a user convenience model.

5. The method for planning an electric vehicle charging path according to claim 1, wherein In the step S4: The reachable charging path set refers to the set of paths corresponding to the locations of all charging piles that the vehicle can reach within the remaining mileage; Screening in combination with the user convenience model and user historical trajectory data means initially scoring or screening the reachable paths according to the preference weights in the user convenience model, and further screening in combination with the route preferences or frequently visited locations reflected in the user historical trajectory data to form a pre-screened charging path set.

6. The method for planning an electric vehicle charging path according to claim 1, wherein, The scoring of each path using the TOPSIS method includes: Defining multiple evaluation attributes for each path in the pre-screened charging path set, constructing a standardized decision matrix, determining the positive ideal solution and the negative ideal solution, calculating the distances of each path to the positive ideal solution and the negative ideal solution, and taking the normalized score as the score.

7. An apparatus for implementing the electric vehicle charging path planning method according to any one of claims 1-6, characterized in that, The device includes: A real-time power monitoring module based on a single-chip microcomputer, designed based on a single-chip microcomputer and integrated into the vehicle battery management system (BMS) for real-time collection of battery voltage, current, and SOC data; An information aggregation module connected to the whole vehicle module based on the CAN bus, implemented based on the CAN bus and AUTOSAR protocol, connecting to each electronic control unit (ECU) of the vehicle, for aggregating the vehicle's real-time position information and the data of the real-time power monitoring module, and uploading it after preliminary preprocessing; ​ ​

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