PTCU-based new energy vehicle travel charging multi-terminal optimization solution

Abstract

The invention relates to the related field of new energy travel charging optimization, and discloses a PTCU-based new energy vehicle travel charging multi-terminal optimization solution, which realizes the provision of an optimal path planning scheme by cooperatively acquiring and managing multi-terminal information and combining the vehicle electric quantity, the real-time traffic condition and the charging pile distribution, and at the same time, realizes the multi-terminal optimization of the new energy vehicle travel charging. The PTCU side end and cloud end system provides real-time monitoring and reservation functions for the state of the charging pile, the problem of mileage anxiety of a user is effectively solved, and more convenient, efficient and whole-process travel auxiliary experience can be provided for the user through an integrated planning, recommendation and reservation system.

Description

Technical Field

[0001] The present invention relates to the field related to new energy travel charging optimization, and specifically to a multi-terminal optimization solution for new energy vehicle travel charging based on PTCU. Background Art

[0002] With the popularization of new energy electric vehicles, in order to meet the endurance requirements during driving, the availability of charging piles and the convenience of the charging process have become the focus of users' attention. In the existing technology, new energy vehicles can plan the user's travel and charging needs through the path planning system provided by the map software, which is mainly based on fixed charging pile locations and basic path planning algorithms, real-time data integration and dynamic optimization functions.

[0003] The path planning function in the existing technology fails to fully consider factors such as vehicle power, traffic conditions and distribution of charging piles, resulting in an inadequately optimized planned route. At the same time, the lack of real-time status monitoring and reservation functions of charging piles prevents users from making advance reservations for charging, leading to a prominent mileage anxiety problem. Moreover, for the charging pile operation side, the usage and fault status of the charging piles cannot be monitored and managed in real time, resulting in inefficient use of the charging piles. In addition, the lack of an effective charging pile management and scheduling mechanism makes it impossible to achieve near-full-load operation of the charging piles, affecting revenue efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-terminal optimization solution for charging new energy vehicles based on PTCU to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A multi-terminal optimization solution for charging of new energy vehicles based on PTCU, including:

[0007] Obtain vehicle positioning data and destination information, generate multiple groups of travel path plans based on the vehicle positioning data and destination information, and obtain traffic condition information and charging pile synchronization information along the corresponding travel path plans;

[0008] Update and synchronize the vehicle's real-time power information, calculate the vehicle's endurance through traffic status information and real-time power information, and obtain the vehicle's safe driving mileage;

[0009] Establishing a recommended range of charging piles based on the safe driving mileage, dynamically generating an optimal path planning scheme within the recommended range of charging piles using an A* algorithm, and performing a minimization evaluation of the total driving cost based on the optimal path planning scheme to generate a corresponding list of optimal charging piles;

[0010] Feedback the real-time information of multiple charging piles in the optimal charging pile list in real time, obtain user selection information and make reservations for the charging piles.

[0011] As a further solution of the present invention: the step of dynamically generating the optimal path planning solution by the A* algorithm specifically includes:

[0012] Based on the current vehicle positioning data, the starting point is set as S, and based on the destination information, the end point is set as T. Then the set of charging piles passed on the corresponding path is C = {C1, C2, ..., C i ,C i+1};

[0013] The optimal path planning is dynamically generated by the A* algorithm, which is characterized by:

[0014]

[0015] Wherein, the d(C i ,C i+1 ) represents the distance between two nodes, t(C i ,C i+1 ) The travel time between two nodes, e(C i ,C i+1 ) represents the power consumption between two nodes, f(C j ) indicates charging pile C j The charging cost is α, β are the weights of time and power consumption.

[0016] As a further solution of the present invention: in the step of evaluating the minimization of the total driving cost based on the optimal path planning solution, the minimization of the total driving cost can be characterized as:

[0017]

[0018] Among them, d i represents the driving distance, c i represents the charging cost, and γ is the weight of the cost.

[0019] As a further solution of the present invention: it also includes a user monitoring feedback re-recommendation step based on the user reservation information, specifically including:

[0020] Obtain the location information of the user's reserved charging station, the user's current vehicle positioning data and real-time traffic information of the optimal planned route;

[0021] Based on the location information of the charging pile and the current vehicle positioning data as the starting and ending nodes, and based on the real-time traffic information, the user's scheduled arrival is predicted. The arrival time is represented as:

[0022] ETA(t)=CurrentTime+TravelTiem(t),

[0023] Among them, CurrentTime represents the current time, and TravelTiem(t) represents the user's travel time;

[0024] The arrival time is judged, and if the judgment result indicates that the user cannot arrive at the scheduled time, feedback is given to the user, the optimal route is re-planned, and the reserved charging pile is updated based on the user's needs.

[0025] As a further solution of the present invention: the charging pile includes a reservation charging standard, a usage charging standard and a breach of contract charging standard;

[0026] The reservation charging standard is used to charge the user according to the standard after the user reserves the charging pile. When the reservation is terminated or charging starts, the charging standard is no longer used;

[0027] The charging standard is used to charge the user according to the standard during the charging process;

[0028] The breach of contract charging standard is used to charge the user when the user has exceeded the reservation time and has not terminated the reservation or charged. The breach of contract charging standard is higher than the reservation charging standard. Both the reservation charging standard and the breach of contract charging standard are set with multiple standard gradients based on the accumulated time.

[0029] As a further solution of the present invention: it also includes a charging pile usage optimization step, including:

[0030] Monitor the status of charging piles in real time and establish a charging pile set C, represented by {S 空闲 ,S 预约 ,S 使用 ,S 故障}, wherein the S 空闲 , S 预约 , S 使用 , S 故障 They are used to represent the status of the charging pile as idle, reserved, in use, and faulty;

[0031] Perform maximum utilization rate fitting to obtain the optimal scheduling solution, and the maximum utilization rate fitting is characterized as follows:

[0032]

[0033] Among them, the S 使用 (C i ) indicates charging pile C i The utilization rate, S 故障 (C i ) indicates charging pile Ci Fault state, T 维修 (C i ) indicates charging pile C i maintenance time.

[0034] As a further solution of the present invention: it also includes a charging pile revenue optimization step, including:

[0035] The target maximum revenue is calculated based on the charging pile usage data to optimize the intelligent scheduling strategy for the charging piles. The maximum revenue is represented by: Among them, R(C i ) represents the income of a single charging pile, τ is the time period, U(C i ,t) is the charging pile C i Efficiency of use at time t;

[0036] The intelligent scheduling strategy P is characterized as follows: Among them, D(C i ,t) represents the charging pile C i The demand at time t, w i (t) is the charging pile C at a specific time t i status.

[0037] Compared with the prior art, the beneficial effects of the present invention are: compared with the prior art, by collaboratively acquiring and managing information from multiple terminals, combined with vehicle power, real-time traffic conditions and charging pile distribution, the provision of optimal path planning solutions is realized. At the same time, the PTCU edge and cloud systems provide real-time monitoring and reservation functions for the charging pile status, which effectively solves the user's mileage anxiety problem, and the integrated planning, recommendation and reservation system can provide users with a more convenient, efficient and full-process travel assistance experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flowchart of a multi-terminal optimization solution for charging new energy vehicles based on PTCU.

[0039] Figure 2 This is a flowchart of the user monitoring feedback and recommendation steps in a multi-terminal optimization solution for charging new energy vehicles based on PTCU. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0042] like Figure 1 The above-mentioned embodiment of the present invention provides a PTCU-based new energy vehicle travel charging multi-terminal optimization solution, which includes the following steps:

[0043] S10, obtaining vehicle positioning data and destination information, generating multiple groups of travel path plans based on the vehicle positioning data and destination information, and obtaining traffic condition information and charging pile synchronization information along the corresponding travel path plans;

[0044] S20, updating and synchronizing the real-time power information of the vehicle, calculating the vehicle endurance through the traffic status information and the real-time power information, and obtaining the safe driving mileage of the vehicle;

[0045] S30, establishing a recommended range of charging piles based on the safe driving mileage, dynamically generating an optimal path planning scheme within the recommended range of charging piles using an A* algorithm, and performing a minimization evaluation of the total driving cost based on the optimal path planning scheme to generate a corresponding list of optimal charging piles;

[0046] S40, providing real-time feedback of the real-time information of multiple charging piles in the optimal charging pile list, obtaining user selection information and making reservations for the charging piles.

[0047] In this embodiment, a PTCU-based cloud system is adopted, namely the Personal Transportation Cost Unit Cloud System (PTCU cloud system), which is a service built on cloud computing technology, designed to help individuals or organizations manage and optimize their transportation costs more effectively. Before traveling, users enter information such as destination and current power level. The system provides the optimal route planning plan and recommends available charging piles based on vehicle power level, real-time traffic information, distribution of charging piles and other information. Compared with the existing technology, the optimal route planning plan is provided by collaboratively acquiring and managing information from multiple terminals, combining vehicle power level, real-time traffic conditions and distribution of charging piles. At the same time, the PTCU edge and cloud systems provide real-time monitoring and reservation functions of the charging pile status, which effectively solves the user's mileage anxiety problem. The integrated planning, recommendation and reservation system can provide users with a more convenient, efficient and full-process travel assistance experience.

[0048] As another preferred embodiment of the present invention, the step of dynamically generating the optimal path planning solution by using the A* algorithm specifically includes:

[0049] Based on the current vehicle positioning data, the starting point is set as S, and based on the destination information, the end point is set as T. Then the set of charging piles passed on the corresponding path is C = {C1, C2, ..., C i ,C i+1};

[0050] The optimal path planning is dynamically generated by the A* algorithm, which is characterized by:

[0051]

[0052] Wherein, the d(C i ,C i+1 ) represents the distance between two nodes, t(C i ,C i+1 ) The travel time between two nodes, e(C i ,C i+1 ) represents the power consumption between two nodes, f(C j ) indicates charging pile C j The charging cost is α, β are the weights of time and power consumption.

[0053] In this embodiment, after the user inputs the destination, the PTCU cloud system automatically calculates the best driving route from the current location to the destination, recommends the most suitable charging station location along the way, estimates the charging cost, and the system prioritizes charging stations with short distances, low charging costs and good availability. In this step, the previous embodiment defines the safe driving mileage. The safe driving mileage is comprehensively judged based on the vehicle's current remaining power, power consumption status and road traffic conditions. It is used to characterize the mileage range that the vehicle can travel stably in the current state. Therefore, within this mileage range, the vehicle must complete the charging behavior at a certain charging station to ensure that the vehicle's endurance can continue to support the vehicle's forward movement.

[0054] As another preferred embodiment of the present invention, in the step of evaluating the minimization of the total driving cost based on the optimal path planning solution, the minimization of the total driving cost can be characterized as:

[0055]

[0056] Among them, d i represents the driving distance, c i represents the charging cost, and γ is the weight of the cost.

[0057] In this embodiment, the purpose is to recommend the most suitable charging pile location along the way and estimate the charging cost after the PTCU cloud system automatically calculates the best driving route from the current location to the destination according to the user's destination, so as to give priority to recommending charging piles with short distance, low charging cost and good availability, so as to minimize the total driving cost.

[0058] like Figure 2 As shown, as another preferred embodiment of the present invention, it also includes a user monitoring feedback re-recommendation step based on the user reservation information, specifically including:

[0059] S51, obtaining the location information of the user's reserved charging pile, the user's current vehicle positioning data and the real-time traffic information of the optimal planned route;

[0060] S52, based on the charging pile location information and the current vehicle positioning data as the starting and ending nodes, and based on the real-time traffic information, the user's scheduled arrival is predicted, and the arrival time is represented by:

[0061] ETA(t)=CurrentTime+TravelTiem(t),

[0062] Among them, CurrentTime represents the current time, and TravelTiem(t) represents the user's travel time;

[0063] S53, judging the arrival time, if the judgment result indicates that the user cannot arrive at the scheduled time, providing feedback to the user, re-planning the optimal route and updating the scheduled charging pile based on the user's needs.

[0064] Furthermore, the charging pile includes reservation charging standards, usage charging standards and breach of contract charging standards;

[0065] The reservation charging standard is used to charge the user according to the standard after the user reserves the charging pile. When the reservation is terminated or charging starts, the charging standard is no longer used;

[0066] The charging standard is used to charge the user according to the standard during the charging process;

[0067] The breach of contract charging standard is used to charge the user when the user has exceeded the reservation time and has not terminated the reservation or charged. The breach of contract charging standard is higher than the reservation charging standard. Both the reservation charging standard and the breach of contract charging standard are set with multiple standard gradients based on the accumulated time.

[0068] In this embodiment, when the user fails to arrive at the reserved charging pile on time, the PTCU cloud system automatically re-plans the route, monitors the user's location and traffic conditions in real time, and ensures that the user can charge in time. The system predicts the user's arrival time through ETA(t) and dynamically adjusts the recommended plan based on real-time traffic information. The user queries the real-time status of the charging piles along the way through the mobile application and selects an idle charging pile for re-booking. In addition, when a charging pile is detected to be faulty or unavailable, the PTCU system immediately notifies the user and recommends other available charging piles based on the current location and real-time path, and recalculates the optimal path:

[0069] As another preferred embodiment of the present invention, the step of optimizing the use of charging piles is also included, including:

[0070] Monitor the status of charging piles in real time and establish a charging pile set C, represented by {S 空闲 ,S 预约 ,S 使用 ,S 故障}, wherein the S 空闲 , S 预约 , S 使用 , S 故障 They are used to represent the status of the charging pile as idle, reserved, in use, and faulty;

[0071] Perform maximum utilization rate fitting to obtain the optimal scheduling solution, and the maximum utilization rate fitting is characterized as follows:

[0072]

[0073] Among them, the S 使用 (C i ) indicates charging pile C i The utilization rate, S 故障 (C i ) indicates charging pile C i Fault state, T 维修 (C i ) indicates charging pile C i maintenance time.

[0074] Furthermore, it also includes charging pile revenue optimization steps, including:

[0075] The target maximum revenue is calculated based on the charging pile usage data to optimize the intelligent scheduling strategy for the charging piles. The maximum revenue is represented by: Among them, R(C i ) represents the income of a single charging pile, τ is the time period, U(C i ,t) is the charging pile C i Efficiency of use at time t;

[0076] The intelligent scheduling strategy P is characterized as follows: Among them, D(C i ,t) represents the charging pile C i The demand at time t, w i (t) is the charging pile C at a specific time t i status.

[0077] In this embodiment, the PTCU cloud monitors the status of all charging piles in real time, including idle, in-use and fault states, to ensure the normal operation of the charging piles. The PTCU edge and cloud systems automatically generate the optimal scheduling plan based on the usage data of the charging piles, dynamically adjust the distribution and configuration of the charging piles, generate the optimal scheduling plan, and improve the usage efficiency and revenue of the charging piles; further, the PTCU edge and cloud systems work together to analyze the charging pile usage data through the data analysis module, optimize the layout and maintenance plan, dynamically adjust the distribution and configuration of the charging piles, and achieve near full load operation of the charging piles; compared with the existing technology, the optimization here includes two points: first, intelligent scheduling and optimization: the PTCU edge and cloud systems automatically generate the optimal scheduling plan based on real-time data analysis to improve the usage efficiency of the charging piles; second, data analysis and revenue optimization: the PTCU edge and cloud systems analyze the usage data of the charging piles, optimize the layout and maintenance strategy of the charging piles, and improve operational efficiency.

[0078] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0079] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0080] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A multi-terminal optimization solution for charging new energy vehicles based on PTCU, characterized in that: Include: Obtain vehicle positioning data and destination information, generate multiple groups of travel path plans based on the vehicle positioning data and destination information, and obtain traffic condition information and charging pile synchronization information along the corresponding travel path plans; Update and synchronize the vehicle's real-time power information, calculate the vehicle's endurance through traffic status information and real-time power information, and obtain the vehicle's safe driving mileage; Establishing a recommended range of charging piles based on the safe driving mileage, dynamically generating an optimal path planning scheme within the recommended range of charging piles using an A* algorithm, and performing a minimization evaluation of the total driving cost based on the optimal path planning scheme to generate a corresponding list of optimal charging piles; Feedback the real-time information of multiple charging piles in the optimal charging pile list in real time, obtain user selection information and make reservations for the charging piles.

2. According to claim 1, a PTCU-based multi-terminal optimization solution for new energy vehicle travel charging is characterized in that: The step of dynamically generating the optimal path planning solution by the A* algorithm specifically includes: Based on the current vehicle positioning data, the starting point is set as S, and based on the destination information, the end point is set as T. Then the set of charging piles passed on the corresponding path is C = {C1, C2, ..., C i ,C i+1 }; The optimal path planning is dynamically generated by the A* algorithm, which is characterized by: Wherein, the d(C i ,C i+1 ) represents the distance between two nodes, t(C i ,C i+1 ) The travel time between two nodes, e(C i ,C i+1 ) represents the power consumption between two nodes, f(C j ) indicates charging pile C j The charging cost is α, β are the weights of time and power consumption.

3. According to claim 2, a PTCU-based multi-terminal optimization solution for new energy vehicle travel charging is characterized in that: In the step of evaluating the minimization of the total driving cost based on the optimal path planning solution, the minimization of the total driving cost can be characterized as: Among them, d i represents the driving distance, c i represents the charging cost, and γ is the weight of the cost.

4. According to claim 3, a PTCU-based multi-terminal optimization solution for new energy vehicle travel charging is characterized in that: It also includes a user monitoring feedback re-recommendation step based on the user reservation information, specifically including: Obtain the location information of the user's reserved charging station, the user's current vehicle positioning data and real-time traffic information of the optimal planned route; Based on the location information of the charging pile and the current vehicle positioning data as the starting and ending nodes, and based on the real-time traffic information, the user's scheduled arrival is predicted. The arrival time is represented as: ETA(t)=CurrentTime+TravelTiem(t), Among them, CurrentTime represents the current time, and TravelTiem(t) represents the user's travel time; The arrival time is judged, and if the judgment result indicates that the user cannot arrive at the scheduled time, feedback is given to the user, the optimal route is re-planned, and the reserved charging pile is updated based on the user's needs.

5. According to claim 4, a PTCU-based multi-terminal optimization solution for new energy vehicle travel charging is characterized in that: The charging piles include reservation charging standards, usage charging standards and breach of contract charging standards; The reservation charging standard is used to charge the user according to the standard after the user reserves the charging pile. When the reservation is terminated or charging starts, the charging standard is no longer used; The charging standard is used to charge the user according to the standard during the charging process; The breach of contract charging standard is used to charge the user when the user has exceeded the reservation time and has not terminated the reservation or charged. The breach of contract charging standard is higher than the reservation charging standard. Both the reservation charging standard and the breach of contract charging standard are set with multiple standard gradients based on the accumulated time.

6. According to claim 5, a PTCU-based multi-terminal optimization solution for new energy vehicle travel charging is characterized in that: It also includes steps to optimize the use of charging piles, including: Monitor the status of charging piles in real time and establish a charging pile set C, represented by {S 空闲 ,S 预约 ,S 使用 ,S 故障 }, wherein the S 空闲 , S 预约 , S 使用 , S 故障 They are used to represent the status of the charging pile as idle, reserved, in use, and faulty; Perform maximum utilization rate fitting to obtain the optimal scheduling solution, and the maximum utilization rate fitting is characterized as follows: Among them, the S 使用 (C i ) indicates charging pile C i The utilization rate, S 故障 (C i ) indicates charging pile C i Fault state, T 维修 (C i ) indicates charging pile C i maintenance time.

7. A PTCU-based multi-terminal optimization solution for new energy vehicle charging according to claim 6, characterized in that: It also includes charging station revenue optimization steps, including: The target maximum revenue is calculated based on the charging pile usage data to optimize the intelligent scheduling strategy for the charging piles. The maximum revenue is represented by: Among them, R(C i ) represents the income of a single charging pile, τ is the time period, U(C i ,t) is the charging pile C i Efficiency of use at time t; The intelligent scheduling strategy P is characterized as follows: Among them, D(C i ,t) represents the charging pile C i The demand at time t, w i (t) is the charging pile C at a specific time t i status.

Images