Charging operation management platform based on smart city

Abstract

The invention discloses a charging operation management platform based on a smart city, and relates to the field of charging management, and the platform comprises a management module which is used for providing instruction editing and submitting for each function module, and providing a distribution network and power supply start-stop authority; the data sensing module is configured at a charging port terminal of each charging station and is used for acquiring electric energy storage data, electric energy output data and dynamic electricity price data in real time; the electricity price analysis module is used for receiving the real-time electricity price data of the data sensing module, predicting an electricity price fluctuation state in a future period and outputting electricity price characteristics; the global optimization level of a charging strategy is improved, the charging cost is minimized, the time efficiency is maximized, the resource utilization rate of a charging station is improved, the peak-valley load pressure of an urban power grid is effectively relieved, the refined requirement of a smart city for energy scheduling is met, the high-power charging process is actively intervened, and the charging efficiency is improved. And irreversible damage of deep circulation to the battery is avoided.

Description

Technical Field

[0001] The present invention relates to the field of charging management technology, and in particular to a charging operation management platform based on a smart city. Background Art

[0002] With the accelerated urbanization process worldwide, dense urban populations, increased traffic pressure, and prominent environmental pollution issues, promoting green travel and reducing automobile emissions have become key measures for sustainable urban development. The construction of charging infrastructure will help promote the use of electric vehicles and reduce the carbon footprint of urban transportation. With the advancement of urban technology, the construction of smart cities has become a new trend in global urban development. Smart cities rely on information technology and the Internet of Things technology, emphasizing the interconnection of various facilities and systems. The vehicle charging operation and management platform is in line with this trend. Through data integration and analysis, it optimizes resource allocation and improves the efficiency of charging facilities.

[0003] The existing system relies solely on the location of charging piles and static electricity prices for decision-making, and fails to integrate variables such as dynamic electricity price predictions, the spatiotemporal status of charging ports, driving behavior characteristics, and battery health constraints. This results in an inability to balance charging costs and efficiency. Traditional fast charging strategies ignore battery aging characteristics and cannot support users to adjust waiting time thresholds and charging start and stop points on demand. They rely on fixed-cycle data updates, resulting in distorted charging queue time estimates and subsequent charging scheduling failures. Summary of the Invention

[0004] In response to the above-mentioned shortcomings of the prior art, the present invention provides a charging operation management platform based on a smart city, which can effectively solve the problems of the prior art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention discloses a charging operation management platform based on a smart city, comprising: The management module is used to provide command editing and submission for each functional module, and provide the authority to start and stop the distribution network and power supply; Data sensing modules are installed at the charging port terminals of each charging station to collect real-time power storage data, power output data, and dynamic electricity price data; The electricity price analysis module is used to receive real-time electricity price data from the data sensor module, predict the fluctuation of electricity prices in future cycles, and output electricity price characteristics; The charging monitoring module is used to aggregate information from various charging stations in real time, including the topological relationship of the physical locations of charging ports, the real-time idle or occupied status of charging ports, and the maximum output power level of charging ports. It also dynamically generates a heat map of available charging ports and the estimated waiting time queue, outputting the information as time features. The user unit is used to obtain vehicle driving data in real time through identity authentication, analyze driving behavior characteristics and calculate the battery loss index, and output the battery life loss status; The fusion modeling module is used to build a charging planning model, integrating driving behavior characteristics, time characteristics, and electricity price characteristics, injecting battery life loss status as a constraint into the model, and generating an initial charging strategy set; The time interaction module is used to calculate the tolerable waiting time range based on the remaining range of the user's vehicle and the real-time queue length of the charging monitoring module. The user can customize the waiting time threshold based on this range to trigger policy recalculation; The strategy definition module is used to receive the sequence of charging start and stop command points set by the user within the user-defined waiting period, calculate the cost-effectiveness ratio of each time point based on the electricity price fluctuation prediction curve, automatically optimize the start and stop point locations to meet the cost-effectiveness objective function set by the user, and generate a balanced cost-effectiveness solution for electricity prices; The execution verification module compiles the final charging plan into a device control instruction set, sends the charging start and stop sequence to the target charging port, monitors the deviation between the actual charging curve and the plan in real time, and activates the feedback adjustment mechanism when the deviation exceeds the threshold.

[0006] Furthermore, the user unit is deployed with submodules at the lower level, including a user authentication module, a feature extraction module and a battery evaluation module. The user authentication module is interactively connected to the feature extraction module and the battery evaluation module via an electrical medium, wherein: The user authentication module is used to receive user login requests, retrieve vehicle CAN bus data after identity verification, and obtain battery charging and power consumption curves in real time; The feature extraction module is used to extract the user's historical driving data packets, including the behavioral feature vectors of acceleration frequency, braking intensity, and cruise driving ratio; The battery evaluation module is used to perform loss analysis on the battery data of the user authentication module, obtain the equivalent aging coefficient of the number of deep cycles and shallow charge and discharge times, and output the battery life loss state index as the life protection constraint condition of the charging strategy.

[0007] Furthermore, the battery evaluation module receives the battery voltage, current and temperature parameters obtained in real time by the user authentication module, and stores the charge and discharge data in time series, calculates the deep discharge DOD percentage and the corresponding cycle number of each charge and discharge cycle based on the charge and discharge data, calculates the corresponding equivalent aging coefficient according to the DOD percentage and the cycle number, and refers to the preset battery aging model or experimental fitting curve, and weightedly accumulates the equivalent aging coefficient with the deep cycle number and the shallow cycle number to generate a battery life loss state index reflecting the comprehensive loss degree of the battery, and uses the battery life loss state index as a constraint condition and passes it to the fusion modeling module as a reference for subsequent charging strategy generation.

[0008] Furthermore, when the electricity price analysis module performs electricity price forecasting, it establishes a model for associating electricity price with grid load, maps the total regional charging power to the distribution network transformer load rate through the load distribution algorithm, updates the electricity price fluctuation status on a rolling basis, and outputs the time-of-use electricity price feature vector for the next 2 hours every 15 minutes.

[0009] Furthermore, the working logic of the charging planning model in the fusion modeling module is calculated as follows: ; Where, represents the charging decision vector, for Charging start and stop status at the moment (0 = stop, 1 = start), and Respectively represent the start and end times of the user-defined tolerable waiting time period. Representative The predicted electricity price at the time, Represents the time discretization unit, the default is 15 minutes, represents the life loss cost function, represent The battery life loss status index at the moment, represent Charging power at the moment, and Represents the corresponding weight coefficient.

[0010] Furthermore, the life loss cost function The calculation formula is: ; Where, represents the battery chemical attenuation coefficient, represents the exponential function with the natural constant e as the base, stands for power stress factor.

[0011] Furthermore, the time interaction module is linked with the user's mobile app, supporting the real-time display of a list of available charging ports and their respective estimated completion times on the app. When the strategy recalculation is triggered, the new charging plan is sent to the user's mobile phone via push or SMS.

[0012] Furthermore, the time interaction module provides an instruction update interface, and users can actively modify the tolerable waiting time threshold, triggering the module to integrate the modeling module and the strategy definition module for recalculation, receive the grid demand response signal, automatically adjust the charging plan and compensate the user's benefits, and all adjustment records are stored in the cloud database.

[0013] Furthermore, the data collected by the data sensing module is compressed and encrypted through the Internet of Things protocol and then uploaded to the cloud data center, and a distributed database with a timestamp index is established.

[0014] Furthermore, the management module is interactively connected to the data sensing module, the charging monitoring module and the user unit through a wireless network, the data sensing module is interactively connected to the electricity price analysis module through a wireless network, the fusion modeling module is interactively connected to the user unit, the time interaction module and the policy definition module through a wireless network, and the policy definition module is interactively connected to the execution verification module through a wireless network.

[0015] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects: 1. By integrating driving behavior characteristics, time characteristics, and electricity price characteristics, and injecting the battery life loss state index as a hard constraint into the charging planning model, the global optimization level of the charging strategy is significantly improved, minimizing charging costs and maximizing time efficiency. At the same time, the resource utilization rate of charging stations is improved, effectively alleviating the peak and valley load pressure of the urban power grid, and meeting the smart city's demand for refined energy scheduling.

[0016] 2. By quantifying the equivalent aging coefficient, the battery life loss state index is calculated in real time based on the depth of discharge percentage and the number of cycles, combined with a preset battery aging model. This is used as a mandatory constraint condition for the charging strategy, actively intervening in the high-power charging process to avoid irreversible damage to the battery caused by deep cycling, significantly extending the battery life, reducing user replacement costs, and at the same time improving the overall safety and reliability of electric vehicles, in line with the development trend of sustainable energy management.

[0017] 3. Through the waiting time threshold customization mechanism, users are allowed to modify the tolerable waiting range in real time through the mobile app, and trigger the system to dynamically recalculate the charging strategy. At the same time, it supports users to set the charging start and stop point sequence, and automatically optimizes the cost-effectiveness plan based on the electricity price fluctuation curve, enhancing user control and participation, generating charging plans that are highly adapted to individual needs, improving user satisfaction and platform stickiness, and providing a flexible interface for grid demand response, promoting the personalized and intelligent upgrade of smart city charging services. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0019] Figure 1 It is a schematic diagram of the framework of the present invention.

[0020] The numbers in the figure represent: 1. Management module; 2. Data sensing module; 3. Electricity price analysis module; 4. Charging monitoring module; 5. User unit; 51. User authentication module; 52. Feature extraction module; 53. Battery evaluation module; 6. Fusion modeling module; 7. Time interaction module; 8. Policy definition module; 9. Execution verification module. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] The present invention will be further described below with reference to the embodiments.

[0023] Example 1: This embodiment is a charging operation management platform based on smart city, such as Figure 1 As shown, it includes: a management module 1, which is used to provide instruction editing and submission for each functional module, and provide distribution network and power supply start and stop permissions.

[0024] The data sensing module 2 is configured at the charging port terminal of each charging station to collect real-time power storage data including battery SOC, SOH, power output data including charging power, voltage and current curves, and dynamic electricity price data; the data collected by the data sensing module 2 is compressed and encrypted through the Internet of Things protocol and uploaded to the cloud data center, and a distributed database with a timestamp index is established.

[0025] The electricity price analysis module 3 is used to receive real-time electricity price data from the data sensing module 2, predict the electricity price fluctuation status in the future cycle, and output electricity price characteristics. When the electricity price analysis module 3 performs electricity price prediction, it establishes a model for associating electricity price with grid load, maps the total regional charging power to the distribution network transformer load rate through the load distribution algorithm, and performs rolling updates on the electricity price fluctuation status, outputting the time-of-use electricity price characteristic vector for the next two hours every 15 minutes.

[0026] The charging monitoring module 4 is used to aggregate the information of each charging station in real time, the topological relationship of the physical location of the charging port, the real-time idle or occupied status of the charging port, and the maximum output power level of the charging port, and dynamically generate a heat map of the available charging ports and the estimated waiting time queue, and output it as a time feature.

[0027] The user unit 5 is used to obtain vehicle driving data in real time through identity authentication, analyze driving behavior characteristics, calculate the battery loss index, and output the battery life loss status. The user unit 5 has submodules deployed below, including a user authentication module 51, a feature extraction module 52, and a battery evaluation module 53. The user authentication module 51 is interconnected with the feature extraction module 52 and the battery evaluation module 53 via an electrical medium, wherein: The user authentication module 51 is used to receive a user login request, retrieve the vehicle CAN bus data after identity verification, and obtain the battery charging and power consumption curve in real time; A feature extraction module 52 is used to extract the user's historical driving data packets, including the behavioral feature vectors of acceleration frequency, braking intensity, and cruise driving ratio; The battery evaluation module 53 is used to perform loss analysis on the battery data of the user authentication module 51, obtain the equivalent aging coefficients of the deep cycle number and the shallow charge and discharge number, and output the battery life loss state index as a life protection constraint condition for the charging strategy; the battery evaluation module 53 receives the battery voltage, current and temperature parameters obtained in real time by the user authentication module 51, and stores the charge and discharge data in time series, calculates the deep discharge DOD percentage and the corresponding cycle number of each charge and discharge cycle based on the charge and discharge data, calculates the corresponding equivalent aging coefficient based on the DOD percentage and the cycle number, and refers to the preset battery aging model or experimental fitting curve. The equivalent aging coefficient is weighted and accumulated with the deep cycle number and the shallow cycle number to generate a battery life loss state index reflecting the comprehensive loss degree of the battery, and the battery life loss state index is passed as a constraint condition to the fusion modeling module 6 as a reference for subsequent charging strategy generation.

[0028] The fusion modeling module 6 is used to build a charging planning model, integrate driving behavior characteristics, time characteristics, and electricity price characteristics, inject the battery life loss status as a constraint condition into the model, and generate an initial charging strategy set.

[0029] The time interaction module 7 is used to calculate the tolerable waiting time range based on the remaining range of the user's vehicle through the real-time queue length of the charging monitoring module 4. The user customizes the waiting time threshold based on the range to trigger policy recalculation.

[0030] The strategy definition module 8 is used to receive the charging start and stop command point sequence set by the user within the waiting time period defined by the user, calculate the cost-effectiveness ratio of each time point in combination with the electricity price fluctuation prediction curve, automatically optimize the start and stop point positions to meet the cost-effectiveness objective function set by the user, and generate a balanced cost-effectiveness solution for electricity prices.

[0031] Execute verification module 9, compile the final charging plan into a device control instruction set, send the charging start and stop sequence to the target charging port, monitor the deviation between the actual charging curve and the plan in real time, and activate the feedback adjustment mechanism when the deviation exceeds the threshold.

[0032] The management module 1 is interactively connected to the data sensing module 2, the charging monitoring module 4 and the user unit 5 through a wireless network, the data sensing module 2 is interactively connected to the electricity price analysis module 3 through a wireless network, the fusion modeling module 6 is interactively connected to the user unit 5, the time interaction module 7 and the policy definition module 8 through a wireless network, and the policy definition module 8 is interactively connected to the execution verification module 9 through a wireless network.

[0033] Compared with the existing technology, the integration of electricity price feature prediction of electricity price analysis module 3, real-time thermal map of charging port of charging monitoring module 4, driving behavior characteristics of feature extraction module 52 and battery life loss status index of battery evaluation module 53 upgrades the traditional single-dimensional decision-making to a global optimization model, effectively solves the technical contradiction that charging cost, efficiency and battery life cannot be coordinated and optimized, and provides an active protection mechanism for battery life. The battery evaluation module 53 calculates the equivalent aging coefficient based on the depth of discharge percentage and the number of cycles, and injects it into the strategy model as a hard constraint, integrating battery health management into the charging decision-making process. The time interaction module 7 allows for custom waiting time thresholds, and the strategy definition module 8 supports start-stop point sequence optimization to achieve personalized real-time adjustment of the strategy. By executing the deviation feedback adjustment of the verification module 9, the system response accuracy and resource utilization are improved.

[0034] Example 2: In other aspects, this example also provides another optimization mechanism based on Example 1, specifically a working logic of a charging planning model, whose calculation formula is: ; Where, represents the charging decision vector, for Charging start / stop status at the moment: 0 = stop, 1 = start, and Respectively represent the start and end times of the user-defined tolerable waiting time period. Representative The predicted electricity price at the time, Represents the time discretization unit, the default is 15 minutes, represents the life loss cost function, represent The battery life loss status index at the moment, represent Charging power at the moment, and Represents the corresponding weight coefficient.

[0035] Life loss cost function The calculation formula is: ; Where, represents the battery chemical attenuation coefficient, represents the exponential function with the natural constant e as the base, stands for power stress factor.

[0036] Compared with existing technologies, it breaks through the single economic orientation of traditional charging strategies and establishes a triple optimization mechanism of cost, life and behavior. It significantly extends the battery life while ensuring economy, and realizes personalized adaptation of users through a dynamic weight mechanism.

[0037] In this embodiment, the time interaction module 7 is linked with the user's mobile app, supporting the real-time display of the list of available charging ports and their respective estimated completion times on the app. When the policy recalculation is triggered, the new charging plan is sent to the user's mobile phone via push or SMS; the time interaction module 7 provides an instruction update interface, and the user can actively modify the tolerable waiting time threshold. The trigger module integrates the modeling module 6 and the strategy definition module 8 for recalculation, receives the grid demand response signal, automatically adjusts the charging plan and compensates the user's benefits, and all adjustment records are stored in the cloud database.

[0038] Compared with the existing technology, the mobile app displays the list of available charging ports and the dynamic heat map of the estimated completion time in real time, supports users to independently set the tolerable waiting threshold according to the remaining battery life, and automatically triggers the collaborative recalculation of the fusion modeling module 6 and the strategy definition module 8 when the threshold is modified. The strategy adjustment results are fed back to the user in real time, synchronously responding to the grid demand side signal and automatically generating a compensation plan to achieve a dynamic balance between user benefits and grid peak regulation. All interactive instructions and adjustment records are traced throughout the cloud database to form an auditable intelligent decision-making chain, which completely solves the technical defects of the traditional system where users passively wait, information lags and cannot participate in grid interaction.

[0039] In summary, the present invention integrates dynamic electricity price prediction, real-time battery loss analysis, and driving behavior characteristics. Using battery life loss status as a hard constraint, it automatically generates a charging strategy that optimizes cost and extends battery life within a user-set waiting time threshold. It aggregates charging port topology, occupancy status, and power levels in real time to generate a heat map and waiting queue. Combined with the grid load distribution algorithm, it updates electricity prices on a rolling basis, significantly improving charging port utilization efficiency. It supports real-time display of available charging ports and waiting times on mobile devices, allowing users to customize waiting thresholds and trigger strategy recalculation. It can also receive grid demand response signals to automatically compensate user benefits, achieve precise execution of charging timing through instruction compilation, and trigger feedback adjustment based on actual charging curve deviations to ensure consistency between planning and execution.

[0040] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A charging operation management platform based on smart city, characterized by: include: Management module, used to provide command editing and submission for each functional module, and provide distribution network and power supply start and stop permissions; Data sensing modules are installed at the charging port terminals of each charging station to collect real-time power storage data, power output data, and dynamic electricity price data; The electricity price analysis module is used to receive real-time electricity price data from the data sensor module, predict the fluctuation of electricity prices in future cycles, and output electricity price characteristics; The charging monitoring module is used to aggregate information from various charging stations in real time, including the topological relationship of the physical locations of charging ports, the real-time idle or occupied status of charging ports, and the maximum output power level of charging ports. It also dynamically generates a heat map of available charging ports and the estimated waiting time queue, outputting the information as time features. The user unit is used to obtain vehicle driving data in real time through identity authentication, analyze driving behavior characteristics and calculate the battery loss index, and output the battery life loss status; The fusion modeling module is used to build a charging planning model, integrating driving behavior characteristics, time characteristics, and electricity price characteristics, injecting battery life loss status as a constraint into the model, and generating an initial charging strategy set; The time interaction module is used to calculate the tolerable waiting time range based on the remaining range of the user's vehicle and the real-time queue length of the charging monitoring module. The user can customize the waiting time threshold based on this range to trigger policy recalculation; The strategy definition module is used to receive the sequence of charging start and stop command points set by the user within the user-defined waiting period, calculate the cost-effectiveness ratio of each time point based on the electricity price fluctuation prediction curve, automatically optimize the start and stop point locations to meet the cost-effectiveness objective function set by the user, and generate a balanced cost-effectiveness solution for electricity prices; The execution verification module compiles the final charging plan into a device control instruction set, sends the charging start and stop sequence to the target charging port, monitors the deviation between the actual charging curve and the plan in real time, and activates the feedback adjustment mechanism when the deviation exceeds the threshold.

2. A charging operation management platform based on smart city according to claim 1, characterized in that: The user unit is deployed with submodules at the lower level, including a user authentication module, a feature extraction module and a battery evaluation module. The user authentication module is interactively connected to the feature extraction module and the battery evaluation module via an electrical medium, wherein: The user authentication module is used to receive user login requests, retrieve vehicle CAN bus data after identity verification, and obtain battery charging and power consumption curves in real time; The feature extraction module is used to extract the user's historical driving data packets, including the behavioral feature vectors of acceleration frequency, braking intensity, and cruise driving ratio; The battery evaluation module is used to perform loss analysis on the battery data of the user authentication module, obtain the equivalent aging coefficient of the number of deep cycles and shallow charge and discharge times, and output the battery life loss state index as the life protection constraint condition of the charging strategy.

3. A charging operation management platform based on smart city according to claim 2, characterized in that: The battery evaluation module receives battery voltage, current, and temperature parameters acquired in real time by the user authentication module, and stores charge and discharge data in a time series. Based on the charge and discharge data, the module calculates the deep discharge (DOD) percentage and the corresponding number of cycles of each charge and discharge cycle. Based on the DOD percentage and the number of cycles, the module refers to a preset battery aging model or an experimental fitting curve to calculate the corresponding equivalent aging coefficient. The module performs a weighted accumulation of the equivalent aging coefficient, the number of deep cycles, and the number of shallow cycles to generate a battery life loss state index that reflects the comprehensive degree of battery loss. The battery life loss state index is used as a constraint condition and is passed to the fusion modeling module as a reference for generating a subsequent charging strategy.

4. The charging operation management platform based on smart city according to claim 1, characterized in that: When the electricity price analysis module performs electricity price forecasting, it establishes a model associating electricity price with grid load, maps the total regional charging power to the distribution network transformer load rate through a load distribution algorithm, performs rolling updates on electricity price fluctuations, and outputs a time-of-use electricity price feature vector for the next two hours every 15 minutes.

5. The charging operation management platform based on smart city according to claim 1, characterized in that: The working logic of the charging planning model in the fusion modeling module is calculated as follows: ； Where, represents the charging decision vector, for Charging start and stop status at the moment, 0 = stop, 1 = start, and Respectively represent the start and end times of the user-defined tolerable waiting time period. Representative The predicted electricity price at the time, Represents the time discretization unit, the default is 15 minutes, represents the life loss cost function, represent The battery life loss status index at the moment, represent Charging power at the moment, and Represents the corresponding weight coefficient.

6. The charging operation management platform based on smart city according to claim 5, characterized in that: The life loss cost function The calculation formula is: ； Where, represents the battery chemical attenuation coefficient, represents the exponential function with the natural constant e as the base, stands for power stress factor.

7. The charging operation management platform based on smart city according to claim 1, characterized in that: The time interaction module is linked to the user's mobile app, supporting the real-time display of a list of available charging ports and their estimated completion times on the app. When the strategy recalculation is triggered, the new charging plan is sent to the user's mobile phone via push or SMS.

8. The charging operation management platform based on smart city according to claim 1, characterized in that: The time interaction module provides an instruction update interface, and users can actively modify the tolerable waiting time threshold. The trigger module integrates the modeling module and the strategy definition module for recalculation, receives the grid demand response signal, automatically adjusts the charging plan and compensates the user's benefits, and all adjustment records are stored in the cloud database.

9. The charging operation management platform based on smart city according to claim 1, characterized in that: The data collected by the data sensing module is compressed and encrypted through the Internet of Things protocol and then uploaded to the cloud data center, and a distributed database with a timestamp index is established.

10. The charging operation management platform based on smart city according to claim 1, characterized in that: The management module is interactively connected to the data sensing module, the charging monitoring module and the user unit through a wireless network, the data sensing module is interactively connected to the electricity price analysis module through a wireless network, the fusion modeling module is interactively connected to the user unit, the time interaction module and the policy definition module through a wireless network, and the policy definition module is interactively connected to the execution verification module through a wireless network.

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