A charging operation management platform based on a smart city

Abstract

The application discloses a kind of based on wisdom city's charging operation management platform, it is related to charging management field, including: management module, for each function module provides instruction editing and delivery, provides network and power supply start-stop authority;Data sensing module is configured in the charging port terminal of each charging site, real-time collection electric energy storage data, electric energy output data and dynamic electricity price data;Electricity price analysis module is used to receive the real-time electricity price data of data sensing module, predict future period electricity price fluctuation state, output electricity price characteristics;It improves the global optimization level of charging strategy, realizes charging cost minimization and time efficiency maximization, while improving charging site resource utilization, effectively relieving urban power grid peak-valley load pressure, meet the fine demand of wisdom city to energy scheduling, actively intervene high-power charging process, avoid irreversible damage to battery of depth cycle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging management, in particular to a charging operation management platform based on smart city. BACKGROUND

[0002] With the acceleration of urbanization process worldwide, the population is densely populated in cities, the traffic pressure is increased, and the environmental pollution problem is highlighted. Under this background, promoting green travel and reducing automobile emissions have become one of the key measures for sustainable development of cities. The construction of charging infrastructure helps to promote the use of electric vehicles and reduce the carbon footprint of urban traffic. With the progress of urban technology, smart city construction has become a new trend of global urban development. Smart city relies on information technology and Internet of Things technology, and emphasizes the interconnection of various facilities and systems. The automobile charging operation management platform is consistent with this trend. Through data integration and analysis, resource allocation is optimized, and the use efficiency of charging facilities is improved.

[0003] The existing system only relies on charging pile position and static electricity price for decision-making, and cannot integrate dynamic electricity price prediction, charging port space-time state, driving behavior characteristics and battery health constraints, resulting in that charging cost and efficiency cannot be considered, traditional fast charging strategy ignores battery aging characteristics, cannot support users to adjust waiting time threshold and charging start-stop point on demand, relies on fixed period data update, and charging queue time estimation is distorted, thereby causing charging scheduling failure. SUMMARY

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

[0005] To achieve the above purpose, the present application is realized by the following technical scheme:

[0006] The present application discloses a charging operation management platform based on smart city, comprising:

[0007] The management module is used for providing instruction editing and submission for each function module, and providing network distribution and power supply start-stop permission.

[0008] The data sensing module is configured at the charging port terminal of each charging station, and is used for real-time collection of electric energy storage data, electric energy output data and dynamic electricity price data.

[0009] The electricity price analysis module is used for receiving real-time electricity price data of the data sensing module, predicting future period electricity price fluctuation state, and outputting electricity price characteristics.

[0010] A charging monitoring module is configured to aggregate information of each charging station in real time, including a physical position topology relationship of a charging port, a real-time idle or occupied state of the charging port, and a maximum output power level of the charging port, dynamically generate a thermal map of available charging ports and a predicted waiting time queue, and output time characteristics;

[0011] A user unit is configured to obtain vehicle driving data in real time through identity authentication, analyze driving behavior characteristics, and calculate a battery consumption index, and output a battery life consumption state;

[0012] A fusion modeling module is configured to build a charging planning model, fuse driving behavior characteristics, time characteristics, and electricity price characteristics, inject the model as a constraint condition with the battery life consumption state, and generate an initial charging strategy set;

[0013] A time interaction module is configured to calculate a tolerable waiting time range according to a remaining range of a user's vehicle and a real-time queue length of the charging monitoring module, and trigger a strategy recalculation according to a waiting time threshold value defined by the user in the range;

[0014] A strategy definition module is configured to receive a charging start-stop command point sequence set by the user within a waiting time period defined by the user, calculate a cost-benefit ratio at each time point in combination with a predicted curve of electricity price fluctuations, automatically optimize a start-stop point position to meet a cost-performance ratio target function set by the user, and generate an electricity price balanced cost-performance ratio scheme;

[0015] An execution verification module is configured to compile a final charging scheme into a device control instruction set, issue a charging start-stop sequence to a target charging port, and monitor a real-time charging curve and a planning deviation value, and start a feedback adjustment mechanism when the deviation exceeds a threshold value.

[0016] Further, the user unit is deployed with a sub-module, the sub-module includes a user authentication module, a feature extraction module, and a battery evaluation module, the user authentication module is connected to the feature extraction module and the battery evaluation module through an electrical medium, and wherein:

[0017] The user authentication module is configured to receive a user login request, access vehicle CAN bus data after verifying the identity, and obtain a battery charging and power consumption curve in real time;

[0018] The feature extraction module is configured to extract a user historical driving data packet, including an acceleration frequency, a braking intensity, and a cruise ratio driving behavior characteristic vector;

[0019] The battery evaluation module is configured to perform consumption analysis on battery data of the user authentication module, obtain an equivalent aging coefficient of a deep cycle number and a shallow charge-discharge number, output a battery life consumption state index, and use the battery life consumption state index as a life protection constraint condition of the charging strategy.

[0020] Further, the battery evaluation module receives the battery voltage, current and temperature parameters obtained in real time by the user authentication module, and stores the charging and discharging data in time sequence, calculates the depth of discharge DOD percentage and the corresponding cycle number of each charging and discharging cycle based on the charging and discharging data, calculates the corresponding equivalent aging coefficient according to the DOD percentage and the cycle number, and generates a battery life loss state index reflecting the comprehensive loss degree of the battery by weighting and accumulating the equivalent aging coefficient, the deep cycle number and the shallow cycle number, and transmits the battery life loss state index as a constraint condition to the fusion modeling module as a reference for subsequent charging strategy generation.

[0021] Further, when the electricity price analysis module performs electricity price prediction, an electricity price and power grid load correlation model is established, the regional charging total power is mapped to the distribution network transformer load rate through a load distribution algorithm, the electricity price fluctuation state is updated rolling, and a time-of-use electricity price feature vector for the next 2 hours is output every 15 minutes.

[0022] Further, the working logic of the charging planning model in the fusion modeling module has a calculation formula as follows:

[0023] ;

[0024] In the formula, x represents the charging decision vector, is the charging start-stop state at time t (0=stop, 1=start), and tstart and tend represent the start time and the end time of the user-defined tolerable waiting time period, respectively, represents the predicted electricity price at time t, represents the time discretization unit, which is 15 minutes by default, represents the battery life loss state index at time t, represents the charging power at time t, and and represent the corresponding weight coefficients.

[0025] Further, the calculation formula of the life loss cost function is as follows:

[0026] ;

[0027] In the formula, represents the battery chemical attenuation coefficient, represents the exponential function with the natural constant e as the base,​​​​​​​​​ stands for power stress factor.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0033] 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.

[0034] 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.

[0035] 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

[0036] 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.

[0037] Figure 1 It is a schematic diagram of the framework of the present invention.

[0038] 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

[0039] 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.

[0040] The present invention will be further described below with reference to the embodiments.

[0041] 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.

[0042] Data sensing module 2 is configured at the charging port terminal of each charging station, which collects real-time power storage data including battery SOC, SOH, power output data including charging power, voltage and current curve, and dynamic price data; the data collected by the data sensing module 2 is uploaded to the cloud data center after being compressed and encrypted through the Internet of Things protocol, and a distributed database with time stamp index is established.

[0043] The price analysis module 3 is used for receiving real-time price data of the data sensing module 2, predicting the future period price fluctuation state, and outputting the price characteristics; when the price analysis module 3 performs price prediction, a price and power grid load correlation model is established, the regional charging total power is mapped to the distribution network transformer load rate through a load distribution algorithm, the price fluctuation state is updated rolling, and the time-of-use price characteristic vector of the next 2 hours is output every 15 minutes.

[0044] The charging monitoring module 4 is used for real-time aggregation of information of each charging station, charging port physical position topological relationship, charging port real-time idle or occupied state and charging port maximum output power level, dynamic generation of available charging port thermal map and predicted waiting time queue, and output of time characteristics.

[0045] The user unit 5 is used for real-time acquisition of vehicle driving data through identity authentication, analysis of driving behavior characteristics and calculation of battery loss index, and output of battery life loss state; the user unit 5 is deployed with a sub-module, the sub-module includes a user authentication module 51, a feature extraction module 52 and a battery evaluation module 53, and the user authentication module 51 is connected with the feature extraction module 52 and the battery evaluation module 53 through an electrical medium.

[0046] The user authentication module 51 is used for receiving user login request, verifying identity, calling vehicle CAN bus data, and real-time acquisition of battery charging and power consumption curve;

[0047] The feature extraction module 52 is used for extracting user historical driving data packet, including acceleration frequency, braking intensity and cruise ratio driving behavior characteristic vector;

[0048] The battery evaluation module 53 is used for loss analysis of the battery data of the user authentication module 51, obtaining the equivalent aging coefficient of the deep cycle number and the shallow charge-discharge number, and outputting the battery life loss state index as the life protection constraint condition of 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-discharge data in time sequence, calculates the deep discharge DOD percentage and the corresponding cycle number of each charge-discharge cycle based on the charge-discharge data, refers to the preset battery aging model or the experimental fitting curve according to the DOD percentage and the cycle number, calculates the corresponding equivalent aging coefficient, and weights and accumulates the equivalent aging coefficient, the deep cycle number and the shallow cycle number to generate the battery life loss state index reflecting the comprehensive loss degree of the battery. The battery life loss state index is used as a constraint condition and is transmitted to the fusion modeling module 6 as a reference for subsequent charging strategy generation.

[0049] The fusion modeling module 6 is used for constructing a charging planning model, fusing driving behavior characteristics, time characteristics and electricity price characteristics, injecting the battery life loss state as a constraint condition into the model, and generating an initial charging strategy set.

[0050] The time interaction module 7 is used for calculating a tolerable waiting time range according to the remaining range of the user's vehicle and the real-time queue length of the charging monitoring module 4, and triggering strategy recalculation according to the range.

[0051] The strategy definition module 8 is used for receiving a charging start-stop command point sequence set by the user within the user-defined waiting time period, calculating the cost-benefit ratio of each time point in combination with the electricity price fluctuation prediction curve, automatically optimizing the start-stop point position to meet the user-set cost-benefit ratio target function, and generating an electricity price balanced cost-benefit ratio scheme.

[0052] The execution verification module 9 compiles the final charging scheme into a device control instruction set, issues a charging start-stop sequence to the target charging port, and monitors the actual charging curve and the planning deviation value in real time. When the deviation exceeds the threshold, a feedback adjustment mechanism is started.

[0053] The management module 1 is 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 connected to the electricity price analysis module 3 through a wireless network, the fusion modeling module 6 is connected to the user unit 5, the time interaction module 7 and the strategy definition module 8 through a wireless network, and the strategy definition module 8 is connected to the execution verification module 9 through a wireless network.

[0054] Compared with the prior art, the integrated electricity price analysis module 3 electricity price feature prediction, the charging monitoring module 4 charging port real-time heat map, the feature extraction module 52 driving behavior characteristics and the battery evaluation module 53 battery life loss state index, the traditional single-dimensional decision is upgraded to a global optimization model, effectively solving the technical contradiction that charging cost, efficiency and battery life cannot be optimized together, providing a battery life active protection mechanism, the battery evaluation module 53 calculates the equivalent aging coefficient based on the deep discharge percentage and the cycle number, and serves as a hard constraint to inject the strategy model, integrates the battery health management into the charging decision process, the time interaction module 7 allows the user to define the waiting time threshold, the strategy definition module 8 supports the start-stop point sequence optimization, realizes the individual real-time adjustment of the strategy, and improves the system response accuracy and resource utilization rate through the deviation feedback adjustment of the execution verification module 9.

[0055] Embodiment 2: In other aspects, the embodiment also provides another optimization mechanism based on embodiment 1, specifically a working logic of a charging planning model, and the calculation formula is:

[0056] ;

[0057] In the formula, represents a charging decision vector, is the charging start-stop state at time t, 0=stop, 1=start, and represent the start time and the end time of the user-defined tolerable waiting time period, respectively, represents the predicted electricity price at time t, represents the time discretization unit, which is 15 minutes by default, represents the life loss cost function, represents the battery life loss state index at time t, represents the charging power at time t, and represent the corresponding weight coefficients. The calculation formula of the life loss cost function is:

[0058] ;

[0059] ;

[0060] In the formula, represents a battery chemical attenuation coefficient, represents an exponential function with the natural constant e as the base, represents a power stress factor.

[0061] ​​Compared with the prior art, the single economic guidance of the traditional charging strategy is broken through, a triple optimization mechanism of cost, life and behavior is established, the battery service life is significantly prolonged while ensuring economy, and the individualization of users is realized through a dynamic weight mechanism.

[0062] In the embodiment, the time interaction module 7 is linked with the user mobile terminal App, supports real-time display of the available charging port list and the respective expected completion time on the App end, triggers strategy recalculation when a new charging scheme is pushed or sent to the user's mobile phone in the form of a short message, the time interaction module 7 provides an instruction update interface, the user can actively modify the tolerable waiting time threshold, trigger the recalculation of the fusion modeling module 6 and the strategy definition module 8, receive the power grid demand response signal, automatically adjust the charging scheme and compensate the user's income, and all adjustment records are stored in the cloud database.

[0063] Compared with the prior art, the available charging port list and the expected completion time dynamic heat map are displayed in real time through the mobile terminal App, the user can set the tolerable waiting threshold according to the remaining endurance, the threshold is modified, the collaborative recalculation of the fusion modeling module 6 and the strategy definition module 8 is automatically triggered, the strategy adjustment result is fed back to the user in real time, the power grid demand side signal is responded synchronously, and the compensation scheme is automatically generated, the dynamic balance between the user's income and the power grid peak shaving is realized, all interaction instructions and adjustment records are recorded through the cloud database throughout the process, forming an auditable intelligent decision link, and the technical defects of the traditional system that the user is passive waiting, information lag and cannot participate in the power grid interaction are completely solved.

[0064] In summary, the present application combines dynamic electricity price prediction, real-time battery wear analysis and driving behavior characteristics, takes the battery life loss state as a hard constraint, generates a cost-optimal and battery life-extending charging strategy within the user-set waiting time threshold, real-time aggregates the charging port topology relationship, occupancy state and power level, generates a heat map and a waiting queue, combines a power grid load distribution algorithm to update the electricity price, and significantly improves the charging port utilization efficiency;

[0065] The mobile terminal supports real-time display of available charging ports and waiting time, allows the user to customize the waiting threshold and trigger strategy recalculation, and can receive the power grid demand response signal to automatically compensate the user's income, realizes accurate execution of the charging sequence through instruction compilation, and triggers feedback adjustment based on the actual charging curve deviation, ensuring consistency between planning and execution.

[0066] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some of the technical features can be replaced by equivalents; and 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 embodiments of the present application.

Claims

1. A charging operation management platform based on smart city, characterized by: include: 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 planned value in real time, and activates the feedback adjustment mechanism when the deviation exceeds the threshold; 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; 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.

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 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.

6. 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.

7. 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.

8. 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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