New energy heavy truck charging data peak valley electricity intelligent identification and early warning method and system

Abstract

The application provides a new energy heavy truck charging data peak valley electricity intelligent identification and early warning method and system, the system comprises a time period configuration module, a charging state sensing module, a peak electricity early warning module, a peak valley electricity cutting module and a statistical analysis module; the method comprises the following sub-steps: S1: customizing time period parameters of each time period; S2: identifying charging behavior in real time; S3: cutting charging records according to electricity price time periods; S4: summarizing charging time lengths and generating a peak valley electricity utilization statistical report; the method can trigger early warning in real time when a vehicle starts charging in a peak electricity time period by setting a peak electricity early warning module, and can push early warning notification texts to fleet managers by calling API interfaces, so that real-time reminding of peak electricity charging behavior is realized, and the method can no longer only be used for post-mortem review, is convenient for managers to timely intervene and correct the charging behavior of drivers, and solves the defects of the prior art, such as the lack of real-time peak electricity early warning capability.

Description

Technical Field

[0001] This invention relates to the field of new energy heavy truck fleet management technology, and in particular to a method and system for intelligent identification and early warning of peak and off-peak electricity rates for new energy heavy truck charging data. Background Technology

[0002] With the deepening of the dual-carbon strategy, the application scale of new energy heavy trucks in the road freight sector continues to expand. Unlike traditional fuel heavy trucks, electricity costs are a major component of the operating costs of new energy heavy trucks. At present, time-of-use pricing mechanisms are widely implemented, dividing 24 hours into four categories: peak electricity, off-peak electricity, flat electricity, and peak hours. The price difference between different time periods is significant. Off-peak electricity prices are usually only 30%-50% of peak electricity prices. For operating heavy trucks with an average daily charging volume of several hundred kWh, prioritizing off-peak charging compared to peak charging can result in a daily cost difference of several hundred yuan. Reasonably selecting charging times is a key means for fleets to control operating costs. Currently, due to constraints such as drivers' operating habits and freight dispatch plans, peak-hour charging is common in actual fleet operations. Fleet managers find it difficult to monitor drivers' actual charging activities in real time, which has the following drawbacks: 1. Lack of real-time peak power warning capability: The existing technology does not link with the time-of-use pricing configuration. When a vehicle starts charging during peak power hours, it cannot promptly send reminders to the fleet management personnel. The process can only be reviewed afterward, missing the opportunity to intervene and correct the charging behavior on-site. 2. Distortion in cross-time period charging statistics: Many charging sessions span two or more electricity price periods. Most existing systems do not have the ability to accurately segment cross-time period charging records. Generally, the entire charging record is directly assigned to a certain electricity price period according to the start time of charging, resulting in large errors in the statistical results of peak and off-peak charging ratios and low data reference value. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for intelligent identification and early warning of peak and off-peak electricity rates for charging data of new energy heavy-duty trucks.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: The intelligent peak-valley electricity identification and early warning system for charging data of new energy heavy trucks includes a time period configuration module, a charging status sensing module, a peak electricity early warning module, a peak-valley electricity segmentation module, and a statistical analysis module; The time period configuration module is used to customize the electricity price time period parameters, perform time interval overlap verification, and store the electricity price time period parameters to the database after the verification is successful. The charging status sensing module is used to acquire charging-related data, identify the start and end states of charging, and generate charging session records; when entering the charging state, it generates a charging status message and transmits it to the peak power warning module. The peak power warning module is used to parse the charging status message, compare the peak power period, generate a warning notification text when charging starts during peak power, call the API to push the warning, and control the same charging session to push the warning only once. The peak-valley electricity segmentation module is used to receive charging session records, segment charging records according to electricity price periods, interpolate and calculate the charging duration and battery SOC increment of sub-intervals, and form charging statistics results by electricity price period segmentation. The statistical analysis module is used to summarize charging data according to the statistical period, calculate the proportion of charging time in each electricity price period, and generate a peak-valley electricity utilization statistical report.

[0005] This invention also proposes a method for intelligent identification and early warning of peak and off-peak electricity rates in charging data for new energy heavy-duty trucks, including the following sub-steps: S1: Customize the time period parameters for each time period; The time-of-use configuration module is based on the time-of-use pricing rules of the local power grid collected in advance. In the system's visual configuration interface, users can customize the electricity price time-of-use type and electricity price time-of-use parameters. The electricity price time-of-use type includes four types of electricity price time-of-use: peak electricity, valley electricity, flat electricity, and peak electricity. The electricity price time-of-use parameters include time interval, applicable date type, and electricity price. The applicable date types include weekdays, public holidays, and all dates; the time interval includes a start time and an end time. The local power grid's time-of-use pricing rules include information such as the time intervals and applicable date types for various pricing periods; among them, multiple time intervals can be configured for the same type of pricing period. After configuration, the time period configuration module performs overlap verification on the time intervals of each electricity price period. If there is overlap, it intercepts and saves the data and outputs a conflict alarm; if there is no overlap, it stores the electricity price period parameters in JSON format in the database.

[0006] S2: Real-time identification of charging behavior; The charging status sensing module connects to the vehicle-mounted T-BOX via a communication interface to acquire charging gun status signals, charging current values, battery SOC values, and vehicle VIN codes at a preset acquisition cycle. The charging gun status signals include 0 and 1, with 0 corresponding to the unplugged state and 1 corresponding to the plugged-in state. The charging status sensing module maintains the charging working status of the corresponding vehicle according to the vehicle VIN. The charging working status includes idle state and charging state, with the initial value of the charging working status of each vehicle being the idle state. The charging status sensing module caches the historical charging gun status signals from the previous collection cycle based on the vehicle's VIN, and determines whether the vehicle has entered the charging state or the charging end state. The specific determination method is as follows: The vehicle is considered to be in charging mode when it is in an idle charging state and the following conditions are met: The historical charging gun status signal of the previous acquisition cycle is 0 and the charging gun status signal of the current acquisition cycle is 1. In the next acquisition cycle following the detection of the above state transition, the charging gun state signal remains at 1, and the charging current value is greater than the preset current threshold. When a vehicle is determined to be in charging mode, the charging status corresponding to that vehicle is switched from idle mode to charging mode. The vehicle is considered to have entered the charging completion state when it is currently charging and meets any of the following conditions: The historical charging gun status signal is 1 and the charging gun status signal in the current acquisition period is 0; Starting from the current acquisition period, the charging current value is lower than the preset current threshold for N consecutive preset acquisition periods; N is a preset positive integer; When a vehicle is determined to have entered the charging end state, the charging working state of the vehicle is switched from charging state to idle state, a charging session record is generated and transmitted to the peak-valley power switching module. The charging session record includes the timestamp T_start when the charging state starts, the timestamp T_end when the charging state ends, and the battery SOC value sampling sequence corresponding to this charging session record. The battery SOC value sampling sequence corresponding to this charging session record is a sequence consisting of each timestamp from timestamp T_start to timestamp T_end and the corresponding battery SOC value; The charging status sensing module updates the cache with the charging gun status signal of each collection cycle, which serves as the historical charging gun status signal for the next collection cycle.

[0007] S3: Segment charging records by electricity price period; The peak-valley power splitting module receives the encapsulated charging session record and reads the timestamp T_start of the charging state start time, the timestamp T_end of the charging state end time, and the battery SOC value sampling sequence corresponding to this charging session record. Obtain the start and end times corresponding to the four types of electricity price periods configured in step S1, and compare them with the timestamps T_start and T_end respectively. Filter out the start and end times between the timestamps T_start and T_end, excluding start or end times that are the same as the timestamps T_start and T_end. Sort the selected start and end times by time, and combine them with the timestamps T_start and T_end to form a segmented time sequence; The two adjacent timestamps in the segmented time sequence are sequentially combined to form a sub-time interval. The sub-time interval is compared with the time intervals of the four types of electricity price periods configured in step S1 to obtain the electricity price period type of each sub-time interval. Linear interpolation is performed on the battery SOC value sampling sequence to obtain the battery SOC value at the start and end times of each sub-time interval; the charging duration and battery SOC increment of each sub-time interval are calculated; The charging duration = end time of the sub-time interval - start time; The battery SOC increment = battery SOC value at the end of the sub-time interval - battery SOC value at the beginning of the sub-time interval; The charging duration and battery SOC increment corresponding to each sub-time interval are accumulated according to the electricity price period type to which the sub-time interval belongs. The accumulated charging duration and battery SOC increment are written into the statistical fields of each electricity price period corresponding to this charging session record to form the charging statistics results split by electricity price period.

[0008] S4: Summarize charging time and generate a peak-valley electricity utilization statistics report; The statistical analysis module obtains a preset statistical period and summarizes the charging duration, battery SOC increment, and total charging duration of the vehicle corresponding to each electricity price period in the charging session records within the statistical period. Based on the vehicle VIN code, the total charging time and battery SOC increment corresponding to each electricity price period type in each vehicle charging status event are summarized. Based on the charging time and battery SOC increment corresponding to each electricity price period type, the percentage of peak charging time, the percentage of off-peak charging time, the percentage of flat charging time, and the percentage of peak charging time are obtained. Among them, for each statistical period, the percentage of charging time for each electricity price period = the charging time corresponding to each electricity price period type / the total charging time of the vehicle; Fill the above fields into the preset peak-valley electricity utilization statistical report template to generate a peak-valley electricity utilization statistical report and display it in the form of a data table.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention features a peak power warning module that can trigger a real-time warning when a vehicle starts charging during peak power hours. The warning notification text is pushed to fleet managers via an API interface, enabling real-time reminders of peak power charging behavior. This eliminates the need for only post-event review and allows managers to intervene and correct drivers' charging behavior in a timely manner, thus solving the deficiency of existing technologies that lack real-time peak power warning capabilities. This invention uses a peak-valley electricity segmentation module to segment charging session records across time periods, dividing a complete charging session into multiple sub-time intervals. By combining linear interpolation, the charging duration and battery SOC increment of each sub-interval are obtained, and the data are accumulated and statistically analyzed according to the electricity price time period type, rather than simply classifying the entire charging record based on the charging start time. This enables accurate segmentation of charging records across time periods and reduces the statistical error of peak-valley electricity charging ratio. This invention summarizes the charging time and battery SOC increment of each vehicle under different electricity price periods according to a preset statistical period, calculates the charging time ratio of each type of electricity price, automatically generates peak and off-peak electricity utilization statistical reports, and can also identify vehicles with a high peak electricity charging ratio based on peak electricity ratio. This allows the fleet to intuitively understand the charging behavior of each vehicle, assists the fleet in optimizing charging scheduling, rationally selects off-peak electricity for charging, and effectively reduces the charging operation cost of new energy heavy trucks. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of the intelligent identification and early warning method for peak and off-peak electricity rates in the charging data of new energy heavy-duty trucks proposed in this invention. Detailed Implementation

[0011] To provide a further understanding of the purpose, structure, features, and functions of the present invention, detailed descriptions are provided below with reference to specific embodiments.

[0012] The intelligent peak-valley electricity identification and early warning system for charging data of new energy heavy trucks includes a time period configuration module, a charging status sensing module, a peak electricity early warning module, a peak-valley electricity segmentation module, and a statistical analysis module; The time period configuration module is used to customize the electricity price time period parameters, perform time interval overlap verification, and store the electricity price time period parameters to the database after the verification is successful. The charging status sensing module is used to acquire charging-related data, identify the start and end states of charging, and generate charging session records; when entering the charging state, it generates a charging status message and transmits it to the peak power warning module. The peak power warning module is used to parse the charging status message, compare the peak power period, generate a warning notification text when charging starts during peak power, call the API to push the warning, and control the same charging session to push the warning only once. The peak-valley electricity segmentation module is used to receive charging session records, segment charging records according to electricity price periods, interpolate and calculate the charging duration and battery SOC increment of sub-intervals, and form charging statistics results by electricity price period segmentation. The statistical analysis module is used to summarize charging data according to the statistical period, calculate the proportion of charging time in each electricity price period, and generate a peak-valley electricity utilization statistical report.

[0013] Please refer to the reference. Figure 1This invention also proposes a method for intelligent identification and early warning of peak and off-peak electricity rates in charging data for new energy heavy-duty trucks, including the following sub-steps: S1: Customize the time period parameters for each time period; The time-of-use configuration module is based on the time-of-use pricing rules of the local power grid collected in advance. In the system's visual configuration interface, users can customize the electricity price time-of-use type and electricity price time-of-use parameters. The electricity price time-of-use type includes four types of electricity price time-of-use: peak electricity, valley electricity, flat electricity, and peak electricity. The electricity price time-of-use parameters include time interval, applicable date type, and electricity price. The applicable date types include weekdays, public holidays, and all dates; the time interval includes a start time and an end time. The local power grid's time-of-use pricing rules include information such as the time intervals and applicable date types for various pricing periods; among them, multiple time intervals can be configured for the same type of pricing period. After configuration, the time period configuration module performs overlap verification on the time intervals of each electricity price period. If there is overlap, it intercepts and saves the data and outputs a conflict alarm; if there is no overlap, it stores the electricity price period parameters in JSON format in the database. The specific verification method is as follows: Electricity price periods are grouped based on applicable date type, and no overlap check is performed between groups with different applicable date types. Electricity price periods within the same group are sorted by start time. The sorted electricity price periods are traversed, and adjacent time intervals are compared. If the start time of the later time interval is less than the end time of the previous time interval, it is determined that there is an overlap; otherwise, it is determined that there is no overlap.

[0014] Furthermore, for the same applicable date type grouping, each time point within a 24-hour day corresponds to only one electricity price period type, ensuring that any charging time can be matched with a unique electricity price period; the time interval of each electricity price period adopts a half-open interval, with the start of the interval included but the end of the interval excluded; For example: peak hours are 10:00-12:00 and 18:00-20:00, peak power hours are 08:00-10:00, 12:00-18:00 and 20:00-22:00, off-peak power hours are 00:00-08:00 and 22:00-24:00, and the off-peak power hours are the rest of the time.

[0015] S2: Real-time identification of charging behavior; The charging status sensing module connects to the vehicle-mounted T-BOX via a communication interface to acquire charging gun status signals, charging current values, battery SOC values, and vehicle VIN codes at a preset acquisition cycle. The charging gun status signals include 0 and 1, with 0 corresponding to the unplugged state and 1 corresponding to the plugged-in state. The charging status sensing module maintains the charging working status of the corresponding vehicle according to the vehicle VIN. The charging working status includes idle state and charging state, with the initial value of the charging working status of each vehicle being the idle state. The specific method for obtaining it is as follows: The battery management system (BMS) deployed inside the vehicle stores the charging gun disconnection voltage range and the charging gun connection voltage range in advance; The battery management system collects the voltage of the CC2 pin in the vehicle's charging socket in real time and determines the charging gun status based on the voltage: When the voltage is within the voltage range where the charging gun is disconnected, the charging gun is determined to be in the unplugged state; when the voltage is within the voltage range where the charging gun is connected, the charging gun is determined to be in the plugged-in state; otherwise, an abnormal connection is determined and a fault prompt is output. The battery management system collects the battery SOC value in real time and the charging current value in real time through the current sampler. According to the preset collection cycle, the timestamps corresponding to the charging gun status, charging current value, and battery SOC value are encapsulated into CAN bus messages and sent to the CAN bus. The vehicle-mounted T-BOX reads and parses CAN bus messages from the CAN bus, converts the charging gun status into a charging gun status signal. If the charging gun is unplugged, the charging gun status signal is 0; if the charging gun is plugged in, the charging gun status signal is 1. The charging gun status signal, charging current value, battery SOC value, and timestamp are reported to the charging status sensing module according to the collection cycle. The charging status sensing module caches the historical charging gun status signals from the previous collection cycle based on the vehicle's VIN, and determines whether the vehicle has entered the charging state or the charging end state. The specific determination method is as follows: The vehicle is considered to be in charging mode when it is in an idle charging state and the following conditions are met: The historical charging gun status signal of the previous acquisition cycle is 0 and the charging gun status signal of the current acquisition cycle is 1. In the next acquisition cycle following the detection of the above state transition, the charging gun state signal remains at 1, and the charging current value is greater than the preset current threshold. When a vehicle is determined to be in charging mode, the charging status corresponding to that vehicle is switched from idle mode to charging mode. The vehicle is considered to have entered the charging completion state when it is currently charging and meets any of the following conditions: The historical charging gun status signal is 1 and the charging gun status signal in the current acquisition period is 0; Starting from the current acquisition period, the charging current value is lower than the preset current threshold for N consecutive preset acquisition periods; N is a preset positive integer; When a vehicle is determined to have entered the charging end state, the charging working state of the vehicle is switched from charging state to idle state, a charging session record is generated and transmitted to the peak-valley power switching module. The charging session record includes the timestamp T_start when the charging state starts, the timestamp T_end when the charging state ends, and the battery SOC value sampling sequence corresponding to this charging session record. The battery SOC value sampling sequence corresponding to this charging session record is a sequence consisting of each timestamp from timestamp T_start to timestamp T_end and the corresponding battery SOC value; The charging status sensing module updates the cache with the charging gun status signal of each collection cycle, which is used as the historical charging gun status signal for the next collection cycle. Furthermore, when the vehicle is detected to be in charging mode, the charging status sensing module generates a charging status message in real time and transmits it to the peak power warning module; the charging status message includes the timestamp T_start of the charging state start time, the battery SOC value at the start time of the charging state, and the vehicle VIN code. When the peak power warning module receives the charging status message, it parses it to obtain the timestamp T_start of the status start time, the battery SOC value at the start time of the charging status, and the vehicle VIN code. The system reads the electricity price period parameters from the database in real time and compares the timestamp T_start with the time interval corresponding to peak electricity. If the timestamp T_start is not within the time interval corresponding to peak electricity, no warning is triggered; if the timestamp T_start is within the time interval corresponding to peak electricity, the following warning is triggered: The peak power warning module retrieves information such as the driver's name, charging pile number, and license plate number bound to the vehicle's VIN code from the system's vehicle file database based on the received vehicle VIN code. It then fills the retrieved information, along with the corresponding electricity price, the timestamp T_start of the charging start time, the battery SOC value at the start of the charging state, and the vehicle VIN code, into a preset warning notification text template to obtain the warning notification text. Finally, it calls the WeChat API interface to push the warning notification text to the corresponding fleet manager's WeChat or enterprise account. Furthermore, the system assigns a unique charging session identifier to each charging status event and sets a push flag bit that corresponds one-to-one with the charging session identifier. Before executing the early warning push, the peak power early warning module queries the push flag bit corresponding to the current charging session identifier. If the push flag bit is in an unpush state, the early warning push is executed. After the push is completed, the push flag bit is updated to a pushed state. If the push flag bit is in a pushed state, the early warning push is abandoned, ensuring that the same charging status event triggers a peak power early warning push at most once.

[0016] S3: Segment charging records by electricity price period; The peak-valley power splitting module receives the encapsulated charging session record and reads the timestamp T_start of the charging state start time, the timestamp T_end of the charging state end time, and the battery SOC value sampling sequence corresponding to this charging session record. Obtain the start and end times corresponding to the four types of electricity price periods configured in step S1, and compare them with the timestamps T_start and T_end respectively. Filter out the start and end times between the timestamps T_start and T_end, excluding start or end times that are the same as the timestamps T_start and T_end. Sort the selected start and end times by time, and combine them with the timestamps T_start and T_end to form a segmented time sequence; For example, if the selected start and end times are denoted as {b_1, b_2, ..., b_n}, then the segmented time sequence is {T_start, b_1, b_2, ..., b_n, T_end}; The two adjacent timestamps in the segmented time sequence are sequentially combined to form a sub-time interval. The sub-time interval is compared with the time intervals of the four types of electricity price periods configured in step S1 to obtain the electricity price period type of each sub-time interval. Linear interpolation is performed on the battery SOC value sampling sequence to obtain the battery SOC value at the start and end times of each sub-time interval; the charging duration and battery SOC increment of each sub-time interval are calculated; The charging duration = end time of the sub-time interval - start time; The battery SOC increment = battery SOC value at the end of the sub-time interval - battery SOC value at the beginning of the sub-time interval; The charging duration and battery SOC increment corresponding to each sub-time interval are accumulated according to the electricity price period type to which the sub-time interval belongs. The accumulated charging duration and battery SOC increment are written into the statistical fields of each electricity price period corresponding to this charging session record to form the charging statistics results split by electricity price period.

[0017] S4: Summarize charging time and generate a peak-valley electricity utilization statistics report; The statistical analysis module obtains a preset statistical period and summarizes the charging duration, battery SOC increment, and total charging duration of the vehicle corresponding to each electricity price period in the charging session records within the statistical period. Based on the vehicle VIN code, the total charging time and battery SOC increment corresponding to each electricity price period type in each vehicle charging status event are summarized. Based on the charging time and battery SOC increment corresponding to each electricity price period type, the percentage of peak charging time, the percentage of off-peak charging time, the percentage of flat charging time, and the percentage of peak charging time are obtained. Among them, for each statistical period, the percentage of charging time for each electricity price period = the charging time corresponding to each electricity price period type / the total charging time of the vehicle; Fill the above fields into the preset peak-valley electricity utilization statistical report template to generate a peak-valley electricity utilization statistical report and display it in the form of a data table; It supports sorting by peak charge percentage and identifying vehicles with a high peak charge percentage.

[0018] The time-period configuration module of this invention supports custom configuration of various electricity price time-period parameters such as peak electricity, valley electricity, flat electricity, and peak period. It can adapt to different applicable date types such as weekdays, holidays, and all dates. Multiple time intervals can be configured for the same electricity price time period. It also has time interval overlap verification and conflict alarm functions, which can adapt to different time-of-use electricity price rules of different power grids in various regions. When configuration errors occur, alarms are promptly intercepted to ensure the accuracy and reliability of electricity price time-period configuration data.

[0019] This invention sets a unique charging session identifier and push flag for each charging session. The same charging session can trigger a peak power warning push at most once, avoiding the generation of multiple duplicate warning messages for the same charging event, preventing managers from receiving a large number of duplicate alarm messages, and improving the availability of warning information.

[0020] The charging status sensing module of this invention judges the charging gun status based on the voltage of the CC2 pin collected by the vehicle BMS. It combines the charging gun status signal jump and the charging current threshold as dual conditions to determine the start and end of charging. It can accurately identify real charging sessions and filter out invalid state disturbances such as plugging in but not charging or brief plugging and unplugging, so as to ensure that the generated charging session records are real and valid.

[0021] The present invention has been described in the above-described embodiments; however, these embodiments are merely examples for implementing the present invention. It must be noted that the disclosed embodiments do not limit the scope of the present invention. Conversely, any modifications and refinements made without departing from the spirit and scope of the present invention are within the scope of patent protection of the present invention.

Claims

1. A method for intelligent identification and early warning of peak and off-peak electricity consumption in charging data of new energy heavy-duty trucks, characterized in that: Includes the following sub-steps: S1: Customize the time period parameters for each time period; The time-of-use configuration module allows users to customize the electricity price time-of-use type and parameters within the system's visual configuration interface. S2: Real-time identification of charging behavior; The charging status sensing module acquires the charging gun status signal, charging current value, battery SOC value, and vehicle VIN code at a preset acquisition cycle, and determines whether the vehicle has entered the charging state or the charging end state. If there is a charging behavior, it generates a charging session record and transmits it to the peak-valley electricity switching module. S3: Segment charging records by electricity price period; The peak-valley electricity segmentation module receives charging session records, compares them with electricity price time period types and electricity price time period parameters, and generates charging statistics results segmented by electricity price time period; S4: Summarize charging time and generate a peak-valley electricity utilization statistics report.

2. The method for intelligent identification and early warning of peak and off-peak electricity consumption for charging data of new energy heavy-duty trucks as described in claim 1, characterized in that: In step S1, the electricity price period type includes four types of electricity price periods: peak electricity, off-peak electricity, flat electricity, and peak electricity. The electricity price period parameters include time interval, applicable date type, and electricity price. The applicable date type includes weekdays, holidays, and all dates. The time interval includes start time and end time. Multiple time intervals can be configured for the same type of electricity price period. After configuration, the time period configuration module performs overlap verification on the time intervals of each electricity price period. If there is overlap, it intercepts and saves the data and outputs a conflict alarm; if there is no overlap, it stores the electricity price period parameters in the database.

3. The method for intelligent identification and early warning of peak and off-peak electricity consumption for charging data of new energy heavy-duty trucks as described in claim 1, characterized in that: In step S2, the charging gun status signal includes 0 and 1; the charging status sensing module maintains the charging working status of the corresponding vehicle according to the vehicle VIN, the charging working status includes idle state and charging state, and the initial value of the charging working status of each vehicle is idle state. The specific method for obtaining it is as follows: The battery management system deployed inside the vehicle stores the charging gun disconnection voltage range and the charging gun connection voltage range in advance. The battery management system collects the voltage of the CC2 pin in the vehicle's charging socket in real time and determines the charging gun status based on the voltage: When the voltage is within the voltage range where the charging gun is disconnected, the charging gun is determined to be in the unplugged state; when the voltage is within the voltage range where the charging gun is connected, the charging gun is determined to be in the plugged-in state. Otherwise, the connection is considered faulty and an error message is output. The battery management system collects the battery SOC value in real time and the charging current value in real time through the current sampler. According to the preset collection cycle, the timestamps corresponding to the charging gun status, charging current value, and battery SOC value are encapsulated into CAN bus messages and sent to the CAN bus. The vehicle-mounted T-BOX reads and parses CAN bus messages from the CAN bus, converting the charging gun status into a charging gun status signal. If the charging gun is unplugged, the charging gun status signal is 0; if the charging gun is plugged in, the charging gun status signal is 1. The charging gun status signal, charging current value, battery SOC value, and timestamp are reported to the charging status sensing module according to the acquisition cycle.

4. The method for intelligent identification and early warning of peak and off-peak electricity consumption for charging data of new energy heavy-duty trucks as described in claim 1, characterized in that: In step S2, the charging status sensing module caches the historical charging gun status signal from the previous collection cycle according to the vehicle VIN, and determines whether the vehicle has entered the charging state or the charging end state. The specific determination method is as follows: The vehicle is considered to be in charging mode when it is in an idle charging state and the following conditions are met: (1) The historical charging gun status signal of the previous acquisition cycle is 0 and the charging gun status signal of the current acquisition cycle is 1; (2) In the next acquisition cycle after the above state transition is detected, the charging gun state signal remains at 1 and the charging current value is greater than the preset current threshold. When a vehicle is determined to be in charging mode, the charging status corresponding to that vehicle is switched from idle mode to charging mode. The vehicle is considered to have entered the charging completion state when it is currently charging and meets any of the following conditions: (1) The historical charging gun status signal is 1 and the charging gun status signal of the current acquisition period is 0; (2) Starting from the current acquisition cycle, the charging current value is lower than the preset current threshold for N consecutive preset acquisition cycles; N is a preset positive integer; When a vehicle is determined to have entered the charging end state, the charging working state of the vehicle is switched from charging state to idle state, a charging session record is generated and transmitted to the peak-valley power switching module. The charging session record includes the timestamp T_start when the charging state starts, the timestamp T_end when the charging state ends, and the battery SOC value sampling sequence corresponding to this charging session record. The battery SOC value sampling sequence corresponding to this charging session record is a sequence consisting of each timestamp from timestamp T_start to timestamp T_end and the corresponding battery SOC value.

5. The method for intelligent identification and early warning of peak and off-peak electricity consumption for charging data of new energy heavy-duty trucks as described in claim 1, characterized in that: In step S3, the peak-valley power splitting module receives the encapsulated charging session record and reads the timestamp T_start of the charging state start time, the timestamp T_end of the charging state end time, and the battery SOC value sampling sequence corresponding to this charging session record. Obtain the start and end times corresponding to the four types of electricity price periods configured in step S1, and compare them with the timestamps T_start and T_end respectively. Filter out the start and end times between the timestamps T_start and T_end, excluding start or end times that are the same as the timestamps T_start and T_end. Sort the selected start and end times by time, and combine them with the timestamps T_start and T_end to form a segmented time sequence; The two adjacent timestamps in the segmented time sequence are sequentially combined to form a sub-time interval. The sub-time interval is compared with the time intervals of the four types of electricity price periods configured in step S1 to obtain the electricity price period type of each sub-time interval. Linear interpolation is performed on the battery SOC value sampling sequence to obtain the battery SOC value at the start and end times of each sub-time interval; the charging duration and battery SOC increment of each sub-time interval are obtained. The charging duration and battery SOC increment corresponding to each sub-time interval are accumulated according to the electricity price period type to which the sub-time interval belongs. The accumulated charging duration and battery SOC increment are written into the statistical fields of each electricity price period corresponding to this charging session record to form the charging statistics results split by electricity price period.

6. The method for intelligent identification and early warning of peak and off-peak electricity consumption for charging data of new energy heavy-duty trucks as described in claim 1, characterized in that: In step S4, the statistical analysis module obtains the preset statistical period and summarizes the charging time, battery SOC increment, and total charging time of the vehicle corresponding to each electricity price period in the charging session records within the statistical period. Based on the vehicle VIN code, the total charging time and battery SOC increment corresponding to each electricity price period type in each vehicle charging status event are summarized. Based on the charging time and battery SOC increment corresponding to each electricity price period type, the percentage of peak charging time, the percentage of off-peak charging time, the percentage of flat charging time, and the percentage of peak charging time are obtained. Fill the above fields into the preset peak-valley electricity utilization statistics report template to generate the peak-valley electricity utilization statistics report.

7. The method for intelligent identification and early warning of peak and off-peak electricity consumption for charging data of new energy heavy-duty trucks as described in claim 4, characterized in that: When the vehicle is detected to be in charging mode, the charging status sensing module generates a charging status message in real time and transmits it to the peak power warning module. The charging status message includes the timestamp T_start of the charging state start time, the battery SOC value at the start time of the charging state, and the vehicle VIN code. When the peak power warning module receives the charging status message, it parses it to obtain the timestamp T_start of the status start time, the battery SOC value at the start time of the charging status, and the vehicle VIN code. The system reads the electricity price period parameters from the database in real time and compares the timestamp T_start with the time interval corresponding to peak electricity. If the timestamp T_start is not within the time interval corresponding to peak electricity, no warning is triggered; if the timestamp T_start is within the time interval corresponding to peak electricity, the following warning is triggered: The peak power warning module retrieves information such as the driver's name, charging pile number, and license plate number bound to the vehicle's VIN code from the system's vehicle file database based on the received vehicle VIN code. It then fills the retrieved information, along with the electricity price corresponding to the peak power, the timestamp T_start of the charging state start time, the battery SOC value at the start of the charging state, and the vehicle VIN code, into a preset warning notification text template to obtain the warning notification text. Call the API interface to push the warning notification text to the corresponding fleet manager.

8. A smart identification and early warning system for peak and off-peak electricity charging data of new energy heavy-duty trucks for implementing the method of any one of claims 1-7, characterized in that: It includes a time period configuration module, a charging status sensing module, a peak power warning module, a peak-valley power segmentation module, and a statistical analysis module.

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