Commercial vehicle battery residual value assessment and grade utilization sorting method based on multi-dimensional data

Abstract

This invention provides a method for assessing the residual value of commercial vehicle batteries and sorting them for secondary use based on multi-dimensional data; it includes the following sub-steps: S1: Collecting multi-source data and preprocessing it; S2: Estimating the battery health status and generating a comprehensive value score; S3: Sorting retired commercial vehicle batteries for secondary use; S4: Outputting a battery health report; This invention achieves multi-dimensional, accurate, and quantitative assessment of battery residual value, providing objective and verifiable value evidence for second-hand transactions, ensuring that transaction prices match the actual condition of the battery, reducing disputes between buyers and sellers, and promoting the standardized development of the second-hand electric commercial vehicle trading market; it significantly improves sorting efficiency, saves on equipment, manpower, and transportation costs for laboratory testing, and enables rapid online sorting of retired batteries; it identifies potential safety hazards such as historical faults, avoiding safety risks in secondary use from the source; it matches battery secondary use scenarios at different levels, explores the remaining value of batteries, and improves the efficiency of power battery resource recycling.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle and power battery life cycle management technology, and in particular to a method for assessing the residual value of commercial vehicle batteries and sorting them for secondary use based on multi-dimensional data. Background Technology

[0002] With the continuous advancement of green and low-carbon development, the new energy electric commercial vehicle industry has entered a period of rapid development, with the market share of various electric commercial vehicles such as logistics vehicles, heavy trucks, sanitation vehicles, and buses growing rapidly. As the core component of electric commercial vehicles, the full life cycle management of power batteries has become a key link in the industry's development. The residual value assessment of power batteries and their efficient tiered utilization after retirement are core bottlenecks that urgently need to be overcome in the current industry development, directly affecting the healthy development of the used electric commercial vehicle trading market and the resource recycling efficiency of the power battery industry. In the practical application of residual value assessment and secondary utilization of power batteries, existing technologies and market models have many shortcomings, making it difficult to meet the needs of high-quality development in the industry. Specific problems are as follows: The valuation of used electric commercial vehicle batteries lacks scientific basis: Traditional battery residual value valuation methods rely only on coarse-grained basic indicators such as mileage and years of use, which cannot accurately reflect the true health status of the power battery. This leads to serious distortion in the transaction price of used electric commercial vehicle batteries, frequent disputes between buyers and sellers, and restricts the standardized development of the used electric commercial vehicle trading market. The screening of retired batteries for secondary use is inefficient, costly, and poses significant safety risks: Current screening of retired batteries for secondary use mainly relies on traditional methods such as laboratory capacity testing and internal resistance measurement. These methods are not only inefficient and costly in terms of manpower and equipment, but also fail to effectively identify potential safety hazards such as historical insulation failures and precursors to thermal runaway, which pose serious safety risks to subsequent secondary use. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for assessing the residual value of commercial vehicle batteries and sorting them for secondary use based on multidimensional data.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: This invention proposes a commercial vehicle battery residual value assessment and cascade utilization sorting system based on multi-dimensional data. The system includes a data acquisition module, an in-vehicle intelligent terminal T-Box, a battery health status estimation model, and a report generation module. The data acquisition module is used to collect multi-source data in real time, including battery data, vehicle usage data, and driving behavior data. The in-vehicle intelligent terminal T-Box is used to receive multi-source data and perform data parsing, data statistical analysis, location and climate data correlation and matching, etc. The battery health status estimation model is used to estimate the battery health status. The report generation module is used to generate battery health check reports.

[0005] This invention also proposes a method for assessing the residual value of commercial vehicle batteries and sorting them for secondary use based on multi-dimensional data, including the following sub-steps: S1: Collect multi-source data and perform preprocessing; The data acquisition module collects multi-source data in real time, including battery data, vehicle usage data, and driving behavior data. Battery data: Battery data is collected from the Battery Management System (BMS) via a data interaction interface. The battery data includes charge / discharge cycle count, cumulative charge / discharge capacity, battery SOC jump frequency, number of serious regulatory alarms, standard deviation of individual cell voltage consistency and maximum voltage difference of individual cells, historical temperature and duration, number of minor alarms, number of moderate alarms, etc. The number of charge-discharge cycles includes the number of full charge-discharge cycles and the number of shallow charge-shallow discharge cycles; The statistics for the critical alarm items mentioned in the regulations include the number of critical level alarms defined in the national standard GB / T 32960, including the number of insulation resistance faults, the number of battery cell / module over-temperature faults, the number of battery system over-voltage / under-voltage faults, the number of communication interruptions, and the number of BMS hardware faults. Vehicle usage data: The vehicle control unit (VCU) collects vehicle usage data in real time and transmits it to the outside via the vehicle CAN bus. The vehicle intelligent terminal (T-Box) receives vehicle usage data in real time through the vehicle's standardized CAN bus interface. Based on the vehicle usage data, the system obtains actual load rate, high load running time, overload time percentage, main operating areas, and climate data. Driving behavior data: The vehicle-mounted intelligent terminal T-Box collects vehicle transportation signals through the vehicle CAN bus. The vehicle transportation signals include real-time vehicle speed, accelerator pedal depth (percentage of accelerator pedal opening), battery remaining charge percentage (SOC), braking signals (switching signals for vehicle braking operation), and cumulative mileage.

[0006] S2: Estimate battery health status and generate a comprehensive value score; Includes the following sub-steps: S21: Estimate battery health status; The aging factors of the following four types of measured data are used as model inputs and fed into the battery health status estimation model. The battery health status estimation model outputs the battery health status. The four types of measured data include cyclic strength, thermal stress accumulation, mechanical load effect, and safety event records; The aging factor of the cycle intensity is the number of full-charge and discharge cycles, denoted as N1; The aging factor accumulated by thermal stress is the equivalent number of hours of high-temperature operation, denoted as H1; The aging factor affected by the mechanical load is the total overload duration, denoted as H2; The aging factor for the security event records is the number of critical alarms according to GB / T 32960, denoted as N2; Among them, the data acquisition module divides different high temperature ranges in advance based on the thermal aging characteristics of the power battery and configures aging weight coefficients for each high temperature range. Based on the historical temperature and duration extracted in step S1, the duration in each high temperature range is multiplied by the corresponding aging weight coefficient, and all weighted results are accumulated to obtain the equivalent high temperature operation hours, denoted as H1. The data acquisition module summarizes and sums the counts of each item in the statistical count of the serious alarm items in the regulations. The sum is the number of serious alarms, denoted as N2. The battery health state estimation model estimates the battery health state using the following formula: ; Wherein, SOH represents the battery health status, α represents the attenuation coefficient for the preset number of full charge and discharge cycles, β represents the attenuation coefficient for the preset equivalent high temperature operating hours, γ represents the attenuation coefficient for the preset total overload duration, and δ represents the attenuation coefficient for the preset number of severe alarms. If the obtained SOH is outside the preset range (e.g., [50%, 100%]), it is considered abnormal and the process is terminated; otherwise, proceed to step S22. S22: Generate a comprehensive value score; A comprehensive value score is generated based on positive factors, negative factors, and security constraints. The positive factors include driving smoothness and voltage consistency; the negative factor is differential pressure penalty. Specifically, driving smoothness: Pre-set weights for the number of rapid accelerations, the frequency of throttle use, and a range of aggressive driving behaviors are defined in advance. The range of aggressive driving behaviors is... ,in, This represents the lower bound of the aggressive value for optimal smooth driving of commercial vehicles within the known industry. This represents the upper limit of the known optimal smooth driving value for commercial vehicles within the industry; The number of rapid accelerations and the frequency of high throttle use obtained in step S1 are weighted and summed to obtain the aggressive value of commercial vehicle driving behavior. By using a linear inverse formula, the weighted aggressive value of driving behavior is... Mapped to driving stability score The specific mapping method is as follows: ; Voltage consistency: Based on measured data from the commercial vehicle power industry, a reasonable range for the standard deviation of individual unit voltage consistency is preset. ,in, To preset the minimum standard deviation of individual unit voltage consistency, The maximum standard deviation of the individual unit voltage consistency is preset. Let U be the standard deviation of the individual unit voltage consistency collected in step S1. Then, map U to the voltage consistency score using a linear inverse formula. The specific mapping method is as follows: ; Differential Pressure Penalty: A differential pressure penalty trigger threshold is preset. If the maximum differential pressure of a single cell collected in step S1 is less than the differential pressure penalty trigger threshold, the differential pressure penalty is not triggered, and the differential pressure penalty score is 0. If the maximum differential pressure of a single cell is greater than or equal to the differential pressure penalty trigger threshold, the differential pressure penalty is triggered, and the score is allocated based on the gradient calibration rule to obtain... ; The gradient calibration rules are as follows: A pre-set threshold for slight pressure differential exceedance is established. If the maximum pressure differential of a single cell is greater than or equal to the pressure differential penalty trigger threshold but less than or equal to the slight pressure differential exceedance threshold, it is judged as a slight exceedance gradient, and a pressure differential penalty score is applied. The value is set to a positive integer 'a' (e.g., 5 points); conversely, if the maximum differential pressure of a single cell exceeds the threshold for slight differential pressure exceedance, it is judged as severe exceedance, and a differential pressure penalty score is applied. The value is set to a positive integer b (e.g., 10 points). The overall value score is generated using the following formula: ; The obtained comprehensive value score is then subject to safety constraint rules. Specifically, if the number of statistical counts of the serious alarm items collected in step S1 is not 0, the upper limit of the comprehensive value score Score is forcibly limited to a pre-set positive integer c (70 points). If the calculated score is higher than the positive integer c, the positive integer c is directly taken as the comprehensive value score of the battery. If the calculated score is less than or equal to the positive integer c, the original calculated score remains unchanged. If the calculated score is lower than 0 points, it is uniformly counted as 0 points. If the calculated score is higher than 100 points, it is uniformly counted as 100 points.

[0007] S3: Sorting retired commercial vehicle batteries for secondary use; Includes the following sub-steps: S31: Conduct initial safety screening; If the battery health status (SOH) obtained in step S21 reaches the set power battery retirement threshold, then check the three indicators of the battery one by one. The three indicators include historical safety record, battery consistency indicator, and SOH stability indicator. The historical safety record corresponds to the number of statistical counts of the serious regulatory alarm items collected in step S1, the battery consistency index corresponds to the maximum differential pressure of a single cell, and the SOH stability index corresponds to the standard deviation of the battery health state SOH fluctuation within the evaluation period. If any of the following conditions are met: (1) The number of times a serious alarm item is recorded according to regulations is greater than or equal to 1; (2) The maximum differential pressure of a single unit is greater than or equal to the differential pressure penalty trigger threshold; (3) The standard deviation of the battery health status (SOH) fluctuation during the assessment period is greater than or equal to the set standard deviation threshold of the battery health status (SOH) fluctuation (e.g., 5%). If the battery fails the initial safety screening, the process ends; otherwise, if it passes the initial safety screening, proceed to step S32. S32: Classify utilization levels into tiers; Pre-define battery health status intervals m1, m2, m3, and comprehensive value score intervals n1, n2, n3; When a battery meets the following three conditions, its secondary utilization level is determined to be a high-value secondary utilization level. (1) The battery health state SOH is in the battery health state range m1; (2) The number of times the serious alarm item was recorded was 0; (3) The overall value score is within the overall value score range n1; When a battery meets the following three conditions, its secondary utilization level is determined to be the conventional value secondary utilization level. (1) The battery health state SOH is in the battery health state range m2; (2) The number of minor alarms is not zero; (3) The overall value score is within the overall value score range n2; A battery is classified as a material recycling grade when it meets any of the following conditions: (1) The battery health state SOH is in the battery health state range m3; (2) The number of moderate alarms is not 0.

[0008] S4: Output battery health report; The report generation module outputs a battery health check report, which includes quantity... Evaluation indicators, analysis of battery aging driving factors, safety risk warnings, and usage / secondary utilization recommendations, etc.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention overcomes the limitations of single coarse-grained indicators by collecting multi-source fine-grained data from three categories: battery body, vehicle usage, and driving behavior, covering core indicators such as charge-discharge cycles, load rate, and driving aggression index. It extracts four types of aging factors, including cycle usage intensity and thermal stress accumulation, to accurately calculate the battery's health status. Based on State of Health (SOH), and combining positive factors such as driving stability and voltage consistency with negative factors such as differential pressure penalty, and overlaying safety constraint rules, it constructs a comprehensive value scoring system to quantify the actual value of the battery. This achieves a multi-dimensional and precise quantitative assessment of battery residual value, providing objective and verifiable value evidence for second-hand transactions, ensuring that transaction prices match the actual condition of the battery, reducing disputes, and promoting the standardized development of the second-hand electric commercial vehicle trading market.

[0010] Sorting is accomplished by utilizing real-time operational data throughout the battery's entire lifecycle, replacing laboratory physical testing methods and eliminating the need for battery disassembly and offline testing. Once a battery's State of Health (SOH) reaches the retirement threshold, a preliminary safety screening is conducted based on three aspects: historical safety records, battery consistency, and SOH stability. If any indicator fails to meet the standard, the battery is excluded from potential reuse. Based on the SOH range, alarm record type, and comprehensive value score range, batteries that pass the preliminary safety screening are classified into three levels: high-value, regular-value, and material recycling, clearly defining the applicable scenarios for each level. The preliminary safety screening is conducted upfront, with only batteries that pass the screening entering the tier classification process. Simultaneously, a standardized battery health check report is generated, including quantitative indicators, aging analysis, safety tips, and utilization recommendations. Significantly improves sorting efficiency, eliminating the need for laboratory testing equipment, manpower, and transportation costs, enabling rapid online sorting of retired batteries; accurately identifies potential safety hazards such as historical faults and poor consistency, mitigating safety risks associated with secondary use from the source; tiered matching of battery secondary use scenarios maximizes the exploitation of remaining battery value and improves the efficiency of power battery resource recycling; the health check report provides comprehensive data support for secondary use decisions, promoting the scientific and standardized development of the secondary use industry. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the steps of the commercial vehicle battery residual value assessment and tiered utilization sorting method based on multidimensional data according to the present invention. Detailed Implementation

[0012] 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.

[0013] This invention proposes a commercial vehicle battery residual value assessment and cascade utilization sorting system based on multi-dimensional data. The system includes a data acquisition module, an in-vehicle intelligent terminal T-Box, and a report generation module. The data acquisition module is used to collect multi-source data in real time, including battery data, vehicle usage data, and driving behavior data. The in-vehicle intelligent terminal T-Box is used to receive multi-source data and perform data parsing, data statistical analysis, location and climate data correlation and matching, etc. The report generation module is used to generate battery health check reports.

[0014] like Figure 1 As shown, this invention also proposes a method for assessing the residual value of commercial vehicle batteries and sorting them for secondary use based on multi-dimensional data, including the following sub-steps: S1: Collect multi-source data and perform preprocessing; The data acquisition module collects multi-source data in real time, including battery data, vehicle usage data, and driving behavior data. Battery data: Battery data is collected from the Battery Management System (BMS) via a data interaction interface. The battery data includes charge / discharge cycle count, cumulative charge / discharge capacity, battery SOC jump frequency, number of serious regulatory alarms, standard deviation of individual cell voltage consistency and maximum voltage difference of individual cells, historical temperature and duration, number of minor alarms, number of moderate alarms, etc. The number of charge-discharge cycles includes the number of full charge-discharge cycles and the number of shallow charge-shallow discharge cycles; The statistics for the critical alarm items mentioned in the regulations include the number of critical level alarms defined in the national standard GB / T 32960, including the number of insulation resistance faults, the number of battery cell / module over-temperature faults, the number of battery system over-voltage / under-voltage faults, the number of communication interruptions, and the number of BMS hardware faults. Vehicle usage data: The vehicle control unit (VCU) collects vehicle usage data in real time and transmits it to the outside via the vehicle CAN bus. The vehicle intelligent terminal (T-Box) receives vehicle usage data in real time through the vehicle's standardized CAN bus interface. Based on the vehicle usage data, the system obtains actual load rate, high load running time, overload time percentage, main operating areas, and climate data. The real-time gross vehicle weight and static parameters of the vehicle are extracted from the vehicle usage data through a message parsing algorithm. The static parameters of the vehicle include the vehicle's tare weight and rated load capacity. The actual load factor of the vehicle is calculated in real time, and the actual load factor = (real-time total weight of the vehicle - vehicle tare weight) / rated load capacity; The in-vehicle intelligent terminal T-Box, based on its built-in timing and data statistics module, obtains the high-load running time and overload time percentage of the vehicle within the evaluation period (vehicle usage period, battery life cycle); Specifically, high-load running time: The actual load rate threshold is set in advance. The timing and data statistics module compares the actual load rate calculated in real time within the evaluation period with the threshold. When it is greater than or equal to the actual load rate threshold, timing is started. When it is less than the actual load rate threshold, timing is stopped. All time periods that meet the timing within the evaluation period are summed to obtain the high-load running time. Overload duration percentage: The timing and data statistics module calculates the actual running time of the vehicle within the evaluation period, and sums up the periods when the actual load rate calculated in real time within the evaluation period is greater than 100%, which is recorded as the total overload duration. The overload duration percentage is calculated as: overload duration percentage = total overload duration / actual running time of the vehicle. The in-vehicle intelligent terminal T-Box collects vehicle location data in real time through its own GPS positioning module. It uses a GPS clustering algorithm to remove scattered driving trajectories from the location data to determine the vehicle's main operating area. The main operating area is then matched with a preset climate database to obtain the climate data of the main operating area. The climate database contains the correspondence between regions and climate data. The climate data includes the average annual temperature, the number of extreme high-temperature days, etc. Driving behavior data: The vehicle-mounted intelligent terminal T-Box collects vehicle transportation signals through the vehicle-mounted CAN bus. The vehicle transportation signals include real-time vehicle speed, accelerator pedal depth (percentage of accelerator pedal opening), battery remaining charge percentage (SOC), braking signals (switching signals for vehicle braking operation), and cumulative mileage. The in-vehicle intelligent terminal T-Box uses a built-in data processing module to calculate derived behavioral data based on vehicle transportation signals. This derived behavioral data includes low battery driving range, number of rapid accelerations, number of rapid decelerations / brakes, frequency of high accelerator use, and driving aggression index. Specifically, the data processing module monitors the battery's remaining charge percentage (SOC) in real time. When the SOC is less than a preset low charge threshold, it is determined to be in a low charge state; otherwise, it is not in a low charge state. The module also calculates the driving mileage corresponding to the low charge state in real time and sums the results to obtain the low charge driving mileage. The data processing module calculates the real-time rate of change of vehicle speed based on real-time vehicle speed statistics, and calculates the instantaneous acceleration of the vehicle based on the real-time rate of change and the corresponding time. When the instantaneous acceleration is detected to be greater than the preset acceleration threshold, it is determined as a rapid acceleration event; otherwise, it is not a rapid acceleration event. The sum of all rapid acceleration events that meet the conditions within the evaluation period is used to obtain the number of rapid acceleration events. When the real-time vehicle speed is greater than the set vehicle speed and the braking signal is 1, and the instantaneous acceleration is less than the preset negative acceleration threshold, it is determined as one emergency deceleration / emergency braking event; otherwise, it is a non-emergency deceleration / emergency braking event. The sum of all emergency deceleration / emergency braking events that meet the conditions within the evaluation period is counted to obtain the number of emergency deceleration / emergency braking events. The data processing module monitors the accelerator pedal depth in real time. When the accelerator pedal depth exceeds a set percentage, it is determined to be in a high accelerator pedal usage state. The cumulative duration of the high accelerator pedal usage state is counted, and the high accelerator pedal usage frequency is calculated as follows: High accelerator pedal usage frequency = (cumulative duration of high accelerator pedal usage state / actual vehicle running time) * 100%. The data processing module assigns preset weights to low battery driving range, number of rapid accelerations, number of rapid decelerations / brakes, and frequency of heavy accelerator use. The final score is obtained by accumulating the weights, and is recorded as the driving aggression index.

[0015] S2: Estimate battery health status and generate a comprehensive value score; Includes the following sub-steps: S21: Estimate battery health status; The aging factors of the following four types of measured data are used as model inputs and fed into the battery health status estimation model. The battery health status estimation model outputs the battery health status. The four types of data include cyclic use intensity, thermal stress accumulation, mechanical load effects, and safety event records; The aging factor of the cycle intensity is the number of full-charge and discharge cycles, denoted as N1; The aging factor accumulated by thermal stress is the equivalent number of hours of high-temperature operation, denoted as H1; The aging factor affected by the mechanical load is the total overload duration, denoted as H2; The aging factor for the security event records is the number of critical alarms according to GB / T 32960, denoted as N2; Among them, the data acquisition module divides different high temperature ranges in advance based on the thermal aging characteristics of the power battery and configures aging weight coefficients for each high temperature range. Based on the historical temperature and duration extracted in step S1, the duration in each high temperature range is multiplied by the corresponding aging weight coefficient, and all weighted results are accumulated to obtain the equivalent high temperature operation hours, denoted as H1. The data acquisition module summarizes and sums the counts of each item in the statistical count of the serious alarm items in the regulations. The sum is the number of serious alarms, denoted as N2. The battery health state estimation model estimates the battery health state using the following formula: ; Wherein, SOH represents the battery health status, α represents the attenuation coefficient for the preset number of full charge and discharge cycles, β represents the attenuation coefficient for the preset equivalent high temperature operating hours, γ represents the attenuation coefficient for the preset total overload duration, and δ represents the attenuation coefficient for the preset number of severe alarms. If the obtained SOH is outside the preset range (e.g., [50%, 100%]), it is considered abnormal and the process is terminated; otherwise, proceed to step S22. S22: Generate a comprehensive value score; A comprehensive value score is generated based on positive factors, negative factors, and security constraints. The positive factors include driving smoothness and voltage consistency; the negative factor is differential pressure penalty. Specifically, driving smoothness: Pre-set weights for the number of rapid accelerations, the frequency of throttle use, and a range of aggressive driving behaviors are defined in advance. The range of aggressive driving behaviors is... ,in, This represents the lower bound of the aggressive value for optimal smooth driving of commercial vehicles within the known industry. This represents the upper limit of the known optimal smooth driving value for commercial vehicles within the industry; The number of rapid accelerations and the frequency of high throttle use obtained in step S1 are weighted and summed to obtain the aggressive value of commercial vehicle driving behavior. By using a linear inverse formula, the weighted aggressive value of driving behavior is... Mapped to driving stability score The specific mapping method is as follows: ; Voltage consistency: Based on measured data from the commercial vehicle power industry, a reasonable range for the standard deviation of individual unit voltage consistency is preset. ,in, To preset the minimum standard deviation of individual unit voltage consistency, The maximum standard deviation of the individual unit voltage consistency is preset. Let U be the standard deviation of the individual unit voltage consistency collected in step S1. Then, map U to the voltage consistency score using a linear inverse formula. The specific mapping method is as follows: ; Differential Pressure Penalty: A differential pressure penalty trigger threshold is preset. If the maximum differential pressure of a single cell collected in step S1 is less than the differential pressure penalty trigger threshold, the differential pressure penalty is not triggered, and the differential pressure penalty score is 0. If the maximum differential pressure of a single cell is greater than or equal to the differential pressure penalty trigger threshold, the differential pressure penalty is triggered, and the score is allocated based on the gradient calibration rule to obtain... ; The gradient calibration rules are as follows: A pre-set threshold for slight pressure differential exceedance is established. If the maximum pressure differential of a single cell is greater than or equal to the pressure differential penalty trigger threshold but less than or equal to the slight pressure differential exceedance threshold, it is judged as a slight exceedance gradient, and a pressure differential penalty score is applied. The value is set to a positive integer 'a' (e.g., 5 points); conversely, if the maximum differential pressure of a single cell exceeds the threshold for slight differential pressure exceedance, it is judged as severe exceedance, and a differential pressure penalty score is applied. The value is set to a positive integer b (e.g., 10 points). The overall value score is generated using the following formula: ; The obtained comprehensive value score is then subject to safety constraint rules. Specifically, if the number of statistical counts of the serious alarm items collected in step S1 is not 0, the upper limit of the comprehensive value score Score is forcibly limited to a pre-set positive integer c (70 points). If the calculated score is higher than the positive integer c, the positive integer c is directly taken as the comprehensive value score of the battery. If the calculated score is less than or equal to the positive integer c, the original calculated score remains unchanged. If the calculated score is lower than 0 points, it is uniformly counted as 0 points. If the calculated score is higher than 100 points, it is uniformly counted as 100 points.

[0016] S3: Sorting retired commercial vehicle batteries for secondary use; Includes the following sub-steps: S31: Conduct initial safety screening; If the battery health status (SOH) obtained in step S21 reaches the set power battery retirement threshold, then check the three indicators of the battery one by one. The three indicators include historical safety record, battery consistency indicator, and SOH stability indicator. The historical safety record corresponds to the number of statistical counts of the serious regulatory alarm items collected in step S1, the battery consistency index corresponds to the maximum differential pressure of a single cell, and the SOH stability index corresponds to the standard deviation of the battery health state SOH fluctuation within the evaluation period. Specifically, the standard deviation of the battery health state (SOH) fluctuation within the assessment period is calculated by simultaneously calculating all values ​​of the battery health state (SOH) within the assessment period according to the method in step S21, and then calculating the standard deviation of the battery health state (SOH) fluctuation within the assessment period using the standard deviation calculation formula. If any of the following conditions are met: The number of serious alarm items according to regulations is greater than or equal to 1. The maximum differential pressure of a single unit is greater than or equal to the differential pressure penalty trigger threshold; The standard deviation of battery health status (SOH) fluctuation during the assessment period is greater than or equal to the set standard deviation threshold of battery health status (SOH) fluctuation (e.g., 5%). If the battery fails the initial safety screening, the process ends; otherwise, if it passes the initial safety screening, proceed to step S32. S32: Classify utilization levels into tiers; Pre-define battery health status intervals m1, m2, m3, and comprehensive value score intervals n1, n2, n3; For example, m1 is [75%, 80%], m2 is [70%, 75%], m3 is [0, 70%]; n1 is [85, 100], n2 is [70, 84], n3 is [0, 70]; When a battery meets the following three conditions, its secondary utilization level is determined to be a high-value secondary utilization level. (1) The battery health state SOH is in the battery health state range m1; (2) The number of times the serious alarm item was recorded was 0; (3) The overall value score is within the overall value score range n1; When a battery meets the following three conditions, its secondary utilization level is determined to be the conventional value secondary utilization level. (1) The battery health state SOH is in the battery health state range m2; (2) The number of minor alarms is not zero; (3) The overall value score is within the overall value score range n2; A battery is classified as a material recycling grade when it meets any of the following conditions: (1) The battery health state SOH is in the battery health state range m3; (2) The number of moderate alarms is not 0.

[0017] S4: Output battery health report; The report generation module outputs a battery health check report, which includes quantity... Evaluation indicators, analysis of driving factors of battery aging, safety risk warnings, and recommendations for use / secondary utilization; The quantitative evaluation indicators include the battery's state of health (SOH) and the battery's overall value assessment. The metrics include: Score, standard deviation of individual cell voltage consistency, maximum voltage difference, driving aggression index, and percentage of overload duration. The analysis of battery aging driving factors includes sorting aging factors by contribution, with the contribution from high to low as primary aging factor, secondary aging factor, inducing factor 1, and inducing factor 2. Calculate the strength decay value during cyclic use, the cumulative thermal stress decay value, the mechanical load effect decay value, and the safety event record decay value; Among them, the intensity decay value of repeated use is The cumulative thermal stress attenuation value is The attenuation value due to mechanical load Security event log decay value ; Total attenuation value ; The contribution of each aging factor is the corresponding decay value / total decay value; The safety risk warnings include the number of serious alarms from regulations, whether the maximum differential pressure of a single cell is greater than or equal to the differential pressure penalty trigger threshold, and whether the standard deviation of the battery health state (SOH) fluctuation is greater than or equal to the set standard deviation threshold for the battery health state (SOH) fluctuation. The recommended use / tiered utilization is as follows: When a vehicle is classified as having a high-value tiered utilization level, the recommendation is: no significant safety risks, suitable for continued use in commercial vehicles; When the battery is determined to be of conventional value and suitable for secondary use, the following recommendations are made: the battery has reached the retirement threshold but has passed the initial safety screening, and the corresponding secondary use scenario is matched according to the classification results. When the battery is determined to be at the material recycling level, the following recommendations are made: If the battery fails the initial safety screening (e.g., there is a serious alarm or excessive differential pressure) or there is a moderate safety risk, it is recommended to recycle the material.

[0018] 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 commercial vehicle battery residual value assessment and tier utilization sorting based on multi-dimensional data, characterized in that: Includes the following steps: S1: Collect multi-source data and perform preprocessing; The data acquisition module collects multi-source data in real time, including battery data, vehicle usage data, and driving behavior data. S2: Estimate battery health status and generate a comprehensive value score; S21: Estimate battery health status; The aging factor obtained from the measured data based on multi-source data is used as the model input and fed into the battery health status estimation model. The battery health status estimation model outputs the battery health status. S22: Generate a comprehensive value score; Based on positive factors, negative factors, and safety constraints, driving smoothness scores, voltage consistency scores, and differential pressure penalty scores are obtained; and a comprehensive value score is generated. S3: Sorting retired commercial vehicle batteries for secondary use; S31: Conduct initial safety screening; S32: Classify utilization levels into tiers; The tiered utilization levels include high-value tiered utilization level, conventional-value tiered utilization level, and material recycling level; S4: Output battery health report.

2. The commercial vehicle battery residual value assessment and tiered utilization sorting method based on multidimensional data as described in claim 1, characterized in that: In step S1, battery body data: battery body data is collected from the battery management system (BMS) through the data interaction interface. The battery body data includes the number of charge-discharge cycles, the number of statistical counts of serious regulatory alarms, the standard deviation of single cell voltage consistency and the maximum voltage difference of single cells, historical temperature and duration, the number of minor alarms, and the number of moderate alarms. The number of charge-discharge cycles includes the number of full charge-discharge cycles and the number of shallow charge-shallow discharge cycles; Vehicle usage data: The vehicle control unit (VCU) collects vehicle usage data in real time and transmits it to the outside via the vehicle CAN bus. The vehicle intelligent terminal (T-Box) receives vehicle usage data in real time through the vehicle's standardized CAN bus interface. Based on the vehicle usage data, the system obtains actual load rate, high load running time, overload time percentage, main operating areas, and climate data. Driving behavior data: The vehicle-mounted intelligent terminal T-Box collects vehicle transportation signals via the vehicle CAN bus. The vehicle transportation signals include real-time vehicle speed, accelerator pedal depth, battery remaining charge percentage (SOC), braking signals, and cumulative mileage. The in-vehicle intelligent terminal T-Box uses a built-in data processing module to calculate derived behavioral data based on vehicle transportation signals. This derived behavioral data includes low battery driving range, number of rapid accelerations, number of rapid decelerations / brakes, frequency of high accelerator usage, and driving aggression index.

3. The commercial vehicle battery residual value assessment and tiered utilization sorting method based on multidimensional data as described in claim 1, characterized in that: In step S21, the four types of data include cyclic use intensity, thermal stress accumulation, mechanical load effect, and safety event records; The aging factor of the cycle intensity is the number of full-charge and discharge cycles, denoted as N1; The aging factor accumulated by thermal stress is the equivalent number of hours of high-temperature operation, denoted as H1; The aging factor affected by the mechanical load is the total overload duration, denoted as H2; The aging factor for the security event records is the number of critical alarms according to GB / T 32960, denoted as N2; Among them, the data acquisition module divides different high temperature ranges in advance based on the thermal aging characteristics of the power battery and configures aging weight coefficients for each high temperature range. Based on the historical temperature and duration extracted in step S1, the duration in each high temperature range is multiplied by the corresponding aging weight coefficient, and all weighted results are accumulated to obtain the equivalent high temperature operation hours, denoted as H1. The data acquisition module summarizes and sums the counts of each item in the statistical count of the serious alarm items in the regulations. The sum is the number of serious alarms, denoted as N2. The battery health state estimation model estimates the battery health state using the following formula: ； Wherein, SOH represents the battery health status, α represents the attenuation coefficient for the preset number of full charge and discharge cycles, β represents the attenuation coefficient for the preset equivalent high temperature operating hours, γ represents the attenuation coefficient for the preset total overload duration, and δ represents the attenuation coefficient for the preset number of severe alarms. If the obtained SOH is outside the preset range, an abnormality is determined and the process is terminated; otherwise, proceed to step S22.

4. The commercial vehicle battery residual value assessment and tiered utilization sorting method based on multi-dimensional data as described in claim 1, characterized in that: In step S22, a comprehensive value score is generated based on positive factors, negative factors, and security constraints; The positive factors include driving smoothness and voltage consistency; the negative factor is differential pressure penalty. Driving smoothness: presetting the number of sudden accelerations, the preset weight of the frequency of using the accelerator pedal, and the driving behavior aggressiveness value interval, which is wherein, is the known industry commercial vehicle optimal smooth driving aggressiveness value lower limit, is the known industry commercial vehicle optimal smooth driving aggressiveness value upper limit; The number of rapid accelerations and the frequency of high throttle use obtained in step S1 are weighted and summed to obtain the aggressive value of commercial vehicle driving behavior. By using a linear inverse formula, the weighted aggressive value of driving behavior is... Mapped to driving stability score The specific mapping method is as follows: ； Voltage consistency: Based on measured data from the commercial vehicle power industry, a reasonable range for the standard deviation of individual unit voltage consistency is preset. ,in, To preset the minimum standard deviation of individual unit voltage consistency, The maximum standard deviation of the individual unit voltage consistency is preset. Let U be the standard deviation of the individual unit voltage consistency collected in step S1. Then, map U to the voltage consistency score using a linear inverse formula. The specific mapping method is as follows: ； Differential Pressure Penalty: A differential pressure penalty trigger threshold is preset. If the maximum differential pressure of a single cell collected in step S1 is less than the differential pressure penalty trigger threshold, the differential pressure penalty is not triggered, and the differential pressure penalty score is 0. If the maximum differential pressure of a single cell is greater than or equal to the differential pressure penalty trigger threshold, the differential pressure penalty is triggered, and the score is allocated based on the gradient calibration rule to obtain... ; The gradient calibration rules are as follows: A pre-set threshold for slight pressure differential exceedance is established. If the maximum pressure differential of a single cell is greater than or equal to the pressure differential penalty trigger threshold but less than or equal to the slight pressure differential exceedance threshold, it is judged as a slight exceedance gradient, and a pressure differential penalty score is applied. The value is set to a positive integer 'a'; conversely, if the maximum differential pressure of a single cell exceeds the threshold for slight differential pressure exceedance, it is judged as a severe exceedance gradient, and a differential pressure penalty score is applied. The value is set to a positive integer b. The overall value score is generated using the following formula: ； The obtained comprehensive value score is then subject to safety constraint rules. Specifically, if the number of statistical counts of the serious alarm items collected in step S1 is not 0, the upper limit of the comprehensive value score Score is forcibly limited to a pre-set positive integer c. If the calculated score is higher than the positive integer c, the positive integer c is directly taken as the comprehensive value score of the battery. If the calculated score is less than or equal to the positive integer c, the original calculated score remains unchanged. Furthermore, if the calculated score is lower than 0, it is uniformly counted as 0 points. If the calculated score is higher than 100 points, it is uniformly counted as 100 points.

5. The commercial vehicle battery residual value assessment and tiered utilization sorting method based on multi-dimensional data as described in claim 1, characterized in that: The specific details of step S3 are as follows: S31: Conduct initial safety screening; If the battery health status (SOH) obtained in step S21 reaches the set power battery retirement threshold, then check the three indicators of the battery one by one. The three indicators include historical safety record, battery consistency indicator, and SOH stability indicator. The historical safety record corresponds to the number of statistical counts of the serious regulatory alarm items collected in step S1, the battery consistency index corresponds to the maximum differential pressure of a single cell, and the SOH stability index corresponds to the standard deviation of the battery health state SOH fluctuation within the evaluation period. Standard deviation of battery health status (SOH) fluctuation within the assessment period: All values ​​of battery health status (SOH) within the assessment period are calculated synchronously according to the method in step S21, and the standard deviation of battery health status (SOH) fluctuation within the assessment period is obtained by calculating all values ​​using the standard deviation calculation formula. If any of the following conditions are met: The number of serious alarm items according to regulations is greater than or equal to 1. The maximum differential pressure of a single unit is greater than or equal to the differential pressure penalty trigger threshold; The standard deviation of battery health status (SOH) fluctuation during the assessment period is greater than or equal to the set standard deviation threshold for battery health status (SOH) fluctuation. If the battery fails the initial safety screening, the process ends. Conversely, if the initial safety screening is passed, proceed to step S32; S32: Classify utilization levels into tiers; Pre-define battery health status intervals m1, m2, m3, and comprehensive value score intervals n1, n2, n3; When a battery meets the following three conditions, its secondary utilization level is determined to be a high-value secondary utilization level. The battery health state SOH is within the battery health state range m1; The number of serious alarms under regulations was 0. The overall value score falls within the overall value score range n1; When a battery meets the following three conditions, its secondary utilization level is determined to be the conventional value secondary utilization level. The battery health state (SOH) is within the battery health state range m2; The number of minor alarms is not zero; The overall value score falls within the overall value score range n2; When a battery meets any of the following conditions, its cascade utilization level is determined to be a material recycling level; (1) The battery health state SOH is in the battery health state range m3; The number of moderate alarms is not zero.

6. The commercial vehicle battery residual value assessment and tiered utilization sorting method based on multidimensional data as described in claim 1, characterized in that: The report generation module outputs a battery health check report, which includes quantity... Evaluation indicators, analysis of driving factors of battery aging, safety risk warnings, and recommendations for use / secondary utilization; The quantitative evaluation indicators include the battery's state of health (SOH) and the battery's overall value assessment. Components include: Score, standard deviation of individual voltage consistency, maximum voltage difference, driving aggression index, and percentage of overload duration; The analysis of battery aging driving factors includes sorting aging factors by contribution, with the contribution from high to low as primary aging factor, secondary aging factor, inducing factor 1, and inducing factor 2. Calculate the strength decay value during cyclic use, the cumulative thermal stress decay value, the mechanical load effect decay value, and the safety event record decay value; Among them, the intensity decay value of repeated use is The cumulative thermal stress attenuation value is The attenuation value due to mechanical load Security event log decay value ; Total attenuation value ; The contribution of each aging factor is the corresponding decay value / total decay value; The safety risk warnings include the number of serious alarms from regulations, whether the maximum differential pressure of a single cell is greater than or equal to the differential pressure penalty trigger threshold, and whether the standard deviation of the battery health state (SOH) fluctuation is greater than or equal to the set standard deviation threshold for battery health state (SOH) fluctuation. The recommended use / tiered utilization is as follows: When a vehicle is classified as having a high-value tiered utilization level, the recommendation is: no significant safety risks, suitable for continued use in commercial vehicles; When the battery is determined to be of conventional value and suitable for secondary use, the following recommendations are made: the battery has reached the retirement threshold but has passed the initial safety screening, and the corresponding secondary use scenario is matched according to the classification results. When the battery is determined to be at the material recycling level, the following recommendation is made: the battery failed the initial safety screening or poses a moderate safety risk, and material recycling is recommended.

7. A commercial vehicle battery residual value assessment and cascade utilization sorting system based on multidimensional data for implementing the method of any one of claims 1-6, characterized in that: The system includes a data acquisition module, an in-vehicle intelligent terminal T-Box, and a report generation module; The data acquisition module is used to collect multi-source data in real time, including battery data, vehicle usage data, and driving behavior data. The in-vehicle intelligent terminal T-Box is used to receive multi-source data and perform data parsing, data statistical analysis, and location and climate data correlation matching. The report generation module is used to generate battery health check reports.

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