New energy automobile battery pack operation management method and device and storage medium

Abstract

The invention relates to the technical field of battery management, in particular to a new energy automobile battery pack operation management method and device and a storage medium, and the method comprises the steps: collecting the driving data of a new energy automobile; analyzing a driving behavior parameter according to the driving data, and performing weighted fusion analysis on the driving behavior parameter to obtain a driving load parameter; collecting battery data when the new energy automobile is charged; analyzing the parameters before charging according to the battery data, and generating a charging tolerance index in combination with the driving load parameters and the parameters before charging; establishing a hierarchical management model based on the charging tolerance index and the pre-charging parameter to generate a charging regulation and control strategy; the driving data and the battery data are stored to generate automobile link data, and a thermal recovery rule and a calibration update rule are triggered according to the automobile link data. Accurate management of battery charging based on the driving data and the battery operation data is realized.

Description

Technical Field

[0001] The present invention relates to the field of battery management technology, and in particular to a method, device and storage medium for managing the operation of a battery pack of a new energy vehicle. Background Art

[0002] With the world's increasing attention to environmental protection and sustainable development, new energy vehicles have become an important development direction in the transportation sector. As the core component of new energy vehicles, the performance and life of batteries are directly related to the vehicle's endurance and cost of use. However, the popularization of fast charging technology, while improving user experience, also puts higher demands on battery management. How to reduce the damage to batteries caused by fast charging while ensuring charging efficiency has become a key issue that the industry urgently needs to solve. It is in this context that the present invention proposes a dynamic management method based on driving data and battery operation data, which aims to achieve precise regulation of battery charging and provide technical support for the widespread application of new energy vehicles.

[0003] In existing technologies, new energy vehicle battery charging management usually formulates charging strategies based on static parameters, failing to fully consider the real-time impact of driving behavior on the battery. In addition, the temperature changes and heat accumulation problems during fast charging are not effectively solved, resulting in shortened battery life or safety hazards. Traditional methods also lack dynamic analysis of historical data and cannot adjust management strategies according to actual usage, limiting the accuracy and adaptability of battery management. Summary of the Invention

[0004] The object of the present invention is to provide a method, device and storage medium for operating and managing a battery pack of a new energy vehicle, so as to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A new energy vehicle battery pack operation management method, comprising: Analyze driving behavior parameters based on driving data and perform weighted fusion analysis on the driving behavior parameters to obtain driving load parameters; Analyze pre-charging parameters based on battery data and generate a charging tolerance index by combining driving load parameters and pre-charging parameters; A hierarchical management model is established based on the charging tolerance index and pre-charging parameters to generate a charging control strategy; Driving data and battery data are stored to generate vehicle link data, and thermal recovery rules and calibration update rules are triggered based on the vehicle link data.

[0006] Preferably, driving data of the new energy vehicle is collected, the standard deviation of the steering wheel torque per second is used as the torque standard deviation parameter, and the ratio of the average value of the torque standard deviation parameter in the driving cycle to the maximum value of the torque standard deviation parameter in the driving cycle is used as the torque parameter; The ratio of the standard deviation of the vehicle acceleration during the driving cycle to the average value of the vehicle acceleration during the driving cycle is used as the acceleration change parameter; The change in battery instantaneous power at adjacent acquisition times is taken as the power instantaneous change parameter. The power instantaneous change parameters within the driving cycle are arranged in descending order, and the ratio of the average value of the first 25% of the power instantaneous change parameters to the battery instantaneous power calibration threshold is taken as the battery instantaneous output parameter.

[0007] Preferably, a weighted fusion analysis is performed on the torque parameter, the acceleration change parameter and the battery instantaneous output parameter to obtain the driving load parameter.

[0008] Preferably, the battery data of the new energy vehicle is collected during charging, the maximum value of the driving battery temperature in a driving cycle before charging the new energy vehicle is extracted as the safety temperature parameter, and the charging safety value is analyzed based on the safety temperature parameter. If the safety temperature parameter is greater than the safety temperature threshold, the expression of the charging safety value is set as: SOCs=0.8-0.005×(T1 max -t1) / T1 max Otherwise, the expression of the charge safety value is set to SOCs=0.8, where SOCs represents the charge safety value, T1 max represents the safety temperature parameter, and t1 represents the safety temperature threshold.

[0009] Preferably, the temperature of the charging battery of the three fast charging types before charging the new energy vehicle is extracted as the fast charging battery temperature, and the difference between the battery temperature at the end time of fast charging and the battery temperature at the start time of fast charging in each fast charging battery temperature is taken as the fast charging temperature rise parameter, and the thermal memory strength is analyzed based on the fast charging temperature rise parameter.

[0010] Preferably, the charging tolerance index is generated by combining the driving load parameter, the battery health state and the thermal memory strength. The expression of the charging tolerance index is C=1 / (1+D)×SOH×e -0.1×Hm , where C represents the charging tolerance index, SOH represents the battery health state, Hm represents the thermal memory strength, and D represents the driving load parameter.

[0011] Preferably, a charging control strategy is generated based on the charging tolerance index, battery state of charge and charge safety value. When C is less than 0.4, the charging power of the new energy vehicle battery is limited to be less than or equal to (0.5×maximum fast charging power); when 0.4≤C<0.7, the charging power of the new energy vehicle battery is limited to be less than or equal to (C×maximum fast charging power); when C≥0.7, the new energy vehicle battery is allowed to use the maximum fast charging power for fast charging.

[0012] Preferably, the driving battery temperature, parking time, driving load parameters and charging tolerance index of the most recent driving cycle before charging the new energy vehicle battery are stored as vehicle link data; The battery temperature at the start of fast charging when analyzing the fast charging temperature rise parameters is corrected based on the parking duration, driving load parameters, and driving battery temperature. When t2 < 30 and D > 0.6, the battery temperature at the start of fast charging when analyzing the fast charging temperature rise parameters is corrected. When the charging tolerance index is less than 0.4 in three consecutive charging tolerance index analyses, the battery instantaneous power calibration threshold is lowered.

[0013] On the other hand, the present invention also provides a new energy vehicle battery pack operation management device, comprising: Driving collection module, used to collect driving data of new energy vehicles; The driving analysis module is used to analyze driving behavior parameters based on driving data and perform weighted fusion analysis on the driving behavior parameters to obtain driving load parameters; Battery collection module, used to collect battery data when new energy vehicles are charging; A comprehensive analysis module is used to analyze pre-charging parameters based on battery data and generate a charging tolerance index by combining driving load parameters and pre-charging parameters; A strategy generation module is used to establish a hierarchical management model based on the charging tolerance index and pre-charging parameters to generate a charging control strategy; The rule update module is used to store driving data and battery data to generate vehicle link data, and trigger thermal recovery rules and calibration update rules based on the vehicle link data.

[0014] On the other hand, the present invention also provides a storage medium, characterized in that it stores instructions, which, when executed on a computer, enable the computer to execute the new energy vehicle battery pack operation management method as described above.

[0015] The beneficial effects of the present invention are as follows: by integrating driving data and battery operation data, precise management of new energy vehicle battery charging is achieved; by dynamically generating a charging tolerance index and formulating a graded charging strategy based on the index, the safety of the charging process and battery life are improved; by storing and analyzing vehicle link data, thermal recovery rules and calibration parameters are continuously optimized to ensure the continuous adaptability of the battery management strategy, thereby reducing the risk of damage to the battery caused by fast charging, improving charging efficiency, and providing reliable protection for the long-term stable operation of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a flow chart of the new energy vehicle battery pack operation management method of this embodiment.

[0018] Figure 2 Flowchart of the method for generating a charging tolerance index according to this embodiment.

[0019] Figure 3 This is a flow chart of the vehicle link data analysis method of this embodiment.

[0020] Figure 4 This is a structural diagram of the new energy vehicle battery pack operation management device of this embodiment. DETAILED DESCRIPTION

[0021] The following is a further detailed description of the new energy vehicle battery pack operation management method, device and storage medium disclosed in the present invention in conjunction with the accompanying drawings and specific embodiments. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve better technical effects. In the drawings of the following embodiments, the same reference numerals appearing in each drawing represent the same features or components, which can be applied to different embodiments. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0022] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not intended to limit the conditions under which the invention can be implemented. Any structural modification, change in proportional relationship, or adjustment of size should fall within the scope of the technical content disclosed in the invention without affecting the efficacy and purpose of the invention. The scope of the preferred embodiments of the present invention includes alternative implementations, in which the functions can be performed in a non-described or discussed order, including performing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art of the art to which the embodiments of the present invention belong.

[0023] Technologies, methods, and apparatus known to persons of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0024] In the description of the embodiments of this application, " / " represents "or," and "and / or" is used to describe the association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" represents the following three situations: A and B exist alone, B exists alone, and A and B exist at the same time. In the description of the embodiments of this application, "multiple" refers to two or more embodiments.

[0025] See also Figure 1 As shown, this is the new energy vehicle battery pack operation management method of this embodiment, including: Step S1: Collect driving data of new energy vehicles, including steering wheel torque, vehicle acceleration, battery instantaneous power and parking time. The unit of steering wheel torque is N·m, and the unit of vehicle acceleration is m / s. 2 The unit of battery instantaneous power is W. The frequency of collecting the steering wheel torque is 10 times per second. The frequency of collecting the vehicle acceleration and battery instantaneous power is 1 time per second. The parking duration is the total parking duration in a driving cycle. The unit of the parking duration is minutes. The driving data is collected through the vehicle CAN bus.

[0026] Specifically, in step S1 of this embodiment, driving data such as steering wheel torque, vehicle acceleration, and battery instantaneous power are collected at a high frequency to fully reflect the real-time impact of driving behavior on the battery. This provides a reliable basis for subsequent analysis and avoids deviations that may be caused by a single data source. At the same time, the recording of parking time helps to analyze the temperature changes of the battery in a static state, providing a basis for subsequent thermal management.

[0027] Please continue reading Figure 1 As shown, the new energy vehicle battery pack operation management method further includes: Step S2: analyzing driving behavior parameters based on the driving data and performing weighted fusion analysis on the driving behavior parameters to obtain driving load parameters. The driving behavior parameters include torque parameters, acceleration change parameters, and battery instantaneous output parameters.

[0028] Specifically, in this embodiment, a complete driving cycle of a user is regarded as a driving cycle.

[0029] Specifically, in step S2 of this embodiment, the standard deviation of the steering wheel torque per second is used as the torque standard deviation parameter, and the ratio of the average value of the torque standard deviation parameter in the driving cycle to the maximum value of the torque standard deviation parameter in the driving cycle is used as the torque parameter.

[0030] Specifically, in step S2 of this embodiment, the ratio of the standard deviation of the vehicle acceleration during the driving cycle to the average value of the vehicle acceleration during the driving cycle is used as the acceleration change parameter.

[0031] Specifically, in step S2 of this embodiment, the change in the battery instantaneous power at adjacent collection times is used as the power instantaneous change parameter, the power instantaneous change parameters within the driving cycle are arranged in descending order, and the ratio of the average value of the first 25% of the power instantaneous change parameters to the battery instantaneous power calibration threshold is taken as the battery instantaneous output parameter.

[0032] Specifically, the battery instantaneous power calibration threshold in this embodiment is the maximum instantaneous discharge power of the battery, which is related to the characteristics and discharge time of the battery used in the new energy vehicle. For example, if a 1kW battery discharges in 1 second, its instantaneous power is 1000W.

[0033] Specifically, in step S2 of this embodiment, a weighted fusion analysis is performed on the torque parameter, the acceleration change parameter, and the battery instantaneous output parameter to obtain a driving load parameter. The expression of the driving load parameter is: D=α1×A1+α2×A2+α3×A3, where D represents the driving load parameter, A1 represents the torque parameter, A2 represents the acceleration change parameter, A3 represents the battery instantaneous output parameter, α1 represents the torque weight, α2 represents the acceleration weight, α3 represents the power weight, and α1+α2+α3=1.

[0034] Specifically, in this embodiment, the torque weight is set to 0.3, the acceleration weight is set to 0.3, and the power weight is set to 0.4. In this embodiment, there is no specific limitation on the setting of the torque weight, acceleration weight, and power weight, and those skilled in the art can freely set them.

[0035] Specifically, the driving load parameter in this embodiment is a parameter ranging from 0 to 1, and a higher value indicates a greater impact of the driving behavior on the battery.

[0036] Specifically, in step S2 described in this embodiment, the torque parameters, acceleration change parameters and battery instantaneous output parameters are weightedly fused to accurately quantify the load of driving behavior on the battery, realize comprehensive mechanical operation and battery dynamic response analysis, and thus more comprehensively evaluate the potential damage risk of driving behavior to the battery.

[0037] Please continue reading Figure 1 As shown, the new energy vehicle battery pack operation management method further includes: Step S3, collecting battery data when the new energy vehicle is charging, the battery data includes driving battery temperature, charging battery temperature, charging type, battery health status, battery state of charge and fast charging maximum power, the driving battery temperature is the battery temperature data before the new energy vehicle is connected to the charging pile for charging, the charging battery temperature is the battery temperature during the charging process of the new energy vehicle connected to the charging pile, the unit of the driving battery temperature and the charging battery temperature is Celsius, the charging type includes normal charging and fast charging, the battery health status is SOH, which reflects the health status of the battery and measures the degree of battery degradation and remaining service life. The battery state of charge is SOC, which reflects the current remaining power of the battery. The fast charging maximum power is the maximum fast charging power allowed for the charging pile when charging the battery of the new energy vehicle. The battery data is collected by importing data from the battery management system.

[0038] Please continue reading Figure 1 As shown, the new energy vehicle battery pack operation management method further includes: Step S4: Analyze pre-charging parameters based on battery data, and generate a charging tolerance index by combining the driving load parameters and the pre-charging parameters.

[0039] See also Figure 2 As shown, it is a method for generating a charging tolerance index, including: Step S41 , analyzing the charge safety value according to the driving battery temperature.

[0040] Specifically, in step S41 of this embodiment, the maximum value of the driving battery temperature in a driving cycle before charging the new energy vehicle is extracted as a safety temperature parameter, and the charging safety value is analyzed based on the safety temperature parameter. If the safety temperature parameter is greater than the safety temperature threshold, the expression for setting the charging safety value is: SOCs=0.8-0.005×(T1 max -t1) / T1 max Otherwise, the expression of the charge safety value is set to SOCs=0.8, where SOCs represents the charge safety value, T1max represents the safety temperature parameter, and t1 represents the safety temperature threshold.

[0041] Specifically, in this embodiment, there is no specific limitation on the setting of the safety temperature threshold. It is the optimal temperature for the operation of the new energy vehicle battery. Those skilled in the art can set it according to the type of battery used in the new energy vehicle. For example, when using a lithium battery, the safety temperature threshold is set to 25°C.

[0042] Specifically, in the calculation of the charge safety value described in this embodiment, 0.005 is a temperature compensation coefficient, which means that the charge safety value decreases by 0.005 for every 1 degree Celsius increase in temperature.

[0043] Please continue reading Figure 2 As shown, the charging tolerance index generation method further includes: Step S42 , analyzing the thermal memory strength according to the rechargeable battery temperature and charging type.

[0044] Specifically, in step S42 of this embodiment, the temperature of the rechargeable battery of the three fast charging types before charging the new energy vehicle is extracted as the fast charging battery temperature, and the difference between the battery temperature at the end of fast charging and the battery temperature at the start of fast charging in each fast charging battery temperature is used as the fast charging temperature rise parameter, and the thermal memory strength is analyzed based on the fast charging temperature rise parameter. The expression of the thermal memory strength is: Where Hm represents the thermal memory strength, T2(i) represents the fast charge temperature rise parameter, and i represents the recent fast charge number. The recent fast charge number is defined as the number that distinguishes which of the three extracted fast charge battery temperatures was the most recent. It is numbered in reverse chronological order, and i=1 represents the most recent fast charge number.

[0045] Please continue reading Figure 2 As shown, the charging tolerance index generation method further includes: Step S43 : generating a charging tolerance index by combining the driving load parameter, the battery health status, and the thermal memory strength.

[0046] Specifically, in step S43 of this embodiment, the charging tolerance index is generated by combining the driving load parameter, the battery health status and the thermal memory strength. The expression of the charging tolerance index is C=1 / (1+D)×SOH×e -0.1×Hm , where C represents the charge tolerance index and SOH represents the battery health status.

[0047] Specifically, the battery tolerance index described in this embodiment is a parameter ranging from 0 to 1, and the lower the value, the weaker the battery's ability to resist fast charging.

[0048] Specifically, in step S4 of this embodiment, the charging tolerance index is generated by combining the driving load parameters, battery health status and thermal memory strength to dynamically reflect the fast charging capability of the battery. The current status of the battery and the historical fast charging temperature rise data are combined and analyzed to more accurately predict the risks of the battery during the fast charging process.

[0049] Please continue reading Figure 1 As shown, the new energy vehicle battery pack operation management method further includes: Step S5: establishing a hierarchical management model based on the charging tolerance index and pre-charging parameters to generate a charging control strategy.

[0050] Specifically, in step S5 described in this embodiment, a charging control strategy is generated based on the charging tolerance index, the battery state of charge and the charge safety value. When C is less than 0.4, the charging power of the new energy vehicle battery is limited to less than or equal to (0.5×the maximum power of fast charging), and staged charging is started. If 0<SOC≤0.3, constant current charging is performed. If 0.3<SOC<SOCs, constant voltage charging is performed. If SOC=SOCs, charging is terminated. When 0.4≤C<0.7, the charging power of the new energy vehicle battery is limited to less than or equal to (C×the maximum power of fast charging), so that the rising slope of the charging current of the new energy vehicle battery is reduced by 50%. When C≥0.7, the new energy vehicle battery is allowed to use the maximum power of fast charging for fast charging.

[0051] Specifically, in step S5 described in this embodiment, a graded charging strategy is formulated based on the charging tolerance index and the battery state of charge to effectively prevent the battery from being damaged by overload during fast charging. For batteries with a lower tolerance index, the charging power is limited or charged in stages to improve charging safety and battery life.

[0052] Please continue reading Figure 1 As shown, the new energy vehicle battery pack operation management method further includes: Step S6: storing the driving data and the battery data to generate vehicle link data, and triggering the thermal recovery rule and the calibration update rule according to the vehicle link data.

[0053] See also Figure 3 As shown in FIG, a method for analyzing automobile link data includes: Step S61: storing driving data and battery data to generate vehicle link data.

[0054] Specifically, in step S61 of this embodiment, the driving battery temperature, parking time, driving load parameters and charging tolerance index of the most recent driving cycle before charging the new energy vehicle battery are stored as vehicle link data.

[0055] Please continue reading Figure 3As shown, the method for analyzing the vehicle link data further includes: Step S62 : triggering a hot recovery rule based on the vehicle link data.

[0056] Specifically, in step S62 of this embodiment, the battery temperature at the time of fast charging start when analyzing the fast charging temperature rise parameter is corrected according to the parking time, the driving load parameter, and the driving battery temperature. If t2 < 30 and D > 0.6, the battery temperature at the time of fast charging start when analyzing the fast charging temperature rise parameter is corrected, and the battery temperature at the time of fast charging start when correcting the fast charging temperature rise parameter is set to Tst, Tst = Tsd + (T1 max -Tsd)×e -0.05×t2 , Tsd represents the battery temperature when the new energy vehicle is turned off, and t2 represents the parking time; otherwise, the battery temperature at the start time of fast charging is not corrected when analyzing the fast charging temperature rise parameters.

[0057] Please continue reading Figure 3 As shown, the method for analyzing the vehicle link data further includes: Step S63: triggering calibration update rules based on vehicle link data.

[0058] Specifically, in step S63 of this embodiment, if the charging tolerance index is less than 0.4 in three consecutive charging tolerance index analyses, the battery instantaneous power calibration threshold is lowered by 10%.

[0059] Specifically, in step S6 of this embodiment, by storing and analyzing vehicle link data, thermal recovery rules and calibration parameters are dynamically adjusted to correct the battery temperature at the start of fast charging or lower the instantaneous power calibration threshold, thereby further optimizing the real-time and adaptability of battery management.

[0060] See also Figure 4 As shown, it is the new energy vehicle battery pack operation management device of this embodiment, including: Driving collection module, used to collect driving data of new energy vehicles; The driving analysis module is used to analyze driving behavior parameters based on driving data and perform weighted fusion analysis on the driving behavior parameters to obtain driving load parameters; Battery collection module, used to collect battery data when new energy vehicles are charging; A comprehensive analysis module is used to analyze pre-charging parameters based on battery data and generate a charging tolerance index by combining driving load parameters and pre-charging parameters; A strategy generation module is used to establish a hierarchical management model based on the charging tolerance index and pre-charging parameters to generate a charging control strategy; The rule update module is used to store driving data and battery data to generate vehicle link data, and trigger thermal recovery rules and calibration update rules based on the vehicle link data.

[0061] An embodiment of the present application also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the new energy vehicle battery pack operation management method as described in the above method embodiment.

[0062] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as a computer-readable program, a data structure, a program module, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable programs, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0063] In the above description, the disclosure of the present invention is not intended to limit itself to these aspects. Rather, within the scope of the intended protection of the present disclosure, the components can be selectively and operationally combined in any number. In addition, terms such as "including", "encompassing" and "having" should be interpreted as inclusive or open by default, rather than exclusive or closed, unless they are explicitly defined to the contrary. All technical, scientific or other terms have the meaning understood by those skilled in the art unless they are defined to the contrary. Common terms found in dictionaries should not be interpreted too idealistically or too impractically in the context of relevant technical documents, unless the present disclosure explicitly defines them as such. Any changes and modifications made by a person of ordinary skill in the field of the present invention based on the above disclosure are within the scope of protection of the claims.

Claims

1. A new energy vehicle battery pack operation management method, characterized in that: include: Analyze driving behavior parameters based on driving data and perform weighted fusion analysis on the driving behavior parameters to obtain driving load parameters; Analyze pre-charging parameters based on battery data and generate a charging tolerance index by combining driving load parameters and pre-charging parameters; A hierarchical management model is established based on the charging tolerance index and pre-charging parameters to generate a charging control strategy; Driving data and battery data are stored to generate vehicle link data, and thermal recovery rules and calibration update rules are triggered based on the vehicle link data.

2. The new energy vehicle battery pack operation management method according to claim 1, characterized in that: Driving data of new energy vehicles is collected, the standard deviation of steering wheel torque per second is used as a torque standard deviation parameter, and the ratio of the average value of the torque standard deviation parameter within a driving cycle to the maximum value of the torque standard deviation parameter within the driving cycle is used as a torque parameter; The ratio of the standard deviation of the vehicle acceleration during the driving cycle to the average value of the vehicle acceleration during the driving cycle is used as the acceleration change parameter; The change in battery instantaneous power at adjacent acquisition times is taken as the power instantaneous change parameter. The power instantaneous change parameters within the driving cycle are arranged in descending order, and the ratio of the average value of the first 25% of the power instantaneous change parameters to the battery instantaneous power calibration threshold is taken as the battery instantaneous output parameter.

3. The new energy vehicle battery pack operation management method according to claim 2, characterized in that: A weighted fusion analysis is performed on the torque parameters, acceleration change parameters and battery instantaneous output parameters to obtain the driving load parameters.

4. The new energy vehicle battery pack operation management method according to claim 3, characterized in that: Collect battery data when new energy vehicles are charging, extract the maximum value of the driving battery temperature in a driving cycle before charging as the safety temperature parameter, and analyze the charging safety value based on the safety temperature parameter. If the safety temperature parameter is greater than the safety temperature threshold, set the charging safety value as follows: SOCs = 0.8-0.005×(T1 max -t1) / T1 max Otherwise, the expression of the charge safety value is set to SOCs=0.8, where SOCs represents the charge safety value, T1 max represents the safety temperature parameter, and t1 represents the safety temperature threshold.

5. The new energy vehicle battery pack operation management method according to claim 4, characterized in that: The battery temperatures of the three fast-charging battery types before charging the new energy vehicle are extracted as the fast-charging battery temperatures. The difference between the battery temperature at the end of each fast-charging battery temperature and the battery temperature at the start of each fast-charging battery temperature is taken as the fast-charging temperature rise parameter, and the thermal memory strength is analyzed based on the fast-charging temperature rise parameter.

6. The new energy vehicle battery pack operation management method according to claim 5, characterized in that: The charging tolerance index is generated by combining driving load parameters, battery health status and thermal memory strength. The expression of the charging tolerance index is C=1 / (1+D)×SOH×e -0.1×Hm , where C represents the charging tolerance index, SOH represents the battery health state, Hm represents the thermal memory strength, and D represents the driving load parameter.

7. The new energy vehicle battery pack operation management method according to claim 6, characterized in that: A charging control strategy is generated based on the charging tolerance index, battery state of charge and charge safety value. When C is less than 0.4, the charging power of the new energy vehicle battery is limited to less than or equal to (0.5×maximum fast charging power). When 0.4≤C<0.7, the charging power of the new energy vehicle battery is limited to less than or equal to (C×maximum fast charging power). When C≥0.7, the new energy vehicle battery is allowed to use the maximum fast charging power for fast charging.

8. The new energy vehicle battery pack operation management method according to claim 7, characterized in that: The driving battery temperature, parking time, driving load parameters and charging tolerance index of the most recent driving cycle of the new energy vehicle before charging are stored as vehicle link data; The battery temperature at the start of fast charging when analyzing the fast charging temperature rise parameters is corrected based on the parking duration, driving load parameters, and driving battery temperature. When t2 < 30 and D > 0.6, the battery temperature at the start of fast charging when analyzing the fast charging temperature rise parameters is corrected. When the charging tolerance index is less than 0.4 in three consecutive charging tolerance index analyses, the battery instantaneous power calibration threshold is lowered.

9. A new energy vehicle battery pack operation management device, applied to the new energy vehicle battery pack operation management method according to any one of claims 1 to 8, characterized in that: include: Driving collection module, used to collect driving data of new energy vehicles; The driving analysis module is used to analyze driving behavior parameters based on driving data and perform weighted fusion analysis on the driving behavior parameters to obtain driving load parameters; Battery collection module, used to collect battery data when new energy vehicles are charging; A comprehensive analysis module is used to analyze pre-charging parameters based on battery data and generate a charging tolerance index by combining driving load parameters and pre-charging parameters; A strategy generation module is used to establish a hierarchical management model based on the charging tolerance index and pre-charging parameters to generate a charging control strategy; The rule update module is used to store driving data and battery data to generate vehicle link data, and trigger thermal recovery rules and calibration update rules based on the vehicle link data.

10. A storage medium, characterized in that: Instructions are stored, which, when executed on a computer, enable the computer to execute the new energy vehicle battery pack operation management method as described in any one of claims 1 to 8.

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