CN112946483B - Comprehensive evaluation method for battery health of electric vehicle and storage medium - Google Patents
Comprehensive evaluation method for battery health of electric vehicle and storage medium Download PDFInfo
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- CN112946483B CN112946483B CN202110158158.1A CN202110158158A CN112946483B CN 112946483 B CN112946483 B CN 112946483B CN 202110158158 A CN202110158158 A CN 202110158158A CN 112946483 B CN112946483 B CN 112946483B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The invention provides a comprehensive evaluation method and a storage medium for the battery health of an electric vehicle, which comprises the steps of S1, extracting the original working condition data of the electric vehicle, and filtering abnormal data in the original working condition data; step S2, calculating the attenuation coefficient A of the battery capacity; step S3, constructing a vehicle fault event table through original working condition data, and calculating a battery fault alarm coefficient B; step S4, constructing a physical examination index configuration table of different battery types according to the types and the characteristics of different batteries, and calculating a monitoring index score C according to a preset physical examination index rating rule; and step S5, combining the battery capacity attenuation coefficient A, the battery failure alarm coefficient B and the monitoring index score C, and obtaining the health degree score of the battery through sorting. The method and the device can be used for rapidly detecting the health state of the battery on line and comprehensively evaluating the health state of the battery in more practical application scenes.
Description
Technical Field
The invention relates to the technical field of new energy battery application, in particular to a comprehensive evaluation method and a storage medium for the health of an electric vehicle battery.
Background
For electric vehicles, the power battery is one of the important factors influencing the overall performance of the vehicle, so that evaluation of the health degree of the power battery is of great significance to consumers and manufacturers.
Currently, most of the existing calculation methods for measuring the health degree of the battery are considered based on the battery capacity, and include a direct discharge method and an equivalent circuit model method. The direct discharge method requires a long time for the battery to stand still for measurement, consumes too long time, cannot estimate in real time, and is not easy to implement. The equivalent circuit model method requires not only an appropriate cell model and accurately measured parameters, but also large-scale calculation power and accurate initialization to ensure estimation accuracy. Meanwhile, the characteristic that the equivalent circuit model is high in pertinence is considered, when different batteries are faced, the equivalent circuit model has uncertainty and is not easy to extend to other batteries, and therefore the equivalent circuit model is not suitable for practical application scenarios.
The method only evaluates the health condition of the battery from the change of the battery health degree before and after the capacity, and cannot integrally reflect the existing overall condition of the battery health. Therefore, it is necessary to invent a comprehensive evaluation method and a storage medium for battery health of an electric vehicle to accurately evaluate the battery health.
Disclosure of Invention
In view of the above, the present invention provides a comprehensive evaluation method for battery health of an electric vehicle, which is used to solve the problems that the traditional calculation method for measuring battery health degree is too complex, consumes too long time, and cannot reflect the battery health as a whole.
In a first aspect, the invention provides a comprehensive evaluation method for battery health of an electric vehicle, which comprises the following steps:
step S1, extracting the original working condition data of the electric automobile, and filtering abnormal data in the original working condition data;
step S2, calculating the attenuation coefficient A of the existing battery capacity;
step S3, constructing a vehicle fault event table through the original working condition data, and calculating a battery fault alarm coefficient B;
step S4, constructing a physical examination index configuration table of different battery types according to the types and the characteristics of different batteries, and calculating a monitoring index score C according to a preset physical examination index rating rule;
step S5, combining the battery capacity attenuation coefficient A, the battery failure alarm coefficient B and the monitoring index score C, the health degree score expression of the battery obtained through arrangement is as follows:
MAX(20*A*B–C+100,0)+80*A*B。
further, in step S1, the filtering the abnormal data in the original working condition data specifically includes:
step S101, calculating statistical data of a battery discharging stroke and a battery charging stroke according to original working condition data;
and step S102, checking data in the charging and discharging stroke, and rejecting the invalid stroke when the stroke is detected to be the invalid stroke.
Further, the step S2 specifically includes:
step S201, constructing a battery capacity attenuation model, analyzing monthly level of battery charging capacity through big data statistics, setting an initial score of 100, and carrying out quantitative evaluation;
step S202, training a battery capacity attenuation model, counting the monthly average level of battery charging capacity, comparing the monthly average level with initial 30-day data, calculating the ring ratio reduction range, and setting a reference as 100 points;
step S203, inputting the counted data as input quantity into the attenuation model of the battery capacity, and calculating to obtain a battery capacity attenuation coefficient through a piecewise function, wherein an expression of the piecewise function is:
and step S204, calculating to obtain the attenuation coefficient A of the battery capacity of a single vehicle and a single month granularity.
Further, the method for calculating the battery failure warning coefficient B in step S3 includes the following steps:
step S301, a vehicle fault event table is constructed through original working condition data, wherein the original working condition data comprise VIN, trigger fault name, trigger fault grade and trigger fault timestamp;
step S302, a battery fault alarm coefficient algorithm is constructed, and the expression of the battery fault alarm coefficient is
Wherein, biRepresenting the base of the exponential function, i ═ 1,2,3, nmA preset index representing an exponential function, m being 1,2, 3;
and step S303, obtaining the evaluation result of the single-vehicle and accumulated battery fault alarm coefficients through calculation.
Further, the specific calculation process of the monitoring index score C in step S4 includes the following steps:
step S401, according to the types and characteristics of different batteries, a physical examination index configuration table of different battery types is constructed;
step S402, setting various index levels of the battery, grading the actual change condition of the battery every month based on the various index levels of the battery, and performing corresponding deduction processing;
step S403, setting a physical examination index rating rule of the battery aiming at each index level of the battery, and calculating a monitoring index score C of the battery according to the physical examination index rating rule.
In a second aspect, the present invention further provides a storage medium storing one or more programs, which when executed by one or more processors, implement the steps of the method for comprehensively evaluating the health of batteries of electric vehicles.
The invention brings the following beneficial effects:
according to the comprehensive evaluation method and the storage medium for the battery health of the electric automobile, a large amount of original working condition data are extracted from a TSP system of a host factory, abnormal data are filtered out and are respectively used for calculating the battery capacity attenuation coefficient, the battery fault alarm coefficient and the monitoring index score C, finally the factors are integrated, the health state of the battery is measured on line, excessive time does not need to be consumed in the whole measurement, the calculation method is simple, and the health state of the battery can be comprehensively evaluated in more practical application scenes.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a comprehensive evaluation method for battery health of an electric vehicle according to the present embodiment;
fig. 2 is a schematic diagram of a piecewise function relationship of the battery attenuation coefficient provided in this embodiment;
fig. 3 is a flowchart of a method for calculating a battery failure alarm coefficient according to this embodiment.
Detailed Description
As shown in fig. 1, a comprehensive evaluation method for battery health of an electric vehicle includes the following steps:
step S1, extracting the original working condition data of the electric automobile, and filtering abnormal data in the original working condition data;
step S2, calculating the attenuation coefficient A of the existing battery capacity;
step S3, constructing a vehicle fault event table through original working condition data, and calculating a battery fault alarm coefficient B;
step S4, constructing a physical examination index configuration table of different battery types according to the types and the characteristics of different batteries, and calculating a monitoring index score C according to a preset physical examination index rating rule;
step S5, combining the battery capacity attenuation coefficient A, the battery failure alarm coefficient B and the monitoring index score C, the health degree score expression of the battery obtained through arrangement is as follows:
MAX(20*A*B–C+100,0)+80*A*B。
it should be noted that the reference of the acquisition standard for extracting the original working condition data of the electric vehicle in step S1 is the data acquisition standard of the electric vehicle GB/T32960, where the original working condition data includes various entire vehicle data (such as VIN, timestamp, vehicle state, charging state, total voltage, total current, and SOC), various battery data (such as maximum voltage of battery cell, minimum voltage of battery cell, maximum temperature, minimum temperature, voltage of battery cell, and serial number of battery cell), and various fault data (such as maximum alarm level, high temperature alarm of battery, type overvoltage alarm of vehicle-mounted energy storage device, type undervoltage alarm of vehicle-mounted energy storage device, too low alarm of SOC, overvoltage alarm of battery cell, undervoltage alarm of battery cell, too high alarm of SOC, uniformity difference alarm of battery cell, and type overcharge of vehicle-mounted energy storage device).
Then, data preprocessing is carried out on the collected original working condition data to filter out abnormal data, and the specific filtering step comprises the following steps:
and step S101, calculating statistical data of a battery discharging stroke and a battery charging stroke according to the original working condition data.
Step S102, data in the charging and discharging process are checked, and when invalid processes are detected in the process, for example, the single driving mileage is less than 1 kilometer, or the single driving mileage exceeds 500 kilometers, or the single charging time is less than or equal to 5 minutes, or the charging SOC is 0, the invalid or unreasonable processes are eliminated.
In this embodiment, the step S2 of calculating the attenuation coefficient a of the battery capacity includes:
step S201, the monthly level of the battery charging capacity is analyzed through big data statistics, a battery capacity attenuation model is built, an initial score is set to be 100, and quantitative evaluation is carried out.
Step S202, training a battery capacity attenuation model, and comparing the monthly average level with initial 30-day data by counting the monthly average level of battery charging capacity for calculating the ring ratio reduction amplitude.
Since the data granularity is monthly data and the effective data amount is small for a single vehicle, a unary linear regression model is constructed using a battery health value (capacity attenuation value) of approximately 12 months (unsatisfied full-volume monthly data) as a training set. The battery capacity attenuation variable is used as a target variable, and the Chinese and English comparison and units of other characteristic contents are shown in the following table:
step S203, inputting the counted data as input quantity to the attenuation model of the battery capacity, and calculating a predicted value of the attenuation coefficient of the battery capacity through a piecewise function, wherein an expression of the piecewise function is:
as can be seen from the piecewise function relationship and fig. 2, when the degree of the battery attenuation coefficient is 100%, the battery capacity is 1, and when the battery capacity is 80%, the power battery directly affects the use and safety risk of the battery, so the 80% attenuation level directly corresponds to the grid line with the attenuation coefficient of 0.6, that is, the capacity attenuation coefficient f (X) ═ X/500-1, X > -80.
And step S204, calculating the attenuation coefficient A of the battery capacity of a single vehicle and a single month granularity as a health standard for evaluating the battery.
Specifically, the method for calculating the battery failure warning coefficient B in step S3, as shown in fig. 3, includes the following steps:
step S301, a vehicle fault event table is constructed through original working condition data, wherein the original working condition data comprise VIN, trigger fault name, trigger fault grade and trigger fault timestamp;
step S302, a battery failure alarm coefficient algorithm is constructed, and the expression of the battery failure alarm coefficient is
Wherein b isiRepresents the base of an exponential function and 0 ≦ bi<1, i is 1,2 and 3. It represents the degree of influence on the battery failure warning coefficient: b is a mixture of1Representing the degree of influence of the primary fault on the battery fault alarm coefficient, b2Representing the degree of influence of the secondary fault on the battery fault alarm coefficient, b3Representing the degree of influence of the three-level fault on the battery fault alarm coefficient. When b isiWhen the time approaches to 1, the larger the change rate of the exponential function expression, the larger the influence on the battery fault alarm coefficientThe greater the degree. When b isiWhen the temperature approaches 0, the smaller the change rate of the exponential function, the smaller the influence degree on the battery fault alarm coefficient.
nmAnd m is 1,2 and 3. It represents the number of times that the vehicle has different levels of battery failure within a natural month. n is1Indicating the number of primary faults occurring, n2Indicating the number of occurrences of a secondary fault, n3Indicating the number of occurrences of a tertiary fault.
And step S303, finally, obtaining the evaluation result of the single-vehicle and accumulated battery fault alarm coefficient B through calculation.
Specifically, the specific calculation process of the monitoring index score C in step S4 includes the following steps:
step S401, a service expert combs out physical examination item indexes of the power battery and a rating standard thereof according to different battery models and battery characteristics to form a physical examination index configuration table aiming at different battery models;
step S402, the detailed index scores are each punishment item of the battery health degree evaluation, specifically, each index level of the battery is subjectively defined through business experience and data distribution conditions, each actual change condition of the battery per month is graded based on each index level of the battery, and corresponding deduction processing is carried out, wherein the grading rule is shown in the following table.
For example: when the temperature extreme value of the battery is in the interval (15, 48), the health degree of the battery is in an excellent state, namely the grade is excellent; when the temperature extreme values of the battery are between (5, 15) and (48, 53), the health degree of the battery is in a good state, namely the grade is good; when the extreme temperature value of the battery is in two intervals of (-999, -5) and (53, +999), the health of the battery is in a poor state, i.e., the level is poor. Similarly, when the extreme voltage value of the battery cell is in the interval of [ 3.6,4.1 ], the health degree of the battery is in an excellent state, namely the grade is excellent; when the extreme voltage values of the battery cells are in the intervals of (3.4, 3.6) and (4.1, 4.35), the health degree of the battery is in a good state, namely the grade is good; when the extreme voltage value of the battery cell is in two intervals of (-999, -5) and (53, +999), the health of the battery is in a poor state, i.e., the level is poor. Similarly, the current state of health of the battery can be known through the above table for various indexes of the battery, such as the highest charging current value, the highest discharging current value, the highest voltage difference extreme value, and the weighted average of the discharging current.
Step S403, a health examination index punishment rule is formulated according to the indexes: taking the total score as 100 as an example, repeating the following operations on each specific index, wherein if the grade is good, the grade is not reduced, if the grade is good, the grade is reduced by 1, if the grade is poor, the grade is reduced by 2, and finally obtaining each monitoring index score C.
Specifically, in the step S5, the battery health score is given by integrating all the factors:
step S501, comprehensively analyzing the battery capacity attenuation coefficient A, the battery failure alarm coefficient B and the monitoring index score C in the steps to obtain a battery health degree score of 100 battery attenuation coefficient A battery failure alarm coefficient B- (100-monitoring index score C)
Step S502, the expression of the health degree score of the battery obtained finally after the arrangement is as follows:
MAX(20*A*B–C+100,0)+80*A*B。
in summary, the invention extracts the original working condition data of the host plant TSP system as an input source for collecting standardized data, then respectively calculates the existing battery capacity attenuation coefficient, battery failure alarm coefficient and monitoring index scores of each item, and synthesizes all the factors to obtain the battery health score, thereby measuring the health state of the battery on line.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Claims (4)
1. A comprehensive evaluation method for the battery health of an electric vehicle is characterized by comprising the following steps:
step S1, extracting the original working condition data of the electric automobile, and filtering abnormal data in the original working condition data;
step S2, calculating an attenuation coefficient a of the existing battery capacity, specifically including:
step S201, constructing a battery capacity attenuation model, analyzing monthly level of battery charging capacity through big data statistics, setting an initial score of 100, and carrying out quantitative evaluation;
step S202, training a battery capacity attenuation model, counting the monthly average level of battery charging capacity, comparing the monthly average level with initial 30-day data, calculating the ring ratio reduction range, and setting a reference as 100 points;
step S203, inputting the counted data as input quantity into the attenuation model of the battery capacity, and calculating to obtain a battery capacity attenuation coefficient through a piecewise function, wherein an expression of the piecewise function is:
step S204, calculating to obtain the attenuation coefficient A of the battery capacity of a single vehicle and a single month granularity;
step S3, a vehicle fault event table is constructed through original working condition data, and a battery fault alarm coefficient B is calculated, and the method specifically comprises the following steps:
step S301, a vehicle fault event table is constructed through original working condition data, wherein the original working condition data comprise VIN, trigger fault name, trigger fault grade and trigger fault timestamp;
step S302, a battery failure alarm coefficient algorithm is constructed, and the expression of the battery failure alarm coefficient is
Wherein, biRepresenting the base of the exponential function, i ═ 1,2,3, nmA preset index representing an exponential function, m being 1,2, 3;
step S303, calculating to obtain the evaluation result of the failure alarm coefficient of the single vehicle and the accumulated battery;
step S4, constructing a physical examination index configuration table of different battery types according to the types and the characteristics of different batteries, and calculating a monitoring index score C according to a preset physical examination index rating rule;
step S5, combining the battery capacity attenuation coefficient A, the battery failure alarm coefficient B and the monitoring index score C, the health degree score expression of the battery obtained through arrangement is as follows:
MAX(20*A*B–C+100,0)+80*A*B。
2. the comprehensive evaluation method for the battery health of the electric vehicle according to claim 1, wherein in the step S1, the filtering of the abnormal data in the original working condition data specifically comprises:
step S101, calculating statistical data of a battery discharging stroke and a battery charging stroke according to original working condition data;
and step S102, checking data in the charging and discharging stroke, and rejecting the invalid stroke when the stroke is detected to be the invalid stroke.
3. The comprehensive evaluation method for the battery health of the electric vehicle according to claim 1 or 2, wherein the specific calculation process of the monitoring index score C in the step S4 includes the following steps:
step S401, according to the types and characteristics of different batteries, a physical examination index configuration table of different battery types is constructed;
step S402, setting various index levels of the battery, grading the actual change condition of the battery every month based on the various index levels of the battery, and performing corresponding deduction processing;
step S403, setting a physical examination index rating rule of the battery aiming at each index level of the battery, and calculating a monitoring index score C of the battery according to the physical examination index rating rule.
4. A storage medium, wherein the storage medium stores one or more programs, and the one or more programs, when executed by one or more processors, implement the steps of the comprehensive evaluation method for battery health of electric vehicles according to any one of claims 1 to 3.
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