CN115453400A - Vehicle-mounted power battery health degree evaluation method, system and medium - Google Patents
Vehicle-mounted power battery health degree evaluation method, system and medium Download PDFInfo
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Abstract
The invention discloses a method, a system and a medium for evaluating the health degree of a vehicle-mounted power battery. The method comprises the steps of collecting, storing and transmitting vehicle operation data; the data platform and the automobile end estimate the health state of the power battery by adopting a Gaussian distribution method based on the extreme voltage temperature value, the standard deviation and the SOC variation value of the power battery respectively; displaying the estimation result at the automobile end; and periodically correcting the estimation result of the data platform to the estimation of the automobile end. The model is constructed in a mode of estimating, comparing and calibrating the cloud end and the vehicle end, the SOC change rate of the charging, the voltage and the temperature extreme value of the power battery and the standard deviation of the extreme value are used as evaluation indexes of the health degree of the power battery, the indexes capable of directly representing the health degree of the power battery are covered, in addition, the charging capacity of the new energy vehicle is integrated by adopting a line segment integration method, and the charging capacity is more accurate.
Description
Technical Field
The invention belongs to the technical field of computer vision, and particularly relates to a method, a system and a medium for evaluating the health degree of a vehicle-mounted power battery.
Background
The power battery is one of important components of the new energy automobile, and the manufacturing cost of the occupied vehicle can reach 40 percent, so that the power battery is the component with the highest cost of the whole vehicle. Meanwhile, the power battery is the only component which provides energy for the running of the vehicle by storing a large amount of electric energy, and the safety and the stability of the power battery are indexes of primary investigation on the performance of the whole vehicle. However, due to the complexity of the composition framework of the power battery, the difference of the response in the charging and discharging processes and the randomness of the discharging process in the using process, the health states of different power batteries and the using conditions of vehicles have obvious difference after the power battery is used for a long time. Therefore, the estimation research aiming at the health state of the power battery is always the key point of the research in the industry, and the accurate estimation of the health state of the power battery can provide real-time health state information of the vehicle for users, provide pricing guidance for buyers and sellers of the second-hand vehicle and provide premium calculation reference for new energy vehicle insurance.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art, and provides a vehicle-mounted power battery health degree evaluation method, a system and a medium, which can accurately estimate the health state of a power battery, provide the real-time health condition of a vehicle for a user, provide pricing guidance for buyers and sellers of second-hand vehicles and provide insurance calculation reference for new energy vehicle insurance.
In order to achieve the purpose, the invention adopts the following technical scheme:
in one aspect of the invention, a method for evaluating the health degree of a vehicle-mounted power battery is provided, which comprises the following steps:
collecting, storing and transmitting vehicle operation data;
the data platform estimates the health state of the power battery by adopting a Gaussian distribution method based on the voltage temperature extreme value, the standard deviation and the SOC variation value of the power battery;
the automobile end estimates the health state of the power battery by adopting a Gaussian distribution method based on the voltage temperature extreme value, the standard deviation and the SOC variation value of the power battery;
displaying the estimation result at the automobile end;
and periodically correcting the estimation of the automobile end by using the estimation result of the data platform.
As a preferred technical scheme, the data acquisition, storage and transmission specifically comprises:
the automobile end collects the vehicle operation data in the operation process, stores the power battery operation data, and transmits the collected data to the new energy automobile data platform.
As a preferred technical scheme, the data platform estimates the state of health of the power battery by adopting a gaussian distribution method based on the extreme voltage temperature value, the standard deviation and the SOC variation value of the power battery, and specifically comprises the following steps:
the data platform extracts voltage, current and SOC data of each vehicle in the power battery operation process in the past year;
calculating the charged quantity N and the initial SOC value S in each charging action 0 ，S 1 ；
The SOC change per degree of electricity per charging operation is defined as α = (S) 0 -S 1 ) In the formula, alpha is the change rate of the charging SOC; s. the 0 ，S 1 Representing the SOC end point and the SOC starting point during each charging action; wherein the charging quantity N in each charging action is obtained by integrating the current and voltage values acquired in the running process of the power battery,
in the formula of U i As value of voltage at the point of acquisition, I i The current value of the acquisition point and delta t are the time interval of the acquisition point;
the data platform extracts voltage and temperature data of each vehicle in the power battery operation process in the past year, and acquires voltage and temperature extreme value data and standard deviation of the voltage and the temperature of each frame of data;
randomly extracting a plurality of trolleys from a data platform, calculating an alpha value of each charging behavior of a sample vehicle, and constructing a Gaussian distribution function S (alpha) of the value; extracting voltage and temperature data of each vehicle in the power battery operation process in the past year, and acquiring voltage and temperature extreme value data and corresponding standard deviation of each frame of data;
constructing a Gaussian distribution function S (U) of voltage maxima max ) Gaussian distribution function S (U) of voltage minima min ) Gaussian distribution function of temperature maximum S (T) max ) Temperature minimum Gaussian distribution function S (T) min ) Gaussian distribution function S (U) of standard deviation of voltage sta ) And a Gaussian distribution function S (T) of standard deviation of temperature sta )。
As a preferred technical scheme, gaussian distribution functions S (alpha), S (U) are obtained max )，S(U min )，S(T max )，S(T min )，S(U sta ) And S (T) sta ) And then, the calculated alpha, voltage and temperature extreme values and voltage and temperature standard deviation of each vehicle are brought into the respective Gaussian distribution function, the confidence of each index value of the vehicle in the respective Gaussian distribution function is obtained, the index values which are lower than q% and are greater than p% confidence are defined as low health degree, the index values which are lower than or equal to p% confidence are defined as unhealthy, and q and p are values which are less than 100 and greater than 0.
As a preferred technical scheme, the automobile end estimates the state of health of the power battery by adopting a gaussian distribution method based on the extreme voltage temperature value, the standard deviation and the SOC variation value of the power battery, and specifically comprises the following steps:
after the running data of the trolley is stored, the voltage, the current and the SOC data of the trolley in the running process of a power battery for 30 days are extracted by the automobile end, and the charged quantity N and the charging initial SOC value S in each charging action are calculated 0 ，S 1 ；
The SOC change per watt hour per charging behavior is defined as α = (S) 0 -S 1 ) Where α is the rate of change of the charging SOC, S 0 ，S 1 Representing the SOC end point and the SOC starting point during each charging action; wherein the charging quantity N in each charging action is obtained by integrating the current and voltage values acquired in the running process of the power battery,
in the formula of U i As value of voltage at the point of acquisition, I i The current value of the acquisition point and delta t are the time interval of the acquisition point;
after the running data of the trolley is stored, the voltage and temperature data of the trolley in the running process of the power battery in the running process of the trolley for 30 days are extracted by the automobile end, and the voltage and temperature extreme value data and the voltage and temperature standard deviation of each frame of data are obtained;
the automobile end obtains the alpha value of the automobile in each charging action, and a Gaussian distribution function S of the value is constructed c (α); the method comprises the steps that the voltage and temperature data of the vehicle end in the process of running a power battery for 30 days in the past are obtained, and voltage and temperature extreme value data of each frame of data are obtained;
constructing a Gaussian distribution function S of voltage maxima c (U max ) Gaussian distribution function S of voltage minima c (U min ) Gaussian distribution function of temperature maximum S c (T max ) Gaussian distribution function S of temperature minima c (T min ) Gaussian distribution function S of standard deviation of voltage c (U sta ) Gaussian distribution function of temperature standard deviation S c (T sta )。
As a preferred technical scheme, a Gaussian distribution function S is obtained c (α)，S c (U max )，S c (U min )，S c (T max )，S c (T min )，S c (U sta ) And S c (T sta ) And then, the alpha and voltage temperature extreme values calculated by each frame of the vehicle are brought into respective Gaussian distribution functions, the confidence of each index value of the vehicle in the respective Gaussian distribution functions is obtained, the index values which are lower than q% and higher than p% confidence are defined as low health degree, the index values which are lower than or equal to p% confidence are defined as unhealthy, wherein q and p are values which are smaller than 100 and higher than 0.
As a preferred technical solution, the displaying of the estimation result at the automobile end specifically includes:
the automobile end displays the health degree of the automobile alpha and the voltage temperature extreme value through the preinstalled APP, the mobile phone APP and the display terminal.
As a preferred technical solution, the periodically correcting the estimation of the vehicle end using the estimation result of the data platform specifically includes:
gaussian distribution function S of charging SOC change rate of each vehicle periodically by data platform c (α), gaussian distribution function S of voltage maximum c (U max ) Gaussian distribution function S of voltage minima c (U min ) Gaussian distribution function of temperature maxima S c (T max ) Gaussian distribution function S of temperature minima c (T min ) Gaussian distribution function S of standard deviation of voltage c (U sta ) Gaussian distribution function of temperature Standard deviation S c (T sta ) The average value and the standard deviation are calibrated, namely, the Gaussian distribution function S (alpha) of the self vehicle and the Gaussian distribution function S (alpha) of the change rate of the charging SOC in the data platform, and the Gaussian distribution function S (U) of the maximum voltage value max ) Gaussian distribution function S (U) of voltage minima min ) Gaussian distribution function of temperature maximum S (T) max ) Temperature minimum Gaussian distribution function S (T) min ) Gaussian distribution function S (U) of standard deviation of voltage sta ) And a Gaussian distribution function S (T) of the standard deviation of temperature sta ) When the deviation of the average value and the standard deviation exceeds a set percentage, the automobile end downloads the Gaussian distribution function of the data platform end and replaces the Gaussian distribution function of the automobile end.
The invention also provides a vehicle-mounted power battery health degree evaluation system, which is applied to the vehicle-mounted power battery health degree evaluation method and comprises a data acquisition module, a data platform battery health estimation module, an automobile-end battery health estimation module, a result display module and a calibration module;
the data acquisition module is used for acquiring, storing and transmitting vehicle operation data;
the data platform battery health estimation module is used for estimating the health state of the power battery by the data platform based on the voltage temperature extreme value, the standard deviation and the SOC variation value of the power battery by adopting a Gaussian distribution method;
the automobile end battery health estimation module is used for estimating the health state of the power battery by adopting a Gaussian distribution method based on the voltage temperature extreme value, the standard deviation and the SOC variation value of the power battery at the automobile end;
the result display module is used for displaying the estimation result at the automobile end;
the calibration module is used for periodically correcting the estimation of the automobile end by using the estimation result of the data platform.
In another aspect of the present invention, a storage medium is provided, which stores a program, and when the program is executed by a processor, the method for evaluating the health of the vehicle-mounted power battery is implemented.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) The invention adopts a mode of cloud end and vehicle end double-end estimation comparison calibration to construct the model, and can customize an estimation model for each vehicle.
(2) The invention adopts the SOC change rate, the voltage of the power battery, the temperature extreme value and the standard deviation thereof as the evaluation indexes of the health degree of the power battery, and covers the indexes which can directly represent the health degree of the power battery.
(3) According to the invention, the charging capacity of the new energy automobile is integrated by adopting a line segment integration method, so that the charging capacity is more accurate.
Drawings
FIG. 1 is a schematic overall flow chart of a method for evaluating the health degree of a vehicle-mounted power battery according to an embodiment of the invention;
FIG. 2 is a schematic structural diagram of a vehicle-mounted power battery health assessment system according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of a storage medium according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
Examples
As shown in fig. 1, the present embodiment provides a method for evaluating health degree of a vehicle-mounted power battery, which includes processes of data acquisition, data storage, data analysis, result display, result calibration, and the like, and specifically includes the following steps:
s1, vehicle data storage and transmission:
the new energy automobile collects vehicle operation data in the operation process, transmits the collected data to a new energy automobile data platform, stores the power battery operation data, and deletes the operation data before 30 days when the new energy automobile is started and operated for the first time every day.
S2, estimating the health state of the power battery by the data platform by adopting a power battery health state estimation algorithm 1:
s2.1, storing the new energy automobile operation data after the new energy automobile operation data is collected by the data platform, extracting voltage, current and SOC data of each automobile in the past year of power battery operation, and calculating the charged amount N and the charging termination initial SOC value S in each charging action 0 ，S 1 . And setting the SOC change value brought by each kilowatt of electricity under each charging action as alpha = (S) 0 -S 1 ) N, in the formula; n is the charge amount in each charging action; s 0 ，S 1 Representing the SOC end point and start point for each charging event. Wherein the charging quantity N is obtained by integrating the current and voltage values acquired in the running process of the power battery
In the formula of U i As value of voltage at the point of acquisition, I i The current value of the acquisition point is, and delta t is the time interval of the acquisition point.
And S2.2, storing the operation data of the new energy automobile by the data platform after the operation data of the new energy automobile is collected, extracting voltage and temperature data of each automobile in the past one year of the operation process of the power battery, and acquiring voltage and temperature extreme value data (including a maximum value and a minimum value) and standard deviation of the voltage and the temperature of each frame of data.
S2.3, 10000 trolleys are randomly drawn from a data platform, the alpha value of each charging behavior of the sample vehicle is calculated, and a Gaussian distribution function S (alpha) of the value is constructed; and extracting voltage and temperature data of each vehicle in the power battery operation process in the past year, and acquiring voltage and temperature extreme value data (including a maximum value and a minimum value) and corresponding standard deviation of each frame of data. Constructing a Gaussian distribution function S (U) of voltage maxima max ) Gaussian distribution function S (U) of voltage minima min ) Gaussian distribution function of temperature maximum S (T) max ) Temperature minimum Gaussian distribution function S (T) min ) Gaussian distribution function S (U) of standard deviation of voltage sta ) And a Gaussian distribution function S (T) of the standard deviation of temperature sta )。
S3, estimating the health degree of the data platform:
obtaining S (alpha), S (U) max )，S(U min )，S(T max )，S(T min )，S(U sta ) And S (T) sta ) After the Gaussian distribution functions are equal, the calculated alpha, voltage and temperature extreme values and voltage and temperature standard deviation of each vehicle are brought into the respective Gaussian distribution functions, the confidence of each index value of the vehicle in the respective Gaussian distribution functions is obtained, the index values which are lower than q% and larger than p% confidence are defined as low health degree, the index values which are lower than or equal to p% confidence are defined as unhealthy, and q and p are values which are smaller than 100 and larger than 0.
S4, estimating the health state of the power battery by the vehicle end by adopting a power battery health state estimation algorithm 2:
s4.1, after the running data of the trolley is stored, the voltage, the current and the SOC data of the trolley in the running process of the power battery for the last 30 days are extracted, and the charged quantity N and the charging termination initial SOC value S in each charging action are calculated 0 ，S 1 . And the SOC change value per degree of electricity in each charging action is set as alpha = (S) 0 -S 1 ) N; n is the charge amount in each charging action; s 0 ，S 1 Representing the SOC end point and start point for each charging event. Wherein the charging quantity N is obtained by integrating the current and voltage values acquired in the running process of the power battery
In the formula of U i As value of voltage at the point of acquisition, I i The current value of the acquisition point is shown, and delta t is the time interval of the acquisition point.
And S4.2, after the vehicle end stores the operation data of the trolley, extracting voltage and temperature data of the power battery in the operation process of the power battery in the last 30 days of the trolley, and acquiring voltage and temperature extreme value data (including a maximum value and a minimum value) and voltage and temperature standard deviations of each frame of data.
S4.3, obtaining the alpha value of the vehicle during each charging action of the vehicle at the vehicle end, and constructing a Gaussian distribution function S of the alpha value c (α); the voltage and temperature data of the vehicle end in the process of the power battery running for the last 30 days of the vehicle are obtained, and voltage and temperature extreme value data (including a maximum value and a minimum value) of each frame of data are obtained. Constructing a Gaussian distribution function S of voltage maxima c (U max ) Gaussian distribution function s of voltage minima c (U min ) Gaussian distribution function of temperature maximum S c (T max ) Gaussian distribution function S of temperature minima c (T min ) Gaussian distribution function S of standard deviation of voltage c (U sta ) Gaussian distribution function of temperature standard deviation S c (T sta )。
S5, estimating the health degree of the vehicle end:
obtaining S c (α)，S c (U max )，S c (U min )，S c (T max )，S c (T min )，S c (U sta ) And S c (T sta ) After the Gaussian distribution function is equal to the others, the extreme values of alpha and voltage temperature calculated in each frame of the vehicle are brought into the respective Gaussian distribution function to obtain the value of each index of the vehicle in the respective Gaussian distribution functionThe index value which is lower than q% and is greater than the confidence of p% is defined as low health degree, the index value which is lower than or equal to the confidence of p% is defined as unhealthy, and q and p are values which are less than 100 and greater than 0.
S6, displaying the vehicle end estimation result:
the APP pre-installed at the vehicle end can be displayed by the mobile phone APP and other display terminals to display the estimation result of the health state of the power battery at the vehicle end, the estimation result comprises the health degree of the vehicle alpha and the voltage temperature extreme value, suggestion is provided, a user can observe the abnormal condition of the index value conveniently, and the abnormal condition of the power battery is appropriately processed by combining the self condition.
S7, calibrating a vehicle-end Gaussian distribution function model:
the platform can perform Gaussian distribution function S on the change rate of the charging SOC of each vehicle at intervals c (α), gaussian distribution function S of voltage maximum c (U max ) Gaussian distribution function S of voltage minima c (U min ) Gaussian distribution function of temperature maximum S c (T max ) Gaussian distribution function S of temperature minima c (T min ) Gaussian distribution function S of standard deviation of voltage c (U sta ) Gaussian distribution function of temperature Standard deviation S c (T sta ) The average value and the standard deviation are calibrated, namely, the Gaussian distribution function S (alpha) of the self vehicle and the Gaussian distribution function S (alpha) of the change rate of the charging SOC in the data platform, and the Gaussian distribution function S (U) of the maximum voltage value max ) Gaussian distribution function S (U) of voltage minima min ) Gaussian distribution function of temperature maximum S (T) max ) Temperature minimum Gaussian distribution function S (T) min ) Gaussian distribution function S (U) of standard deviation of voltage sta ) And a Gaussian distribution function S (T) of the standard deviation of temperature sta ) The average value and the standard deviation in the vehicle model and the platform model are calibrated, namely when the deviation of the average value and the standard deviation of variables in the Gaussian distribution function in the vehicle model and the platform model exceeds n percent (n is a natural number which is more than 0 and less than 100), the vehicle end downloads the platform model and replaces the self model.
As shown in fig. 2, in another embodiment of the present application, a vehicle-mounted power battery health assessment system is provided, which includes a data acquisition module, a data platform battery health estimation module, an automobile-side battery health estimation module, a result display module, and a calibration module;
the data acquisition module is used for acquiring, storing and transmitting vehicle operation data;
the data platform battery health estimation module is used for estimating the health state of the power battery by the data platform based on the voltage temperature extreme value, the standard deviation and the SOC variation value of the power battery by adopting a Gaussian distribution method;
the automobile end battery health estimation module is used for estimating the health state of the power battery by adopting a Gaussian distribution method based on the voltage temperature extreme value, the standard deviation and the SOC variation value of the power battery at the automobile end;
the result display module is used for displaying the estimation result at the automobile end;
the calibration module is used for periodically correcting the estimation of the automobile end by using the estimation result of the data platform.
It should be noted that the system provided in the above embodiment is only exemplified by the division of the above functional modules, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the above described functions.
As shown in fig. 3, in another embodiment of the present application, a storage medium is further provided, where a program is stored, and when the program is executed by a processor, the method for evaluating health degree of a vehicle-mounted power battery of the foregoing embodiment is implemented, specifically:
s1, vehicle operation data are collected, stored and transmitted;
s2, estimating the health state of the power battery by a Gaussian distribution method on the basis of the extreme voltage and temperature value, the standard deviation and the SOC variation value of the power battery by a data platform;
s3, estimating the health state of the power battery by the automobile end based on the extreme voltage temperature value, standard deviation and SOC variation value of the power battery by adopting a Gaussian distribution method;
s4, displaying an estimation result at the automobile end;
and S5, periodically correcting the estimation of the automobile end by using the estimation result of the data platform.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A vehicle-mounted power battery health degree assessment method is characterized by comprising the following steps:
collecting, storing and transmitting vehicle operation data;
the data platform estimates the health state of the power battery by adopting a Gaussian distribution method based on the extreme voltage and temperature value, the standard deviation and the SOC variation value of the power battery;
the automobile end estimates the health state of the power battery by adopting a Gaussian distribution method based on the extreme voltage and temperature value, the standard deviation and the SOC variation value of the power battery;
displaying the estimation result at the automobile end;
and periodically correcting the estimation of the automobile end by using the estimation result of the data platform.
2. The vehicle-mounted power battery health assessment method according to claim 1, wherein the data acquisition, storage and transmission specifically comprises:
the automobile end collects the vehicle operation data in the operation process, stores the power battery operation data, and transmits the collected data to the new energy automobile data platform.
3. The vehicle-mounted power battery health degree evaluation method according to claim 1, wherein the data platform estimates the health state of the power battery by adopting a Gaussian distribution method based on the extreme voltage temperature value, the standard deviation and the SOC variation value of the power battery, and specifically comprises the following steps:
the data platform extracts voltage, current and SOC data of each vehicle in the power battery operation process in the past year;
calculating the charged quantity N and the initial SOC value S in each charging action 0 ，S 1 ；
The SOC change per watt hour per charging behavior is defined as α = (S) 0 -S 1 ) In the formula, alpha is the change rate of the charging SOC; s. the 0 ，S 1 Representing the SOC end point and the SOC starting point during each charging action; wherein the charging quantity N in each charging action is obtained by integrating the current and voltage values acquired in the running process of the power battery,
in the formula of U i As the voltage value, I, of the acquisition point i The current value of the acquisition point and delta t are the time interval of the acquisition point;
the data platform extracts voltage and temperature data of each vehicle in the power battery operation process in the past year, and acquires voltage and temperature extreme value data and standard deviation of the voltage and the temperature of each frame of data;
randomly extracting a plurality of trolleys from a data platform, calculating an alpha value of each charging behavior of the sample trolley, and constructing a Gaussian distribution function S (alpha) of the value; extracting voltage and temperature data of each vehicle in the power battery operation process in the past year, and acquiring voltage and temperature extreme value data and corresponding standard deviation of each frame of data;
constructing a Gaussian distribution function S (U) of voltage maxima max ) Gaussian distribution function S (U) of voltage minima min ) Gaussian distribution function of temperature maximum S (T) max ) Temperature minimum Gaussian distribution function S (T) min ) Gaussian distribution function S (U) of standard deviation of voltage sta ) And a Gaussian distribution function S (T) of the standard deviation of temperature sta )。
4. The vehicle-mounted power battery health assessment method according to claim 3, characterized in that a Gaussian distribution function S (α), S (U) is obtained max )，S(U min )，S(T max )，S(T min )，S(U sta ) And S (T) sta ) And then, substituting the alpha, the voltage temperature extreme value and the voltage temperature standard deviation which are obtained by each vehicle through calculation into the respective Gaussian distribution function, obtaining the confidence of each index value of the vehicle in the respective Gaussian distribution function, defining the index values which are lower than q% and are greater than p% confidence as low health degree, defining the index values which are lower than or equal to p% confidence as unhealthy, and defining the q and the p as values which are less than 100 and greater than 0.
5. The method for evaluating the health degree of the vehicle-mounted power battery according to claim 1, wherein the vehicle end estimates the health state of the power battery by adopting a Gaussian distribution method based on a power battery voltage temperature extreme value, a standard deviation and an SOC variation value, and specifically comprises the following steps:
after the running data of the trolley is stored, the voltage, the current and the SOC data of the trolley in the running process of the power battery for 30 days are extracted by the automobile end, and the charged quantity N and the charging initial SOC value S in each charging action are calculated 0 ，S 1 ；
The SOC change per degree of electricity per charging operation is defined as α =(S 0 -S 1 ) Where α is the rate of change of the charging SOC, S 0 ，S 1 Representing the SOC end point and the SOC starting point during each charging action; wherein the charging quantity N in each charging action is obtained by integrating the current and voltage values acquired in the running process of the power battery,
in the formula of U i As value of voltage at the point of acquisition, I i The current value of the acquisition point and delta t are the time interval of the acquisition point;
after the running data of the trolley is stored, the voltage and temperature data of the trolley in the running process of the power battery in the running process of the trolley for 30 days are extracted by the automobile end, and the voltage and temperature extreme value data and the voltage and temperature standard deviation of each frame of data are obtained;
the automobile end obtains the alpha value of the automobile in each charging action, and a Gaussian distribution function S of the value is constructed c (alpha); the method comprises the steps that the voltage and temperature data of the vehicle end in the process of running a power battery for 30 days in the past are obtained, and voltage and temperature extreme value data of each frame of data are obtained;
constructing a Gaussian distribution function S of voltage maxima c (U max ) Gaussian distribution function S of voltage minima c (U min ) Gaussian distribution function of temperature maxima S c (T max ) Gaussian distribution function of temperature minima S c (T min ) Gaussian distribution function S of standard deviation of voltage c (U sta ) Gaussian distribution function of temperature standard deviation S c (T sta )。
6. The vehicle-mounted power battery health assessment method according to claim 5, wherein a Gaussian distribution function S is obtained c (α)，S c (U max )，S c (U min )，S c (T max )，S c (T min )，S c (U sta ) And S c (T sta ) Then, the alpha and voltage temperature extreme values calculated by each frame of the vehicle are brought into the vehicleThe confidence degree of each index value of the vehicle in the Gaussian distribution function is obtained, the index values which are lower than q% and larger than p% confidence degree are defined as low health degree, the index values which are lower than or equal to p% confidence degree are defined as unhealthy, and q and p are values which are smaller than 100 and larger than 0.
7. The method for evaluating the health degree of the vehicle-mounted power battery according to claim 1, wherein the display of the estimation result at the vehicle end specifically comprises:
the automobile end displays the health state estimation result of the power battery at the automobile end through the preinstalled APP, the mobile phone APP and the display terminal, and the health degree of the automobile alpha and the voltage temperature extreme value is included.
8. The vehicle-mounted power battery health assessment method according to claim 1, wherein the estimation result of the data platform is periodically used to correct the estimation of the vehicle side, specifically:
gaussian distribution function S of charging SOC change rate of each vehicle periodically by data platform c (α), gaussian distribution function S of voltage maxima c (U max ) Gaussian distribution function S of voltage minima c (U min ) Gaussian distribution function of temperature maximum S c (T max ) Gaussian distribution function S of temperature minima c (T min ) Gaussian distribution function S of standard deviation of voltage c (U sta ) Gaussian distribution function of temperature standard deviation S c (T sta ) The mean value and standard deviation of the vehicle are calibrated, that is, when the Gaussian distribution function of the vehicle itself and the Gaussian distribution function S (alpha) of the change rate of the charging SOC in the data platform, the Gaussian distribution function S (U) of the voltage maximum value max ) Gaussian distribution function S (U) of voltage minima min ) Gaussian distribution function of temperature maximum S (T) max ) Temperature minimum Gaussian distribution function S (T) min ) Gaussian distribution function S (U) of standard deviation of voltage sta ) And a Gaussian distribution function S (T) of the standard deviation of temperature sta ) Mean and standard deviation of (1)And when the deviation exceeds a set percentage, the automobile end downloads the Gaussian distribution function of the data platform end and replaces the Gaussian distribution function of the automobile end.
9. The vehicle-mounted power battery health degree evaluation system is applied to the vehicle-mounted power battery health degree evaluation method in any one of claims 1 to 8, and comprises a data acquisition module, a data platform battery health degree estimation module, an automobile end battery health degree estimation module, a result display module and a calibration module;
the data acquisition module is used for acquiring, storing and transmitting vehicle operation data;
the data platform battery health estimation module is used for estimating the health state of the power battery by adopting a Gaussian distribution method on the basis of the voltage temperature extreme value, the standard deviation and the SOC variation value of the power battery by the data platform;
the automobile end battery health estimation module is used for estimating the health state of the power battery by adopting a Gaussian distribution method based on the voltage temperature extreme value, the standard deviation and the SOC variation value of the power battery at the automobile end;
the result display module is used for displaying the estimation result at the automobile end;
the calibration module is used for periodically correcting the estimation of the automobile end by using the estimation result of the data platform.
10. A storage medium storing a program, characterized in that: the program, when executed by a processor, implements the vehicle-mounted power battery health assessment method of any one of claims 1-8.
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