CN112666464A - Battery health state prediction method and device, electronic equipment and readable storage medium - Google Patents
Battery health state prediction method and device, electronic equipment and readable storage medium Download PDFInfo
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- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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- 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 embodiment of the invention provides a battery health state prediction method, a battery health state prediction device, electronic equipment and a readable storage medium, and relates to the technical field of computers.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for predicting a battery health status, an electronic device, and a readable storage medium.
Background
At present, with the enhancement of environmental awareness of people, the development of new energy vehicles is more and more rapid, and more people select new energy vehicles as transportation means for traveling, wherein the new energy vehicles use electric energy as main energy, and therefore, the battery life of the new energy vehicles is strongly related to the performance of the new energy vehicles.
The battery life can be expressed by the State of health (SOH) of the battery, which is a parameter that cannot be directly obtained and needs to be determined by a prediction algorithm.
In the related art, the battery SOH at a future time is often predicted by external characteristic parameters such as current and voltage, but it is difficult to ensure the accuracy of the battery SOH by such a prediction method.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a method and an apparatus for predicting a state of health of a battery, an electronic device, and a readable storage medium, so as to improve an accuracy of predicting the state of health of the battery.
In a first aspect, a battery state of health prediction method is provided, where the method is applied to an electronic device, and the method includes:
and determining the current driving mileage and the current battery parameters of the target electric vehicle.
And determining the current battery health state corresponding to the current driving mileage based on a preset first corresponding relation between the driving mileage and the battery health state.
And inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model so as to determine a target battery health state corresponding to the target driving mileage of the target electric vehicle.
In a second aspect, an apparatus for predicting a state of health of a battery is provided, the apparatus being applied to an electronic device, and the apparatus including:
the first determination module is used for determining the current driving mileage and the current battery parameters of the target electric vehicle.
And the second determination module is used for determining the current battery health state corresponding to the current driving mileage based on the preset first corresponding relation between the driving mileage and the battery health state.
And the prediction module is used for inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model so as to determine the target battery health state corresponding to the target driving mileage of the target electric vehicle.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer program instructions, where the one or more computer program instructions are executed by the processor to implement the method according to the first aspect.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium on which computer program instructions are stored, which when executed by a processor implement the method according to the first aspect.
In a fifth aspect, embodiments of the present invention provide a computer program product comprising computer programs/instructions which, when executed by a processor, implement the method according to the first aspect.
According to the embodiment of the invention, the current health state corresponding to the current driving mileage of the target electric vehicle can be determined through the preset first corresponding relation, then the current battery health state, the current driving mileage and the current battery parameters can be synthesized based on the pre-trained machine learning model, and the target battery health state corresponding to the target driving mileage of the target electric vehicle is predicted.
Drawings
The above and other objects, features and advantages of the embodiments of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram of a method for predicting a state of health of a battery according to an embodiment of the present invention;
fig. 2 is a flowchart of a battery health status prediction method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a range section in a first mapping relationship according to an embodiment of the present invention;
FIG. 4 is a flow chart of another method for predicting the state of health of a battery according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a battery capacity curve at 25 degrees Celsius according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a second corresponding relationship according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a first mapping relationship according to an embodiment of the present invention;
FIG. 8 is a graph comparing experimental results provided by embodiments of the present invention;
fig. 9 is a schematic structural diagram of a battery state of health predicting apparatus according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present invention.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the description, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
At present, with the development of new energy vehicles becoming more and more rapid, more and more people select new energy vehicles as transportation means for traveling, taking new energy vehicles as an example, the new energy vehicles are applied to various scenes (such as new energy private cars, new energy buses, shared electric cars and the like), the new energy vehicles take electric energy as main energy, that is, the new energy vehicles obtain electric energy through batteries, and therefore the battery life of the new energy vehicles is strongly related to the performance of the new energy vehicles.
The battery life can be expressed by the State of health (SOH) of the battery, which is a parameter that cannot be directly obtained and needs to be determined by a prediction algorithm.
In the related art, the SOH of the battery can be estimated from two dimensions, one of which is that the SOH of the battery can be estimated by observing the reaction mechanism and parameters inside the battery, such as loss of active lithium ions, collapse of material lattices, anode and cathode materials inside the battery, diaphragm size, electrolyte ion concentration and diffusion coefficient, and the like, based on the dimension of an electrochemical mechanism. Secondly, the SOH of the battery can be estimated through external characteristic parameters (such as current, voltage, temperature, and the like) of the battery, but the method cannot accurately represent the attenuation of the battery, that is, the method cannot accurately predict the future SOH of the battery.
In order to accurately predict the future SOH of the battery, an embodiment of the present invention provides a method for predicting a state of health of a battery, where the method may be applied to an electronic device, and predict the SOH of the battery of a target electric train at a target driving mileage in the future through the electronic device, where the electronic device may be a terminal or a server, the terminal may be a smart phone, a tablet Computer, a Personal Computer (PC), or the like, and the server may be a single server, a server cluster configured in a distributed manner, or a cloud server.
As shown in fig. 1, fig. 1 is a schematic diagram of a battery state of health prediction method according to an embodiment of the present invention, where the schematic diagram includes: a target electric train 11, a first correspondence relation 12, and a machine learning model 13.
First, the electronic device may obtain a current driving mileage of the target electric car 11 and a current battery parameter, where the target electric car 11 may be an electric car shown in fig. 1, and may also be an electric two-wheeled vehicle, an electric three-wheeled vehicle, and so on, and this is not limited in the embodiment of the present invention, and the current driving mileage is used to represent the accumulated driving mileage of the target electric car 11, and the current battery parameter is a relevant parameter of the battery in the target electric car 11.
Then, the electronic device may determine the current battery health state corresponding to the current driving range according to a preset first corresponding relationship 12, where the first corresponding relationship 12 is a corresponding relationship between the driving range and the battery health state, and when the driving range is known, may determine to determine the battery health state corresponding to the driving range according to the first corresponding relationship 12.
Then, the electronic device may input the current driving mileage, the current battery health state, and the current battery parameter of the target electric train 11 into the machine learning model 13 trained in advance, so that the machine learning model 13 outputs the target battery health state of the target electric train 11, where the target battery health state is used to characterize the battery health state of the target electric train 11 at a certain future mileage, that is, the machine learning model 13 may predict the battery health state of the target electric train 11 at any future mileage from the current driving mileage, the current battery health state, and the current battery parameter of the target electric train 11.
The following describes a method for predicting a state of health of a battery according to an embodiment of the present invention in detail with reference to a specific embodiment, as shown in fig. 2, the specific steps are as follows:
in step 21, the current driving range and the current battery parameters of the target electric vehicle are determined.
In practical applications, the current driving mileage is used to represent the accumulated driving mileage of the target electric vehicle, and the service life of one electric vehicle can be generally represented by accumulating the driving mileage, for example, for one electric vehicle, when the electric vehicle drives to about 12 kilometers, the electric vehicle will run out of service life and be in a scrapped state.
In addition, in an alternative embodiment, the current battery parameter may include a battery temperature, a discharge current and a discharge voltage, wherein the battery temperature refers to a phenomenon that the battery surface generates heat due to chemical, electrochemical change, electron transfer, material transfer and the like of an internal structure of the battery when the battery is in use, and the battery temperature can be generally measured by a battery temperature sensor.
Of course, the battery temperature, the discharge current, and the discharge voltage are only an alternative implementation of the current battery parameters, and the current battery parameters may also include other data, which is not limited in the embodiment of the present invention.
In step 22, a current battery health status corresponding to the current driving mileage is determined based on a first correspondence relationship between the preset driving mileage and the battery health status.
The first corresponding relationship may be a pre-established corresponding relationship, which may represent a corresponding relationship between the driving mileage of an electric car of a certain type and the battery health status of the electric car of the certain type under normal conditions.
For example, as shown in fig. 3, fig. 3 is a schematic diagram of a mileage section in a first corresponding relationship according to an embodiment of the present invention, where the schematic diagram includes: a number axis comprising a plurality of consecutive mileage sections.
In practical applications, the mileage life of an electric vehicle is about 12 kilometers, that is, when an electric vehicle runs to about 12 kilometers, the life will be exhausted.
Therefore, in the embodiment of the present invention, 12 kilometers may be used as the upper limit of the mileage, and a plurality of mileage sections are preset, as shown in fig. 3, the axis in fig. 3 is used to represent a mileage range of 0 to 12 kilometers, where in the embodiment of the present invention, 0.5 kilometer is used as a length of one mileage section, and 12 kilometers are divided into 24 mileage sections.
In practical applications, an excessively large range of the mileage segment (for example, 1 kilometer is used as a length of one mileage segment) may cause too many or too few sample trolleys corresponding to a certain mileage segment, for example, 15 sample trolleys correspond to a mileage segment of 1 kilometer to 2 kilometers, and 1 sample trolley corresponds to 2 kilometers to 3 kilometers, so that the distribution of samples is not uniform, and the accuracy of prediction is low.
Accordingly, if the range included in the mileage segment is too small (for example, 0.1 kilometer is used as a length of one mileage segment), the difference between data corresponding to adjacent mileage segments is small, and the computing resources are wasted.
Therefore, in a preferred mode, as shown in fig. 3, 0.5 kilometer can be used as the length of the mileage segment, which can ensure that the samples are uniformly distributed, and can also ensure that the data corresponding to the adjacent mileage segments have a certain difference.
In an alternative embodiment, as shown in fig. 4, the first corresponding relationship may be determined based on the following steps:
in step 41, real-time battery parameters of a plurality of sample electric cars and attenuation coefficients of corresponding models of the sample electric cars are obtained.
The model of the sample electric car is the same as that of the target electric car, and the real-time travel mileage of each sample electric car corresponds to a preset travel mileage section.
At step 42, for each sample trolley, a real-time battery state of health of the sample trolley is determined based on the real-time battery parameters and the attenuation coefficient.
Wherein the attenuation coefficient may correct the real-time battery parameter when determining the real-time battery health status of the sample electric vehicle.
In the embodiment of the invention, the attenuation coefficient can be determined based on a battery capacity curve of the corresponding model of the sample tram, and the battery capacity curve is a curve obtained by fitting at a specific temperature.
Specifically, as shown in fig. 5, fig. 5 is a schematic diagram of a battery capacity curve at 25 degrees celsius according to an embodiment of the present invention, where the schematic diagram includes: time axis (in hours), battery voltage axis (in volts), current/battery capacity axis (in amps/amp-hours), battery voltage curve, battery capacity curve, and charging current curve.
In the embodiment of the present invention, the schematic diagram of the battery capacity curve shown in fig. 5 is obtained by fitting battery data of electric vehicles of the same model during charging, and in the fitting process, data such as current, voltage, battery temperature, and time during charging the State of charge (SOC) of each electric vehicle of the same model from 20% to 100% can be obtained, and the data are fitted to obtain the battery capacity curve shown in fig. 5.
The SOC is used to represent the state of charge of the battery, and is usually used to reflect the remaining capacity of the battery, and is numerically defined as the ratio of the remaining capacity to the battery capacity, and is usually expressed as a percentage. The value range of the SOC is 0 to 1 (i.e., 0% to 100%), and when the SOC is 0, the battery is completely discharged, and when the SOC is 1, the battery is completely charged.
Since the battery temperature is often not kept constant during the charging process, that is, the battery temperature cannot be maintained at 25 degrees celsius all the time, there is a difference between the data during actual charging and the data during charging in an ideal state (the ideal state is a curve shown in fig. 5), and further, the attenuation coefficient can be determined by the difference, specifically, the formula of the attenuation coefficient may be as follows:
wherein, P is used for representing a damping coefficient, the damping coefficient can correct the battery data to a level of 25 ℃ when the battery is charged, and Charge Energy is used for representing Charge Energy, namely the current of the battery in a full-Charge state, socendFor characterizing the remaining charge of the battery at the end of the charge, socbeginFor characterizing the remaining battery charge at the beginning of charging, tem _ minendThe method is used for representing the lowest temperature of the battery cell during battery charging.
After determining the attenuation coefficient, a real-time battery health status of each sample trolley may be determined based on the real-time battery parameter and the attenuation coefficient.
In an alternative embodiment, the real-time battery parameters may include a charging current and a cumulative charging time, and step 42 may be specifically performed as: determining an accumulated charging capacity of the sample electric car based on the charging current, the accumulated charging time and the attenuation coefficient, and determining a real-time battery health state of the sample electric car based on the accumulated charging capacity and a nominal capacity of the sample electric car.
Specifically, for the accumulated charging capacity of the sample electric car, the accumulated charging capacity of the sample electric car may be calculated based on the following formula:
wherein, WtCumulative charge capacity, P, for characterizing a sample trolleytFor characterizing the attenuation coefficient, ItFor characterizing the charging current and t for characterizing the time.
In practical application, the current of the battery is always constant in the charging process, and meanwhile, the electric quantity discharged in the working process of the battery is approximately equal to the electric quantity charged by the battery, so that the accuracy of the accumulated charging electric quantity calculated based on the charging current is high.
Further, based on the accurate accumulated charged power and the nominal capacity of the sample trolley, an accurate real-time battery health state of the sample trolley can be determined.
In an alternative embodiment, the process of determining the real-time battery health state of the sample trolley based on the accumulated charged capacity and the nominal capacity of the sample trolley may be performed as: and determining the real-time cycle number of the sample electric car based on the accumulated charging electric quantity and the nominal capacity of the sample electric car, and determining the real-time battery health state corresponding to the real-time cycle number based on the real-time cycle number and a preset second corresponding relation.
The real-time cycle number is used for representing the number of charge/discharge cycles of the sample electric car, the second corresponding relation is used for representing the corresponding relation between the cycle number and the health state of the battery, and the nominal capacity refers to the capacity of the battery measured after the battery is completely discharged, namely the nominal capacity of the battery can be used for representing the electric quantity which can be stored by the battery.
Specifically, after the accumulated charging capacity of the sample electric car (i.e., the accumulated charging capacity of the battery in the sample electric car) is determined, the accumulated charging capacity of the sample electric car may be divided by the nominal capacity of the battery of the sample electric car to determine the number of real-time cycles.
The formula for determining the real-time cycle number of the sample tramcar can be as follows:
wherein N istFor characterizing the real-time cycle number, i.e. the current cycle number, W, of the sample trolley batterytIs used for representing the accumulated charging Capacity of the sample electric carBOLFor characterizing the nominal capacity of the sample trolley battery.
In the embodiment of the invention, the accumulated charging capacity has higher precision, and the nominal capacity is also a more accurate numerical value, so the real-time cycle number corresponding to the sample electric car can represent the accurate battery cycle number of the sample electric car.
Further, based on the real-time cycle number and a preset second corresponding relationship, the real-time battery health state corresponding to the real-time cycle number can be determined.
For example, as shown in fig. 6, fig. 6 is a schematic diagram of a second corresponding relationship provided in an embodiment of the present invention, where the schematic diagram includes: the battery cycle number axis, SOH/% axis, and line segment used to characterize SOH/% as a function of battery cycle number.
Fig. 6 is a corresponding relationship between the number of battery cycles and the battery capacity fade (i.e., SOH) determined with reference to the current rate of 1C, where C is used to represent the battery charge/discharge capacity rate, and 1C represents the current intensity when the battery is completely discharged for one hour. For example, a cell with a nominal capacity of 2200mAh was discharged at 1C intensity for 1 hour to completion, at which time the discharge current was 2200 mA.
As shown in fig. 6, when the battery cycle number is 0, the SOH/% of the battery is 100, that is, when the battery cycle number is 0, the life consumption of the battery is 0 (i.e., the SOH of the battery at this time is 100%), and as the battery cycle number increases, the life of the battery is gradually consumed, and the SOH/% of the battery becomes lower.
Through the second corresponding relationship shown in fig. 6, after the real-time cycle number of the sample electric car is determined, the SOH/%, which corresponds to the real-time cycle number, can be determined, that is, the SOH which corresponds to the real-time cycle number is determined.
In another alternative implementation, the second corresponding relationship may also be in the form of a table, as shown in the following table i, where the table i is another table of the second corresponding relationship provided in the embodiment of the present invention, and the table is as follows:
| Number of battery cycles | SOH/％ |
| 0-199 | 100 |
| 200-399 | 95 |
| 400-599 | 90 |
| 600-799 | 85 |
| 800-899 | 80 |
| 900-1000 | 75 |
As can be seen from table one, the relationship between the battery cycle number and the SOH can also be determined through the form of the table, and in addition, the second corresponding relationship can also be represented based on other forms, which is not described in detail in the embodiment of the present invention.
At step 43, a first correspondence is established for each mileage run based on the real-time battery health status of each sample electric vehicle.
In an alternative embodiment, step 43 may be performed as: the method comprises the steps of determining the average value of the real-time battery health states of the sample electric cars corresponding to the traveled mileage sections, and establishing a first corresponding relation between the average value of the real-time battery health states and the traveled mileage sections.
For example, as shown in fig. 7, fig. 7 is a schematic diagram of a first corresponding relationship provided in an embodiment of the present invention, where the schematic diagram includes: the SOH value comprises a plurality of axes of continuous mileage sections and the SOH value corresponding to each mileage section.
As can be seen from fig. 7, when the current driving range of the target electric vehicle is determined, the SOH corresponding to the current driving range may be determined according to the first correspondence relationship shown in fig. 7, and further, the target battery health state corresponding to the target driving range of the target electric vehicle may be determined based on the SOH corresponding to the current driving range.
In step 23, the current battery health status, the current driving mileage and the current battery parameters are input into a machine learning model trained in advance to determine a target battery health status corresponding to the target driving mileage of the target electric vehicle.
In an optional implementation manner, the Machine learning model may be constructed based on xgboost (extreme Gradient boosting), Support Vector Machine (SVM), or linear regression algorithm (linear regression).
The XGboost is an optimized distributed Gradient enhancement library, is an improvement on a Gradient enhancement algorithm, uses a Newton method when solving an extreme value of a loss function, expands a loss function Taylor to a second order, and adds a regularization term into the loss function, so that the XGboost is essentially improved on the basis of a Gradient Boosting Decision Tree (GBDT) algorithm, and is more efficient, flexible and portable.
The SVM is a generalized linear classifier (generalized linear classifier) for binary classification of data in a supervised learning mode, a decision boundary of the SVM is a maximum-margin hyperplane (maximum-margin hyperplane) for solving learning samples, the SVM calculates an empirical risk (empirical risk) by using a hinge loss function (change loss), and a regularization term is added into a solving system to optimize a structural risk (structural risk), and the SVM is a classifier with sparsity and robustness. SVMs can be classified non-linearly by a kernel method, which is one of the common kernel learning (kernel learning) methods.
Linear regression algorithms, which are statistical analysis methods that use regression analysis in mathematical statistics to determine the quantitative relationships of interdependencies between two or more variables, are widely used, where linear regression algorithms are a regression analysis that uses a least squares function called a linear regression equation to model the relationship between one or more independent variables and dependent variables, where the function is a linear combination of one or more model parameters called regression coefficients, where the case of only one independent variable is called simple regression, and the case of more than one independent variable is called multiple regression.
In one example, the current driving range of the target electric train a is 5 kilometers, the SOH (i.e., the current battery health state) corresponding to the current driving range (5 kilometers) of the target electric train a can be determined through a first corresponding relationship (e.g., the corresponding relationship shown in fig. 7) according to an embodiment of the present invention, and then the current battery health state, the current driving range, and the current battery parameters can be input into a machine learning model trained in advance to determine the battery health state (i.e., the target battery health state) of the target electric train a at 10 kilometers.
According to the embodiment of the invention, the current health state corresponding to the current driving mileage of the target electric vehicle can be determined through the preset first corresponding relation, then the current battery health state, the current driving mileage and the current battery parameters can be synthesized based on the pre-trained machine learning model, and the target battery health state corresponding to the target driving mileage of the target electric vehicle is predicted.
In one example, 4 types of target electric vehicles (the target electric vehicle a, the target electric vehicle B, the target electric vehicle C, and the target electric vehicle D) are randomly selected and tested, and the 4 types of target electric vehicles are respectively predicted by the battery health state prediction method provided by the embodiment of the invention.
The specific results are shown in the following table:
watch two
Where from the capacity in table two to the battery capacity, the predicted capacity may be calculated based on the predicted SOH. In addition, the first data set and the second data set represent mileage parameters and battery parameters of each target electric car in two different time periods respectively.
As can be seen from table two, according to the embodiment of the present invention, the prediction errors of the battery capacities of the target electric vehicles (i.e., the battery SOH of the target electric vehicle) are all less than 5%, and the accuracy is high.
In another example, the embodiment of the present invention extracts data (a mileage parameter and a battery parameter) of the target electric car E at a first time period and predicts the SOH of the target electric car E at 12 kilometers (a target travel mileage) based on the data of the first time period, and then, the embodiment of the present invention also extracts data (a mileage parameter and a battery parameter) of the target electric car E at a second time period and predicts the SOH of the target electric car E at 12 kilometers (a target travel mileage) based on the data of the second time period.
Specifically, as shown in fig. 8, fig. 8 is a comparison graph of experimental results provided by the embodiment of the present invention, where the comparison graph includes: a mileage axis (in ten thousand kilometers), an SOH (%) axis, an SOH variation tendency line segment of the target electric car E predicted based on the data of the first time period, an SOH variation tendency line segment of the target electric car E predicted based on the data of the second time period, and actually measured data.
As can be seen from fig. 8, according to the embodiment of the present invention, the error between the predicted SOH value and the measured SOH value of the target electric train E is small, that is, the prediction method according to the embodiment of the present invention has a high accuracy.
Based on the same technical concept, an embodiment of the present invention further provides a battery state of health prediction apparatus, as shown in fig. 9, the apparatus includes: a first determination module 91, a second determination module 92 and a prediction module 93.
The first determining module 91 is used for determining the current driving mileage and the current battery parameters of the target electric vehicle.
And the second determining module 92 is configured to determine a current battery health state corresponding to the current driving mileage based on a preset first corresponding relationship between the driving mileage and the battery health state.
And the prediction module 93 is configured to input the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model, so as to determine a target battery health state corresponding to the target driving mileage of the target electric vehicle.
Optionally, the first corresponding relationship is determined based on the following modules:
the acquisition module is used for acquiring real-time battery parameters of a plurality of sample electric cars and attenuation coefficients of the corresponding models of the sample electric cars, the models of the sample electric cars are the same as that of the target electric car, and the real-time travel mileage of each sample electric car corresponds to a preset travel mileage section.
And the third determination module is used for determining the real-time battery health state of the sample electric car according to the real-time battery parameters and the attenuation coefficient.
And the establishing module is used for establishing a first corresponding relation aiming at each driving mileage section based on the real-time battery health state of each sample electric car.
Optionally, the real-time battery parameters include a charging current and an accumulated charging time.
A third determining module, specifically configured to:
and determining the accumulated charging electric quantity of the sample electric car based on the charging current, the accumulated charging time and the attenuation coefficient.
And determining the real-time battery health state of the sample electric car based on the accumulated charging capacity and the nominal capacity of the sample electric car.
Optionally, the third determining module is specifically configured to:
and determining the real-time cycle number of the sample electric car based on the accumulated charging electric quantity and the nominal capacity of the sample electric car, wherein the real-time cycle number is used for representing the number of charging/discharging cycles of the sample electric car.
And determining the real-time battery health state corresponding to the real-time cycle number based on the real-time cycle number and a preset second corresponding relation, wherein the second corresponding relation is used for representing the corresponding relation between the cycle number and the battery health state.
Optionally, the establishing module is specifically configured to:
and determining the average value of the real-time battery health states of the sample electric vehicles corresponding to the traveled mileage sections.
And establishing a first corresponding relation between the average value of the real-time battery health state and the driving mileage section.
Optionally, the attenuation coefficient is determined based on a battery capacity curve of the corresponding model of the sample electric car, and the battery capacity curve is a curve obtained by fitting at a specific temperature.
Optionally, the current battery parameters include a battery temperature, a discharge current, and a discharge voltage.
Optionally, the machine learning model is constructed based on XGBoost, a support vector machine, or a linear regression algorithm.
According to the embodiment of the invention, the current health state corresponding to the current driving mileage of the target electric vehicle can be determined through the preset first corresponding relation, then the current battery health state, the current driving mileage and the current battery parameters can be synthesized based on the pre-trained machine learning model, and the target battery health state corresponding to the target driving mileage of the target electric vehicle is predicted.
Fig. 10 is a schematic diagram of an electronic device of an embodiment of the invention. As shown in fig. 10, the electronic device shown in fig. 10 is a general address query device, which includes a general computer hardware structure, which includes at least a processor 101 and a memory 102. The processor 101 and the memory 102 are connected by a bus 103. The memory 102 is adapted to store instructions or programs executable by the processor 101. Processor 101 may be a stand-alone microprocessor or a collection of one or more microprocessors. Thus, the processor 101 implements the processing of data and the control of other devices by executing instructions stored by the memory 102 to perform the method flows of embodiments of the present invention as described above. The bus 103 connects the above-described components together, and also connects the above-described components to a display controller 104 and a display device and an input/output (I/O) device 105. Input/output (I/O) devices 105 may be a mouse, keyboard, modem, network interface, touch input device, motion sensing input device, printer, and other devices known in the art. Typically, the input/output devices 105 are coupled to the system through input/output (I/O) controllers 106.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus (device) or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations of methods, apparatus (devices) and computer program products according to embodiments of the invention. It will be understood that each flow in the flow diagrams can be implemented by computer program instructions.
These computer program instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
Another embodiment of the invention is directed to a non-transitory storage medium storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method of the above embodiments may be accomplished by specifying related hardware through a program, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps in the method of 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.
Another embodiment of the invention relates to a computer program product comprising computer programs/instructions which, when executed by a processor, may implement some or all of the above-described method embodiments.
That is, as will be understood by those skilled in the art, the embodiments of the present invention may be implemented by a processor executing a computer program product (computer program/instructions) to specify relevant hardware (including the processor itself), so as to implement all or part of the steps in the method of the above embodiments.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The embodiment of the application discloses TS1 and a battery state of health prediction method, wherein the method comprises the following steps:
determining the current driving mileage and current battery parameters of the target electric car;
determining the current battery health state corresponding to the current driving mileage based on a first corresponding relation between the preset driving mileage and the battery health state; and
and inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model so as to determine a target battery health state corresponding to the target driving mileage of the target electric vehicle.
TS2, the method of TS1, wherein the first correspondence is determined based on:
obtaining real-time battery parameters of a plurality of sample electric cars and attenuation coefficients of the sample electric cars of corresponding models, wherein the sample electric cars and the target electric car are of the same model, and the real-time travel mileage of each sample electric car corresponds to a preset travel mileage section;
for each sample trolley, determining the real-time battery health state of the sample trolley based on the real-time battery parameters and the attenuation coefficient; and
and aiming at each driving mileage section, establishing the first corresponding relation based on the real-time battery health state of each sample electric car.
TS3, the method of TS2, wherein the real-time battery parameters include a charging current and a cumulative charging time;
the determining a real-time battery state of health of the sample trolley based on the real-time battery parameters and the attenuation coefficient includes:
determining an accumulated charging capacity of the sample electric car based on the charging current, the accumulated charging time and the attenuation coefficient; and
and determining the real-time battery health state of the sample electric car based on the accumulated charging capacity and the nominal capacity of the sample electric car.
TS4, the method of TS3, wherein the determining the real-time battery health status of the sample trolley based on the accumulated amount of charge and a nominal capacity of the sample trolley comprises:
determining the real-time cycle number of the sample electric car based on the accumulated charging electric quantity and the nominal capacity of the sample electric car, wherein the real-time cycle number is used for representing the number of charging/discharging cycles of the sample electric car; and
and determining the real-time battery health state corresponding to the real-time cycle number based on the real-time cycle number and a preset second corresponding relation, wherein the second corresponding relation is used for representing the corresponding relation between the cycle number and the battery health state.
TS5, the method of TS2, wherein the establishing the first correspondence based on the real-time battery health status of each sample tram comprises:
determining the average value of the real-time battery health states of the sample electric vehicles corresponding to the traveled mileage sections; and
and establishing a first corresponding relation between the average value of the real-time battery health state and the driving mileage section.
TS6, the method of TS2, wherein the attenuation coefficient is determined based on a battery capacity curve of the sample electric car corresponding to the model, the battery capacity curve being a curve fitted at a specific temperature.
TS7, the method of TS1, wherein the current battery parameters include battery temperature, discharge current, and discharge voltage.
TS8, the method of TS1, wherein the machine learning model is constructed based on XGBoost, support vector machine, or linear regression algorithm.
TS9, a battery state of health predicting apparatus, wherein the apparatus comprises:
the first determination module is used for determining the current driving mileage and the current battery parameters of the target electric vehicle;
the second determination module is used for determining the current battery health state corresponding to the current driving mileage based on a first corresponding relation between the preset driving mileage and the battery health state; and
and the prediction module is used for inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model so as to determine the target battery health state corresponding to the target driving mileage of the target electric vehicle.
TS10, the apparatus of TS9, wherein the first correspondence is determined based on:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring real-time battery parameters of a plurality of sample electric vehicles and attenuation coefficients of corresponding models of the sample electric vehicles, the models of the sample electric vehicles are the same as that of the target electric vehicle, and the real-time travel mileage of each sample electric vehicle corresponds to a preset travel mileage section;
a third determining module, configured to determine, for each sample electric car, a real-time battery health status of the sample electric car based on the real-time battery parameter and the attenuation coefficient; and
and the establishing module is used for establishing the first corresponding relation aiming at each driving mileage section based on the real-time battery health state of each sample electric car.
TS11, the apparatus of TS10, wherein the real-time battery parameters include a charging current and a cumulative charging time;
the third determining module is specifically configured to:
determining an accumulated charging capacity of the sample electric car based on the charging current, the accumulated charging time and the attenuation coefficient; and
and determining the real-time battery health state of the sample electric car based on the accumulated charging capacity and the nominal capacity of the sample electric car.
TS12, the apparatus of TS11, wherein the third determining module is specifically configured to:
determining the real-time cycle number of the sample electric car based on the accumulated charging electric quantity and the nominal capacity of the sample electric car, wherein the real-time cycle number is used for representing the number of charging/discharging cycles of the sample electric car; and
and determining the real-time battery health state corresponding to the real-time cycle number based on the real-time cycle number and a preset second corresponding relation, wherein the second corresponding relation is used for representing the corresponding relation between the cycle number and the battery health state.
TS13, the apparatus as set forth in TS10, wherein the establishing module is specifically configured to:
determining the average value of the real-time battery health states of the sample electric vehicles corresponding to the traveled mileage sections; and
and establishing a first corresponding relation between the average value of the real-time battery health state and the driving mileage section.
TS14, the apparatus of TS10, wherein the attenuation coefficient is determined based on a battery capacity curve of the sample electric car corresponding to the model, the battery capacity curve being a curve fitted at a specific temperature.
TS15, the apparatus of TS9, wherein the current battery parameters include battery temperature, discharge current, and discharge voltage.
TS16, the apparatus of TS9, wherein the machine learning model is constructed based on XGBoost, support vector machine, or linear regression algorithm.
TS17, an electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any one of TS 1-8.
TS18, a computer readable storage medium, wherein the computer readable storage medium has stored therein a computer program which, when being executed by a processor, carries out the method of any one of TS 1-8.
TS19, a computer program product comprising computer programs/instructions, wherein the computer programs/instructions, when executed by a processor, implement the method of any of TS 1-8.
Claims (10)
1. A battery state of health prediction method, the method comprising:
determining the current driving mileage and current battery parameters of the target electric car;
determining the current battery health state corresponding to the current driving mileage based on a first corresponding relation between the preset driving mileage and the battery health state; and
and inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model so as to determine a target battery health state corresponding to the target driving mileage of the target electric vehicle.
2. The method of claim 1, wherein the first correspondence is determined based on:
obtaining real-time battery parameters of a plurality of sample electric cars and attenuation coefficients of the sample electric cars of corresponding models, wherein the sample electric cars and the target electric car are of the same model, and the real-time travel mileage of each sample electric car corresponds to a preset travel mileage section;
for each sample trolley, determining the real-time battery health state of the sample trolley based on the real-time battery parameters and the attenuation coefficient; and
and aiming at each driving mileage section, establishing the first corresponding relation based on the real-time battery health state of each sample electric car.
3. The method of claim 2, wherein the real-time battery parameters include a charge current and a cumulative charge time;
the determining a real-time battery state of health of the sample trolley based on the real-time battery parameters and the attenuation coefficient includes:
determining an accumulated charging capacity of the sample electric car based on the charging current, the accumulated charging time and the attenuation coefficient; and
and determining the real-time battery health state of the sample electric car based on the accumulated charging capacity and the nominal capacity of the sample electric car.
4. The method of claim 3, wherein the determining the real-time battery health state of the sample trolley based on the accumulated amount of charge and a nominal capacity of the sample trolley comprises:
determining the real-time cycle number of the sample electric car based on the accumulated charging electric quantity and the nominal capacity of the sample electric car, wherein the real-time cycle number is used for representing the number of charging/discharging cycles of the sample electric car; and
and determining the real-time battery health state corresponding to the real-time cycle number based on the real-time cycle number and a preset second corresponding relation, wherein the second corresponding relation is used for representing the corresponding relation between the cycle number and the battery health state.
5. The method of claim 2, wherein establishing the first correspondence based on the real-time battery state of health of each sample trolley comprises:
determining the average value of the real-time battery health states of the sample electric vehicles corresponding to the traveled mileage sections; and
and establishing a first corresponding relation between the average value of the real-time battery health state and the driving mileage section.
6. The method according to claim 2, wherein the attenuation coefficient is determined based on a battery capacity curve of the corresponding model of the sample electric vehicle, the battery capacity curve being a curve fitted at a specific temperature.
7. A battery state of health prediction apparatus, the apparatus comprising:
the first determination module is used for determining the current driving mileage and the current battery parameters of the target electric vehicle;
the second determination module is used for determining the current battery health state corresponding to the current driving mileage based on a first corresponding relation between the preset driving mileage and the battery health state; and
and the prediction module is used for inputting the current battery health state, the current driving mileage and the current battery parameters into a pre-trained machine learning model so as to determine the target battery health state corresponding to the target driving mileage of the target electric vehicle.
8. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any of claims 1-6.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 6.
10. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the method of any of claims 1-6.
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