Battery life prediction method and device, vehicle and program product

Abstract

The invention relates to the technical field of vehicles, in particular to a battery life prediction method and device, a vehicle and a program product, and the method comprises the steps: obtaining a final cumulative charging capacity based on a final capacity retention ratio according to a first fitting relation between the capacity retention ratio and the cumulative charging capacity, and obtaining a battery life prediction result based on the final cumulative charging capacity; obtaining a final cumulative mileage according to a second fitting relationship between the cumulative charging capacity and the cumulative mileage, obtaining a final cumulative time according to a third fitting relationship between the cumulative charging capacity and the cumulative time, and obtaining a remaining mileage according to the final cumulative mileage and the current cumulative mileage, and obtaining the remaining use time according to the final accumulated time and the current accumulated use time. Therefore, the problems that in the related technology, a battery life prediction method cannot be applied to commercial vehicles with special working conditions, the generalization ability is poor and the like are solved, the accuracy of battery life prediction is improved, the safety and efficiency of battery use are ensured, and a powerful guarantee is provided for the cruising ability of electric vehicles.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a battery life prediction method, device, vehicle and program product. Background Art

[0002] With the rapid development of the new energy industry, electric vehicles have been widely used. As the core of electric vehicles, the monitoring of the health status of batteries has always been a technical problem in the industry, and the accurate prediction of the remaining life of batteries is also the most core issue. The method and system for realizing the prediction of power battery life can improve the safety and efficiency of battery use, and also provide a strong guarantee for the endurance of electric vehicles.

[0003] Among the related technologies, there are mainly physical mechanism model method and data-driven method for predicting the battery life of new energy commercial vehicles. The main applications of physical model method include: equivalent circuit model, electrochemical model, aging mechanism model, etc.; the main applications of data-driven method include: machine learning method, cluster analysis, principal component analysis, etc. The physical model method can provide in-depth insights and better reflect the actual working conditions, but it requires complex modeling and calculations. The data-driven method relies on a large amount of data and may lack an intuitive understanding of the internal processes of the battery.

[0004] In the related technology, a double exponential capacity decay model is used as a battery capacity degradation model. Through regression analysis, trend analysis is performed using data such as capacity retention rate, charge and discharge times, and time. When performing mechanism analysis, the capacity loss caused by reaction concentration polarization and two-level polarization is considered at the same time. The EKPF (Extened Kalman Particle Filter) algorithm is used to track the decay state of the battery power and obtain the predicted value of the remaining life of the lithium battery.

[0005] However, the above solutions are mainly aimed at passenger cars with pure mileage, and are not suitable for commercial vehicles with special working conditions. They are limited in application types and have poor generalization capabilities, which need to be urgently addressed. Summary of the invention

[0006] The present application provides a battery life prediction method, device, vehicle and program product to solve the problems in the related technology that the battery life prediction method cannot be applied to commercial vehicles with special working conditions and has poor generalization ability, improves the accuracy of battery life prediction, ensures the safety and efficiency of battery use, and provides a strong guarantee for the endurance of electric vehicles.

[0007] The first embodiment of the present application provides a method for predicting the life of a battery, comprising the following steps:

[0008] Obtain the current cumulative mileage, current cumulative usage time and final capacity retention rate of the battery to be predicted at the end of its life;

[0009] Based on the final capacity retention rate, a final cumulative charging capacity is obtained according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, and based on the final cumulative charging capacity, a final cumulative mileage is obtained according to a second fitting relationship between the cumulative charging capacity and the cumulative mileage, and a final cumulative time is obtained according to a third fitting relationship between the cumulative charging capacity and the cumulative time;

[0010] The remaining mileage is obtained according to the final accumulated mileage and the current accumulated mileage, and the remaining usage time is obtained according to the final accumulated time and the current accumulated usage time, and the life prediction result of the battery to be predicted is generated according to the remaining mileage and / or the remaining usage time.

[0011] Optionally, in some embodiments, before obtaining the final cumulative charging capacity based on the final capacity retention rate according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, the method further comprises:

[0012] Acquire charging data of the battery to be predicted each time it is charged;

[0013] The charging data is preprocessed, and the first fitting relationship, the second fitting relationship, and the third fitting relationship are obtained by fitting according to the preprocessed charging data.

[0014] Optionally, in some embodiments, the fitting of the first fitting relationship, the second fitting relationship and the third fitting relationship according to the preprocessed charging data further includes:

[0015] Calculate the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge from the pre-processed charging data;

[0016] The first fitting relationship is obtained by fitting the capacity retention rate corresponding to each charging and the cumulative charging capacity corresponding to each charging;

[0017] The second fitting relationship is obtained by fitting the accumulated charging capacity corresponding to each charging and the accumulated mileage corresponding to each charging;

[0018] The third fitting relationship is obtained by fitting according to the cumulative charging capacity corresponding to each charging and the cumulative usage time corresponding to each charging.

[0019] Optionally, in some embodiments, after acquiring the charging data of each charging of the battery to be predicted, the method further includes:

[0020] Determine whether the capacity retention rate corresponding to each charging, the cumulative charging capacity corresponding to each charging, the cumulative mileage corresponding to each charging, and the cumulative usage time corresponding to each charging all meet preset fitting requirements;

[0021] If any of the capacity retention rate corresponding to each charging, the cumulative charging capacity corresponding to each charging, the cumulative mileage corresponding to each charging, and the cumulative usage time corresponding to each charging does not meet the preset fitting requirement, the first fitting relationship is stopped to the third fitting relationship.

[0022] Optionally, in some embodiments, the first fitting relationship is:

[0023] f(x)=k0*exp(k1*x)+k2*exp(k3*x);

[0024] Among them, f(x) is the capacity retention rate, x is the charging capacity, k0 is the initial value of the first exponential term exp(k1*x), k1 is the growth rate or decay rate of the first exponential term exp(k1*x), k2 is the initial amplitude or baseline value of the second exponential term exp(k3*x), and k3 is the growth rate or decay rate of the second exponential term exp(k3*x).

[0025] A second aspect of the present application provides a battery life prediction device, including:

[0026] An acquisition module is used to obtain the current cumulative mileage, current cumulative usage time and final capacity retention rate of the battery to be predicted at the end of its life;

[0027] a calculation module, configured to obtain, based on the final capacity retention rate, a final cumulative charging capacity according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, obtain, based on the final cumulative charging capacity, a final cumulative mileage according to a second fitting relationship between the cumulative charging capacity and the cumulative mileage, and obtain, based on the final cumulative charging capacity, a final cumulative time according to a third fitting relationship between the cumulative charging capacity and the cumulative time;

[0028] A generation module is used to obtain the remaining mileage according to the final accumulated mileage and the current accumulated mileage, and to obtain the remaining usage time according to the final accumulated time and the current accumulated usage time, and to generate a life prediction result of the battery to be predicted according to the remaining mileage and / or the remaining usage time.

[0029] Optionally, in some embodiments, before obtaining the final cumulative charging capacity based on the final capacity retention rate according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, the calculation module is specifically configured to:

[0030] Acquire charging data of the battery to be predicted each time it is charged;

[0031] The charging data is preprocessed, and the first fitting relationship, the second fitting relationship, and the third fitting relationship are obtained by fitting according to the preprocessed charging data.

[0032] Optionally, in some embodiments, the computing module is further configured to:

[0033] Calculate the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge from the pre-processed charging data;

[0034] The first fitting relationship is obtained by fitting the capacity retention rate corresponding to each charging and the cumulative charging capacity corresponding to each charging;

[0035] The second fitting relationship is obtained by fitting the accumulated charging capacity corresponding to each charging and the accumulated mileage corresponding to each charging;

[0036] The third fitting relationship is obtained by fitting according to the cumulative charging capacity corresponding to each charging and the cumulative usage time corresponding to each charging.

[0037] Optionally, in some embodiments, after acquiring the charging data of each charging of the battery to be predicted, the calculation module is further used to:

[0038] Determine whether the capacity retention rate corresponding to each charging, the cumulative charging capacity corresponding to each charging, the cumulative mileage corresponding to each charging, and the cumulative usage time corresponding to each charging all meet preset fitting requirements;

[0039] When any one of the capacity retention rate corresponding to each charging, the cumulative charging capacity corresponding to each charging, the cumulative mileage corresponding to each charging, and the cumulative usage time corresponding to each charging does not meet the preset fitting requirement, the first fitting relationship is stopped to the third fitting relationship.

[0040] Optionally, in some embodiments, the first fitting relationship is:

[0041] f(x)=k0*exp(k1*x)+k2*exp(k3*x);

[0042] Among them, f(x) is the capacity retention rate, x is the charging capacity, k0 is the initial value of the first exponential term exp(k1*x), k1 is the growth rate or decay rate of the first exponential term exp(k1*x), k2 is the initial amplitude or baseline value of the second exponential term exp(k3*x), and k3 is the growth rate or decay rate of the second exponential term exp(k3*x).

[0043] A third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the battery life prediction method as described in the above embodiment.

[0044] A fourth aspect of the present application provides a computer program product having a computer program stored thereon, which is executed to implement the battery life prediction method as described in the above embodiment.

[0045] Therefore, the present application obtains the current cumulative mileage, current cumulative usage time and final capacity retention rate of the battery to be predicted at the end of its life, obtains the final cumulative charging capacity based on the final capacity retention rate according to the first fitting relationship between the capacity retention rate and the cumulative charging capacity, obtains the final cumulative mileage based on the final cumulative charging capacity according to the second fitting relationship between the cumulative charging capacity and the cumulative mileage, obtains the final cumulative time according to the third fitting relationship between the cumulative charging capacity and the cumulative time, obtains the remaining mileage according to the final cumulative mileage and the current cumulative mileage, obtains the remaining usage time according to the final cumulative time and the current cumulative usage time, and generates the life prediction result of the battery to be predicted according to the remaining mileage and / or the remaining usage time. Therefore, the problems in the related art that the battery life prediction method cannot be applied to commercial vehicles with special working conditions and the generalization ability is poor are solved, the accuracy of battery life prediction is improved, the safety and efficiency of battery use are guaranteed, and the endurance of electric vehicles is provided with a strong guarantee.

[0046] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0048] Figure 1 A flowchart of a battery life prediction method provided according to an embodiment of the present application;

[0049] Figure 2 A flowchart of a battery life prediction method according to a specific embodiment of the present application;

[0050] Figure 3 A block diagram of a battery life prediction device provided according to an embodiment of the present application;

[0051] Figure 4 It is a block diagram of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0053] The following describes the battery life prediction method, device, vehicle and program product of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the battery life prediction method mentioned in the above background technology cannot be applied to commercial vehicles with special working conditions and has poor generalization ability, the present application provides a battery life prediction method, in which the current cumulative mileage, current cumulative use time and final capacity retention rate of the battery to be predicted at the end of life are obtained; based on the final capacity retention rate, the final cumulative charging capacity is obtained according to the first fitting relationship between the capacity retention rate and the cumulative charging capacity, and based on the final cumulative charging capacity, the final cumulative mileage is obtained according to the second fitting relationship between the cumulative charging capacity and the cumulative mileage, and the final cumulative time is obtained according to the third fitting relationship between the cumulative charging capacity and the cumulative time; the remaining mileage is obtained according to the final cumulative mileage and the current cumulative mileage, and the remaining use time is obtained according to the final cumulative time and the current cumulative use time, and the life prediction result of the battery to be predicted is generated according to the remaining mileage and / or the remaining use time. Thus, the problems that the battery life prediction method cannot be applied to commercial vehicles with special working conditions and has poor generalization ability in the related technology are solved, the accuracy of battery life prediction is improved, the safety and efficiency of battery use are guaranteed, and the endurance of electric vehicles is provided with strong guarantee.

[0054] Specifically, Figure 1 A schematic flow chart of a battery life prediction method provided in an embodiment of the present application.

[0055] like Figure 1 As shown, the battery life prediction method includes the following steps:

[0056] In step S101, the current accumulated mileage, the current accumulated usage time and the final capacity retention rate at the end of the life of the battery to be predicted are obtained.

[0057] Among them, the battery to be predicted in the embodiment of the present application is a battery whose performance and life have not been fully determined, and it is necessary to estimate its remaining mileage and remaining usage time through analysis and prediction models, especially the prediction of battery life of commercial vehicles.

[0058] The current accumulated mileage of the embodiment of the present application refers to the total mileage accumulated since the battery was put into use. The current accumulated usage time refers to the total time accumulated since the battery started to be used, which includes usage time and standby time. The final capacity retention rate refers to the capacity ratio that the battery is expected to maintain at the end of its expected life.

[0059] It is understandable that for electric vehicles, the accumulated mileage helps to assess the degree of battery aging. The life of the battery is not only affected by the frequency of use, but also by time. Therefore, the embodiment of the present application considers the current accumulated mileage and the current accumulated usage time to accurately predict the remaining mileage and remaining usage time of the battery. In addition, the final capacity retention rate at the end of life reflects a direct indicator of battery performance degradation, providing an important basis for battery life prediction.

[0060] Therefore, in step S101, the present application evaluates the performance of the battery by detecting the current cumulative mileage, the current cumulative usage time and the final capacity retention rate, accurately predicts the remaining life of the battery, reduces unnecessary battery replacement, and thus reduces operating costs.

[0061] In step S102, based on the final capacity retention rate, the final cumulative charging capacity is obtained according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, and based on the final cumulative charging capacity, the final cumulative mileage is obtained according to a second fitting relationship between the cumulative charging capacity and the cumulative mileage, and the final cumulative time is obtained according to a third fitting relationship between the cumulative charging capacity and the cumulative time.

[0062] Among them, the final cumulative charging capacity of the embodiment of the present application refers to the total amount of charge that the battery can accumulate throughout its entire service life; the final cumulative mileage refers to the total mileage of the electric vehicle supported by the battery to be predicted during its entire service life; the final cumulative time refers to the total time the battery experiences from the beginning of use to the predicted end of its life.

[0063] Based on the introduction in the background technology, in the related technologies of new energy commercial vehicle battery life prediction, the physical model method requires complex modeling and calculation, the data-driven method relies on a large amount of data, and lacks an intuitive understanding of the internal process of the battery. Therefore, this application combines the physical model with the data-driven model, using the theoretical basis provided by the physical model and the generalization ability of the data-driven model to improve the accuracy of the model prediction.

[0064] Specifically, the embodiment of the present application establishes a first fitting relationship between the capacity retention rate and the cumulative charging capacity, a second fitting relationship between the cumulative charging capacity and the cumulative mileage, and a third fitting relationship between the cumulative charging capacity and the cumulative time. Based on this, an analysis is performed based on the particularity of the operating conditions of commercial vehicles, the historical data of the battery is obtained, and a fusion model that combines the physical model with the data-driven model is constructed to predict the remaining service life of the battery.

[0065] Optionally, in some embodiments, before obtaining the final cumulative charging capacity based on the final capacity retention rate according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, it includes: acquiring charging data of the battery to be predicted at each charging; preprocessing the charging data, and fitting the first fitting relationship, the second fitting relationship and the third fitting relationship according to the preprocessed charging data.

[0066] It is understandable that the charging and discharging data, temperature data, current data, mileage, etc. of commercial vehicles are different from those of passenger vehicles. The battery life prediction method of the embodiment of the present application analyzes the historical data of the actual vehicle, taking into account the special working conditions of commercial vehicles (freight transportation, engineering operations, fire fighting, etc.) and the regularity of driving (mainly manifested in the regularity of daily operations, fixed routes, long driving distances, etc.) The impact on the data. Therefore, the charging data of each charge of the battery to be predicted in the embodiment of the present application includes: capacity retention rate, cumulative charging capacity, cumulative throughput, mileage, time, etc.

[0067] Furthermore, after obtaining the charging data of each charging of the battery to be predicted, in order to reduce random fluctuations in the data, the embodiment of the present application uses appropriate filtering and denoising technology to clean and process the data to avoid data fluctuations, abnormal changes, losses, etc. caused by special working conditions.

[0068] Optionally, the embodiments of the present application can collect real-time data of new energy vehicles through a preset data acquisition module, one frame every 10 seconds. The collected data includes fields such as data acquisition time, total current, mileage, SOC (State of Charge), charging status, maximum value of battery cell voltage, etc. The original data set is cleaned, including but not limited to deleting empty fields, error fields, abnormal fields, etc.

[0069] Then, the data is standardized, that is, the data collection time, insulation alarm, vehicle speed and other data are standardized. The collection time is converted into a standard time format (datetime, unit: ms), and the mileage, SOC, total current, and the maximum value of the battery cell voltage are converted into a floating point (float) data type, so as to obtain standard data and form a matrix according to the time series.

[0070] Optionally, in some embodiments, the first fitting relationship, the second fitting relationship and the third fitting relationship are obtained by fitting the preprocessed charging data, and the method further includes: calculating the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge and the cumulative usage time corresponding to each charge from the preprocessed charging data; fitting the first fitting relationship according to the capacity retention rate corresponding to each charge and the cumulative charging capacity corresponding to each charge; fitting the second fitting relationship according to the cumulative charging capacity corresponding to each charge and the cumulative mileage corresponding to each charge; and fitting the third fitting relationship according to the cumulative charging capacity corresponding to each charge and the cumulative usage time corresponding to each charge.

[0071] Specifically, the embodiment of the present application can calculate the capacity retention rate corresponding to each charging time, the cumulative charging capacity (pure electric vehicle), the cumulative throughput (hybrid vehicle), and the cumulative mileage array based on the above data. Optionally, the capacity retention rate can use a dual Kalman filter algorithm to ensure the output of data under standard working conditions. For pure electric vehicles, the charging amount of each time is counted and accumulated to obtain the cumulative charging capacity. For hybrid vehicles, the capacity of each charge and discharge is counted and accumulated to obtain the cumulative throughput.

[0072] Optionally, in some embodiments, the first fitting relationship is:

[0073] f(x)=k0*exp(k1*x)+k2*exp(k3*x);

[0074] Among them, f(x) is the capacity retention rate, x is the charging capacity, k0 is the initial value of the first exponential term exp(k1*x), k1 is the growth rate or decay rate of the first exponential term exp(k1*x), k2 is the initial amplitude or baseline value of the second exponential term exp(k3*x), and k3 is the growth rate or decay rate of the second exponential term exp(k3*x).

[0075] It should be noted that, in the first fitting relationship, k0 is the initial value of the first exponential term exp(k1*x), which represents the value of the first exponential term exp(k1*x) when x=0; k1 is the growth rate or decay rate of the exponential term exp(k1*x), which determines the speed at which the first exponential term exp(k1*x) increases or decreases with x. If k1 is positive, it means that the term grows exponentially with the increase of x. If k1 is negative, it means that the term decays exponentially with the increase of x. k2 is the initial amplitude or reference value of the second exponential term exp(k3*x), which represents the value of the second exponential term exp(k3*x) when x=0; k3 is the growth rate or decay rate of the second exponential term exp(k3*x), which determines the speed at which the second exponential term exp(k3*x) increases or decreases with x. Similar to k1, the positive or negative value of k3 determines the growth or decay trend of the second exponential term exp(k3*x).

[0076] Specifically, the embodiment of the present application transforms the double exponential model, and establishes a relationship expression between the capacity retention rate and the cumulative charging capacity according to the capacity retention rate corresponding to each charge and the cumulative charging capacity corresponding to each charge, that is, the first fitting relationship of the embodiment of the present application. Therefore, the embodiment of the present application can obtain the final cumulative charging capacity based on the final capacity retention rate according to the first fitting relationship.

[0077] It should be noted that, for hybrid vehicles, the embodiments of the present application can also establish a relationship expression between the capacity retention rate and the cumulative throughput based on a double exponential model according to the capacity retention rate corresponding to each charge and the cumulative throughput corresponding to each charge.

[0078] Furthermore, the embodiment of the present application performs fitting according to the cumulative charging capacity corresponding to each charging and the cumulative mileage corresponding to each charging to obtain a second fitting relationship. Specifically, the embodiment of the present application performs fitting on the cumulative charging capacity and the cumulative mileage, and adopts the support vector regression model (SVR) in the machine learning algorithm to obtain a relational expression, that is, the second fitting relationship of the embodiment of the present application is:

[0079] g(x)=kx+b;

[0080] Among them, g(x) is the cumulative charging capacity, x is the cumulative mileage, k is the slope, which represents the rate of change of the charging capacity required for each unit mileage, and b is the intercept, which represents the initial value of the cumulative charging capacity g(x) when the cumulative mileage x is zero.

[0081] It should be noted that, for hybrid vehicles, the embodiment of the present application can also fit the cumulative throughput and the cumulative mileage to obtain a second fitting relationship.

[0082] Furthermore, the embodiment of the present application performs fitting according to the cumulative charging capacity corresponding to each charging and the cumulative usage time corresponding to each charging to obtain a third fitting relationship. Specifically, the embodiment of the present application establishes a relationship expression between the cumulative charging capacity and time. The embodiment of the present application adopts a support vector regression model (SVR) in a machine learning algorithm. The nonlinear SVR expression, i.e., the third fitting relationship is:

[0083] g(x)=w,φ(T)+b;

[0084] Among them, g(x) is the cumulative charging capacity, T is the time, w, φ(T) represents the dot product of the weight vector w and the mapped time φ(T) in the high-dimensional space, and b is the bias term.

[0085] It should be noted that, in the third fitting relationship, the bias term b allows the model to be translated along the y-axis (here, the cumulative charging capacity) in the feature space, thereby improving the fitting of the model.

[0086] Therefore, the embodiment of the present application can obtain the relationship between the capacity retention rate and the mileage based on the first fitting relationship and the second fitting relationship, and obtain the relationship between the capacity retention rate and the time based on the first fitting relationship and the third fitting relationship.

[0087] Based on the above fitting process, the embodiment of the present application can obtain the relationship between the cumulative charging capacity / cumulative throughput and time, as well as the relationship between the cumulative charging capacity / cumulative throughput and mileage, thereby obtaining the corresponding expected mileage and time based on the predicted demand for capacity retention rate.

[0088] Specifically, the embodiment of the present application counts the capacity retention rate, cumulative charging capacity / cumulative throughput, mileage, time, etc., constructs the relationship between the capacity retention rate and the cumulative charging capacity / cumulative throughput, performs formula fitting, and calculates the final cumulative charging capacity at the end of the life according to the fitted curve. Based on the final cumulative charging capacity, the final cumulative mileage is obtained according to the second fitting relationship, and the final cumulative time is obtained according to the third fitting relationship.

[0089] That is to say, the embodiment of the present application first describes the relationship between the capacity retention rate and the cumulative charging capacity / cumulative throughput by constructing a fitting curve of the capacity retention rate and the cumulative charging capacity / cumulative throughput. By constructing the relationship between the two, the attenuation of commercial vehicle battery performance over time can be more accurately reflected. Furthermore, the cumulative charging capacity is introduced as an intermediate variable to obtain the relationship between the capacity retention rate and mileage, as well as the relationship between the capacity retention rate and time, thereby accurately predicting the battery life of new energy commercial vehicles.

[0090] Optionally, in some embodiments, after obtaining the charging data of the battery to be predicted each time it is charged, it also includes: judging whether the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge all meet the preset fitting requirements; if any of the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge does not meet the preset fitting requirements, then stopping the first fitting relationship to the third fitting relationship.

[0091] It can be understood that the number of points of the capacity retention rate directly affects the accuracy and reliability of the fitting model. Generally speaking, the more points there are, the stronger the predictive ability of the model. Therefore, the fitting requirements preset in the embodiments of the present application include a threshold value for the number of capacity retention rate points.

[0092] The accumulated usage time should be evenly distributed throughout the entire life cycle of the battery to ensure that the long-term change trend of the battery performance can be captured. Therefore, the preset fitting requirements of the embodiment of the present application include restrictions on the accumulated usage time.

[0093] The accumulated mileage should include the driving conditions of the vehicle under different conditions, such as urban roads, highways, etc., to reflect the performance of the battery under different usage modes. Therefore, the preset fitting requirements of the embodiment of the present application include the limit of accumulated mileage and driving conditions.

[0094] In addition, the embodiment of the present application also needs to ensure that there are enough cumulative charging capacity data points to conduct effective statistical analysis and establish a reliable fitting model. Therefore, the preset fitting requirements of the embodiment of the present application include a threshold value for the cumulative charging capacity.

[0095] When making battery life predictions, determining whether the points of capacity retention rate, cumulative charging capacity, cumulative mileage, and cumulative usage time meet the fitting requirements is a key step in ensuring the accuracy of data analysis.

[0096] Specifically, if any of the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge does not meet the above preset fitting requirements, no fitting is performed.

[0097] Therefore, in step S102, the present application improves the double exponential model based on the analysis of the special operating conditions and driving regularity of commercial vehicles, integrates the physical mechanism model method and the data-driven method, introduces the cumulative charging capacity as an intermediate variable to obtain the relationship between the capacity retention rate and mileage, as well as the relationship between the capacity retention rate and time, so as to more accurately predict the final state of the battery.

[0098] In step S103, the remaining mileage is obtained according to the final accumulated mileage and the current accumulated mileage, and the remaining usage time is obtained according to the final accumulated time and the current accumulated usage time, and the life prediction result of the battery to be predicted is generated according to the remaining mileage and / or the remaining usage time.

[0099] Specifically, in step S101, the embodiment of the present application obtains the current cumulative mileage and the current cumulative usage time of the battery. In step S102, the cumulative charging capacity is introduced as an intermediate variable to obtain the relationship between the capacity retention rate and the mileage, and the relationship between the capacity retention rate and the time, and the final cumulative mileage and the final cumulative time are obtained. Furthermore, the embodiment of the present application can use the difference between the final cumulative mileage and the current cumulative mileage as the remaining mileage, and the difference between the final cumulative time and the current cumulative usage time as the remaining usage time, thereby obtaining the life prediction result of the battery to be predicted.

[0100] It should be noted that the life prediction result of the battery to be predicted in the embodiment of the present application may be the remaining mileage or the remaining usage time or the remaining mileage and the remaining usage time.

[0101] In order to enable those skilled in the art to further understand the battery life prediction method of the embodiment of the present application, the following examples are listed to schematically illustrate the implementation steps of the method.

[0102] Specifically, Figure 2 This is a flowchart of a battery life prediction method according to a specific embodiment of the present application. Figure 2 As shown, the battery life prediction method includes the following steps:

[0103] Step S201, obtaining raw data, and calculating the charging time, capacity retention rate, cumulative charging capacity and cumulative mileage corresponding to each charging;

[0104] Step S202, judging whether the points of the capacity retention rate, the vehicle running time, and the accumulated mileage meet the fitting requirements, if they meet the fitting requirements, executing step S204, otherwise, executing step S203;

[0105] Step S203, no fitting is performed, and the process ends;

[0106] Step S204, fitting the capacity retention rate and cumulative charging capacity curve;

[0107] Step S205, calculating the remaining cumulative charging capacity at the end of life according to the fitted curve;

[0108] Step S206, obtaining the total mileage and total time according to the fitting relationship among the mileage, time and capacity retention rate;

[0109] Step S207, calculate the remaining mileage and remaining time based on the current accumulated mileage and time, and end the process.

[0110] Therefore, this application analyzes the historical data of real vehicles, considers the impact of special operating conditions and driving regularity of commercial vehicles on the data, uses appropriate filtering and denoising techniques to reduce random fluctuations in the data, and statistics the capacity retention rate, cumulative charging capacity / cumulative throughput, mileage, time, etc. By constructing the relationship between the capacity retention rate and the cumulative charging capacity / cumulative throughput, a formula is fitted, and the remaining cumulative charging capacity to reach the end of life is calculated according to the fitted curve, and the remaining mileage and time to reach the end of life are calculated based on the proportional relationship between the cumulative charging capacity and mileage and time in a recent period of time.

[0111] According to the battery life prediction method proposed in the embodiment of the present application, by obtaining the current cumulative mileage, current cumulative usage time and final capacity retention rate of the battery to be predicted at the end of its life, based on the final capacity retention rate, the final cumulative charging capacity is obtained according to the first fitting relationship between the capacity retention rate and the cumulative charging capacity, and based on the final cumulative charging capacity, the final cumulative mileage is obtained according to the second fitting relationship between the cumulative charging capacity and the cumulative mileage, the final cumulative time is obtained according to the third fitting relationship between the cumulative charging capacity and the cumulative time, the remaining mileage is obtained according to the final cumulative mileage and the current cumulative mileage, and the remaining usage time is obtained according to the final cumulative time and the current cumulative usage time, and the life prediction result of the battery to be predicted is generated according to the remaining mileage and / or the remaining usage time. Thus, the problems in the related art that the battery life prediction method cannot be applied to commercial vehicles with special working conditions and the generalization ability is poor are solved, the accuracy of battery life prediction is improved, the safety and efficiency of battery use are guaranteed, and the endurance of electric vehicles is provided with a strong guarantee.

[0112] Next, a battery life prediction device according to an embodiment of the present application is described with reference to the accompanying drawings.

[0113] Figure 3 It is a block diagram of a battery life prediction device according to an embodiment of the present application.

[0114] like Figure 3 As shown, the battery life prediction device 10 includes: an acquisition module 100 , a calculation module 200 and a generation module 300 .

[0115] Specifically, the acquisition module 100 is used to obtain the current cumulative mileage, the current cumulative usage time and the final capacity retention rate at the end of the life of the battery to be predicted; the calculation module 200 is used to obtain the final cumulative charging capacity based on the final capacity retention rate according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, and to obtain the final cumulative mileage based on the final cumulative charging capacity according to a second fitting relationship between the cumulative charging capacity and the cumulative mileage, and to obtain the final cumulative time according to a third fitting relationship between the cumulative charging capacity and the cumulative time; the generation module 300 is used to obtain the remaining mileage based on the final cumulative mileage and the current cumulative mileage, and to obtain the remaining usage time based on the final cumulative time and the current cumulative usage time, and to generate a life prediction result of the battery to be predicted based on the remaining mileage and / or the remaining usage time.

[0116] Optionally, in some embodiments, before obtaining the final cumulative charging capacity based on the final capacity retention rate according to the first fitting relationship between the capacity retention rate and the cumulative charging capacity, the calculation module 200 is specifically used to: obtain charging data of the battery to be predicted during each charging; preprocess the charging data, and obtain a first fitting relationship, a second fitting relationship and a third fitting relationship based on the preprocessed charging data.

[0117] Optionally, in some embodiments, the calculation module 200 is further used to: calculate the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge from the preprocessed charging data; fit the capacity retention rate corresponding to each charge and the cumulative charging capacity corresponding to each charge to obtain a first fitting relationship; fit the cumulative charging capacity corresponding to each charge and the cumulative mileage corresponding to each charge to obtain a second fitting relationship; fit the cumulative charging capacity corresponding to each charge and the cumulative mileage corresponding to each charge to obtain a third fitting relationship.

[0118] Optionally, in some embodiments, after obtaining the charging data of the battery to be predicted at each charge, the calculation module 200 is also used to: determine whether the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge all meet the preset fitting requirements; if any of the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge does not meet the preset fitting requirements, stop the first fitting relationship to the third fitting relationship.

[0119] Optionally, in some embodiments, the first fitting relationship is:

[0120] f(x)=k0*exp(k1*x)+k2*exp(k3*x);

[0121] Among them, f(x) is the capacity retention rate, x is the charging capacity, k0 is the initial value of the first exponential term exp(k1*x), k1 is the growth rate or decay rate of the first exponential term exp(k1*x), k2 is the initial amplitude or baseline value of the second exponential term exp(k3*x), and k3 is the growth rate or decay rate of the second exponential term exp(k3*x).

[0122] It should be noted that the above explanation of the battery life prediction method embodiment is also applicable to the battery life prediction device of this embodiment, and will not be repeated here.

[0123] According to the battery life prediction device proposed in the embodiment of the present application, by obtaining the current cumulative mileage, current cumulative usage time and final capacity retention rate of the battery to be predicted at the end of its life, based on the final capacity retention rate, the final cumulative charging capacity is obtained according to the first fitting relationship between the capacity retention rate and the cumulative charging capacity, and based on the final cumulative charging capacity, the final cumulative mileage is obtained according to the second fitting relationship between the cumulative charging capacity and the cumulative mileage, the final cumulative time is obtained according to the third fitting relationship between the cumulative charging capacity and the cumulative time, the remaining mileage is obtained according to the final cumulative mileage and the current cumulative mileage, and the remaining usage time is obtained according to the final cumulative time and the current cumulative usage time, and the life prediction result of the battery to be predicted is generated according to the remaining mileage and / or the remaining usage time. Thus, the problems in the related art that the battery life prediction method cannot be applied to commercial vehicles with special working conditions and the generalization ability is poor are solved, the accuracy of battery life prediction is improved, the safety and efficiency of battery use are guaranteed, and the endurance of electric vehicles is provided with a strong guarantee.

[0124] Figure 4 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0125] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0126] When the processor 402 executes the program, the battery life prediction method provided in the above embodiment is implemented.

[0127] Furthermore, the vehicle also includes:

[0128] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0129] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0130] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0131] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0132] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0133] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0134] An embodiment of the present application also provides a computer program product, on which a computer program is stored, and when the program is executed, the battery life prediction method as described above is implemented.

[0135] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0136] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0137] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0138] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0139] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0140] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for predicting battery life, characterized in that: The following steps are involved: Obtain the current cumulative mileage, current cumulative usage time and final capacity retention rate of the battery to be predicted at the end of its life; Based on the final capacity retention rate, a final cumulative charging capacity is obtained according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, and based on the final cumulative charging capacity, a final cumulative mileage is obtained according to a second fitting relationship between the cumulative charging capacity and the cumulative mileage, and a final cumulative time is obtained according to a third fitting relationship between the cumulative charging capacity and the cumulative time; The remaining mileage is obtained according to the final accumulated mileage and the current accumulated mileage, and the remaining usage time is obtained according to the final accumulated time and the current accumulated usage time, and the life prediction result of the battery to be predicted is generated according to the remaining mileage and / or the remaining usage time.

2. The method according to claim 1, characterized in that Before obtaining the final accumulated charging capacity based on the final capacity retention rate according to a first fitting relationship between the capacity retention rate and the accumulated charging capacity, the method comprises: Acquire charging data of the battery to be predicted each time it is charged; The charging data is preprocessed, and the first fitting relationship, the second fitting relationship, and the third fitting relationship are obtained by fitting according to the preprocessed charging data.

3. The method according to claim 2, characterized in that The step of obtaining the first fitting relationship, the second fitting relationship and the third fitting relationship by fitting the preprocessed charging data further includes: Calculate the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge from the pre-processed charging data; The first fitting relationship is obtained by fitting the capacity retention rate corresponding to each charging and the cumulative charging capacity corresponding to each charging; The second fitting relationship is obtained by fitting the accumulated charging capacity corresponding to each charging and the accumulated mileage corresponding to each charging; The third fitting relationship is obtained by fitting according to the cumulative charging capacity corresponding to each charging and the cumulative usage time corresponding to each charging.

4. The method according to claim 2, characterized in that: After acquiring the charging data of the battery to be predicted at each charging time, the method further includes: Determine whether the capacity retention rate corresponding to each charging, the cumulative charging capacity corresponding to each charging, the cumulative mileage corresponding to each charging, and the cumulative usage time corresponding to each charging all meet preset fitting requirements; If any of the capacity retention rate corresponding to each charging, the cumulative charging capacity corresponding to each charging, the cumulative mileage corresponding to each charging, and the cumulative usage time corresponding to each charging does not meet the preset fitting requirement, the first fitting relationship is stopped to the third fitting relationship.

5. The method according to claim 1, characterized in that: The first fitting relationship is: f(x)=k0*exp(k1*x)+k2*exp(k3*x); Among them, f(x) is the capacity retention rate, x is the charging capacity, k0 is the initial value of the first exponential term exp(k1*x), k1 is the growth rate or decay rate of the first exponential term exp(k1*x), k2 is the initial amplitude or baseline value of the second exponential term exp(k3*x), and k3 is the growth rate or decay rate of the second exponential term exp(k3*x).

6. A battery life prediction device, characterized in that: include: An acquisition module is used to obtain the current cumulative mileage, current cumulative usage time and final capacity retention rate of the battery to be predicted at the end of its life; a calculation module, configured to obtain, based on the final capacity retention rate, a final cumulative charging capacity according to a first fitting relationship between the capacity retention rate and the cumulative charging capacity, obtain, based on the final cumulative charging capacity, a final cumulative mileage according to a second fitting relationship between the cumulative charging capacity and the cumulative mileage, and obtain, based on the final cumulative charging capacity, a final cumulative time according to a third fitting relationship between the cumulative charging capacity and the cumulative time; A generation module is used to obtain the remaining mileage according to the final accumulated mileage and the current accumulated mileage, and to obtain the remaining usage time according to the final accumulated time and the current accumulated usage time, and to generate a life prediction result of the battery to be predicted according to the remaining mileage and / or the remaining usage time.

7. The device according to claim 6, characterized in that Before obtaining the final accumulated charging capacity based on the final capacity retention rate according to a first fitting relationship between the capacity retention rate and the accumulated charging capacity, the calculation module is specifically configured to: Acquire charging data of the battery to be predicted each time it is charged; The charging data is preprocessed, and the first fitting relationship, the second fitting relationship, and the third fitting relationship are obtained by fitting according to the preprocessed charging data.

8. The device according to claim 7, characterized in that The computing module is further used for: Calculate the capacity retention rate corresponding to each charge, the cumulative charging capacity corresponding to each charge, the cumulative mileage corresponding to each charge, and the cumulative usage time corresponding to each charge from the pre-processed charging data; The first fitting relationship is obtained by fitting the capacity retention rate corresponding to each charging and the cumulative charging capacity corresponding to each charging; The second fitting relationship is obtained by fitting the accumulated charging capacity corresponding to each charging and the accumulated mileage corresponding to each charging; The third fitting relationship is obtained by fitting according to the cumulative charging capacity corresponding to each charging and the cumulative usage time corresponding to each charging.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the battery life prediction method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the battery life prediction method as described in any one of claims 1 to 5.

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