Battery life prediction method

Abstract

The invention provides a battery life prediction method. The battery life prediction method comprises the steps of obtaining electrical characteristic data of a to-be-tested battery and constructing an electrical module; constructing a battery cycle aging prediction module; obtaining calendar aging data of the to-be-tested battery at different temperatures and different SOCs, and constructing a battery calendar aging prediction module; obtaining calorific value data of the to-be-tested battery under different multiplying powers, and constructing a battery cell heat production module; obtaining four-season temperature change data of an area where the to-be-measured battery is located, and constructing an environment temperature module; a battery life prediction model is obtained through the coupling electricity module, the battery cycle aging prediction module, the battery calendar aging prediction module, the battery cell heat production module and the environment temperature module, and the attenuation life of a to-be-tested battery is predicted through the battery life prediction model. According to the technical scheme of the invention, the problem of low prediction accuracy of a prediction method in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular, to a method for predicting battery life. Background Art

[0002] In the context of the rapid development of new energy vehicles, as the core component of the vehicle, the performance and life of the power battery system directly affect the overall performance of the vehicle, user satisfaction, and market competitiveness. Therefore, vehicle manufacturers require that the service life of power batteries meet high standards. In particular, based on the national "Three Guarantees" policy, more stringent quality assurance requirements are put forward: after the power battery has been used for 8 years or traveled more than 200,000 kilometers, its state of health (SOH) should not be lower than 80%. When evaluating whether the battery meets the above quality assurance requirements using traditional power battery system life test methods, it is usually necessary to conduct tests throughout the entire life cycle of the battery, covering several years of actual use, which not only consumes a large amount of time but also incurs high test costs. Therefore, there is an urgent need for a high-precision quality assurance life prediction during the design and development stage of power batteries.

[0003] In recent years, battery life prediction technology has been vigorously developed. Among them, Chinese Patent CN115840142A discloses a technical solution for predicting battery life using an electrochemical model. This solution predicts the battery life decay trend by constructing an electrochemical model based on a series of complex battery internal reaction parameters, such as porous SEI (solid electrolyte interface) film formation reaction parameters, broken SEI film formation reaction parameters, changes in the solid-phase diffusion coefficients of the positive and negative electrodes, changes in the surface reaction rate constants of the positive and negative electrodes, and test data of the lithium battery to be tested under different charge-discharge cycles. This technology can theoretically provide a relatively detailed analysis of battery aging mechanisms and achieve life prediction. However, the above method overly relies on complex mechanism parameters inside the battery and ignores the influence of factors such as environmental temperature and vehicle operating conditions (such as acceleration, deceleration, driving speed, etc.), resulting in low prediction accuracy. Summary of the Invention

[0004] The main objective of the present invention is to provide a method for predicting battery life, which can solve the problem of low prediction accuracy of the existing prediction methods.

[0005] To achieve the above objective, the present invention provides a method for predicting battery life, including: obtaining the electrical characteristic data of the battery to be tested and constructing an electrical module; obtaining the cycle test data and battery temperature rise data of the battery to be tested under different charge-discharge rates and the same initial temperature conditions, and the cycle test data and battery temperature rise data of the battery to be tested under different initial temperatures and the same charge-discharge rate conditions, and then fitting the Arrhenius formula based on the above data to obtain the Arrhenius formula containing the corrected factor temperature T: To construct a battery cycle aging prediction module, where X is the number of cycles, E act is the reaction activation energy of a single cell, C rate is the charging rate, Ah is the ampere-hour capacity of the battery, R is the molar gas constant, a, b, and c are fitting values, and z 1 is the first power exponent factor; obtain the calendar aging data of the battery to be tested at different temperatures and different SOCs and construct a battery calendar aging prediction module; obtain the heat generation data of the battery to be tested at different rates and construct a cell heat generation module; obtain the seasonal temperature change data of the region where the battery to be tested is located and construct an ambient temperature module; obtain a battery life prediction model by coupling the electrical module, the battery cycle aging prediction module, the battery calendar aging prediction module, the cell heat generation module, and the ambient temperature module, and predict the attenuation life of the battery to be tested through the battery life prediction model.

[0006] Through the above settings, an electrical module is constructed, which can perform state of charge estimation, health state monitoring, electrical behavior simulation, and thermal effect analysis of the battery, and can accurately reflect the behavior of the battery in the actual use environment. When constructing the battery cycle aging prediction module, the Arrhenius formula with the corrected factor temperature T is adopted, which considers the non-linear effect of temperature on the battery aging rate, enabling the model to accurately capture the battery aging rate varying with temperature and solving the problem of ignoring or simplifying the temperature effect in the prior art; the data input into the battery calendar aging prediction module not only includes the calendar aging attenuation of batteries with different initial SOCs at the same initial temperature, but also considers the calendar aging attenuation of batteries with the same SOC state at different initial temperatures, thus being able to more comprehensively reflect the influence of calendar aging attenuation of the cell in different states, avoiding the limitations of the fitting model under a single condition, and improving the comprehensiveness and accuracy of the prediction; by constructing the cell heat generation module and the ambient temperature module, the actual temperature change of the battery during use can be predicted more accurately, and further the prediction accuracy of the battery aging process can be improved. By coupling the electrical module, the battery cycle aging prediction module, the battery calendar aging prediction module, the cell heat generation module, and the ambient temperature module, multi-factor comprehensive analysis is realized, and more comprehensive and practical battery life prediction is achieved, thereby improving the prediction accuracy.

[0007] Further, the value range of the initial temperature is -30°C to 55°C, and the value range of the charge-discharge rate is 0.3C to 3C.

[0008] Through the above settings, it can be ensured that the performance of the battery under various climatic conditions is accurately simulated and predicted. The value range of the charge and discharge rate is 0.3C to 3C, covering a variety of charge and discharge rates from slow charge to fast charge, which helps to evaluate the performance degradation and life change of the battery under different usage intensities, and can significantly improve the prediction accuracy of the battery life prediction model.

[0009] Further, the steps of obtaining the calendar aging data of the battery to be tested at different temperatures and different SOCs and constructing the battery calendar aging prediction module include: establishing the functional relationship between capacity attenuation and battery capacity and storage time: where t is time, B is the fitting value, and z 2 is the second power exponent factor.

[0010] Through the above settings, the calendar aging rate of the battery at different temperatures and states of charge can be quantified, which can accurately reflect the true law of battery aging. Coupling the battery calendar aging prediction module with the battery cycle aging prediction module can provide a more comprehensive battery life prediction model, considering the storage cycles experienced by the battery in actual use, and thus improve the prediction accuracy of the battery life prediction model.

[0011] Further, the value of B is directly proportional to the initial SOC value of the battery to be tested during aging.

[0012] Through the above settings, the influence of SOC on the aging of the battery to be tested can be evaluated more precisely, thereby improving the prediction accuracy.

[0013] Further, the battery life prediction method further includes: obtaining the energy consumed by the battery to be tested under the thermal management strategy, and combining the above energy consumption to use the battery life prediction model to predict the degradation life.

[0014] Through the above settings, the influence of different thermal management strategies on the battery life can be evaluated more comprehensively. The battery life prediction model combines the above energy consumption to predict the degradation life, and can more accurately predict the life degradation trend of the battery under complex working conditions.

[0015] Further, the battery life prediction method further includes: obtaining the SOH value before charging, using this SOH value as the current correction factor to make the charging current I1 satisfy: I1 = I2 * SOH, and constructing a charging module, where I2 is the rated current, and coupling the charging module to the battery life prediction model.

[0016] Through the above settings, the electrochemical reaction and thermodynamic state during the charging process of the battery to be tested in different health states can be more accurately simulated. Coupling the charging module to the battery life prediction model can further improve the prediction accuracy of the battery life prediction model.

[0017] Further, the battery life prediction method further includes: obtaining the discharge parameters of the battery to be tested and constructing a discharge module, and coupling the discharge module to the battery life prediction model.

[0018] Through the above settings, the electrochemical reaction and energy conversion process of the battery under different discharge conditions can be accurately simulated. Coupling the discharge module with the battery life prediction model enables the battery life prediction model to not only consider the characteristics of the battery to be tested during the charging process, but also comprehensively consider the performance and losses of the battery to be tested during discharge. As a result, the capacity decay trend of the battery to be tested during actual use can be predicted more accurately, further improving the prediction accuracy of the battery life prediction model.

[0019] Further, the steps of obtaining the discharge parameters of the battery to be tested and constructing a discharge module include: obtaining the discharge parameters of the battery to be tested by combining the daily driving mileage, average vehicle speed, and operating condition information of the whole vehicle.

[0020] Through the above settings, a discharge module closer to the actual use scenario can be constructed, which can more realistically simulate the discharge process of the battery under different conditions, thereby improving the accuracy and reliability of the battery life prediction model.

[0021] Further, the steps of obtaining the electrical characteristic data of the battery to be tested and constructing the electrical module of the battery to be tested include: performing a hybrid pulse power characteristic test on the battery to be tested at different preset temperatures to obtain the electrical characteristic data of the battery to be tested at different preset temperatures. The electrical characteristic data includes SOC, internal resistance, and open circuit voltage value; processing the electrical characteristic data through a second-order equivalent circuit model to obtain the corresponding relationship between SOC and open circuit voltage, and the corresponding relationship between internal resistance and SOC at different preset temperatures, so as to construct an electrical module.

[0022] Through the above settings, the electrical characteristic data of the battery under different temperature conditions can be collected. Processing the electrical characteristic data through a second-order equivalent circuit model can more accurately simulate the electrochemical process inside the battery, improve the accuracy of the electrical module, and make the battery life prediction more accurate.

[0023] Further, the steps of obtaining the electrical characteristic data of the battery to be tested and constructing the electrical module of the battery to be tested further include: using the linear interpolation method to obtain the data corresponding to the temperature outside the preset temperature, and then processing the electrical characteristic data and the data corresponding to the temperature outside the preset temperature obtained by the linear interpolation method through a second-order equivalent circuit model to construct an electrical module.

[0024] Through the above settings, the mixed pulse power characteristics can be tested at fewer temperature points, thereby simplifying the data collection process, reducing the test cost and time, enabling the battery life prediction model to be more widely applied to the prediction of battery performance under different temperature conditions, and improving the continuity of prediction. In addition, by inputting the interpolated data and the actual test data into the second-order equivalent circuit model for processing, the electrochemical behavior of the battery at different temperatures can be more comprehensively simulated, thereby improving the accuracy of battery life prediction.

[0025] Applying the technical solution of the present invention, electrical characteristic data of the battery to be tested are obtained and an electrical module is constructed. Through the electrical module, the state of charge estimation, health state monitoring, electrical behavior simulation, and thermal effect analysis of the battery can be carried out, which can accurately reflect the behavior of the battery to be tested in the actual use environment. When constructing the battery cycle aging prediction module, the Arrhenius formula with the modified factor temperature T is adopted, considering the non-linear influence of temperature on the battery aging rate, which enables the model to accurately capture the battery aging rate changing with temperature and solves the problem of ignoring or simplifying the temperature effect in the prior art; the data input into the battery calendar aging prediction module not only includes the battery calendar aging attenuation of different initial SOCs at the same initial temperature, but also considers the battery calendar aging attenuation under the same SOC state at different initial temperatures, so as to more comprehensively reflect the influence of the calendar aging attenuation of different states of the battery cell, avoid the limitation of the fitting model under a single condition, and improve the comprehensiveness and accuracy of prediction; by constructing the battery heat generation module and the ambient temperature module, the actual temperature change of the battery during use can be more accurately predicted, and further improve the prediction accuracy of the battery aging process. By coupling the electrical module, the battery cycle aging prediction module, the battery calendar aging prediction module, the battery heat generation module, and the ambient temperature module, multi-factor comprehensive analysis is realized, and more comprehensive and practical battery life prediction is achieved, thereby improving the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0027] Figure 1 The flowchart of the battery life prediction method according to the embodiment of the present invention is shown;

[0028] Figure 2 The change trend diagram of the state of charge (SOC) and open circuit voltage (OCV) of the battery to be tested in the first embodiment of the present invention at different temperatures is shown;

[0029] Figure 3Shows the changing trend graph of the state of charge (SOC) and internal resistance of the battery under test in the first embodiment of the present invention at different temperatures;

[0030] Figure 4 Shows the battery life attenuation curve of the battery under test in the first embodiment of the present invention. Detailed implementation manners

[0031] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0032] As Figure 1 shown, the present invention provides a battery life prediction method, and the battery life prediction method includes: obtaining the electrical characteristic data of the battery under test and constructing an electrical module; obtaining the cycle test data and battery temperature rise data of the battery under test under different charge-discharge rates and the same initial temperature condition, and the cycle test data and battery temperature rise data of the battery under test under different initial temperatures and the same charge-discharge rate condition, and then performing Arrhenius formula fitting according to the above data, and obtaining the Arrhenius formula containing the corrected factor temperature T: to construct a battery cycle aging prediction module, where X is the number of cycles, E act is the reaction activation energy of a single battery cell, C rate is the charge rate, Ah is the battery ampere-hour capacity, R is the molar gas constant, a, b, c are fitting values, z 1 is the first power exponent factor; obtaining the calendar aging data of the battery under test at different temperatures and different SOCs and constructing a battery calendar aging prediction module; obtaining the heat generation data of the battery under test at different rates and constructing a battery cell heat generation module; obtaining the seasonal temperature change data of the region where the battery under test is located and constructing an environmental temperature module; obtaining a battery life prediction model by coupling the electrical module, the battery cycle aging prediction module, the battery calendar aging prediction module, the battery cell heat generation module, and the environmental temperature module, and predicting the attenuation life of the battery under test through the battery life prediction model.

[0033] In this embodiment, electrical characteristic data of the battery to be tested are obtained and an electrical module is constructed. Through the electrical module, the state of charge estimation, health state monitoring, electrical behavior simulation, and thermal effect analysis of the battery can be carried out, which can accurately reflect the behavior of the battery in the actual use environment. When constructing the battery cycle aging prediction module, the temperature-corrected Arrhenius formula is adopted, which takes into account the non-linear effect of temperature on the battery aging rate. This enables the model to accurately capture the battery aging rate varying with temperature and solves the problem of ignoring or simplifying the temperature effect in the prior art. The data input into the battery calendar aging prediction module not only includes the calendar aging attenuation of batteries with different initial SOCs at the same initial temperature but also takes into account the calendar aging attenuation of batteries with the same SOC at different initial temperatures, thereby being able to more comprehensively reflect the influence of calendar aging attenuation in different states of the battery cell, avoiding the limitations of the fitting model under a single condition, and improving the comprehensiveness and accuracy of the prediction. By constructing the battery cell heat generation module and the ambient temperature module, the actual temperature change of the battery during use can be predicted more accurately, and further the prediction accuracy of the battery aging process can be improved. By coupling the electrical module, the battery cycle aging prediction module, the battery calendar aging prediction module, the battery cell heat generation module, and the ambient temperature module, multi-factor comprehensive analysis is achieved, and more comprehensive and practical battery life prediction is realized, thereby improving the prediction accuracy.

[0034] It should be noted that the battery life prediction model of the present application is applicable to the life prediction of batteries for BEV (battery electric vehicle), PHEV (plug-in hybrid electric vehicle), and HEV (hybrid electric vehicle).

[0035] Obtaining the electrical characteristic data of the battery refers to obtaining the SOC-OCV data and the correlation data among the internal resistance, SOC, and temperature T. The cycle test data refers to the change in performance parameters recorded after the battery has undergone multiple charge and discharge cycles. Usually, it includes the changes in key parameters such as the capacity, internal resistance, and voltage of the battery with the increase in the number of cycles. The battery temperature rise data refers to the degree of temperature rise of the battery during charge and discharge. The battery will generate heat during charge and discharge due to internal chemical reactions and Joule heat generated by the current passing through the internal resistance of the battery. The battery temperature rise data includes: initial temperature: the temperature of the battery before the start of charge and discharge; maximum temperature: the highest temperature that the battery can reach during charge and discharge; temperature rise rate: the amount of temperature increase of the battery per unit time. The calendar aging data refers to the capacity retention rate, battery internal resistance, voltage, self-discharge rate, etc.

[0036] In addition, the battery life prediction model of the present application is calculated by being embedded in simulation software (such as Matlab, etc.) to achieve battery life prediction. a, b, and c are all fitting values, which depend on the change in charging temperature rise. When performing simulation, multiple groups of battery temperature rise data are brought in and satisfy the above Arrhenius formula, and the fitting values of a, b, and c can be obtained.

[0037] z 1 is the first power exponent factor, which is used to control the growth trend of the function, z 1 ranges from [0.4, 0.8], and the value of R is 8.314.

[0038] In one embodiment of the present invention, the steps of obtaining the seasonal temperature change data of the area where the battery under test is located and constructing the ambient temperature module include: obtaining the seasonal temperature change data of the area where the battery under test operates, and then using a wave function to simulate the seasonal temperature change trend of the above data to construct the ambient temperature module of the battery operating area.

[0039] Specifically, first, collect the seasonal temperature change data of the area where the battery under test operates. These data can be obtained from the historical records of the weather station. The temperature change data includes the temperature values at different seasons and different time points in a year to fully reflect the laws and characteristics of the temperature change in this area. Combine the above data and use a sine wave function to simulate the periodic fluctuation of temperature over time in a year to ensure that the temperature change corresponds to the actual seasonal change.

[0040] In one embodiment of the present invention, the value range of the initial temperature is -30°C to 55°C, and the value range of the charge-discharge rate is 0.3C to 3C.

[0041] In this embodiment, the value range of the initial temperature is set to -30°C to 55°C, considering the extreme low and high temperature environments that the battery may experience. Low temperature will significantly reduce the discharge capacity of the battery, while high temperature will accelerate the chemical reaction and aging process of the battery. Therefore, the above setting of the initial temperature can ensure that the performance of the battery under various climate conditions is accurately simulated and predicted. The value range of the charge-discharge rate is 0.3C to 3C, covering a variety of charge-discharge rates from slow charge to fast charge, which helps to evaluate the performance decay and life change of the battery under different usage intensities and can significantly improve the prediction accuracy of the battery life prediction model.

[0042] In one embodiment of the present invention, the steps of obtaining the calendar aging data of the battery under test at different temperatures and different SOCs and constructing the battery calendar aging prediction module include: establishing a functional relationship between capacity decay and battery capacity and storage time: where t is time, B is the fitting value, z 2 is the second power exponent factor.

[0043] In this embodiment, z 2The value range of [] is [0.4, 0.8]. By establishing the functional relationship between capacity attenuation and temperature, SOC, and storage time, the calendar aging rate of the battery at different temperatures and states of charge can be quantified, which can accurately reflect the true law of battery aging. Coupling the battery calendar aging prediction module with the battery cycle aging prediction module can provide a more comprehensive battery life prediction model, considering the storage cycles experienced by the battery during actual use, thereby improving the prediction accuracy of the battery life prediction model.

[0044] It should be noted that the battery life prediction model of this application is calculated by embedding simulation software (such as Matlab, etc.) to achieve battery life prediction. B is the fitted value, which depends on the initial SOC of the battery. When performing simulation, substituting the calendar aging data under different SOCs and satisfying the above formula can obtain the fitted value of B.

[0045] In an embodiment of the present invention, the value of B is directly proportional to the initial SOC value of the battery under test for aging.

[0046] Through the above settings, the influence of SOC on the aging of the battery under test can be evaluated more precisely, thereby improving the prediction accuracy.

[0047] In an embodiment of the present invention, the battery life prediction method further includes: obtaining the energy consumed by the battery under test under the thermal management strategy, and combining the above energy consumption to perform attenuation life prediction using the battery life prediction model.

[0048] In this embodiment, the thermal management strategy, such as the cooling or heating of the battery system, consumes additional energy. These energy consumptions not only reduce the available energy of the battery but also impose an additional cycle burden on the battery, indirectly affecting the life attenuation of the battery. By accurately quantifying the energy consumption under the thermal management strategy, the influence of different thermal management strategies on the battery life can be evaluated more comprehensively. The battery life prediction model combines the above energy consumption to perform attenuation life prediction, which can more accurately predict the life attenuation trend of the battery under complex working conditions.

[0049] In an embodiment of the present invention, the battery life prediction method further includes: obtaining the SOH value before charging, using this SOH value as the current correction factor to make the charging current I1 satisfy: I1 = I2 * SOH, and constructing a charging module, where I2 is the rated current, and coupling the charging module to the battery life prediction model.

[0050] In this embodiment, the SOH value reflects the current health state of the battery. During the use of the battery, the SOH will decrease over time and with the increase in the number of cycles. Taking the SOH as a correction factor for the charging current can more accurately simulate the electrochemical reactions and thermodynamic states during the charging process of the battery under different health states. Coupling the charging module to the battery life prediction model can further improve the prediction accuracy of the battery life prediction model.

[0051] In one embodiment of the present invention, the battery life prediction method further includes: obtaining the discharge parameters of the battery to be tested and constructing a discharge module, and coupling the discharge module to the battery life prediction model.

[0052] In this embodiment, the discharge parameters include discharge current, discharge rate, discharge time, depth of discharge (DOD), etc. By constructing a discharge module, the electrochemical reactions and energy conversion processes of the battery to be tested under different discharge conditions can be accurately simulated. Coupling the discharge module with the battery life prediction model enables the battery life prediction model to not only consider the characteristics of the battery to be tested during the charging process but also comprehensively consider the performance and losses of the battery to be tested during discharge, thereby being able to more accurately predict the capacity attenuation trend of the battery to be tested during actual use and further improving the prediction accuracy of the battery life prediction model.

[0053] In one embodiment of the present invention, the steps of obtaining the discharge parameters of the battery to be tested and constructing a discharge module include: obtaining the discharge parameters of the battery to be tested by combining the daily driving mileage, average vehicle speed, and operating condition information of the whole vehicle.

[0054] In this embodiment, during the actual use of an electric vehicle, the discharge behavior of the battery is affected by various factors, including the driving mileage, driving speed, road conditions, driving style, etc. By collecting this operating condition information, a discharge module closer to the actual use scenario can be constructed, which can more realistically simulate the discharge process of the battery under different conditions, thereby improving the accuracy and reliability of the battery life prediction model.

[0055] In one embodiment of the present invention, the steps of obtaining the electrical characteristic data of the battery to be tested and constructing an electrical module of the battery to be tested include: performing a hybrid pulse power characteristic test on the battery to be tested at different preset temperatures to obtain the electrical characteristic data of the battery to be tested at different preset temperatures. The electrical characteristic data includes SOC, internal resistance, and open-circuit voltage value; processing the electrical characteristic data through a second-order equivalent circuit model to obtain the corresponding relationship between SOC and open-circuit voltage, and the corresponding relationship between internal resistance and SOC at different preset temperatures to construct an electrical module.

[0056] In this embodiment, the hybrid pulse power characteristic test is carried out at different preset temperatures, and the electrical characteristic data of the battery under different temperature conditions can be collected. By processing the electrical characteristic data through a second-order equivalent circuit model, the electrochemical process inside the battery can be more accurately simulated, the accuracy of the electrical module can be improved, and the prediction of the battery life can be made more accurate.

[0057] It should be noted that the hybrid pulse power characteristic test adopts the existing technology, and the specific steps are not elaborated here.

[0058] In an embodiment of the present invention, the steps of obtaining the electrical characteristic data of the battery to be tested and constructing the electrical module of the battery to be tested further include: obtaining the data corresponding to the temperature outside the preset temperature by using the linear interpolation method, and then processing the electrical characteristic data and the data corresponding to the temperature outside the preset temperature obtained by using the linear interpolation method through a second-order equivalent circuit model to construct the electrical module.

[0059] In this embodiment, the linear interpolation method can estimate the electrical characteristic data of the battery at the intermediate temperature point based on the limited test data points. By using the linear interpolation method, the hybrid pulse power characteristic test can be carried out at fewer temperature points, thereby simplifying the data collection process, reducing the test cost and time, and enabling the battery life prediction model to be more widely applied to the prediction of the battery performance under different temperature conditions, improving the continuity of the prediction. In addition, by inputting the interpolated data and the actual test data into the second-order equivalent circuit model for processing together, the electrochemical behavior of the battery at different temperatures can be more comprehensively simulated, thereby improving the accuracy of the battery life prediction.

[0060] Embodiment 1

[0061] Taking a certain battery system as the detection object for battery life prediction, the relevant information of the battery system is shown in Table 1.

[0062] Table 1 Basic Information Table of the Battery System

[0063]

[0064] The specific process of predicting the life of the above battery system is as follows: Conduct HPPC tests on the 171Ah battery to be tested at 55°C, 25°C, 0°C, and -20°C respectively, and identify the parameters through a second-order equivalent circuit model to obtain the SOC-OCV data of the 171Ah battery to be tested, the correlation data between the internal resistance and SOC, and temperature T. The corresponding data at other temperature points are obtained by linear interpolation method. Combine the above data to construct the electrical module of the 171Ah battery to be tested; Conduct cyclic aging tests on the 171Ah battery to be tested at 0°C, 25°C, 45°C, DOD = 100%, and charge-discharge rate of 1C. Based on the above test results, determine the Arrhenius formula with temperature T correction factor:

[0065] To construct a battery cyclic aging prediction module; Conduct calendar aging tests on the 171Ah battery to be tested to obtain the calendar aging data of the battery to be tested at 0°C, 25°C, 45°C, and 30% SOC, 50% SOC, 100% SOC, and establish the functional relationship between capacity decay and battery capacity, storage time Construct a battery calendar aging prediction module, and the B values corresponding to different SOC are shown in Table 2;

[0066] Table 2 B values in the calendar aging formula corresponding to different SOC

[0067] SOC(%) 10％ 30％ 50％ 80％ 100％ B 0.42 0.47 0.52 0.6 0.71

[0068] Then, charge-discharge tests are carried out on the 171Ah battery to be tested at rates of 0.33C, 1C, 2C, and 3C respectively in a closed space without heat exchange to obtain the heat generation data of the battery to be tested. Based on the above heat generation data, a core heat generation module is constructed; it is determined that the battery system to be tested is assembled from 1 parallel and 99 series of cells to be tested to obtain the power and capacity of the battery system to be tested. The main operating area of this battery system to be tested is Sanya area. The annual environmental temperature change trend in this area is simulated by a wave function, and an environmental temperature module for the operating area is constructed; during the discharge process of the battery system to be tested, when the minimum temperature of the core is > 36°C, a cooling strategy is activated, and when the minimum temperature of the internal core is less than 32°C, the cooling strategy is deactivated. The energy consumed by the battery to be tested under the thermal management strategy is obtained as 3kW; the SOH value before charging is obtained, and this SOH value is used as a current correction factor to make the charging current I1 satisfy: I1 = I2 * SOH, and a charging module is constructed; the vehicle travels 125 km per day, with an average speed of 40 km / h, the energy consumption per 100 km is controlled at 12 kWh, the charging frequency is once a day, and it travels 300 days a year. Combining this working condition, the daily operating time and storage time of the battery system are obtained as 4 hours and 16 hours respectively; through the above constructed core electrical module, battery cycle aging prediction module, battery calendar aging prediction module, core heat generation module, environmental temperature module, charging module, and discharge module, a battery life prediction model is coupled. Through the battery life prediction model, the capacity decay of the vehicle corresponding to this battery system during a driving process of 180,000 km is predicted, and the life decay is obtained as 13.16%. Figure 2 Shows the change trend diagram of the state of charge (SOC) and open circuit voltage (OCV) of the battery to be tested in the first embodiment at different temperatures; Figure 3 Shows the change trend diagram of the state of charge (SOC) and internal resistance of the battery to be tested in the first embodiment at different temperatures; Figure 4 Shows the battery life decay curve of the battery to be tested in the first embodiment.

[0069] From the above description, it can be seen that the above embodiments of the present invention achieve the following technical effects: obtaining the electrical characteristic data of the battery to be tested and constructing an electrical module, through which the state of charge estimation, health state monitoring, electrical behavior simulation and thermal effect analysis of the battery can be carried out, and it can accurately reflect the behavior of the battery to be tested in the actual use environment. When constructing the battery cycle aging prediction module, the Arrhenius formula containing the corrected factor temperature T is adopted, which takes into account the non-linear influence of temperature on the battery aging rate, enabling the model to accurately capture the battery aging rate varying with temperature and solving the problem of ignoring or simplifying the temperature effect in the prior art; the data input into the battery calendar aging prediction module not only includes the battery calendar aging attenuation of different initial SOCs at the same initial temperature, but also considers the battery calendar aging attenuation of the same SOC state at different initial temperatures, so as to more comprehensively reflect the influence of the calendar aging attenuation of different states of the battery cell, avoid the limitation of the fitting model under a single condition, and improve the comprehensiveness and accuracy of the prediction; by constructing the battery cell heat generation module and the ambient temperature module, the actual temperature change of the battery during use can be predicted more accurately, and further the prediction accuracy of the battery aging process can be improved. By coupling the electrical module, the battery cycle aging prediction module, the battery calendar aging prediction module, the battery cell heat generation module and the ambient temperature module, multi-factor comprehensive analysis is realized, and more comprehensive and practical battery life prediction is achieved, thereby improving the prediction accuracy.

[0070] Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0071] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0072] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A battery life prediction method, characterized in that: include: Obtain the electrical characteristic data of the battery to be tested and construct an electrical module; The cycle test data and battery temperature rise data of the battery to be tested under different charge and discharge rates and the same initial temperature conditions as well as the cycle test data and battery temperature rise data of the battery to be tested under different initial temperatures and the same charge and discharge rate conditions are obtained, and then the Arrhenius formula is fitted according to the above data to obtain the Arrhenius formula containing the correction factor temperature T: To build a battery cycle aging prediction module, where X is the number of cycles, E act is the reaction activation energy of a single cell, C rate is the charging rate, Ah is the battery ampere-hour, R is the molar gas constant, a, b, c are fitting values, and z1 is the first power exponential factor; Acquire calendar aging data of the battery to be tested at different temperatures and different SOCs and construct a battery calendar aging prediction module; Obtaining the calorific value data of the battery under test at different rates and constructing a battery cell heat generation module; Obtaining the temperature change data of the region where the battery to be tested is located in four seasons and constructing an environmental temperature module; A battery life prediction model is obtained by coupling the electrical module, the battery cycle aging prediction module, the battery calendar aging prediction module, the battery cell heat generation module and the ambient temperature module, and the attenuation life of the battery to be tested is predicted by the battery life prediction model.

2. The battery life prediction method according to claim 1, characterized in that: The initial temperature ranges from -30°C to 55°C, and the charge and discharge rate ranges from 0.3C to 3C.

3. The battery life prediction method according to claim 1, characterized in that: The steps of obtaining calendar aging data of the battery to be tested at different temperatures and different SOCs and constructing a battery calendar aging prediction module include: establishing a functional relationship between capacity attenuation and battery capacity and storage time: Where t is time, B is the fitted value, and z2 is the second power exponential factor.

4. The battery life prediction method according to claim 3, characterized in that: The value of B is proportional to the initial SOC value of the battery under test.

5. The battery life prediction method according to any one of claims 1 to 4, characterized in that: The battery life prediction method further includes: obtaining the energy consumed by the battery to be tested under the thermal management strategy, and performing attenuation life prediction using the battery life prediction model in combination with the above energy consumption.

6. The battery life prediction method according to any one of claims 1 to 4, characterized in that: The battery life prediction method also includes: obtaining the SOH value before charging, using the SOH value as a current correction factor to make the charging current I1 satisfy: I1=I2*SOH, and constructing a charging module, wherein I2 is the rated current, and coupling the charging module to the battery life prediction model.

7. The battery life prediction method according to any one of claims 1 to 3, characterized in that: The battery life prediction method further includes: acquiring discharge parameters of the battery to be tested and constructing a discharge module, and coupling the discharge module to the battery life prediction model.

8. The battery life prediction method according to claim 7, characterized in that: The step of obtaining the discharge parameters of the battery to be tested and constructing a discharge module includes: obtaining the discharge parameters of the battery to be tested in combination with the daily mileage, average vehicle speed and operating condition information of the whole vehicle.

9. The battery life prediction method according to any one of claims 1 to 4, characterized in that: The steps of obtaining the electrical characteristic data of the battery to be tested and constructing the electrical module of the battery to be tested include: Performing a mixed pulse power characteristic test on the battery to be tested at different preset temperatures to obtain electrical characteristic data of the battery to be tested at different preset temperatures, wherein the electrical characteristic data includes SOC, internal resistance and open circuit voltage value; The electrical characteristic data is processed by a second-order equivalent circuit model to obtain the corresponding relationship between the SOC and the open circuit voltage and the corresponding relationship between the internal resistance and the SOC at different preset temperatures, so as to construct the electrical module.

10. The battery life prediction method according to claim 9, characterized in that: The step of obtaining the electrical characteristic data of the battery to be tested and constructing the electrical module of the battery to be tested also includes: using a linear interpolation method to obtain data corresponding to temperatures other than the preset temperature, and then processing the electrical characteristic data and the data corresponding to temperatures other than the preset temperature obtained using the linear interpolation method through the second-order equivalent circuit model to construct the electrical module.

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