Battery life prediction method, device, electronic device, storage medium and vehicle

Abstract

The present application relates to the field of battery technology, and discloses a battery life prediction method, device, electronic device, storage medium and vehicle. By obtaining test batteries with different sample battery lives and obtaining the sample initial voltages corresponding to each test battery, a charging function is performed on each test battery to obtain the sample charging time. A corresponding relationship is established according to the sample battery life, the sample initial voltage and the sample charging time to obtain a charging data relationship table. In response to the current initial voltage and the current charging time corresponding to the target battery, matching is performed from the charging data relationship table according to the current initial voltage and the current charging time to obtain the current battery life corresponding to the target battery. The above method can provide a simpler, more practical and more accurate battery life prediction method, and accurately predict the battery life of the target battery.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery life prediction method, device, electronic equipment, storage medium and vehicle. Background Art

[0002] With the development of vehicle electrification, new energy vehicles are becoming more and more popular. As the power supply for low-voltage electrical appliances in vehicles, the reliability and stability of batteries are crucial. Usually, the battery life is estimated in the following two ways to determine whether to replace the battery: 1. Users determine whether to replace the small battery based on the durability or service life of the current vehicle battery. The estimation accuracy is low, which leads to missing the best time for replacement; 2. Some models add additional devices to detect and estimate the battery life, which increases the cost, increases the vehicle energy consumption, and deteriorates the stability of the vehicle control system. The intelligent charging function has been widely used in new energy vehicles. By using the existing intelligent charging function, the battery life is estimated through software algorithms, which reduces the cost of the whole vehicle and improves the stability of the vehicle control system.

[0003] Document CN116840695A discloses a method and system for measuring the power value of an automobile battery. The method obtains the on-load battery voltage under different conditions by collecting data, matches the on-load battery voltage and SOC value under different conditions and establishes a database, measures the output power value, and uploads the battery history data, charge and discharge depth and number of times to the cloud. The cloud is based on the battery life cycle data, considers the characteristic factors that affect the battery life, uses feature engineering, and constructs a life prediction AI algorithm model to achieve battery life estimation and prediction. This method not only requires the collection of a large amount of data, but also the process of constructing the life prediction AI algorithm model is complicated, which is disadvantageous. For promotion and application; Document CN108931742B discloses a method for online real-time detection of vehicle battery life, which includes: real-time acquisition of the battery voltage value of the vehicle to be detected, uploading the acquired battery voltage value to the platform server, generating a voltage curve from the acquired battery voltage value, and cloud analysis of the voltage curve to form a voltage curve trajectory diagram that changes with the battery usage time, and judging whether the current battery voltage curve deviates from the trajectory. If the deviation value reaches the expected value, it is predicted that the battery needs to be replaced and fed back to the terminal device, and additional voltage acquisition equipment is required, which increases the user's vehicle cost and the vehicle power consumption. In order to reduce the complexity of battery life prediction and improve the accuracy of battery life prediction, this application proposes a new battery life prediction method.

[0004] It should be noted that the above content only provides background technical information related to the present invention and does not necessarily constitute prior art. Summary of the invention

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides a battery life prediction method, device, electronic device, storage medium and vehicle to reduce the complexity of battery life prediction and improve the accuracy of battery life prediction.

[0006] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0007] According to one aspect of an embodiment of the present invention, a battery life prediction method is provided, comprising: obtaining batteries to be tested with different sample battery lives, and obtaining sample initial voltages corresponding to each of the batteries to be tested; performing a power replenishment function on each of the batteries to be tested to obtain a sample power replenishment duration, wherein the power replenishment function is performed by the battery to be tested supplying power to the battery to be tested by inputting a current at the sample initial voltage, and stopping supplying power to the battery to be tested when a current difference of the input current is less than a preset threshold; establishing a corresponding relationship according to the sample battery life, the sample initial voltage, and the sample power replenishment duration to obtain a power replenishment data relationship table; in response to a current initial voltage and a current power replenishment duration corresponding to a target battery, matching is performed from the power replenishment data relationship table according to the current initial voltage and the current power replenishment duration to obtain a current battery life corresponding to the target battery.

[0008] In one embodiment of the present invention, based on the above solution, before obtaining the sample initial voltages respectively corresponding to the batteries to be tested, the method includes: adjusting the voltages of the batteries to be tested to the sample initial voltages respectively corresponding to the batteries to be tested.

[0009] In one embodiment of the present invention, based on the above scheme, the charging function is performed on each of the batteries to be tested to obtain a sample charging time, including: performing the charging function on each of the batteries to be tested for a preset number of times, and calculating the average charging time corresponding to the preset number of times, and using the average charging time as the sample charging time corresponding to the battery to be tested.

[0010] In one embodiment of the present invention, based on the aforementioned scheme, a preset number of times of recharging function is performed on each of the batteries to be tested, including: each of the batteries to be tested is installed on a vehicle to be tested, so as to perform a preset number of times of recharging function on each of the batteries to be tested, wherein the vehicle to be tested and a mass-produced vehicle are in the same state and the vehicle to be tested has no faults.

[0011] In one embodiment of the present invention, based on the aforementioned scheme, in response to the current initial voltage and the current charging time corresponding to the target battery, the method further includes: obtaining charging parameters, the charging parameters including the battery capacity of the target battery, the vehicle power consumption and dark current of the target vehicle; setting the charging detection time and the charging start voltage according to the charging parameters; collecting the current voltage of the target battery at each interval of the charging detection time; if the current voltage is greater than or equal to the charging start voltage, the target vehicle is in normal sleep; if the current voltage is less than the charging start voltage, performing the charging function on the target battery.

[0012] In one embodiment of the present invention, based on the above scheme, the method further includes: starting to recharge the target battery, obtaining the current initial voltage and the recharge start time; monitoring the input current of the target battery, if the current difference of the input current within a preset time is less than the preset threshold, stopping recharging the target battery, and taking the time when the current difference is less than the preset threshold as the recharge end time; and calculating the current recharge duration according to the recharge start time and the recharge end time.

[0013] In one embodiment of the present invention, based on the aforementioned scheme, performing the charging function on the target battery also includes: detecting the current battery temperature of the target battery; if at least one of the following conditions is met: the current battery temperature is greater than a preset temperature threshold, the current voltage is greater than a preset charging voltage threshold, and the current voltage is less than a preset discharging voltage threshold, then stopping charging the target battery.

[0014] In one embodiment of the present invention, based on the aforementioned scheme, after matching the current initial voltage and the current charging time from the charging data relationship table to obtain the current battery life corresponding to the target battery, the method further includes at least one of the following: sending a prompt message to the vehicle system to prompt the user on the vehicle side, wherein the prompt message is generated based on the current battery life; sending the prompt message to the cloud so that the terminal can obtain the prompt message from the cloud and prompt the user at the terminal.

[0015] In one embodiment of the present invention, based on the aforementioned scheme, after matching the current initial voltage and the current charging time from the charging data relationship table to obtain the current battery life corresponding to the target battery, the method further includes: if the current battery life is less than a preset battery life threshold, prompting the user through a preset prompt method, and the preset prompt method includes at least one of prompting the user through an interactive interface, prompting the user through voice, and prompting the user through terminal message push.

[0016] According to one aspect of an embodiment of the present invention, a battery life prediction device is provided, comprising: an acquisition module, used to acquire batteries to be tested with different sample battery lives, and acquire sample initial voltages corresponding to each of the batteries to be tested; an execution module, used to execute a power replenishment function on each of the batteries to be tested, and obtain a sample power replenishment duration, wherein the execution of the power replenishment function comprises the battery to be tested replenishing the battery to be tested by inputting current at the sample initial voltage, and stopping replenishing the battery to be tested when the current difference of the input current is less than a preset threshold; a corresponding module, used to establish a corresponding relationship according to the sample battery life, the sample initial voltage, and the sample power replenishment duration, and obtain a power replenishment data relationship table; a prediction module, used to respond to a current initial voltage and a current power replenishment duration corresponding to a target battery, and match the current initial voltage and the current power replenishment duration from the power replenishment data relationship table to obtain a current battery life corresponding to the target battery.

[0017] The present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements a battery life prediction method as described in any one of the above embodiments.

[0018] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer is enabled to execute the battery life prediction method as described in any one of the above embodiments.

[0019] The present invention also provides a vehicle, which includes the battery life prediction device as described in the above embodiment or the electronic device as described in the above embodiment.

[0020] Beneficial effects of the present invention: The present application performs the charging function on the batteries to be tested with different sample battery lives respectively to obtain the sample charging time, establishes a corresponding relationship according to the sample battery life, the sample initial voltage and the sample charging time, obtains the charging data relationship table, responds to the current initial voltage and the current charging time corresponding to the target battery, matches the current initial voltage and the current charging time from the charging data relationship table, and obtains the current battery life corresponding to the target battery, which can reduce the complexity of battery life prediction and improve the accuracy of battery life prediction, avoid misleading the operating personnel due to inaccurate prediction, avoid delaying the maintenance of the battery module, and thus improve driving safety.

[0021] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0023] Figure 1 is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application;

[0024] Figure 2 is a flowchart of a battery life prediction method shown in an exemplary embodiment of the present application;

[0025] Figure 3 is a flowchart of a battery life prediction method shown in another exemplary embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of a charging time calibration test process of a battery life prediction method shown in an exemplary embodiment of the present application;

[0027] Figure 5 is a schematic diagram of a current battery life estimation process of a battery life prediction method shown in an exemplary embodiment of the present application;

[0028] Figure 6 is a block diagram of a battery life prediction device shown in an exemplary embodiment of the present application;

[0029] Figure 7 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0030] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0031] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner, and thus the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0032] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0033] First of all, it should be noted that VCU (Vehicle Control Unit) is the core electronic control unit that realizes the whole vehicle control decision. It is mainly used in new energy vehicles. When the car is equipped with VCU, it can collect basic information of the vehicle during driving and use this information to protect the car. VCU software, namely vehicle control unit software, is the core software in the control system of new energy vehicles and bears the core responsibility of the entire control system.

[0034] DCDC is the abbreviation of "DC-to-DC Converter". It is a device that converts a DC power supply of a certain voltage level into a DC power supply of another voltage level. The working principle of the DCDC converter is to convert the input DC power into AC power through a self-excited oscillation circuit, and then convert it into DC output after changing the voltage through a transformer, or convert AC power into high-voltage DC output through a voltage doubler rectifier circuit. A DC-DC converter, also known as a DC-DC converter, is a circuit or electromechanical device for power conversion that can convert a DC power supply into a DC (or near-DC) power supply of different voltages. Its power range can range from very small (small battery) to very large (high-voltage power conversion).

[0035] In a battery management system, SOH (State of Health) is an important parameter used to describe the performance of a battery relative to its original state. It is usually a percentage value that indicates how much charge the battery is currently able to store and release, relative to its capabilities when it was new.

[0036] Figure 1 It is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application.

[0037] Reference Figure 1As shown, the system architecture may include a data acquisition device 101 and a computer device 102. The computer device 102 may be at least one of a desktop graphics processing unit (GPU) computer, a GPU computing cluster, a neural network computer, etc. The data acquisition device 101 is used to obtain batteries to be tested with different sample battery lifespans, and obtain sample initial voltages corresponding to each battery to be tested. In this embodiment, the data acquisition device 101 acquires the above data and provides it to the computer device 102 for processing. Relevant technicians can use the computer device 102 to perform the charging function on each battery to be tested, obtain the sample charging duration, establish a corresponding relationship according to the sample battery life, the sample initial voltage, and the sample charging duration, obtain a charging data relationship table, and in response to the current initial voltage and the current charging duration corresponding to the target battery, match the current initial voltage and the current charging duration from the charging data relationship table to obtain the current battery life corresponding to the target battery. It should be noted that the data acquisition device 101 and the computer device 102 provided in this embodiment are only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0038] It should be noted that the battery life prediction method provided in the embodiment of the present application is generally executed by the computer device 102 , and accordingly, the battery life prediction device is generally disposed in the computer device 102 .

[0039] Figure 2 is a flowchart of a battery life prediction method shown in an exemplary embodiment of the present application. The battery life prediction method can be executed by a computing and processing device. The computing and processing device can be Figure 1 The computer device 102 shown in FIG. Figure 2 As shown, the battery life prediction method includes at least steps S210 to S240, which are described in detail as follows:

[0040] In step S210, batteries to be tested with different sample battery lifespans are obtained, and sample initial voltages corresponding to the batteries to be tested are obtained.

[0041] In one embodiment of the present application, before obtaining the sample initial voltages corresponding to the batteries to be tested, the voltages of the batteries to be tested are adjusted to the sample initial voltages corresponding to the batteries to be tested. The adjustment includes discharging, that is, discharging the voltages of the batteries to be tested to the sample initial voltages corresponding to the batteries to be tested, so that the discharged batteries to be tested are installed on the prepared vehicles to be tested to perform the charging function.

[0042] In this embodiment, a battery to be tested with the same specifications as mass production is prepared, and the battery to be tested includes different sample battery lifespans (including new and different life states) for charging data testing. The battery to be tested can be a low-voltage battery, which generally refers to a battery with a rated voltage below 12V.

[0043] In step S220, the charging function is performed on each battery to be tested to obtain a sample charging time.

[0044] The execution method of the charging function includes charging the battery to be tested by inputting current when the battery to be tested has an initial voltage of the sample, and stopping charging the battery to be tested when the current difference of the input current is less than a preset threshold.

[0045] In one embodiment of the present application, each battery to be tested is recharged, and the recharge start time is obtained when the recharge starts; the input current of the DC-DC converter is monitored, and if the current difference of the input current within the preset time is less than the preset threshold, the recharge of the battery to be tested is stopped, and the time when the current difference is less than the preset threshold is used as the recharge end time; the current recharge duration is calculated according to the recharge start time and the recharge end time. It should be noted that, in the process of intelligent recharge of the whole vehicle, the DCDC power, i.e., the power of the DC-DC converter, is not only used to charge the low-voltage battery, but also needs to supply power to the low-voltage electrical appliances of the whole vehicle (such as the starter, headlights, turn signals, etc.); therefore, during the battery recharge process, the DCDC input terminal current, i.e., the input current, is relatively high. When the recharge is completed, the DCDC power is only used for the low-voltage electrical appliances of the whole vehicle, and the input terminal current is reduced and tends to be stable; therefore, the time from the start of recharge to the reduction and stabilization of the DCDC input terminal current is determined as the intelligent recharge duration, i.e., the sample recharge duration.

[0046] In this embodiment, the vehicle VCU software has an intelligent charging function during the test and development stage. At the same time, the interval time of the intelligent charging can be adjusted at will through calibration, or the intelligent charging function can be turned on at any time through calibration. It should be noted that the intelligent charging function refers to the vehicle system replenishing electricity through a series of intelligent control methods to extend the vehicle's mileage. The intelligent charging function has the advantages of efficient charging speed, convenient charging method, extended battery life, energy saving and environmental protection, real-time monitoring of the charging status of the equipment through the intelligent charging protection system, avoiding safety hazards such as overcharging and overheating, thereby ensuring the safety of users.

[0047] In one embodiment of the present application, the charging function is performed for each battery to be tested for a preset number of times, and the average charging duration corresponding to the preset number of times is calculated, and the average charging duration is used as the sample charging duration corresponding to the battery to be tested.

[0048] In this embodiment, preferably, a plurality of tested batteries with the same sample battery life are respectively subjected to the power replenishment function for a preset number of tests, and the power replenishment duration of each tested battery each time the power replenishment function is performed is obtained, and the average value is calculated based on the power replenishment duration of each tested battery each time the power replenishment function is performed, and the corresponding average power replenishment duration after the plurality of tested batteries perform the power replenishment function for the preset number of tests is obtained, and the average power replenishment duration is used as the sample power replenishment duration corresponding to the tested battery. For example, three tested batteries with a sample battery life of 80% are respectively subjected to the power replenishment function for 2 times, and the power replenishment duration of each tested battery each time the power replenishment function is performed is obtained, which are t11, t12, t21, t22, t31, and t32, and the average value is calculated based on t11, t12, t21, t22, t31, and t32 to obtain the average power replenishment duration t, and the average power replenishment duration t is used as the sample power replenishment duration corresponding to the tested battery.

[0049] In this embodiment, the battery to be tested with different sample battery life is used to perform the charging test at different sample initial voltages, and the charging data of the entire charging process is recorded, including the charging time of the sample; in order to reduce the error of the experimental data, each group of tests is performed three times, and the average of the three data is taken when there is no special abnormality. It should be noted that the three times in this embodiment is a preferred value, and the number of repetitions of each group of tests can be set according to the actual application process, and this application does not limit this.

[0050] In one embodiment of the present application, each battery to be tested is installed on a vehicle to be tested to perform a preset number of charging functions on each battery to be tested, wherein the vehicle to be tested and the mass-produced vehicle are in the same state and the vehicle to be tested has no faults.

[0051] In this embodiment, a vehicle to be tested is selected whose software and hardware status of each controller of the vehicle is close to that of a mass-produced vehicle, ensuring that the power consumption, dark current and sleep state of each controller of the vehicle to be tested are normal and that the vehicle functions normally without faults.

[0052] In step S230, a corresponding relationship is established according to the sample battery life, the sample initial voltage, and the sample charging time to obtain a charging data relationship table.

[0053] In one embodiment of the present application, a corresponding relationship is established based on the sample battery life, sample initial voltage, and sample charging time in each group of test results, and a corresponding relationship between the sample battery life, sample initial voltage, and sample charging time is obtained, and a charging data relationship table is obtained based on the corresponding relationship.

[0054] In this embodiment, all experimental data are summarized to form a statistical table, from which the sample recharging time of a sample battery life at the sample initial voltage can be obtained.

[0055] In step S240, in response to the current initial voltage and the current charging duration corresponding to the target battery, a match is performed from the charging data relationship table according to the current initial voltage and the current charging duration to obtain the current battery life corresponding to the target battery.

[0056] In one embodiment of the present application, the above-mentioned experimental data can be used as calibration data to calibrate different charging characteristic data, i.e., sample charging duration, for vehicles under test of different models; after the calibration test is completed, the VCU software can monitor the charging current, i.e., the input current, after the target battery turns on the intelligent charging function, and then match it with the original experimental data to predict the current battery life of the target battery. It can also be understood that the VCU uses the current initial voltage and the current charging duration as input variables and compares them with the reference calibration data to derive the current battery life of the target battery. Among them, the target battery can be a battery under test (the battery under test includes the target battery), or it can be a battery of the same model as the battery under test.

[0057] In one embodiment of the present application, in response to the current initial voltage and the current charging duration corresponding to the target battery, charging parameters are obtained, the charging parameters including the battery capacity of the target battery, the vehicle power consumption and dark current of the target vehicle; the charging detection time and the charging start voltage are set according to the charging parameters; the current voltage of the target battery is collected at each charging detection time interval; if the current voltage is greater than or equal to the charging start voltage, the target vehicle goes into sleep normally; if the current voltage is less than the charging start voltage, the charging function is performed on the target battery.

[0058] In this embodiment, during normal use of the vehicle by the user, the charging detection time of the intelligent charging function is designed according to different vehicle models. The vehicle will periodically wake up the intelligent charging function according to the charging detection time, and monitor the voltage value of the target battery, that is, the current voltage, through the VCU; if the voltage value is above the intelligent charging start threshold, that is, the current voltage is greater than or equal to the charging start voltage, the target vehicle will be in normal sleep mode; if it is detected that the current voltage of the target battery is less than the intelligent charging start threshold, that is, the charging start voltage, the relevant controller will be woken up to start the intelligent charging function, and the charging function will be executed on the target battery.

[0059] In one embodiment of the present application, the target battery is started to be recharged, and the current initial voltage and the recharge start time are obtained; the input current of the target battery is monitored, and if the current difference of the input current within a preset time is less than a preset threshold, the target battery is stopped from being recharged, and the time when the current difference is less than the preset threshold is used as the recharge end time; the current recharge time is calculated based on the recharge start time and the recharge end time.

[0060] In this embodiment, after the intelligent charging is turned on, the VCU collects the current initial voltage of the target battery when the charging is turned on, then starts timing, and monitors the input current of the DCDC end during the charging process; when the DCDC input current drops and stabilizes, the timing is stopped, and the current charging duration is determined based on the timing result. Among them, when the VCU monitors that the current difference of the DCDC input current for several consecutive cycles, that is, within a certain range within a preset time, it is determined that the current is stable.

[0061] In one embodiment of the present application, the current battery temperature of the target battery is detected; if at least one of the following conditions is met: the current battery temperature is greater than a preset temperature threshold, the current voltage is greater than a preset charging voltage threshold, and the current voltage is less than a preset discharging voltage threshold, then charging of the target battery is stopped.

[0062] In one embodiment of the present application, after matching the current initial voltage and the current charging time from the charging data relationship table to obtain the current battery life corresponding to the target battery, the method further includes at least one of the following: sending a prompt message to the vehicle system to prompt the user at the vehicle end, wherein the prompt message is generated according to the current battery life; sending the prompt message to the cloud, so that the terminal can obtain the prompt message from the cloud and prompt the user at the terminal. The prompt message includes but is not limited to text such as "Battery life is low, please check the battery status in time", "Battery life is low", "Current battery life is XX", or displaying a battery icon, and setting the color of the icon to a preset color, such as red.

[0063] In this embodiment, if the current battery life is less than a preset battery life threshold, the user is prompted by a preset prompting method, which includes at least one of prompting the user through an interactive interface, prompting the user through voice, and prompting the user through terminal message push. For example, the prompt information is displayed in a pop-up window on the display screen or dashboard of the target vehicle, the prompt information is played through the voice device of the target vehicle, the prompt information is forwarded to the user's terminal, and the user is prompted through the software or voice of the user's terminal.

[0064] In this embodiment, a battery life threshold is set to remind the user according to the service life characteristics of the low-voltage battery; each time the intelligent charging function is turned on, the VCU will perform a life estimation and compare the estimated life result, i.e., the current battery life, with the battery life threshold. When it is estimated that the current battery life is greater than or equal to the battery life threshold, the current estimation result, i.e., the current battery life, is cleared; when the current battery life is less than the battery life threshold, the estimation result is used to prompt the user to check the battery life in one or more of the following ways: 1. The user is prompted through a pop-up window on the instrument panel or the vehicle computer the next time the user gets on the vehicle; 2. The estimation result is pushed to the user's mobile phone software through a remote control message to remind the user to check the battery. Avoid the vehicle being unable to get on high voltage due to battery problems.

[0065] Figure 3 FIG. 1 is a flow chart of a battery life prediction method according to another exemplary embodiment of the present application. Figure 3 As shown, in an exemplary embodiment, the overall battery life prediction method can be divided into four parts: experimental testing, calibration debugging, software estimation, and prompting the user, wherein prompting the user is an optional (non-essential) part. Figure 3 As shown, the battery life prediction method includes at least steps S1 to S4, which are described in detail as follows:

[0066] In step S1, a test vehicle, i.e., a vehicle to be tested, is prepared. The test vehicle is required to meet the requirements of power-on and power-off software logic, low-voltage battery model, vehicle controller power consumption and mass production status, and the vehicle is powered on and off normally without vehicle failure. By controlling the variable method, complex problems are simplified and the interference of other factors is eliminated, so as to more accurately evaluate the impact of specific variables. By ensuring the stability of other variables during the experiment, the changes and effects of the research object can be more accurately measured and evaluated, thereby improving the accuracy and reliability of the experiment.

[0067] In step S2, low-voltage batteries with different sample battery life SOH, i.e., the batteries to be tested, are installed on the sample vehicles prepared in step S1 at different sample initial voltages to start intelligent charging, and the charging time is monitored and recorded. In order to reduce the dispersion, each group of combined tests is tested three times and the average value is taken as the final sample charging time. By averaging multiple tests, inaccurate charging time of the tested samples due to errors is avoided, thereby improving the accuracy of battery life prediction.

[0068] In step S3, after completing the charging test of all different sample battery life and different sample initial voltage in step S2, the intelligent charging time experimental data of different sample initial voltage under each sample battery life can be obtained, as shown in Table 1; this data will be used as the calibration basis for the VCU intelligent charging function of estimating low-voltage battery life. After sorting the data, the VCU data calibration of this function is completed.

[0069] Table 1 Recharging time record table

[0070]

[0071] As can be seen from Table 1, the factors that affect the charging time include battery life and initial voltage. Different battery life and different initial voltage correspond to different charging times. The specific correspondence is determined by the actual measurement results. Table 1 only shows the correspondence. The specific values ​​are not the focus of this application. This application does not limit this, nor does it cite specific values ​​as examples. It should be noted that the data in this embodiment are only for illustration and should not bring any limitations to the functions and scope of use of the embodiments of this application.

[0072] In step S4, the VCU of the target battery of the mass-produced vehicle, i.e. the target vehicle, first detects the current initial voltage of the low-voltage battery before each intelligent charging is started. After the charging is started, the VCU can monitor the change of the DCDC input current. Similarly, based on the test principle in step S2, the VCU can calculate the current charging time; and then, based on the calibrated data, the current battery life of the target battery is calculated by looking up the table.

[0073] In step S5, the VCU sets the lower limit of the battery life as the battery life threshold according to the product characteristics of the low-voltage battery, i.e., the target battery. When the current battery life calculated in step S4 is less than the lower limit of the battery life threshold set in advance, the VCU issues a life display prompt to prompt the user to go to the 4S shop to check the battery status to avoid the vehicle being unable to go on high voltage due to battery problems.

[0074] In step S6, the VCU sends the life reminder to the vehicle computer and uploads the life reminder to the cloud at the same time; after receiving the life reminder, the vehicle computer prompts the user to check the battery on the vehicle side, and the cloud pushes the reminder information of the life reminder to the mobile phone APP side, and the user can receive the reminder to check the battery on the mobile phone.

[0075] It should be noted that in this embodiment, step S5 and step S6 are not necessary steps. Step S5 and step S6 are additional steps in the preferred case. In actual application, step S5 and step S6 can be deleted, or step S5 and step S6 can be replaced by other steps. This application does not impose any restrictions on this.

[0076] Figure 4 FIG. 1 is a schematic diagram of a battery life prediction method for a battery charging time calibration test flow chart of an exemplary embodiment of the present application. Figure 4 As shown, in an exemplary embodiment, a group of test examples are selected and detailed Figure 4 The steps of the charging time calibration test process are explained in the following.

[0077] Prepare low-voltage batteries with different sample battery life (SOH), i.e., the batteries to be tested, where the SOH can be 100%, 90%, 80%, 70%, 60%, and 50%. First, select a low-voltage battery with an SOH of 100%, which is recorded as variable Y1; discharge the battery voltage to 12V, which is recorded as variable X1, and then install the discharged battery on the sample vehicle prepared by S1. Collect vehicle CAN data (CAN (Controller Area Network) data usually refers to the information exchanged between various vehicle controllers (ECU, such as engine control unit, body control unit, etc.)) through the CAN bus interface). Check the CAN data to see that the voltage collected by the VCU is about 12V. Then the vehicle is powered on normally and the vehicle intelligent charging function is turned on through VCU calibration. This moment is recorded as the charging start time ta; check the DCDC input current I from time to time. When the DCDC input current drops to a stable state and does not change, this moment is recorded as the charging end time tb; calculate the sample charging time t1 based on the charging start time and the charging end time, as shown in the following formula:

[0078] t1=tb-ta formula (1)

[0079] In formula (1), t1 is the sample charging duration, tb represents the charging end time, and ta is the charging start time.

[0080] The same set of variables Y1 and X1 were tested three times, and then the average value was taken to get the final sample charging time.

[0081] By completing all combination experiments of different SOH and different sample initial voltages as above, the sample recharging time t under different combinations of variables Y and X can be obtained, as shown in Table 1, which serves as the basis for functional calibration.

[0082] Figure 5 FIG. 1 is a schematic diagram of a current battery life estimation process of a battery life prediction method according to an exemplary embodiment of the present application. Figure 5As shown, in an exemplary embodiment, the vehicle to be tested has an intelligent charging function. According to different vehicle models and vehicle electrical architectures, the intelligent charging function will set different charging detection times and voltage values ​​for starting the charging function, namely, the charging start voltage. When the intelligent charging timing reaches the charging detection time, the VCU is awakened and collects the current voltage v of the low-voltage battery. If v is greater than or equal to the charging start voltage, the vehicle normally sleeps and enters the next charging timing; if v is less than the charging start voltage, the VCU wakes up other controllers such as DCDC to start charging the low-voltage battery. Specifically, the whole vehicle starts charging, and the VCU starts timing at the same time at tA. The VCU records the current initial voltage v collected by the battery; the current initial voltage v collected at this time is used as the initial voltage input variable for VCU life estimation. After the charging starts, the VCU constantly monitors the current at the DCDC input end. When the VCU monitors that the difference in the DCDC input current for several consecutive cycles is within a certain range, it is determined that the current is stable and the intelligent charging ends. The moment when the VCU detects that the current at the DCDC input end starts to stabilize is recorded as tB. By monitoring the charging process, the VCU can obtain the current charging time T of the low-voltage battery in this intelligent charging process, where T=tB-tA. At this time, the T value estimated by the VCU is used as the charging time input variable t for VCU life estimation. The t value has been calibrated into the VCU software through the previous charging calibration test. During the intelligent charging process, the VCU collects the input variable v (X) of the current initial voltage and calculates the current charging time T (t); then uses the calibrated charging time value and the relationship data between the initial voltage and SOH. The current SOH (Y) of the battery can be calculated by looking up the table.

[0083] The present application obtains the sample charging time by performing the charging function on the batteries to be tested with different sample battery lives respectively, establishes a corresponding relationship according to the sample battery life, the sample initial voltage and the sample charging time, obtains the charging data relationship table, responds to the current initial voltage and the current charging time corresponding to the target battery, matches the current initial voltage and the current charging time from the charging data relationship table, obtains the current battery life corresponding to the target battery, can reduce the complexity of battery life prediction, improve the accuracy of battery life prediction, avoid misleading the operating personnel due to inaccurate prediction, avoid delaying the maintenance of the battery module, and thus improve driving safety.

[0084] Figure 6 is a block diagram of a battery life prediction device shown in an exemplary embodiment of the present application. The device can be applied to Figure 1 The implementation environment shown in the figure is specifically configured in the computer device 102. The device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applied.

[0085] like Figure 6 As shown, the exemplary battery life prediction device includes: an acquisition module 610 , an execution module 620 , a corresponding module 630 and a prediction module 640 .

[0086] Among them, the acquisition module 610 is used to obtain batteries to be tested with different sample battery lives, and obtain the sample initial voltages corresponding to each battery to be tested; the execution module 620 is used to execute the power replenishment function on each battery to be tested respectively, and obtain the sample power replenishment time, wherein the execution method of the power replenishment function includes the battery to be tested replenishing the battery to be tested by inputting current at the sample initial voltage, and stopping replenishing the battery to be tested when the current difference of the input current is less than a preset threshold; the corresponding module 630 is used to establish a corresponding relationship according to the sample battery life, the sample initial voltage, and the sample power replenishment time, and obtain a power replenishment data relationship table; the prediction module 640 is used to respond to the current initial voltage and the current power replenishment time corresponding to the target battery, and match from the power replenishment data relationship table according to the current initial voltage and the current power replenishment time to obtain the current battery life corresponding to the target battery.

[0087] It should be noted that the battery life prediction device provided in the above embodiment and the battery life prediction method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In practical applications, the battery life prediction device provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0088] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the electronic device implements the battery life prediction method provided in the above-mentioned embodiments.

[0089] Figure 7 The structure diagram of the computer system of the electronic device suitable for implementing the embodiment of the present invention is shown. It should be noted that: Figure 7 The computer system 700 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0090] like Figure 7As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 702 or the program loaded from the storage part 708 to the random access memory (RAM) 703, such as executing the methods provided in the above-mentioned various embodiments. Various programs and data required for system operation are also stored in the RAM 703. The CPU 701, the ROM 702 and the RAM 703 are connected to each other through the bus 704. The input / output (I / O) interface 705 is also connected to the bus 704.

[0091] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed so that a computer program read therefrom is installed into the storage section 708 as needed.

[0092] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 709, and / or installed from a removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, various functions defined in the system of the present invention are executed.

[0093] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0095] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

[0096] Another aspect of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor of a computer, causes the computer to execute the battery life prediction method provided in the above-mentioned embodiments. The computer-readable storage medium may be included in the electronic device described in the above-mentioned embodiments, or may exist independently without being assembled into the electronic device.

[0097] Another aspect of the present invention provides a vehicle, comprising the battery life prediction device as in the above embodiment or the electronic device as in the above embodiment.

[0098] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0099] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the battery life prediction method provided in each of the above embodiments.

[0100] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0101] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0102] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A battery life prediction method, characterized in that: include: Obtaining batteries to be tested with different sample battery lifespans, and obtaining sample initial voltages corresponding to each of the batteries to be tested; The charging function is respectively performed on each of the batteries to be tested to obtain a sample charging time, wherein the charging function is performed by inputting a current to the battery to be tested at the sample initial voltage, and when the current difference of the input current is less than a preset threshold, the charging of the battery to be tested is stopped; Establish a corresponding relationship according to the sample battery life, the sample initial voltage, and the sample charging time to obtain a charging data relationship table; In response to the current initial voltage and the current charging duration corresponding to the target battery, matching is performed from the charging data relationship table according to the current initial voltage and the current charging duration to obtain the current battery life corresponding to the target battery; The current charging duration is determined by: Starting to recharge the target battery, obtaining the current initial voltage and the recharging start time; Monitor the input terminal current of the target battery, and if the current difference of the input terminal current within a preset time is less than the preset threshold, stop charging the target battery, and use the time when the current difference is less than the preset threshold as the charging end time; The current charging duration is calculated according to the charging start time and the charging end time.

2. The battery life prediction method according to claim 1, characterized in that: Before obtaining the sample initial voltages corresponding to the batteries to be tested, the method includes: The voltage of each of the batteries to be tested is adjusted to the sample initial voltage corresponding to each of the batteries to be tested.

3. The battery life prediction method according to claim 1, characterized in that: Performing a charging function on each of the batteries to be tested to obtain a sample charging time, including: The charging function is performed for a preset number of times on each of the batteries to be tested, and an average charging duration corresponding to the preset number of times is calculated, and the average charging duration is used as a sample charging duration corresponding to the battery to be tested.

4. The battery life prediction method according to claim 3, characterized in that: The charging function is performed for each of the batteries to be tested for a preset number of times, including: The batteries to be tested are respectively installed on the vehicles to be tested to perform a preset number of charging functions on the batteries to be tested, wherein the vehicle to be tested is in the same state as a mass-produced vehicle and the vehicle to be tested has no faults.

5. The battery life prediction method according to claim 1, characterized in that: In response to the current initial voltage and the current charging duration corresponding to the target battery, the method further includes: Acquiring charging parameters, wherein the charging parameters include the battery capacity of the target battery, the vehicle power consumption and dark current of the target vehicle; Setting the supplementary power detection time and supplementary power start voltage according to the supplementary power parameters; Collecting the current voltage of the target battery at each interval of the power replenishment detection time; If the current voltage is greater than or equal to the supplementary power-on voltage, the target vehicle is in sleep mode; If the current voltage is less than the power replenishment start voltage, the power replenishment function is performed on the target battery.

6. The battery life prediction method according to claim 5, characterized in that: The method further comprises: performing a charging function on the target battery. Detecting a current battery temperature of the target battery; If at least one of the following conditions is satisfied: the current battery temperature is greater than a preset temperature threshold, the current voltage is greater than a preset charging voltage threshold, and the current voltage is less than a preset discharging voltage threshold, then charging of the target battery is stopped.

7. The battery life prediction method according to any one of claims 1 to 6, characterized in that: After obtaining the current battery life corresponding to the target battery by matching the current initial voltage and the current charging time from the charging data relationship table, the method further includes at least one of the following: Sending a prompt message to the vehicle system to prompt the user at the vehicle end, wherein the prompt message is generated according to the current battery life; The prompt information is sent to the cloud, so that the terminal can obtain the prompt information from the cloud and prompt the user at the terminal.

8. The battery life prediction method according to any one of claims 1 to 6, characterized in that: After obtaining the current battery life corresponding to the target battery by matching the current initial voltage and the current charging time from the charging data relationship table, the method further includes: If the current battery life is less than a preset battery life threshold, the user is prompted by a preset prompting method, wherein the preset prompting method includes at least one of prompting the user by an interactive interface, prompting the user by voice, and prompting the user by terminal message push.

9. A battery life prediction device, characterized in that: include: An acquisition module, used to acquire batteries to be tested with different sample battery lifespans, and to acquire sample initial voltages corresponding to the batteries to be tested; an execution module, configured to respectively execute a power replenishment function on each of the batteries to be tested to obtain a sample power replenishment duration, wherein the execution of the power replenishment function comprises: the battery to be tested performs power replenishment on the battery to be tested by inputting a current at a sample initial voltage, and stops performing power replenishment on the battery to be tested when a current difference of the input current is less than a preset threshold; A corresponding module, used to establish a corresponding relationship according to the sample battery life, the sample initial voltage, and the sample charging time to obtain a charging data relationship table; A prediction module is used for responding to a current initial voltage and a current charging duration corresponding to a target battery, matching from the charging data relationship table according to the current initial voltage and the current charging duration, and obtaining a current battery life corresponding to the target battery; the prediction module is also used for determining the current charging duration in the following manner: starting to charge the target battery, obtaining the current initial voltage and the charging start time; monitoring the input terminal current of the target battery, if the current difference of the input terminal current within a preset time is less than the preset threshold, stopping charging the target battery, and taking the time when the current difference is less than the preset threshold as the charging end time; and calculating the current charging duration according to the charging start time and the charging end time.

10. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the battery life prediction method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the battery life prediction method according to any one of claims 1 to 8.

12. A vehicle, characterized in that: The vehicle includes the battery life prediction device according to claim 9 or the electronic device according to claim 10.

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