Method, device and equipment for predicting residual life of power battery and medium

Abstract

The invention provides a power battery remaining life prediction method, device and equipment and a medium, and the method comprises the steps: determining influence parameters which influence the service life of a battery in a target vehicle use period, and the influence parameters comprise influence factors which influence the service life of the battery, the number of the influence factors and an influence coefficient; determining the original residual life of the target vehicle under the influence of the single influence factor based on the mapping relation between the influence factor and the battery cycle index; determining the theoretical residual life of each single influence factor according to the original residual life, the influence factor and the influence coefficient; and based on the theoretical residual life and the number of the influence factors, determining the actual residual life of the power battery at the end of the target vehicle use period. According to the scheme, the actual remaining life of the battery is calculated according to the actual vehicle using condition of the user, and calculation accuracy can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobiles, and particularly relates to a method, device, equipment and medium for predicting the remaining life of a power battery. Background Art

[0002] With the development of new energy vehicles, the number of electric vehicles in the market is increasing. At present, lithium-ion power batteries are mostly used for the power batteries of electric vehicles, and lithium-ion power batteries are a type of chemical battery. During continuous use, the number of available lithium ions will become less and less. Since the capacity of the power battery is determined by the number of available lithium ions, as the number of charge and discharge cycles of the power battery increases, the loss of lithium ions is also more, so the capacity will also decrease. When the capacity decreases to a certain limit, the power battery will no longer be usable. The process from the start of use to the inability to use of the power battery is called the life of the power battery.

[0003] Currently, the commonly used methods for predicting the life of power batteries usually establish an electrochemical model or set up an equivalent circuit model. These methods only consider the theoretical remaining life of the power battery and do not determine the remaining life of the power battery by combining actual data, resulting in a large error between the theoretical remaining life and the actual remaining life, which affects the customer experience. Summary of the Invention

[0004] In view of the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a method, device, equipment and medium for predicting the remaining life of a power battery to solve the above problems.

[0005] The method for predicting the remaining life of a power battery provided by the present invention includes: Determine the influencing parameters that affect the battery life within the target vehicle usage period, where the influencing parameters include the influencing factors, the number of influencing factors, and the influencing coefficients that affect the battery life; Based on the mapping relationship between the influencing factor and the battery cycle count, determine the original remaining life of the target vehicle under the influence of a single influencing factor; According to the original remaining life, the influencing factor, and the influencing coefficient, determine the theoretical remaining life of each single influencing factor; Based on the theoretical remaining life and the number of influencing factors, determine the actual remaining life of the power battery at the end of the target vehicle usage period.

[0006] In an embodiment of the present invention, determining the influencing parameters that affect the battery life within the target vehicle usage period includes: Determine the duration of the target vehicle usage period; Determine the target discharge rate of the target vehicle within the target vehicle usage period, and according to the mapping relationship between the discharge rate and the number of power on and off times, determine the target number of power on and off times of the target discharge rate; Determine the average discharge rate according to the target discharge rate and the target number of power-on and power-off times. Determine the first influence coefficient based on the average discharge rate and the cycle duration. Wherein, the discharge rate is the influencing factor, and the first influence coefficient is the influence coefficient of the discharge rate. In an embodiment of the present invention, determining the average discharge rate according to the target discharge rate and the target number of power-on and power-off times includes: Multiply the target discharge rate by the mapped target number of power-on and power-off times to obtain first intermediate data, and there is at least one target discharge rate. Add the first intermediate data of each target discharge rate to obtain second intermediate data. Add the number of power-on and power-off times of each target discharge rate to obtain third intermediate data. Determine the average discharge rate based on the second intermediate data and the third intermediate data.

[0007] In an embodiment of the present invention, determining the influencing parameters affecting the battery life within the target vehicle usage cycle includes: Determine the cycle duration of the target vehicle usage cycle. Determine the non-usage cycle duration within the target vehicle usage cycle. Determine the second influence coefficient based on the cycle duration and the non-usage cycle duration. The second influence coefficient is the influence coefficient of the static duration of the target vehicle, and the static duration is the influencing factor.

[0008] In an embodiment of the present invention, determining the influencing parameters affecting the battery life within the target vehicle usage cycle includes: Determine the cycle duration of the target vehicle usage cycle. Determine the charge and discharge range of the power battery of the target vehicle within the target vehicle usage cycle. Determine the third influence coefficient based on the cycle duration and the charge and discharge range. Wherein, the charge and discharge range is the influencing factor, and the third influence coefficient is the influence coefficient of the charge and discharge range.

[0009] In an embodiment of the present invention, determining the theoretical remaining life under each single influencing factor according to the original remaining life, the influencing factor, and the influence coefficient includes: Based on the influencing factor and the influence coefficient, respectively determine the life loss values of each influencing factor affecting the remaining life of the power battery. Determine the theoretical remaining life based on the difference between the original remaining life and each life loss value.

[0010] In one embodiment of the present invention, determining the actual remaining life of the power battery at the end of the target vehicle usage cycle based on the theoretical remaining life and the number of influencing factors includes: Determining the sum of the theoretical remaining lives according to each of the theoretical remaining lives; Based on the sum and the number of influencing factors, determining the actual remaining life.

[0011] The power battery remaining life prediction device provided by the present invention includes: A data determination module, configured to determine influencing parameters affecting the battery life during the target vehicle usage cycle, where the influencing parameters include influencing factors affecting the battery life, the number of influencing factors, and the influence coefficient; An original remaining life determination module, configured to determine the original remaining life of the target vehicle under the influence of a single influencing factor based on the mapping relationship between the influencing factor and the battery cycle count; A first processing module, configured to determine the theoretical remaining life under each single influencing factor according to the original remaining life, the influencing factor, and the influence coefficient; A second processing module, configured to determine the actual remaining life of the power battery at the end of the target vehicle usage cycle based on the theoretical remaining life and the number of influencing factors.

[0012] The electronic device provided by the present invention, the electronic device includes: One or more processors; A storage device, configured to store one or more programs, when the one or more programs are executed by the one or more processors, enabling the electronic device to implement the power battery remaining life prediction method described above.

[0013] The computer-readable storage medium provided by the present invention, on which a computer program is stored, when the computer program is executed by a processor of a computer, enabling the computer to execute the power battery remaining life prediction method described above.

[0014] Advantages of the present invention: The present invention takes a target vehicle usage cycle as a unit, determines the corresponding influencing factors, influence coefficients, and the number of influencing factors according to the user's vehicle usage situation during the target vehicle usage cycle, and determines the original remaining life under a single influencing factor based on the mapping relationship between the influencing factor and the battery cycle count. Considering that there is more than one influencing factor affecting the battery life, therefore, it is necessary to comprehensively calculate the actual remaining life of the battery according to the influence coefficient, the original remaining life, and the number of influencing factors, etc., to increase the accuracy of the estimated actual remaining life.

[0015] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. Description of the Drawings The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application. Obviously, the accompanying drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings: Figure 1 is a schematic diagram of the implementation environment for predicting the remaining life of a power battery shown in an exemplary embodiment of this application.

[0016] Figure 2 is a flowchart of a method for predicting the remaining life of a power battery shown in an exemplary embodiment of this application.

[0017] Figure 3 is a flowchart of determining the influence coefficient of the discharge rate shown in an exemplary embodiment of this application.

[0018] Figure 4 is a flowchart of determining the average discharge rate shown in an exemplary embodiment of this application.

[0019] Figure 5 is a flowchart of determining the influence factor of the static duration shown in an exemplary embodiment of this application.

[0020] Figure 6 is a flowchart of determining the influence factor of the charge-discharge interval shown in an exemplary embodiment of this application.

[0021] Figure 7 is a flowchart of determining the theoretical remaining life shown in an exemplary embodiment of this application.

[0022] Figure 8 is a flowchart of determining the actual remaining life shown in an exemplary embodiment of this application.

[0023] Figure 9 is a block diagram of a device for predicting the remaining life of a power battery shown in an exemplary embodiment of this application. Detailed implementation manners

[0024] The following will describe the implementation manners 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 content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed 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 explaining the present invention, rather than for limiting the protection scope of the present invention.

[0025] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0026] In the following description, a large number of details are explored 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.

[0027] Figure 1 is a schematic diagram of the implementation environment for predicting the remaining life of a power battery shown in an exemplary embodiment of the present application. As Figure 1 shown, it includes an intelligent terminal 120, a cloud platform 110, and a target vehicle 130. The target vehicle 130 is the user vehicle for which the remaining life needs to be calculated. The intelligent terminal 120 can be an in-vehicle terminal or other terminal devices not installed on the target vehicle 130. The target vehicle 130 records the collected data information that affects the battery life. The collected data information includes data such as the vehicle usage cycle, non-usage cycle, and discharge rate. The collected data information is uploaded to the cloud platform 110 for storage. When it is necessary to determine the actual remaining life of the target vehicle 130, the intelligent terminal 120 obtains the collected data information corresponding to the target vehicle usage cycle from the cloud platform 110, determines the influencing factors, the number of influencing factors, and the influencing coefficients and other influencing parameters that affect the battery life in each collected data information, and calculates the actual remaining life of the target vehicle 130 at the end of the first vehicle usage cycle according to the influencing parameters and the original remaining life designed for the target vehicle 130 (determined by the performance of the power battery itself).

[0028] Among them, Figure 1 the intelligent terminal 120 shown can be a terminal device such as a smart phone, an in-vehicle computer, a tablet computer, a notebook computer, or a wearable device, but is not limited thereto.

[0029] Please refer to Figure 2 , Figure 2 is a flowchart of a method for predicting the remaining life of a power battery shown in an exemplary embodiment of the present application. This method can be applied to Figure 1 the implementation environment shown, and is specifically executed by the intelligent terminal in this implementation environment. It should be understood that this method can also be applicable to other exemplary implementation environments and is specifically executed by devices in other implementation environments. This embodiment does not limit the implementation environment to which this method is applicable.

[0030] As Figure 2 shown, in an exemplary embodiment, the method for predicting the remaining life of a power battery at least includes steps S210 to S240, which are introduced in detail as follows: Step S210: Determine the influencing parameters that affect the battery life within the target vehicle usage cycle. The influencing parameters include the influencing factors, the number of influencing factors, and the influencing coefficients that affect the battery life.

[0031] Exemplarily, the influencing factors may include the discharge rate, the static duration, the charge-discharge range, and the static temperature, etc., which are specifically determined according to the user's vehicle usage situation. The number of influencing factors is determined according to the user's vehicle usage situation within the target vehicle usage cycle.

[0032] Exemplarily, the influencing coefficient is the weight value for calculating the actual remaining life, and this weight value can be determined according to the influencing degree of each influencing factor.

[0033] Exemplarily, the influencing coefficient is a value calculated based on the user's actual vehicle usage data within the target vehicle usage cycle.

[0034] Exemplarily, the influencing coefficient is the product of the weight value and the value calculated from the user's actual vehicle usage data within the target vehicle usage cycle.

[0035] Step S220: Based on the mapping relationship between the influencing factor and the battery cycle count, determine the original remaining life of the target vehicle under the influence of a single influencing factor.

[0036] Exemplarily, a mapping table between the single influencing factor and the battery cycle count and the original remaining life is obtained through calibration. The mapping table reflects the mapping relationship between the corresponding influencing factor, the battery cycle count, and the original remaining life.

[0037] It should be noted that the discharge rate, the static duration, the static temperature, and the charge-discharge range all affect the battery cycle count (or charge-discharge times), and the battery cycle count is directly related to the remaining battery life. One battery cycle count refers to completing charging and discharging in a certain way. For example, after charging to 100% SOC at 1 / 3C and then standing for a certain time, and then discharging to the cut-off voltage at 1C and standing for a certain time, it is one cycle. The higher the discharge rate, the shorter the battery life. By charging to 100% SOC at 1 / 3C and then standing for a certain time, and then discharging at different rates, the cycle counts corresponding to different discharge rates can be established, generating the corresponding mapping relationship, that is, obtaining the original remaining life L1 under the influence of the charge-discharge rate.

[0038] It should also be noted that when the vehicle is stationary, if the stationary temperature is lower or higher than the fixed range, the lower the stationary temperature, the fewer the battery cycle times. When the stationary temperature is within the fixed range, the battery life is hardly affected. Therefore, as the temperature rises, the cycle times first increase and then decrease. By placing a fully charged battery in different ambient temperatures (such as -30°C to 60°C) for a fixed time and then discharging it at different temperatures, the relationship between the cycle times and the stationary temperature can be established to generate the corresponding mapping relationship. Similarly, the mapping relationship between the stationary duration and the cycle times can be generated to determine the original remaining life L2 corresponding to the stationary duration.

[0039] Exemplarily, the fixed range can be 25 to 35°C. When the stationary temperature is lower than and higher than this fixed range, it will cause a reduction in the battery cycle times; when the stationary temperature is within this range, regardless of the stationary duration, it has little impact on the battery life.

[0040] In addition, the width of the charge-discharge range affects the battery cycle times, that is, it affects the remaining battery life. When the charge-discharge range is narrow, the change in cycle times is small. For example, when ΔSOC ≤ 20%, the change in the battery cycle times equivalent to the battery capacity loss is small, and at this time, it can be considered that the cycle times remain unchanged; while when the discharge range is wide, such as when ΔSOC > 20%, the change in the battery cycle times equivalent to the battery capacity becomes larger. At this time, there will be differences in the battery cycle times. ΔSOC (SOC refers to the ratio of the remaining battery charge to its total capacity) can be divided into several ranges, such as 30%, 40%, 50%, ……, 90%, 100%. The relationship between the battery charge-discharge range and the cycle times can be obtained through calibration. By setting different ΔSOCs and following the same charge-discharge regime during charging and discharging, the cycle times for different ΔSOCs are recorded separately and organized into the corresponding mapping relationship, and thus the original remaining life L3 corresponding to the discharge range can be obtained.

[0041] Exemplarily, the original remaining lives L1, L2, and L3 can also be obtained through testing.

[0042] Exemplarily, according to the different degrees of influence of different influencing factors on the battery life in different scenarios, the degrees of influence can be sorted from high to low and weight coefficients can be assigned respectively to be incorporated into the calculation of the remaining battery life.

[0043] Exemplarily, in a low-temperature scenario, when the vehicle usage temperature < 10°C, the lithium-ion activity of the battery cell is relatively low, and the lower the temperature, the easier it is to cause lithium plating. At this time, the discharge rate has a greater impact on the number of battery cycles, and the weights of the influencing factors from largest to smallest are: battery storage > discharge rate > charge-discharge interval. In a high-temperature scenario, when the battery temperature > 40°C, the lithium-ion activity of the battery cell is relatively strong, the risk of lithium plating is relatively low, the battery is charged and discharged more frequently, and the charge-discharge interval has a greater impact on the number of cycles. The weights of the influencing factors from largest to smallest are: battery storage > charge-discharge interval > discharge rate. In a medium-temperature scenario, 10°C ≤ battery temperature ≤ 40°C, the battery state is in the best state, and the risk of lithium plating is very low. The weights of the influencing factors from largest to smallest are: (discharge rate = charge-discharge interval) > battery storage.

[0044] Exemplarily, in a low-temperature scenario, the weight of battery storage (including standing time and standing temperature) is 3 / 4 - 1, the weight of the discharge rate is 1 / 4 - 3 / 4, and the weight of the charge-discharge interval of the battery is 0 - 1 / 4. In a high-temperature scenario, the weight of battery storage is 2 / 3 - 1, the weight of the charge-discharge interval is 1 / 3 - 2 / 3, and the weight of the discharge rate is 0 - 1 / 3. In a medium-temperature scenario, the weights of the discharge rate and the charge-discharge interval are 3 / 8 - 1, and the weight of battery storage is 0 - 1 / 8.

[0045] Exemplarily, in a low-temperature scenario, the weights of battery storage, discharge rate, and charge-discharge interval are 3 / 4, 1 / 4, and 1 / 5 respectively. In a high-temperature scenario, the weights among battery storage, charge-discharge interval, and discharge rate are 2 / 3, 1 / 3, and 1 / 6 respectively. In a medium-temperature scenario, the weights of the discharge rate, charge-discharge interval, and battery storage are 3 / 8, 3 / 8, and 1 / 9 respectively.

[0046] Step S230: Determine the theoretical remaining life under each single influencing factor according to the original remaining life, influencing factors, and influence coefficients.

[0047] It should be noted that since the usage conditions of the target vehicle by the user are inconsistent in each vehicle usage cycle, all differences such as the influencing factors and the number of influencing factors for calculating the theoretical remaining life occur in each vehicle usage cycle.

[0048] In this embodiment, in order to facilitate the calculation of the actual remaining life after adding influencing factors, the theoretical remaining life of a single influencing factor is calculated.

[0049] Step S240: Determine the actual remaining life of the power battery at the end of the target vehicle usage cycle based on the theoretical remaining life and the number of influencing factors. In this embodiment, taking one target vehicle usage cycle as a unit, the actual remaining life of the target vehicle is calculated. Moreover, the influencing parameters used in calculating the actual remaining life are all in line with the actual usage conditions of the target vehicle users, so as to more accurately estimate the remaining battery life.

[0050] Exemplarily, the vehicle usage cycle can be half a year, one year or two years, which is specifically set according to requirements. Figure 3 The flowchart for determining the discharge rate influence coefficient shown in an exemplary embodiment of the present application.

[0051] As Figure 3 shown, the process of determining the influencing parameters that affect the battery life within the target vehicle usage cycle may include steps S310 to S340, which are introduced in detail as follows: Step S310, determine the cycle duration of the target vehicle usage cycle. Exemplarily, if the past year is taken as the target vehicle usage cycle, the cycle duration is one year.

[0052] Step S320, determine the target discharge rate of the target vehicle within the target vehicle usage cycle, and determine the target number of power-on and power-off times of the target discharge rate according to the mapping relationship between the discharge rate and the number of power-on and power-off times.

[0053] Exemplarily, if the number of power-on and power-off times corresponding to a discharge rate of 0.5C within the target vehicle usage cycle is 120 times, and the number of power-on and power-off times corresponding to a discharge rate of 1C is 30 times, then the target discharge rates include 0.5C and 1C. The target number of power-on and power-off times corresponding to 0.5C is 120 times, and the target number of power-on and power-off times corresponding to 1C is 30 times.

[0054] Step S330, determine the average discharge rate according to the target discharge rate and the target number of power-on and power-off times.

[0055] Exemplarily, if the target discharge rates include Y1 and Y2, the target number of power-on and power-off times of Y1 is D1, and the target number of power-on and power-off times of Y2 is D2, then the calculation formula for the average discharge rate H is as follows:

[0056] Exemplarily, if the target number of power-on and power-off times corresponding to 0.5C is 120 times, and the target number of power-on and power-off times corresponding to 1C is 30 times, the calculation process of the calculated average discharge rate H is as follows: H = (120 * 0.5 + 30 * 1) / (120 + 30) = 0.6C.

[0057] Step S340, determine the first influence coefficient based on the average discharge rate and the cycle duration; wherein, the discharge rate is the influencing factor, and the first influence coefficient is the influence coefficient of the discharge rate. It should be noted that due to different driving habits of users, the calculated first influence coefficient is different, so as to determine the actual remaining life of the battery according to the actual driving situation of the user and improve the prediction accuracy.

[0058] Figure 4 The flowchart of determining the average discharge rate shown in an exemplary embodiment of the present application.

[0059] As Figure 4 shown, the process of determining the influence parameters affecting the battery life within the target driving cycle may include steps S410 to S440, which are introduced in detail as follows: Step S410, multiply the target discharge rate by the mapped target number of power-on and power-off times to obtain the first intermediate data, and there is at least one target discharge rate.

[0060] Exemplarily, the target discharge rate can be one, two or three, which is specifically determined according to the actual driving situation. Step S420, add the first intermediate data of each target discharge rate to obtain the second intermediate data.

[0061] In this embodiment, the determination of the first intermediate data and the second intermediate data is for subsequent calculations.

[0062] Step S430, add the number of power-on and power-off times of each target discharge rate to obtain the third intermediate data.

[0063] In this embodiment, the determination of the third intermediate data is for subsequent calculations.

[0064] Step S440, determine the average discharge rate based on the second intermediate data and the third intermediate data.

[0065] Exemplarily, the first influence coefficient is denoted as K1, and the calculation formula of K1 is as follows:

[0066] Among them, n is the cycle duration, and J1 is the cycle duration of the target driving cycle.

[0067] Figure 5 The flowchart of determining the influence factor of the static duration shown in an exemplary embodiment of the present application.

[0068] As Figure 5 shown, the process of determining the influence parameters affecting the battery life within the target driving cycle may include steps S510 to S530, which are introduced in detail as follows: Step S510, determine the cycle duration of the target driving cycle.

[0069] Exemplarily, if the target vehicle usage period is the past six months, the period duration is six months.

[0070] Step S520: Determine the duration of the non-usage period within the target vehicle usage period.

[0071] It should be noted that before determining the duration of the non-usage period, it is necessary to determine the usage period and non-usage period within the target vehicle usage period, and then determine the duration of the non-usage period based on the non-usage period. The usage period refers to the process of the vehicle powering on and off, and the non-usage period refers to the period when the vehicle is stationary.

[0072] Step S530: Based on the period duration and the duration of the non-usage period, determine the second influence coefficient. The second influence coefficient is the influence coefficient of the stationary duration of the target vehicle, and the stationary duration is the influencing factor.

[0073] Exemplarily, the second influence coefficient is denoted as K2, and its calculation formula is as follows:

[0074] Among them, J2 is the duration of the non-usage period within the target vehicle usage period.

[0075] In this embodiment, the second influence coefficient will vary according to the user's vehicle usage situation to increase the accuracy of the estimated actual remaining life.

[0076] Figure 6 It is a flowchart for determining the influencing factors of the charge and discharge interval shown in an exemplary embodiment of the present application.

[0077] As Figure 6 shown, the process of determining the influencing parameters affecting the battery life within the target vehicle usage period may include Step S610 to Step S630, which are introduced in detail as follows: Step S610: Determine the period duration of the target vehicle usage period.

[0078] Exemplarily, the period duration of the target vehicle usage period is set according to requirements.

[0079] Step S620: Determine the charge and discharge interval of the power battery of the target vehicle within the target vehicle usage period.

[0080] It should be noted that the diameter of the charge and discharge interval affects the remaining life of the battery.

[0081] Step S630: Based on the period duration and the charge and discharge interval, determine the third influence coefficient. Among them, the charge and discharge interval is the influencing factor, and the third influence coefficient is the influence coefficient of the charge and discharge interval.

[0082] Exemplarily, the third influence coefficient is denoted as K3, and its calculation formula is as follows:

[0083] Among them, J3 is the charge and discharge duration determined according to the charge and discharge interval.

[0084] Figure 7 It is a flowchart for determining the theoretical remaining life shown in an exemplary embodiment of the present application.

[0085] As Figure 7 shown, according to the original remaining life, influence factors and influence coefficients, the process of determining the theoretical remaining life under each single influence factor may include step S710 and step S720, which are introduced in detail as follows: Step S710, based on the influence factors and influence coefficients, respectively determine the life loss values of each influence factor affecting the remaining life of the power battery.

[0086] Exemplarily, the life loss value caused by the discharge rate is denoted as M1, the life loss value caused by the static duration is denoted as M2, and the life loss value caused by the charge and discharge interval is denoted as M3.

[0087] Exemplarily, M1 = L1 * K1, M2 = L2 * K2, M3 = L3 * K3.

[0088] Step S720, based on the difference between the original remaining life and each life loss value, determine the theoretical remaining life.

[0089] Exemplarily, if n = (1, 2, 3,..., i), where n represents the number of influence factors, then the difference F between the original remaining life Li and each life loss value Mi is denoted as F = Li - Mi.

[0090] Figure 8 It is a flowchart for determining the actual remaining life shown in an exemplary embodiment of the present application.

[0091] As Figure 8 shown, based on the theoretical remaining life and the number of influence factors, the process of determining the actual remaining life of the power battery at the end of the target vehicle usage cycle may include step S810 and step S820, which are introduced in detail as follows: Step S810, according to each theoretical remaining life, determine the sum of each theoretical remaining life.

[0092] Exemplarily, the sum of each theoretical remaining life is denoted as G, and its calculation formula is as follows: G = (L1 - M1) + (L2 - M2) + (L3 - M3) +... + (Li - Mi).

[0093] Step S820, based on the sum and the number of influence factors, determine the actual remaining life.

[0094] Exemplarily, the actual remaining useful life is denoted as RUL, and its calculation formula is as follows: RUL = G / n.

[0095] Exemplarily, if the influencing factors affecting the remaining useful life of the battery are only the discharge rate, the standing time, and the charge-discharge interval, the calculation formula for the actual remaining useful life RUL is as follows: RUL = [(L1 - L1*K1) + (L2 - L2*K2) + (L3 - L3*K3)] / 3.

[0096] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0097] Figure 9 is a block diagram of a power battery remaining useful life prediction device shown in an exemplary embodiment of the present application. This device can be applied to Figure 1 the shown implementation environment and is specifically configured in an intelligent terminal. This device can also be applicable to other exemplary implementation environments and is specifically configured in other devices. This embodiment does not limit the implementation environment applicable to this device.

[0098] As Figure 9 shown, this exemplary power battery remaining useful life prediction device includes: A data determination module 910, configured to determine influencing parameters affecting the battery life within a target vehicle usage cycle, where the influencing parameters include influencing factors affecting the battery life, the number of influencing factors, and the influence coefficient; An original remaining useful life determination module 920, configured to determine the original remaining useful life of the target vehicle under the influence of a single influencing factor based on the mapping relationship between the influencing factor and the battery cycle count; A first processing module 930, configured to determine the theoretical remaining useful life under each single influencing factor according to the original remaining useful life, the influencing factor, and the influence coefficient; A second processing module 940, configured to determine the actual remaining useful life of the power battery at the end of the target vehicle usage cycle based on the theoretical remaining useful life and the number of influencing factors.

[0099] In this exemplary power battery remaining useful life prediction device, the influence coefficient of each influencing factor is determined according to the actual vehicle usage situation of the target vehicle by the user, and the final actual remaining useful life is calculated according to the original remaining useful life, the influence coefficient, and the number of influencing factors, so that the life prediction is more in line with the actual situation of the battery and the accuracy of the life prediction is increased.

[0100] It should be noted that the power battery remaining life prediction device provided in the above embodiments and the power battery remaining life prediction method provided in the above embodiments belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiments, and will not be repeated here. In practical applications, the power battery remaining life prediction device provided in the above embodiments can, as needed, allocate the above functions to different functional modules, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0101] An embodiment of the present application also provides an electronic device, including: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the power battery remaining life prediction method provided in each of the above embodiments.

[0102] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of 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 blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0103] The units described in the embodiments of the present application can be implemented in software or in hardware, and the described units can also be provided in the processor. Among them, the names of these units do not, in some cases, constitute a limitation to the unit itself.

[0104] On the other hand, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor of the computer, the computer is caused to execute the power battery remaining life prediction method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist separately and not be assembled into the electronic device.

[0105] Another aspect of the present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the power battery remaining life prediction method provided in each of the above embodiments.

[0106] The above embodiments are only used to exemplarily illustrate the principles and effects of the present invention, rather than to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for predicting the remaining life of a power battery, characterized in that: include: Determine influencing parameters that affect battery life within a target vehicle use cycle, wherein the influencing parameters include influencing factors that affect battery life, the number of influencing factors, and an influencing coefficient; Based on the mapping relationship between the influencing factors and the number of battery cycles, determining the original remaining life of the target vehicle under the influence of a single influencing factor; Determining the theoretical remaining life of each single influencing factor according to the original remaining life, the influencing factor and the influencing coefficient; Based on the theoretical remaining life and the number of influencing factors, the actual remaining life of the power battery at the end of the target vehicle use cycle is determined.

2. The method for predicting the remaining life of a power battery according to claim 1, characterized in that: Determine the parameters that affect battery life during the target vehicle usage cycle, including: Determine the duration of the target vehicle use cycle; Determine a target discharge rate of the target vehicle within a target vehicle use cycle, and determine a target number of power-ups and power-downs for the target discharge rate based on a mapping relationship between the discharge rate and the number of power-ups and power-downs; Determining an average discharge rate according to the target discharge rate and the target power-on and power-off times; Determining a first influence coefficient based on the average discharge rate and the cycle duration; The discharge rate is an influencing factor, and the first influencing coefficient is an influencing coefficient of the discharge rate.

3. The method for predicting the remaining life of a power battery according to claim 2, characterized in that: Determining an average discharge rate according to the target discharge rate and the target power-on and power-off times includes: Multiplying the target discharge rate by the mapped target power-on and power-off times to obtain first intermediate data, wherein the target discharge rate is at least one; Adding the first intermediate data of the target discharge rates to obtain second intermediate data; Adding the number of power-ups and power-downs at each target discharge rate to obtain third intermediate data; The average discharge rate is determined based on the second intermediate data and the third intermediate data.

4. The method for predicting the remaining life of a power battery according to claim 1, characterized in that: Determine the parameters that affect battery life during the target vehicle usage cycle, including: Determine the duration of the target vehicle use cycle; Determine the length of the non-use period within the target use period; Based on the cycle duration and the non-vehicle use cycle duration, a second influence coefficient is determined, where the second influence coefficient is an influence coefficient of the stationary duration of the target vehicle, and the stationary duration is the influence factor.

5. The method for predicting the remaining life of a power battery according to claim 1, characterized in that: Determine the parameters that affect battery life during the target vehicle usage cycle, including: Determine the duration of the target vehicle use cycle; Determine the charging and discharging range of the power battery of the target vehicle within the target vehicle use cycle; Determining a third influence coefficient based on the cycle duration and the charge and discharge interval; The charging and discharging interval is the influencing factor, and the third influencing coefficient is the influencing coefficient of the charging and discharging interval.

6. The method for predicting the remaining life of a power battery according to claim 1, characterized in that: According to the original remaining life, the influencing factor and the influencing coefficient, the theoretical remaining life under each single influencing factor is determined, including: Based on the influencing factors and the influencing coefficients, respectively determining life loss values ​​of the influencing factors affecting the remaining life of the power battery; The theoretical remaining life is determined based on the difference between the original remaining life and each of the life loss values.

7. The method for predicting the remaining life of a power battery according to any one of claims 1 to 6, characterized in that: Based on the theoretical remaining life and the number of influencing factors, determining the actual remaining life of the power battery at the end of the target vehicle use cycle includes: Determine the sum of the theoretical remaining lives according to the theoretical remaining lives; Based on the sum and the number of influencing factors, the actual remaining lifetime is determined.

8. A power battery remaining life prediction device, characterized in that: include: A data determination module, used to determine influencing parameters that affect battery life within a target vehicle use cycle, wherein the influencing parameters include influencing factors that affect battery life, the number of influencing factors, and an influencing coefficient; An original remaining life determination module, used to determine the original remaining life of the target vehicle under the influence of a single influencing factor based on the mapping relationship between the influencing factor and the number of battery cycles; A first processing module, used to determine the theoretical remaining life under each single influencing factor according to the original remaining life, the influencing factor and the influencing coefficient; The second processing module is used to determine the actual remaining life of the power battery at the end of the target vehicle use cycle based on the theoretical remaining life and the number of influencing factors.

9. A device, characterized in that: include: one or more processors and memory, A computer program is stored in the memory, and when the one or more processors execute the computer program, the device is caused to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, which, when executed by one or more processors, causes the device to perform the method according to any one of claims 1 to 7.

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