Battery life prediction method and device

Abstract

The invention relates to the technical field of batteries, and discloses a battery life prediction method and device, and the method comprises the steps: obtaining the related data of a battery at a target moment; wherein the related data comprises current, voltage, temperature and a first operation multiplying power of a previous moment of the target moment; determining a second operation rate of the battery at the target moment according to the related data of the battery at the target moment; based on the second operation rate of the battery at the target moment, the current of the battery at the target moment and a preset time period, determining an equivalent cumulative capacity corresponding to the preset time period; according to the equivalent cumulative capacity corresponding to the preset time period, the cycle index corresponding to the preset time period and the battery health degree corresponding to the preset time period are determined; and predicting the battery life based on the cycle index corresponding to the preset time period and the battery health degree corresponding to the preset time period.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and more specifically to a method and apparatus for predicting battery life. Background Technology

[0002] In related technologies, battery life prediction methods often rely on life decay patterns obtained under laboratory conditions to infer the remaining life of the battery under actual operating conditions.

[0003] However, due to the significant differences between actual operating conditions and laboratory environments, prediction methods based on laboratory lifespan decay patterns often have large errors under complex operating conditions.

[0004] Therefore, how to accurately predict battery life has become a technical problem that needs to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a method and apparatus for predicting battery life.

[0006] In a first aspect, the present invention provides a battery life prediction method, the method comprising: acquiring relevant data of the battery at a target time; wherein the relevant data includes: current, voltage, temperature, and a first operating rate at a previous time of the target time; determining a second operating rate of the battery at the target time based on the relevant data of the battery at the target time; determining the equivalent cumulative capacity corresponding to the preset time period based on the second operating rate of the battery at the target time, the current of the battery at the target time, and the preset time period; determining the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period based on the equivalent cumulative capacity corresponding to the preset time period; and predicting the battery life based on the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period.

[0007] The battery life prediction method provided by this invention determines a second operating rate by acquiring data such as current, voltage, temperature at a target time and a first operating rate from previous times. This more accurately reflects the actual operating condition of the battery under complex current conditions. Furthermore, based on the second operating rate, current, and preset time period at the target time, the equivalent cumulative capacity is determined, equating the charging and discharging process at different operating rates to capacity accumulation under a unified standard. This solves the problem of inaccurate cumulative capacity calculation caused by changes in operating rate, making the assessment of battery usage more objective and accurate.

[0008] Based on this, by accurately calculating the equivalent cumulative capacity, reasonably correlating the cycle count and battery health, the performance degradation pattern of the battery under actual complex operating conditions can be reflected more accurately. Predicting battery life based on these accurate intermediate results can effectively improve the accuracy of prediction, reduce prediction errors caused by complex and variable operating conditions, and avoid problems such as equipment failure, safety hazards, and unnecessary cost increases caused by inaccurate battery life prediction.

[0009] In one possible implementation, the equivalent cumulative capacity corresponding to the preset time period is determined based on the battery's second operating rate at the target time, the battery's current at the target time, and the preset time period. This includes: obtaining the first rated capacity corresponding to the rated rate; determining the battery's second rated capacity at the target time based on the battery's second operating rate at the target time; and determining the equivalent cumulative capacity corresponding to the preset time period based on the battery's second rated capacity at the target time, the first rated capacity, the battery's current at the target time, and the preset time period.

[0010] The battery life prediction method provided by this invention addresses the fact that the rated capacity of a battery is not fixed and varies with the operating rate. At different operating rates, the internal chemical reaction rate and polarization phenomena of the battery differ, thus affecting the actual output capacity. By obtaining the first rated capacity corresponding to the rated operating rate and determining the second rated capacity based on the second operating rate at the target time, the method fully considers the impact of the operating rate on capacity, more accurately reflecting the actual capacity of the battery under current operating conditions, and providing a more reliable data foundation for subsequent battery management and performance evaluation.

[0011] In one possible implementation, when there are multiple preset time periods, the equivalent cumulative capacity corresponding to the preset time period is determined based on the battery's second rated capacity, first rated capacity, battery current at the target time, and the preset time period. This includes: determining the equivalent cumulative capacity corresponding to the subsequent adjacent time periods of the target time period based on the equivalent cumulative capacity corresponding to the target time period, the first rated capacity, battery current at the target time period, and the subsequent adjacent time periods of the target time period. Specifically, when the target time period is a time period adjacent to the target time among multiple preset time periods, determining the equivalent cumulative capacity corresponding to the target time period includes: determining the equivalent cumulative capacity corresponding to the target time period based on the battery's second rated capacity, first rated capacity, battery current at the target time, and the target time period. When the target time period is not a time period adjacent to the target time among multiple preset time periods, the equivalent cumulative capacity corresponding to the target time period is determined based on the equivalent cumulative capacity corresponding to the previous adjacent time period of the target time period, the first rated capacity, battery current at the previous adjacent time period, and the target time period.

[0012] The battery life prediction method provided by this invention first obtains the first rated capacity at the rated rate, and then determines the second rated capacity based on the second operating rate at the target time. This fully considers the influence of the operating rate on the battery capacity and avoids calculation errors caused by ignoring the influence of the operating rate.

[0013] In one possible implementation, the number of cycles and the battery health corresponding to the preset time period are determined based on the equivalent cumulative capacity corresponding to the preset time period. This includes: detecting whether the equivalent cumulative capacity and the first rated capacity corresponding to the rated rate are the same; when the equivalent cumulative capacity and the first rated capacity corresponding to the rated rate are not the same, determining the third rated capacity corresponding to the preset time period based on the operating rate corresponding to the preset time period; determining the cumulative rated rate equivalent capacity corresponding to the preset time period based on the third rated capacity; determining the number of cycles corresponding to the preset time period based on the cumulative rated rate equivalent capacity; and determining the battery health corresponding to the preset time period using a pre-set lifespan degradation model based on the preset time period.

[0014] The battery life prediction method provided by this invention first detects whether the equivalent cumulative capacity corresponding to a preset time period and the first rated capacity corresponding to the rated rate are the same. Since the equivalent cumulative capacity of the battery in actual operation will differ from the first rated capacity due to different operating conditions, this method can accurately capture this difference. When the two are different, a third rated capacity is determined based on the operating rate of the preset time period, and then the cumulative rated rate equivalent capacity is calculated, based on which the number of cycles is determined. This can more accurately reflect the actual charge and discharge cycle situation of the battery under different operating conditions, avoiding errors in cycle count calculation caused by ignoring capacity differences. For example, under the condition of frequent start-stop operation of electric vehicles, the actual charge and discharge capacity of the battery will differ significantly from the rated capacity; this method can accurately calculate the actual number of cycles.

[0015] Furthermore, based on a preset time period, a pre-defined lifespan degradation model is used to determine battery health. This model comprehensively considers the battery's degradation characteristics under different operating conditions, and, combined with previously accurately calculated cycle counts and other relevant parameters, can more accurately assess the battery's health status within the preset time period. Compared to traditional methods, this reduces health assessment biases caused by complex and variable operating conditions and inaccurate calculations, providing a more reliable basis for battery maintenance and replacement.

[0016] In one possible implementation, the number of cycles corresponding to the preset time period is determined based on the cumulative rated multiplier equivalent capacity corresponding to the preset time period. This includes: determining the number of cycles corresponding to the preset time period based on the correspondence between the preset cumulative rated multiplier equivalent capacity and the number of cycles, and the cumulative rated multiplier equivalent capacity.

[0017] The battery life prediction method provided by this invention addresses the issue that various factors (such as temperature and self-discharge) can lead to the accumulation of measurement errors during long-term battery use. Calculating the cycle count based on the cumulative rated rate equivalent capacity can reduce the impact of these errors on the results to some extent. This is because the method comprehensively considers the equivalent capacity over a period of time, rather than relying solely on data from a single measurement or a few measurements, thus making the calculated cycle count more stable and reliable.

[0018] In one possible implementation, battery life is predicted based on the number of cycles corresponding to a preset time period and the battery health level corresponding to the preset time period. This includes: determining a cycle number determination function based on the number of cycles corresponding to the preset time period and the battery health level corresponding to the preset time period; determining a target number of cycles corresponding to a preset battery health level based on the cycle number determination function and the preset battery health level; and predicting battery life based on the target number of cycles and the number of cycles corresponding to the preset time period.

[0019] The battery life prediction method provided by this invention considers two important factors simultaneously: the number of battery cycles over a preset time period and battery health. The number of battery cycles reflects the charging and discharging frequency of the battery in actual use and is one of the key factors affecting battery life; while battery health directly reflects the current performance state of the battery, including capacity decay and changes in internal resistance. By combining information from these two dimensions, the battery's degradation can be grasped more comprehensively and accurately, avoiding the bias that may arise from relying on a single factor for prediction, thereby improving the accuracy of battery life prediction.

[0020] In one possible implementation, the aforementioned relevant data also includes the charging rate; determining the second operating rate of the battery at the target time based on the relevant data of the battery at the target time includes: determining the power of the battery at the target time based on the current and voltage of the battery at the target time; determining the cumulative discharge energy of the battery at the target time based on the power of the battery at the target time and a preset time period; determining energy decay parameters based on the cumulative discharge energy of the battery at the target time, the charging rate, and the current temperature; determining temperature correction parameters based on the current temperature at the target time; determining calendar aging fitting parameters based on the current temperature at the target time and a preset aging coefficient; and determining the second operating rate of the battery at the target time based on the energy decay parameters, the temperature correction parameters, the calendar aging fitting parameters, and the cumulative discharge energy.

[0021] The battery life prediction method provided by this invention determines the power based on current and voltage, and further calculates the cumulative discharge energy. This method can accurately reflect the battery's energy output at a target time. The energy decay parameter considers the influence of charging rate and current temperature on battery energy, making the evaluation of battery energy characteristics more comprehensive. Based on the operating rate at different times, the charging current and discharging current can be reasonably adjusted to avoid overcharging, over-discharging, and overloading, thereby extending the battery's lifespan.

[0022] Secondly, the present invention provides a battery life prediction device, the device comprising: an acquisition module for acquiring relevant data of the battery at a target time; wherein the relevant data includes: current, voltage, temperature, and a first operating rate at a previous time of the target time; a first determination module for determining a second operating rate of the battery at the target time based on the relevant data of the battery at the target time; a second determination module for determining the equivalent cumulative capacity corresponding to a preset time period based on the second operating rate of the battery at the target time, the current of the battery at the target time, and a preset time period; a third determination module for determining the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period based on the equivalent cumulative capacity corresponding to the preset time period; and a prediction module for predicting the battery life based on the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period.

[0023] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the battery life prediction method of the first aspect or any corresponding embodiment described above.

[0024] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the battery life prediction method of the first aspect or any corresponding embodiment thereof.

[0025] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the battery life prediction method of the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a schematic flowchart of a battery life prediction method according to an embodiment of the present invention;

[0028] Figure 2 This is a structural block diagram of a battery life prediction device according to an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] According to an embodiment of the present invention, a battery life prediction method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] This embodiment provides a battery life prediction method, which can be used in computer devices such as computers and servers. Figure 1 This is a flowchart illustrating a battery life prediction method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps:

[0033] Step S101: Obtain relevant data of the battery at the target time; wherein, the relevant data includes: current, voltage, temperature and the first operating rate of the previous time at the target time.

[0034] A target time point indicates a specific point in time during battery operation where special attention is needed for data collection and analysis. It can be a fixed time interval or a time triggered by a specific event, used to acquire relevant battery data at the target time.

[0035] Relevant battery data at the target time may include: current, voltage, temperature, and the first operating rate at a previous time point. The first operating rate can indicate the battery's charge / discharge rate at a previous time point, which is the ratio of the actual charge / discharge current to the rated current, used to describe the battery's charge / discharge rate.

[0036] In practice, current, voltage, and temperature sensors are used to collect real-time data on the battery's current, voltage, and temperature at the target time. The first operating rate from the previous time point is obtained from the battery management system's records. For example, assuming the battery management system records data every minute, if the target time is 10:00, then the previous time point could be 9:59, and the first operating rate at 9:59 is read from the records.

[0037] Step S102: Determine the second operating rate of the battery at the target time based on the relevant data of the battery at the target time.

[0038] The second operating rate can be indicated as the battery's charge / discharge rate at that moment, calculated based on relevant data from the target moment, and also reflects the battery's current charge / discharge rate. After determining the relevant data for the battery at the target moment, the second operating rate of the battery at that target moment can be further determined.

[0039] As an example, after determining the current, voltage, temperature, and the first operating rate of the target time, the rated energy of the most recent cycle at the target time can be determined based on a pre-set model. Then, based on the rated energy, the current, voltage, and temperature data at the target time, the second operating rate of the battery at the target time can be determined.

[0040] As an example, a pre-set mathematical model can be used to determine the second operating rate of the battery at the target time based on relevant data of the battery at the target time.

[0041] As an example, the second operating rate of the battery at the target time can be determined using the following formula:

[0042] Among them, I t For the target time current, V t For the voltage at the target time, E t The rated energy at the target time and P rate·t This represents the second operating rate of the battery at the target time.

[0043] Step S103: Based on the second operating rate of the battery at the target time, the current of the battery at the target time, and the preset time period, determine the equivalent cumulative capacity corresponding to the preset time period.

[0044] The preset time period can be a pre-defined time interval. A single moment may have multiple sampling points, and the time interval between two adjacent sampling points can be used as the preset time interval. The preset time interval can be 30 seconds, 40 seconds, etc., without specific limitations.

[0045] By determining the battery's second operating rate at the target time, the battery's current at the target time, and the preset time period, the equivalent cumulative capacity corresponding to the preset time period can be further determined.

[0046] As an example, a preset equivalent cumulative capacity model can be used to determine the equivalent cumulative capacity corresponding to the preset time period based on the battery's second operating rate at the target time, the battery's current at the target time, and the preset time period. Other methods can also be used to determine the equivalent cumulative capacity, which are not specifically limited here and can be implemented by those skilled in the art.

[0047] Step S104: Determine the number of cycles and the battery health corresponding to the preset time period based on the equivalent cumulative capacity corresponding to the preset time period.

[0048] The preset time period corresponds to the number of cycles, which indicates the number of battery cycles during that time period. Battery health indicates the degree of battery degradation.

[0049] In practice, after determining the equivalent cumulative capacity corresponding to the preset time period, the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period can be further determined.

[0050] As an example, a preset model can be used to determine the number of cycles and the battery health corresponding to a preset time period.

[0051] As an example, a relationship mapping table can be pre-defined. This table records the correspondence between equivalent cumulative capacity and cycle count, and the correspondence between battery health and preset time periods. After determining the equivalent cumulative capacity, the cycle count and the corresponding battery health for the preset time period can be determined by looking up the relationship mapping table.

[0052] Step S105: Predict battery life based on the number of cycles corresponding to a preset time period and the battery health status corresponding to the preset time period.

[0053] After determining the number of cycles and the battery health corresponding to the preset time period, the battery life can be predicted based on these parameters.

[0054] As an example, a neural network model can be used to predict battery life based on the number of cycles corresponding to a preset time period and the battery health corresponding to the preset time period. Other methods can also be used to predict battery life, which are not specifically limited here and can be implemented by those skilled in the art.

[0055] The battery life prediction method provided by this invention determines a second operating rate by acquiring data such as current, voltage, temperature at a target time and a first operating rate from previous times. This more accurately reflects the actual operating condition of the battery under complex current conditions. Furthermore, based on the second operating rate, current, and preset time period at the target time, the equivalent cumulative capacity is determined, equating the charging and discharging process at different operating rates to capacity accumulation under a unified standard. This solves the problem of inaccurate cumulative capacity calculation caused by changes in operating rate, making the assessment of battery usage more objective and accurate.

[0056] Based on this, by accurately calculating the equivalent cumulative capacity, reasonably correlating the cycle count and battery health, the performance degradation pattern of the battery under actual complex operating conditions can be reflected more accurately. Predicting battery life based on these accurate intermediate results can effectively improve the accuracy of prediction, reduce prediction errors caused by complex and variable operating conditions, and avoid problems such as equipment failure, safety hazards, and unnecessary cost increases caused by inaccurate battery life prediction.

[0057] In one possible implementation, step S103 above includes:

[0058] Step a1: Obtain the first rated capacity corresponding to the rated multiplier.

[0059] Rated rate is a quantitative indicator of the battery's charge / discharge rate, representing the ratio of current to the battery's rated capacity. For example, 1C indicates charging and discharging at the battery's rated capacity current (e.g., 3A for a 3000mAh battery at 1C), while 2C is 6A. First rated capacity indicates the nominal capacity at the rated rate. First rated capacity can be obtained directly from a table.

[0060] Step a2: Determine the second rated capacity of the battery at the target time based on the second operating rate of the battery at the target time.

[0061] After determining the second operating rate of the battery at the target time, the second rated capacity of the battery at the target time can be determined based on the conversion of the second operating rate.

[0062] As an example, the charge-discharge curves of batteries at different rates are tested to obtain the maximum discharge capacity under different rate conditions. The maximum discharge capacity under arbitrary rate conditions is then obtained through data fitting and interpolation. Based on this data, equivalent capacity conversion under different rate conditions can be performed. Assume the rated capacity at the rated rate Prate is Qrate, and the rated capacity at time t at the rate Prate·t is Qrate·t.

[0063] Step a3: Determine the equivalent cumulative capacity corresponding to the preset time period based on the battery's second rated capacity at the target time, the first rated capacity, the battery's current at the target time, and the preset time period.

[0064] After determining the battery's second rated capacity, first rated capacity, current at the target time, and preset time period, the equivalent cumulative capacity corresponding to the preset time period can be further determined.

[0065] As an example, the equivalent cumulative capacity corresponding to a preset time period can be determined using the following formula:

[0066] Among them, Q Δt For the equivalent cumulative capacity, Δt is the preset time period, Qrate is the first rated capacity, and Q rate·t This is the second rated capacity.

[0067] The battery life prediction method provided by this invention addresses the fact that the rated capacity of a battery is not fixed and varies with the operating rate. At different operating rates, the internal chemical reaction rate and polarization phenomena of the battery differ, thus affecting the actual output capacity. By obtaining the first rated capacity corresponding to the rated operating rate and determining the second rated capacity based on the second operating rate at the target time, the method fully considers the impact of the operating rate on capacity, more accurately reflecting the actual capacity of the battery under current operating conditions, and providing a more reliable data foundation for subsequent battery management and performance evaluation.

[0068] In one possible implementation, when there are multiple preset time periods, step a3 above includes:

[0069] Step a31: Determine the equivalent cumulative capacity corresponding to the subsequent adjacent time periods of the target time period based on the equivalent cumulative capacity corresponding to the target time period, the first rated capacity, the current of the battery in the target time period, and the subsequent adjacent time periods of the target time period.

[0070] Step a32: When the target time period is a time period adjacent to the target time among multiple preset time periods, determine the equivalent cumulative capacity corresponding to the target time period, including: determining the equivalent cumulative capacity corresponding to the target time period based on the second rated capacity and the first rated capacity of the battery at the target time, the current of the battery at the target time, and the target time period.

[0071] Step a33: When the target time period is not a time period adjacent to the target time among multiple preset time periods, the equivalent cumulative capacity corresponding to the target time period is determined based on the equivalent cumulative capacity corresponding to the previous adjacent time period, the first rated capacity, the current of the battery in the previous adjacent time period, and the target time period.

[0072] The target time period can be the time period for which calculations need to be performed. The target time period can be a time period adjacent to the target time from among multiple preset time periods, or it can be any other time period from among the multiple preset time periods.

[0073] When the target time period is a time period adjacent to the target time among multiple preset time periods, the equivalent cumulative capacity corresponding to the target time period can be determined directly based on the battery's second rated capacity, first rated capacity, battery current at the target time, and the target time period.

[0074] When the target time period is not a time period adjacent to the target time among multiple preset time periods, the equivalent cumulative capacity corresponding to the target time period is determined based on the equivalent cumulative capacity corresponding to the previous adjacent time period, the first rated capacity, the current of the battery in the previous adjacent time period, and the target time period.

[0075] In other words, when the target time period is a time period adjacent to the target time among multiple preset time periods, the equivalent cumulative capacity corresponding to the target time period can be directly determined using the formula:

[0076] Among them, Q Δt For the equivalent cumulative capacity, Δt is the preset time period, Qrate is the first rated capacity, and Q rate·t This is the second rated capacity.

[0077] Then, after determining the equivalent cumulative capacity corresponding to the target time period, the equivalent cumulative capacity of the next time period can be calculated based on the equivalent cumulative capacity corresponding to the target time period, and so on, to calculate the equivalent cumulative capacity of each time period.

[0078] The battery life prediction method provided by this invention first obtains the first rated capacity at the rated rate, and then determines the second rated capacity based on the second operating rate at the target time. This fully considers the influence of the operating rate on the battery capacity and avoids calculation errors caused by ignoring the influence of the operating rate.

[0079] In one possible implementation, step S104 above includes:

[0080] Step c1: Check whether the equivalent cumulative capacity corresponding to the preset time period and the first rated capacity corresponding to the rated multiplier are the same.

[0081] After determining the equivalent cumulative capacity corresponding to the preset time period, it is necessary to determine whether it is the same as the rated capacity. If it is not the same as the rated capacity, then the equivalent cumulative capacity needs to be converted.

[0082] Step c2: When the equivalent cumulative capacity corresponding to the preset time period and the first rated capacity corresponding to the rated multiplier are not the same, determine the third rated capacity corresponding to the preset time period according to the operating multiplier corresponding to the preset time period.

[0083] As mentioned above, by testing the charge-discharge curves of batteries at different rates, the maximum discharge capacity under different rate conditions can be obtained. Furthermore, by data fitting and interpolation, the maximum discharge capacity under any rate condition can be obtained. Based on the above data, equivalent capacity conversion under different rate conditions can be performed. Assume the rated capacity at the rated rate Prate is Qrate, and the rated capacity at time t at the rate Prate·t is Qrate·t.

[0084] When the equivalent cumulative capacity corresponding to the preset time period and the first rated capacity corresponding to the rated multiplier are not the same, the third rated capacity corresponding to the preset time period can be determined by conversion.

[0085] Step c3: Determine the cumulative rated capacity equivalent to the preset time period based on the third rated capacity corresponding to the preset time period.

[0086] The cumulative rated capacity is obtained by adding the third rated capacity for a preset time period to the equivalent capacity at the target time.

[0087] As an example, the cumulative rated capacity equivalent can be determined using the following formula:

[0088] Among them, Q total The cumulative rated capacity is the equivalent capacity, where n is the number of preset time periods, and Q is the value of Q. n The third rated capacity for the preset time period.

[0089] Step c4: Determine the number of cycles corresponding to the preset time period based on the cumulative rated capacity equivalent to the preset time period.

[0090] Based on the charge-discharge cycle curve of the battery under rated rate (Prate) conditions, a correspondence between the battery's cumulative discharge capacity range and the number of cycles can be established. Specifically, after the cumulative rated rate equivalent capacity corresponding to a preset time period, the number of cycles corresponding to that preset time period can be determined based on this correspondence.

[0091] Step c5: Based on the preset time period, use a pre-set lifespan degradation model to determine the battery health corresponding to the preset time period.

[0092] The preset battery life degradation model can be a pre-trained model. After determining the preset time period, inputting this time period into the preset battery life degradation model will output the battery health status corresponding to the preset time period.

[0093] In one possible implementation, the method for constructing a pre-defined lifetime decay model can include:

[0094] Choose a suitable battery degradation model, which can include empirical models, semi-empirical models, and physicochemically based models. Collect battery operating data over different time periods, including charge / discharge current, voltage, temperature, and cycle count. This data will be used for model parameter calibration and validation. Determine the length of the analysis period based on the specific application requirements, such as in hours, days, or months.

[0095] Using collected historical data, statistical methods such as least squares and maximum likelihood estimation were employed to calibrate the parameters of the lifespan degradation model, enabling the model to better fit the actual battery degradation process. The calibrated model parameters were then validated using an independent validation dataset to evaluate the model's accuracy and generalization ability. If the validation results did not meet the requirements, the model parameters needed to be readjusted or an alternative model selected.

[0096] Relevant data for a preset time period is extracted from preprocessed historical data, such as the number of charge / discharge cycles, average current, and average temperature within that period. The extracted data is then input into a calibrated battery degradation model, and the battery's health within that time period is calculated using the model's formula. For example, for a cycle-based degradation model, the amount of battery health degradation can be calculated based on the number of cycles within the preset time period and the model parameters, thus obtaining the battery's current health status.

[0097] The battery life prediction method provided by this invention first detects whether the equivalent cumulative capacity corresponding to a preset time period and the first rated capacity corresponding to the rated rate are the same. Since the equivalent cumulative capacity of the battery in actual operation will differ from the first rated capacity due to different operating conditions, this method can accurately capture this difference. When the two are different, a third rated capacity is determined based on the operating rate of the preset time period, and then the cumulative rated rate equivalent capacity is calculated, based on which the number of cycles is determined. This can more accurately reflect the actual charge and discharge cycle situation of the battery under different operating conditions, avoiding errors in cycle count calculation caused by ignoring capacity differences. For example, under the condition of frequent start-stop operation of electric vehicles, the actual charge and discharge capacity of the battery will differ significantly from the rated capacity; this method can accurately calculate the actual number of cycles.

[0098] Furthermore, based on a preset time period, a pre-defined lifespan degradation model is used to determine battery health. This model comprehensively considers the battery's degradation characteristics under different operating conditions, and, combined with previously accurately calculated cycle counts and other relevant parameters, can more accurately assess the battery's health status within the preset time period. Compared to traditional methods, this reduces health assessment biases caused by complex and variable operating conditions and inaccurate calculations, providing a more reliable basis for battery maintenance and replacement.

[0099] In one possible implementation, step c4 above includes: determining the number of cycles corresponding to a preset time period based on the correspondence between the preset cumulative rated capacity and the number of cycles, and the cumulative rated capacity.

[0100] The correspondence between the preset cumulative rated capacity and the number of cycles can be indicated as the correspondence between the preset cumulative rated capacity and the number of cycles for each preset cumulative rated capacity. For example, the preset cumulative rated capacity is A, and the number of cycles can be B, etc., without specific limitations.

[0101] After determining the cumulative rated multiplier equivalent capacity, the cumulative rated multiplier equivalent capacity can be found from the preset cumulative rated multiplier equivalent capacity. Then, based on the cumulative rated multiplier equivalent capacity and the corresponding relationship, the number of cycles corresponding to the preset time period can be determined.

[0102] The battery life prediction method provided by this invention addresses the issue that various factors (such as temperature and self-discharge) can lead to the accumulation of measurement errors during long-term battery use. Calculating the cycle count based on the cumulative rated rate equivalent capacity can reduce the impact of these errors on the results to some extent. This is because the method comprehensively considers the equivalent capacity over a period of time, rather than relying solely on data from a single measurement or a few measurements, thus making the calculated cycle count more stable and reliable.

[0103] In one possible implementation, step S105 above includes:

[0104] Step d1: Determine the loop count determination function based on the number of loops corresponding to the preset time period and the battery health level corresponding to the preset time period.

[0105] The number of cycles corresponding to a preset time period and the battery health level corresponding to the preset time period can be used as a data point, for example, (N t SOH t ); where N t The number of cycles can be any preset time period, SOH t You can set the battery health level for any preset time period.

[0106] Among them, the loop count determination function can be obtained by fitting multiple data points based on the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period.

[0107] Step d2: Determine the function and preset battery health based on the number of loops, and determine the target number of loops corresponding to the preset battery health.

[0108] The cycle count determination function characterizes the relationship between battery health and the number of cycles. After determining the cycle count determination function, the target number of cycles can be determined based on a preset battery health level. The preset battery health level can be SOH = 80%.

[0109] Step d3: Predict battery life based on the target number of cycles and the number of cycles corresponding to the preset time period.

[0110] After determining the target number of cycles, the number of cycles corresponding to a preset time period can be subtracted from the target number of cycles to predict battery life.

[0111] The battery life prediction method provided by this invention considers two important factors simultaneously: the number of battery cycles over a preset time period and battery health. The number of battery cycles reflects the charging and discharging frequency of the battery in actual use and is one of the key factors affecting battery life; while battery health directly reflects the current performance state of the battery, including capacity decay and changes in internal resistance. By combining information from these two dimensions, the battery's degradation can be grasped more comprehensively and accurately, avoiding the bias that may arise from relying on a single factor for prediction, thereby improving the accuracy of battery life prediction.

[0112] In one possible implementation, step S102 above includes:

[0113] Step e1: Determine the battery power at the target time based on the battery's current and voltage at the target time.

[0114] The power of a battery at a target time can be the product of the current and voltage at that target time.

[0115] As an example, the battery power at a target time can be determined using the following formula:

[0116] P i =V i ·I i Among them, V i For voltage, I i For current, P i The power at the target time.

[0117] Step e2: Determine the cumulative discharge energy of the battery at the target time based on the battery's power at the target time and the preset time period.

[0118] After determining the battery's power at the target time, the cumulative discharge energy of the battery at the target time can be determined based on the battery's power at the target time and the preset time period.

[0119] As an example, the cumulative discharge energy of the battery at the target time can be determined using the following formula:

[0120] Where E0 is the cumulative discharge energy of the battery at the target time, and P i The battery power at the target time, Δt i This is the preset time interval.

[0121] Step e3: Determine the energy decay parameters based on the battery's cumulative discharge energy, charging rate, and current temperature at the target time.

[0122] The energy decay parameter can be determined using the following formula:

[0123] Where k0, k1, k2 are the parameters fitted in the energy decay model, Crate is the charging rate, and W input This is the previous capacity, set to W here. n Ea is the activation energy, R is the gas constant, T is the current temperature, and t is the target time.

[0124] Step e4: Determine the temperature correction parameters based on the current temperature at the target time.

[0125] The temperature correction parameter can be determined using the following formula:

[0126] λ2=1+β×(T-T0); where T0 is 25℃, i.e. no temperature correction is performed at this time; β: is the effect of temperature on OCV under 100% SOC conditions, i.e. the slope of OCV as a function of temperature determined at two temperatures of 25℃ and 45℃.

[0127] Step e5: Determine the calendar aging fitting parameters based on the current temperature at the target time and the preset aging coefficient.

[0128] The calendar aging fitting parameters can be determined using the following formula:

[0129] Where a0, a1, and a2 are the fitting parameters for calendar aging.

[0130] Step e6: Determine the rated energy of the battery at the target time based on the energy decay parameters, temperature correction parameters, calendar aging fitting parameters, and cumulative discharge energy.

[0131] The second running factor can be determined using the following formula:

[0132] Among them, W input-now For rated energy, W input To accumulate discharge energy.

[0133] Step e7: Determine the second operating rate based on the battery's rated energy at the target time and the first operating rate at the previous time before the target time.

[0134] After determining the battery's rated energy at the target time and the first operating rate at a previous time before the target time, the second operating rate can be determined based on a preset model.

[0135] The battery life prediction method provided by this invention determines the power based on current and voltage, and further calculates the cumulative discharge energy. This method can accurately reflect the battery's energy output at a target time. The energy decay parameter considers the influence of charging rate and current temperature on battery energy, making the evaluation of battery energy characteristics more comprehensive. Based on the operating rate at different times, the charging current and discharging current can be reasonably adjusted to avoid overcharging, over-discharging, and overloading, thereby extending the battery's lifespan.

[0136] This embodiment also provides a battery life prediction device for implementing the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0137] This embodiment provides a battery life prediction device, such as... Figure 2 As shown, the system includes: an acquisition module 201, used to acquire relevant data of the battery at a target time; wherein the relevant data includes: current, voltage, temperature, and a first operating rate at a previous time of the target time; a first determination module 202, used to determine a second operating rate of the battery at the target time based on the relevant data of the battery at the target time; a second determination module 203, used to determine the equivalent cumulative capacity corresponding to a preset time period based on the second operating rate of the battery at the target time, the current of the battery at the target time, and a preset time period; a third determination module 204, used to determine the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period based on the equivalent cumulative capacity corresponding to the preset time period; and a prediction module 205, used to predict the battery life based on the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period.

[0138] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0139] In this embodiment, the battery life prediction device is presented in the form of a functional unit. Here, a functional unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0140] This invention also provides a computer device having the above-described features. Figure 2 The battery life prediction device shown.

[0141] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 3 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 3 Take a processor 10 as an example.

[0142] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0143] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0144] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0145] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0146] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0147] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0148] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0149] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for predicting battery life, characterized in that, The method includes: Acquire relevant data of the battery at the target time; wherein, the relevant data includes: current, voltage, temperature, and the first operating rate of the previous time at the target time; Based on the relevant data of the battery at the target time, determine the second operating rate of the battery at the target time; Based on the second operating rate of the battery at the target time, the current of the battery at the target time, and the preset time period, the equivalent cumulative capacity corresponding to the preset time period is determined. Based on the equivalent cumulative capacity corresponding to the preset time period, determine the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period; Battery life is predicted based on the number of cycles corresponding to the preset time period and the battery health status corresponding to the preset time period.

2. The battery life prediction method according to claim 1, characterized in that, Based on the battery's second operating rate at the target time, the battery's current at the target time, and the preset time period, the equivalent cumulative capacity corresponding to the preset time period is determined, including: Obtain the first rated capacity corresponding to the rated multiplier; The second rated capacity of the battery at the target time is determined based on the second operating rate of the battery at the target time. The equivalent cumulative capacity corresponding to the preset time period is determined based on the second rated capacity of the battery at the target time, the first rated capacity, the current of the battery at the target time, and the preset time period.

3. The battery life prediction method according to claim 2, characterized in that, When there are multiple preset time periods, the equivalent cumulative capacity corresponding to the preset time period is determined based on the battery's second rated capacity at the target time, the first rated capacity, the battery's current at the target time, and the preset time period, including: The equivalent cumulative capacity corresponding to the target time period is determined based on the equivalent cumulative capacity corresponding to the target time period, the first rated capacity, the current of the battery during the target time period, and the subsequent adjacent time periods of the target time period; wherein, when the target time period is a time period adjacent to the target time among multiple preset time periods, determining the equivalent cumulative capacity corresponding to the target time period includes: determining the equivalent cumulative capacity corresponding to the target time period based on the second rated capacity of the battery at the target time, the first rated capacity, the current of the battery at the target time, and the target time period; When the target time period is not a time period adjacent to the target time among multiple preset time periods, the equivalent cumulative capacity corresponding to the target time period is determined based on the equivalent cumulative capacity corresponding to the previous adjacent time period of the target time period, the first rated capacity, the current of the battery in the previous adjacent time period, and the target time period.

4. The battery life prediction method according to claim 1, characterized in that, Based on the equivalent cumulative capacity corresponding to the preset time period, determine the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period, including: Detect whether the equivalent cumulative capacity corresponding to the preset time period and the first rated capacity corresponding to the rated multiplier are the same; When the equivalent cumulative capacity corresponding to the preset time period and the first rated capacity corresponding to the rated multiplier are not the same, the third rated capacity corresponding to the preset time period shall be determined according to the operating multiplier corresponding to the preset time period. Based on the third rated capacity corresponding to the preset time period, determine the cumulative rated multiplier equivalent capacity corresponding to the preset time period; The number of cycles corresponding to the preset time period is determined based on the equivalent capacity of the cumulative rated multiplier corresponding to the preset time period. Based on a preset time period, the battery health level corresponding to the preset time period is determined using a pre-set lifespan degradation model.

5. The battery life prediction method according to claim 4, characterized in that, Based on the cumulative rated capacity equivalent to the preset time period, determine the number of cycles corresponding to the preset time period, including: Based on the correspondence between the preset cumulative rated capacity and the number of cycles, the number of cycles corresponding to the preset time period is determined by the cumulative rated capacity.

6. The battery life prediction method according to claim 1, characterized in that, Based on the number of cycles corresponding to the preset time period and the battery health status corresponding to the preset time period, predict battery life, including: A function for determining the number of cycles is determined based on the number of cycles corresponding to the preset time period and the battery health status corresponding to the preset time period. Based on the cycle count determination function and the preset battery health, determine the target cycle count corresponding to the preset battery health; Battery life is predicted based on the target number of cycles and the number of cycles corresponding to the preset time period.

7. The battery life prediction method according to claim 1, characterized in that, The relevant data also includes the charging rate; determining the second operating rate of the battery at the target time based on the relevant data of the battery at the target time includes: Determine the battery power at the target time based on the battery's current and voltage at the target time; The cumulative discharge energy of the battery at the target time is determined based on the battery's power at the target time and the preset time period. The energy decay parameters are determined based on the battery's cumulative discharge energy at the target time, the charging rate, and the current temperature. Determine the temperature correction parameters based on the current temperature at the target time; Based on the current temperature at the target time and the preset aging coefficient, determine the calendar aging fitting parameters; The rated energy of the battery at the target time is determined based on the energy decay parameter, the temperature correction parameter, the calendar aging fitting parameter, and the cumulative discharge energy. The second operating rate is determined based on the battery's rated energy at the target time and the first operating rate at a previous time of the target time.

8. A battery life prediction device, characterized in that, The device includes: The acquisition module is used to acquire relevant data of the battery at a target time; wherein, the relevant data includes: current, voltage, temperature, and the first operating rate of the previous time at the target time; The first determining module is used to determine the second operating rate of the battery at the target time based on the relevant data of the battery at the target time; The second determining module is used to determine the equivalent cumulative capacity corresponding to the preset time period based on the second operating rate of the battery at the target time, the current of the battery at the target time, and the preset time period. The third determining module is used to determine the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period based on the equivalent cumulative capacity corresponding to the preset time period. The prediction module is used to predict battery life based on the number of cycles corresponding to the preset time period and the battery health corresponding to the preset time period.

9. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the battery life prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the battery life prediction method according to any one of claims 1 to 7.

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