Battery charge state correction method, system and equipment and medium
By acquiring the discrete entropy of the current for matching degree analysis and using cloud data to deduce the voltage difference, the problem of SOC accuracy decline caused by battery aging was solved, and high-precision SOC correction was achieved.
Patent Information
- Application Number
- CN202511762542.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-27
- Publication Date
- 2026-02-24
AI Technical Summary
Existing battery state of charge (SOC) estimation methods rely on accurate battery models, which leads to a decrease in accuracy after battery aging and makes it impossible to achieve efficient and stable SOC correction.
By acquiring the current discrete entropy of multiple time windows before vehicle operation, matching degree analysis is performed, and the voltage difference is derived in reverse using standard data in the cloud, achieving high-precision SOC correction that does not rely on the battery model.
It achieves highly adaptive and accurate SOC closed-loop correction that does not rely on the battery model, adapts to parameter changes caused by battery aging, and provides stable SOC correction effect.
Smart Images
Figure CN121559331A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery technology, and in particular to a method, system, device and medium for correcting the state of charge of a battery. Background Technology
[0002] In the field of battery management technology, accurate estimation of battery state of charge (SOC) is a core technology for ensuring the safe and efficient operation of electric vehicles, energy storage systems, and other applications. Current SOC estimation methods typically rely on the accuracy of battery models, requiring extensive experimental identification and optimization of model parameters. However, in actual use, batteries gradually age with increasing cycle counts, leading to changes in their internal parameters. This causes a decrease in the accuracy of pre-calibrated battery models, resulting in increased SOC estimation errors and insignificant correction effects. Summary of the Invention
[0003] This application provides a method, system, device, and medium for correcting the state of charge (SOC) of a battery, which solves the technical problem of decreased accuracy in related SOC estimation and achieves the technical effect of adaptive online SOC correction of battery state.
[0004] To achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, embodiments of this application provide a battery state of charge correction method, the method comprising: acquiring the current discrete entropy of a current vehicle over a series of consecutive first preset time periods before the vehicle stops, the current discrete entropy being used to quantify the operating conditions of the current vehicle; performing a matching degree analysis on the current discrete entropy and a preset standard current discrete entropy to obtain a matching degree analysis result; if the matching degree analysis result meets a preset condition, acquiring the terminal voltage sequence of the current vehicle over a series of consecutive second preset time periods from the time of stopping, and determining a voltage difference sequence between the terminal voltage sequence and the preset standard terminal voltage sequence; and determining a battery state of charge correction amount matching the current vehicle based on the voltage difference sequence.
[0005] The battery state of charge correction method provided in this application obtains the current discrete entropy within multiple consecutive time windows before parking to fully quantify the vehicle's operating conditions. The current discrete entropy is then compared with the standard discrete entropy preset in the cloud for matching degree analysis. If the matching degree analysis result meets the preset conditions, it is determined that the voltage relaxation behavior of the current vehicle after parking is comparable to the standard behavior in the cloud. By calculating the difference between the current vehicle voltage sequence and the standard voltage sequence in the cloud, the correction amount of the current vehicle's battery state of charge is derived in reverse. This achieves a closed-loop correction of SOC that is independent of an accurate battery model, highly adaptive, and highly accurate.
[0006] Optionally, the current discrete entropy includes positive current discrete entropy and negative current discrete entropy, and the current discrete entropy is determined as follows: the current sequence within the first preset time period is divided into a positive current sequence and a negative current sequence according to the current direction; the current change coefficient of each current data point in the current sequence is determined; the positive current discrete entropy is obtained based on the first probability distribution of the positive current sequence and the corresponding current change coefficient; the negative current discrete entropy is obtained based on the second probability distribution of the negative current sequence and the corresponding current change coefficient.
[0007] The intensity of driving conditions is quantified by positive current discrete entropy, and the intensity of braking conditions is quantified by negative current discrete entropy. By introducing a current change coefficient to capture the dynamic abrupt changes in current, the calculated current discrete entropy can reflect not only the distribution of current magnitude but also the rate and frequency of its change, thus enabling a more realistic quantification of transient and intense behaviors in actual driving.
[0008] Optionally, a matching degree analysis is performed on the current discrete entropy and a preset standard current discrete entropy to obtain a matching degree analysis result, including: obtaining a discrete entropy difference sequence based on the current discrete entropy and the preset standard current discrete entropy; calculating the variance of the discrete entropy difference sequence, and using the variance as the matching degree analysis result.
[0009] By calculating the variance of this discrete entropy difference sequence, the consistency between the current vehicle and the standard vehicle in the cloud in terms of current variation patterns can be quantitatively assessed. The smaller the variance, the higher the consistency between the two in terms of dynamic change characteristics under operating conditions, thus providing a basis for judging the comparability of subsequent voltage relaxation behavior.
[0010] Optionally, the preset standard current discrete entropy includes standard positive current discrete entropy and standard negative current discrete entropy. The method further includes: determining a first discrete entropy difference sequence based on the positive current discrete entropy and the corresponding standard positive current discrete entropy, and calculating a first variance of the first discrete entropy difference sequence; determining a second discrete entropy difference sequence based on the negative current discrete entropy and the corresponding standard negative current discrete entropy, and calculating a second variance of the second discrete entropy difference sequence; if both the first variance and the second variance are less than a preset variance threshold, then the matching degree analysis result is determined to meet the preset condition.
[0011] By calculating the first and second variances, and verifying the consistency of driving and braking modes respectively, it is possible to finally conclude that the voltage relaxation process of the battery of the current vehicle and the cloud standard vehicle after parking should follow similar physical laws. This is a key prerequisite for achieving high-precision model-free correction in the future.
[0012] Optionally, for any two adjacent first current data points and second current data points in the current sequence, the current change coefficient of the second current data point is set as follows: if the current direction of the second current data point is opposite to that of the first current data point, then the current change coefficient of the second current data point is greater than 1; if the current direction of the second current data point is the same as that of the first current data point, then the current change coefficient of the second current data point is equal to 1.
[0013] When the current variation coefficient equals 1, it indicates that the current is in a calm period, and this current data point contributes to the entropy calculation with normal weight. When the current variation coefficient is greater than 1, it means that the current is in a period of rapid change, i.e., a change in the charging and discharging direction, and the period of rapid change is given higher weight in the entropy calculation. By introducing the current variation coefficient, the calculated discrete entropy of the current can simultaneously describe the disorder and dynamic intensity, providing a reliable basis for high-precision SOC correction.
[0014] Optionally, determining the battery state of charge correction amount matching the current vehicle based on the voltage difference sequence includes: calculating the mean voltage difference of the voltage difference sequence; and determining the battery state of charge correction amount matching the current vehicle based on the mean voltage difference and a preset mean difference-battery state of charge correction amount mapping table.
[0015] By establishing a standard data warehouse in the cloud, the corresponding standard voltage mean difference is calculated for each data unit. By learning the strong correlation between the actual SOC and the voltage relaxation behavior after parking, a mean difference-battery state of charge correction mapping table is obtained. This converts the voltage mean difference into a high-precision SOC correction instruction, realizing a highly reliable SOC closed-loop correction that is independent of the battery model and can adapt to the battery state.
[0016] Optionally, the preset standard current discrete entropy and the preset standard terminal voltage sequence are determined as follows: historical operating data of multiple reference vehicles are acquired; the historical operating data are divided into multiple data units according to battery health, battery temperature, and calibrated standard charge data; for any data unit, a corresponding standard current discrete entropy is generated based on the current data of each reference vehicle in the data unit within a first preset time period before the parking time; a corresponding standard terminal voltage sequence is generated based on the terminal voltage data of each reference vehicle in the data unit within a second preset time period from the parking time.
[0017] By using calibrated high-precision source data, multiple data units are established based on battery health, temperature, and state of charge. Based on historical data within each data unit, standard current discrete entropy representing typical driving conditions and standard terminal voltage sequences representing corresponding voltage relaxation behavior are statistically generated. This constructs a cloud-based standard data warehouse, providing a reliable data benchmark and comparison basis for achieving high-precision SOC correction on the vehicle side that is independent of battery models and adaptive to battery state.
[0018] Secondly, embodiments of this application provide a battery state of charge correction system, the system comprising: an acquisition module, configured to acquire the current discrete entropy of a current vehicle over a series of consecutive first preset time periods before the vehicle stops, the current discrete entropy being used to quantify the operating conditions of the current vehicle; a matching module, configured to perform a matching degree analysis on the current discrete entropy and a preset standard current discrete entropy to obtain a matching degree analysis result, and if the matching degree analysis result meets a preset condition, to acquire the terminal voltage sequence of the current vehicle over a series of consecutive second preset time periods from the time of stopping, and to determine the voltage difference sequence between the terminal voltage sequence and the preset standard terminal voltage sequence; and a correction module, configured to determine a battery state of charge correction amount matching the current vehicle based on the voltage difference sequence.
[0019] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-described battery state of charge correction method.
[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the above-described battery state-of-charge correction method.
[0021] Fifthly, embodiments of this application provide a computer program product, including computer instructions, which are used to cause a computer to execute the above-described battery state of charge correction method. Attached Figure Description
[0022] To more clearly illustrate the technical solutions in the specific embodiments of this application or 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 this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0023] Figure 1 A flowchart of a battery state of charge correction method provided in an embodiment of this application; Figure 2 A schematic diagram of a battery state of charge correction system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0025] With the widespread application of lithium-ion batteries in energy storage systems, passenger vehicles, and commercial vehicles, the accuracy requirements for estimating the core parameter of a Battery Management System (BMS)—State of Charge (SOC)—are increasing. Accurate SOC estimation is crucial for ensuring battery safety, optimizing energy management, and extending battery life. Currently, mainstream SOC estimation methods typically employ a fusion approach, primarily using the ampere-hour integration method, supplemented by characteristic point correction and online correction. The ampere-hour integration method estimates SOC by directly integrating the current. While simple, it suffers from inaccurate initial SOC and accumulated current measurement errors, causing the estimation results to drift over time. The characteristic point correction method uses a fixed correspondence between battery voltage and SOC under specific states (e.g., resting, fully charged) for calibration. Although effective at specific points, the correction opportunities are limited and cannot cover the entire dynamic operating process. To correct SOC in real time during dynamic processes, online correction algorithms have been widely researched and applied. These methods typically begin by establishing a battery model (such as an equivalent circuit model), and then continuously adjust the model's internal states (including SOC) using algorithms such as Kalman filtering and extended Kalman filtering to minimize the error between the predicted terminal voltage output and the actual measured value. However, these methods have significant inherent drawbacks, including: 1) High model dependence: Their accuracy heavily depends on the accuracy of the established battery model. To obtain a high-precision model, extensive and complex cell testing is required in the early stages to identify model parameters, resulting in high development costs and long development cycles. 2) Model aging mismatch: Batteries inevitably age during long-term use, and their internal parameters (such as internal resistance and capacity) change. This leads to deviations between the initially established battery model and the actual characteristics of the aged battery, and these deviations gradually increase, significantly reducing the accuracy of model-based online correction algorithms and even introducing error correction. 3) High computational complexity: Algorithms such as Kalman filtering require a certain level of computing power from the onboard processor, increasing hardware costs. Furthermore, for cells with a flat open-circuit voltage platform (such as lithium iron phosphate LFP), the voltage does not change significantly with SOC in most of the SOC range. This poses a huge challenge to voltage-based correction methods (including feature point correction and model correction), and the correction effect is greatly reduced.
[0026] Therefore, there is an urgent need in the field for a SOC correction method that can overcome the above-mentioned defects. This method should be able to provide stable and reliable SOC correction in daily vehicle use without relying on a high-precision battery model, avoid model mismatch problems caused by battery aging, and not rely on a high-precision battery model.
[0027] This application provides a battery state of charge correction method. 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. 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.
[0028] Please refer to Figure 1 , Figure 1 A flowchart of a battery state of charge correction method provided in this application embodiment is shown below. Figure 1 As shown, the process includes the following steps: Step S1: Obtain the current discrete entropy of the current vehicle within a series of first preset time periods before the parking time. The current discrete entropy is used to quantify the operating conditions of the current vehicle.
[0029] The first preset duration is the time window used to calculate the discrete entropy of a single current. Its length can be set according to actual needs, such as 10 seconds. Based on the current data collected within each first preset duration, the corresponding discrete entropy value of the current can be calculated. During vehicle operation, all driving actions (such as acceleration, deceleration, and constant speed driving) are mapped to the output or input current signal of the power battery in real time. By calculating the discrete entropy of the current signal, the intensity and change pattern of the operating conditions can be quantified.
[0030] The discrete entropy of the current is obtained over multiple consecutive first preset time periods to form an entropy time series. This series fully reflects the overall operating conditions of the vehicle during a continuous period before parking, avoiding the representational bias caused by random fluctuations in operating conditions within a single time window.
[0031] Step S3: Perform a matching degree analysis on the current discrete entropy and the preset standard current discrete entropy to obtain the matching degree analysis result. If the matching degree analysis result meets the preset conditions, obtain the terminal voltage sequence of the current vehicle for a continuous second preset time period from the time of parking, and determine the voltage difference sequence between the terminal voltage sequence and the preset standard terminal voltage sequence.
[0032] Based on the current state parameters of the vehicle's battery, including battery health (SOH), battery temperature, and the current estimated state of charge (SOC), a matching query is performed in the cloud-based standard data warehouse to identify data units of the same battery model that match the battery's state parameters. The standard current discrete entropy and standard terminal voltage sequence of this data unit are calculated. The standard current discrete entropy characterizes the current change pattern of the data unit, and the standard terminal voltage sequence is used to determine its voltage relaxation behavior. A matching degree analysis is performed between the real-time current discrete entropy of the current vehicle and the standard current discrete entropy to quantify the similarity of their driving conditions. If the matching degree analysis results meet preset conditions, it indicates that the operating conditions experienced by the current vehicle before parking are highly similar to the driving conditions of the target data unit, meaning their current change patterns are highly consistent. Based on this, it can be inferred that the voltage relaxation process of the battery after parking—that is, the dynamic behavior of the terminal voltage recovering and stabilizing over time—should also follow similar physical laws, providing a reliable basis for subsequent SOC correction.
[0033] Specifically, the standard data warehouse in the cloud is constructed as follows: The cloud server collects historical operating data from thousands of vehicles with the same battery model as the current vehicle. This data includes current, voltage, and battery state parameters (battery temperature, standard SOC, and battery health). Standard SOC refers to data from vehicles that have just undergone open circuit voltage (OCV) calibration or full charge correction, ensuring the battery's accurate SOC. The collected historical operating data is divided into battery aging intervals (e.g., every 5%), battery temperature intervals (e.g., every 10°C), and SOC intervals (e.g., every 10%), forming a three-dimensional data matrix. This creates multiple data units, forming a standard data warehouse.
[0034] Step S5: Based on the voltage difference sequence, determine the battery state of charge correction amount to match the current vehicle.
[0035] Under different State of Charge (SOC) states, the ion concentration and electrode potential inside the battery vary, resulting in unique relaxation behavior. Specifically, voltage relaxation behavior is strongly correlated only with the actual SOC. By comparing the current vehicle's voltage relaxation behavior with the standard voltage relaxation behavior in the cloud, the SOC correction amount can be derived in reverse, enabling high-precision online SOC correction independent of battery models.
[0036] The battery state of charge correction method provided in this application obtains the current discrete entropy within multiple consecutive time windows before parking to fully quantify the vehicle's operating conditions. The current discrete entropy is then compared with the standard discrete entropy preset in the cloud for matching degree analysis. If the matching degree analysis result meets the preset conditions, it is determined that the voltage relaxation behavior of the current vehicle after parking is comparable to the standard behavior in the cloud. By calculating the difference between the current vehicle voltage sequence and the standard voltage sequence in the cloud, the correction amount of the current vehicle's battery state of charge is derived in reverse. This achieves a closed-loop correction of SOC that is independent of an accurate battery model, highly adaptive, and highly accurate.
[0037] In some specific embodiments, the current discrete entropy includes positive current discrete entropy and negative current discrete entropy, and the current discrete entropy is determined as follows: the current sequence within the first preset time period is divided into a positive current sequence and a negative current sequence according to the current direction; the current change coefficient of each current data point in the current sequence is determined; the positive current discrete entropy is obtained based on the first probability distribution of the positive current sequence and the corresponding current change coefficient; the negative current discrete entropy is obtained based on the second probability distribution of the negative current sequence and the corresponding current change coefficient.
[0038] Positive current (discharging) corresponds to energy-consuming driving conditions such as vehicle acceleration and constant-speed driving. Negative current (charging) corresponds to energy-recovering braking conditions such as vehicle coasting and braking. To distinguish between driving and braking, two physically distinct conditions, the current sequence is divided into positive and negative values. The intensity of driving conditions is quantified by the discrete entropy of positive current, and the intensity of braking conditions is quantified by the discrete entropy of negative current. By introducing a current change coefficient to capture the dynamic abrupt changes in current, the calculated current discrete entropy not only reflects the distribution of current magnitude but also its rate and frequency of change, thus enabling a more realistic quantification of transient and intense behaviors in actual driving.
[0039] Specifically, the probability distribution is calculated using the kernel density estimation method, with the first probability distribution Pp. i Second probability distribution Pn i The calculation formula is as follows: i j In the above formula, For positive current kernel density function, The kernel density function is the negative current; the unit time within the first preset duration is t, t i t represents the time point corresponding to the i-th current data point.j Ip represents the time point corresponding to the j-th current data point; i Represents t i The corresponding positive current value, In j Represents t j The corresponding negative current value; n is the total number of sampling points within the first preset time period; h is the bandwidth coefficient, which takes a value of 0-1 and can be set to 0.5; Ip is the average positive current within the first preset time period; In is the average negative current within the first preset time period.
[0040] The discrete entropy of the current is calculated based on the probability distribution and the current change coefficient. The specific formula is as follows: In the above formula, The discrete entropy of the positive current within the r-th time window (the first preset time period); The discrete entropy of the negative current within the r-th time window (the first preset time period); is the current variation coefficient for the i-th current data point; Let be the current variation coefficient for the j-th current data point; Let be a probability constant, which can be set as .
[0041] In some specific embodiments, a matching degree analysis is performed on the current discrete entropy and a preset standard current discrete entropy to obtain a matching degree analysis result, including: obtaining a discrete entropy difference sequence based on the current discrete entropy and the preset standard current discrete entropy; calculating the variance of the discrete entropy difference sequence, and using the variance as the matching degree analysis result.
[0042] During the matching degree analysis, the discrete entropy sequences of current over N consecutive time periods calculated by the vehicle end are compared with the corresponding points of N consecutive standard discrete entropy sequences of current under the same operating parameters sent from the cloud, thus constructing a discrete entropy difference sequence. By calculating the variance of this discrete entropy difference sequence, the degree of consistency between the current vehicle and the standard vehicle in the cloud in terms of current change patterns is quantitatively evaluated. The smaller the variance, the higher the consistency between the two in terms of dynamic change characteristics under operating conditions, thus providing a basis for judging the comparability of subsequent voltage relaxation behavior.
[0043] In some specific embodiments, the preset standard current discrete entropy includes standard positive current discrete entropy and standard negative current discrete entropy. The method further includes: determining a first discrete entropy difference sequence based on the positive current discrete entropy and the corresponding standard positive current discrete entropy, and calculating a first variance of the first discrete entropy difference sequence; determining a second discrete entropy difference sequence based on the negative current discrete entropy and the corresponding standard negative current discrete entropy, and calculating a second variance of the second discrete entropy difference sequence; if both the first variance and the second variance are less than a preset variance threshold, then the matching degree analysis result is determined to meet the preset condition.
[0044] Positive current discrete entropy is used to quantify the intensity of driving conditions. Negative current discrete entropy is used to quantify the intensity of braking conditions. Since driving and braking conditions affect the battery's polarization state and internal ion concentration through different mechanisms, the consistency of driving and braking patterns is verified by calculating the first and second variances respectively. Only then can we definitively conclude that the voltage relaxation process of the battery in the current vehicle and the cloud-based standard vehicle after parking should follow similar physical laws. This is a crucial prerequisite for achieving high-precision model-free correction.
[0045] Specifically, the formulas for calculating the first variance Mp and the second variance Mn are as follows: In the above formula, Let be the discrete entropy of the positive current of the current vehicle in the r-th time window; is the standard positive current discrete entropy of the r-th time window in the corresponding data unit in the cloud; N is the number of time windows (first preset time period); Let be the positive current discrete entropy difference value in the r-th time window; It is the arithmetic mean of a discrete entropy difference sequence; Let be the discrete entropy of the negative current of the current vehicle in the r-th time window; The standard negative current discrete entropy of the r-th time window in the corresponding data unit in the cloud; The negative current discrete entropy difference value in the r-th time window; It is the arithmetic mean of the second discrete entropy difference sequence.
[0046] If |Mp|<=H_S and |Mn|<=H_S, where H_S is a preset variance threshold, which can be set to 20 mv, then the matching degree analysis result is determined to meet the preset conditions.
[0047] In some specific embodiments, for any two adjacent first current data points and second current data points in the current sequence, the current change coefficient of the second current data point is set as follows: if the current direction of the second current data point is opposite to that of the first current data point, then the current change coefficient of the second current data point is greater than 1; if the current direction of the second current data point is the same as that of the first current data point, then the current change coefficient of the second current data point is equal to 1.
[0048] The current variation coefficient is used to characterize the drastic change in current between adjacent time points. When the current variation coefficient equals 1, it indicates that the current is in a calm period, and this current data point contributes to the entropy calculation with normal weight. When the current variation coefficient is greater than 1, it means that the current is in a period of dramatic change, i.e., a change in the charging and discharging direction, and this period of dramatic change is given higher weight in the entropy calculation. By introducing the current variation coefficient, the calculated discrete entropy of the current can simultaneously describe the disorder and dynamic intensity, providing a reliable basis for high-precision SOC correction.
[0049] Specifically, for any adjacent first current data point (current value I) i-1 ) and the second current data point (current value I) i If the current values at the first and second current data points are in opposite directions, then the current variation coefficient... ,in The preset weighting coefficient can be set to 0.1; if the current values of the first and second current data points are in the same direction, then the current variation coefficient... It equals 1.
[0050] In some specific embodiments, determining the battery state of charge correction amount matching the current vehicle based on the voltage difference sequence includes: calculating the mean voltage difference of the voltage difference sequence; and determining the battery state of charge correction amount matching the current vehicle based on the mean voltage difference and a preset mean difference-battery state of charge correction amount mapping table.
[0051] The voltage difference sequence contains the difference between the current vehicle's terminal voltage and the standard terminal voltage at each time point. To filter out random noise, the system offset between the two voltage sequences is captured by calculating the voltage mean difference. By establishing a standard data warehouse in the cloud, the corresponding standard voltage mean difference is calculated for each data unit. By learning the strong correlation between the actual SOC and the voltage relaxation behavior after parking, a mean difference-battery state of charge correction mapping table is obtained. This converts the voltage mean difference into a high-precision SOC correction command, achieving a highly reliable SOC closed-loop correction that is independent of the battery model and can adapt to the battery state.
[0052] Specifically, the average voltage difference The calculation formula is: In the above formula, A is the voltage sequence number within the second preset time period. Let a be the a-th voltage difference in the voltage difference sequence. It is the arithmetic mean of the voltage difference sequence.
[0053] Based on the voltage mean difference By querying the preset mean difference-battery state of charge correction mapping table, the corresponding SOC correction amount can be determined.
[0054] In some specific embodiments, the preset standard current discrete entropy and the preset standard terminal voltage sequence are determined as follows: historical operating data of multiple reference vehicles are acquired; the historical operating data are divided into multiple data units according to battery health, battery temperature, and calibrated standard charge data; for any data unit, a corresponding standard current discrete entropy is generated based on the current data of each reference vehicle in the data unit within a first preset time period before the parking time; a corresponding standard terminal voltage sequence is generated based on the terminal voltage data of each reference vehicle in the data unit within a second preset time period from the parking time.
[0055] The reference vehicle uses the same battery model as the current vehicle to ensure consistent electrochemical characteristics. The SOC data of the reference vehicle is confirmed as highly accurate standard charge data; for example, a precise SOC can be obtained after OCV calibration or full-charge correction. Each data unit is defined by unique coordinates (battery health, battery temperature, standard charge data), for example (SOH: 90-95%, temperature: 20-30℃, SOC: 40-50%). Each standard current discrete entropy and standard terminal voltage sequence belongs to a specific data unit. The current vehicle, based on its own battery state parameters, including battery health, battery temperature, and the currently estimated state of charge, can be matched to the corresponding data unit to obtain the standard current discrete entropy and standard terminal voltage sequence for that data unit.
[0056] Specifically, for any given data unit, the steps for calculating the standard current discrete entropy of that data unit include: acquiring the current data of each reference vehicle in that data unit within a first preset time period before parking; dividing the data into positive and negative current sequences; calculating the corresponding probability distributions to obtain the positive and negative current discrete entropies of that reference vehicle; calculating the statistical average of the positive current discrete entropies calculated for all reference vehicles in that data unit to obtain the standard positive current discrete entropy of that data unit; similarly, the standard negative current discrete entropy can be obtained.
[0057] The specific steps for calculating the standard terminal voltage sequence of the data unit include: acquiring the terminal voltage data of each reference vehicle in the data unit within a second preset time after parking, for example, the second preset time is 10 seconds; taking the average of the voltage values of each reference vehicle at the 1st second after parking as the first data point of the standard terminal voltage sequence; taking the average of the voltage values of all reference vehicles at the 2nd second after parking as the second data point of the standard terminal voltage sequence; and so on, to obtain the standard terminal voltage sequence.
[0058] By using calibrated high-precision source data, multiple data units are established based on battery health, temperature, and state of charge. Based on historical data within each data unit, standard current discrete entropy representing typical driving conditions and standard terminal voltage sequences representing corresponding voltage relaxation behavior are statistically generated. This constructs a cloud-based standard data warehouse, providing a reliable data benchmark and comparison basis for achieving high-precision SOC correction on the vehicle side that is independent of battery models and adaptive to battery state.
[0059] In some specific implementations, the preset mean difference-battery state of charge correction mapping table is constructed as follows: In the cloud-based standard data warehouse, the same battery health range and battery temperature range are selected, and multiple data units corresponding to different states of charge (SOC) under these conditions are obtained. Based on the standard terminal voltage sequence corresponding to each data unit, the mean difference of its standard voltage is calculated to establish a mapping relationship between the SOC value and the mean difference of the standard voltage. Based on this mapping relationship, the difference between the mean differences of the standard voltage corresponding to adjacent SOC ranges is further calculated, thereby generating a mean difference-battery state of charge correction mapping table for real-time querying.
[0060] Accordingly, please refer to Figure 2 , Figure 2 A schematic diagram of a battery state-of-charge correction system provided in this application embodiment is shown below. Figure 2 As shown, the system includes: an acquisition module, used to acquire the current discrete entropy of the current vehicle over multiple consecutive first preset time periods before the parking time, the current discrete entropy being used to quantify the operating condition of the current vehicle; a matching module, used to perform a matching degree analysis on the current discrete entropy and a preset standard current discrete entropy to obtain a matching degree analysis result; if the matching degree analysis result meets a preset condition, then acquiring the terminal voltage sequence of the current vehicle over a continuous second preset time period from the parking time, and determining the voltage difference sequence between the terminal voltage sequence and the preset standard terminal voltage sequence; and a correction module, used to determine a battery state of charge correction amount matching the current vehicle based on the voltage difference sequence.
[0061] 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.
[0062] In this embodiment, the battery state-of-charge correction system is presented in the form of a functional unit. Here, a 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.
[0063] Please see Figure 3 , Figure 3 This application provides a schematic diagram of the structure of a computer device, as shown in the embodiment of the present application. 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.
[0064] 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 (GPA), or any combination thereof.
[0065] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.
[0066] 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.
[0067] 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.
[0068] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.
[0069] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently 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. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.
[0070] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method of any embodiment of this application.
[0071] The systems and modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.
[0072] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.
[0073] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0074] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0077] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0078] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0079] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
[0080] Although embodiments of this application 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 this application, and such modifications and variations all fall within the scope defined by the appended claims.
Claims
1. A method for correcting the state of charge of a battery, characterized in that, The method includes: The discrete entropy of the current of the current vehicle is obtained over a series of consecutive first preset time periods before the vehicle stops. The discrete entropy of the current is used to quantify the operating condition of the current vehicle. The current discrete entropy is matched with the preset standard current discrete entropy to obtain the matching degree analysis result. If the matching degree analysis result meets the preset condition, the terminal voltage sequence of the current vehicle within a second preset time period from the time of parking is obtained, and the voltage difference sequence between the terminal voltage sequence and the preset standard terminal voltage sequence is determined. Based on the voltage difference sequence, a battery state of charge correction amount matching the current vehicle is determined.
2. The method according to claim 1, characterized in that, The current discrete entropy includes positive current discrete entropy and negative current discrete entropy, and the current discrete entropy is determined as follows: The current sequence within the first preset time period is divided into a positive current sequence and a negative current sequence according to the current direction; Determine the current variation coefficient for each current data point in the current sequence; Based on the first probability distribution of the positive current sequence and the corresponding current change coefficient, the discrete entropy of the positive current is obtained; The negative current discrete entropy is obtained based on the second probability distribution of the negative current sequence and the corresponding current change coefficient.
3. The method according to claim 2, characterized in that, A matching degree analysis is performed between the current discrete entropy and a preset standard current discrete entropy to obtain the matching degree analysis results, including: Based on the current discrete entropy and the preset standard current discrete entropy, a discrete entropy difference sequence is obtained; Calculate the variance of the discrete entropy difference sequence, and use the variance as the matching degree analysis result.
4. The method according to claim 3, characterized in that, The preset standard current discrete entropy includes standard positive current discrete entropy and standard negative current discrete entropy, and the method further includes: Based on the positive current discrete entropy and the corresponding standard positive current discrete entropy, a first discrete entropy difference sequence is determined, and the first variance of the first discrete entropy difference sequence is calculated. Based on the negative current discrete entropy and the corresponding standard negative current discrete entropy, a second discrete entropy difference sequence is determined, and the second variance of the second discrete entropy difference sequence is calculated. If both the first variance and the second variance are less than a preset variance threshold, then the matching degree analysis result is determined to meet the preset condition.
5. The method according to claim 2, characterized in that, For any two adjacent first current data points and second current data points in the current sequence, the current change coefficient of the second current data point is set as follows: If the current direction of the second current data point is opposite to that of the first current data point, then the current change coefficient of the second current data point is greater than 1; If the current direction of the second current data point is the same as that of the first current data point, then the current change coefficient of the second current data point is equal to 1.
6. The method according to claim 1, characterized in that, Based on the voltage difference sequence, determine the battery state-of-charge correction amount to match the current vehicle, including: Calculate the mean voltage difference of the voltage difference sequence; Based on the voltage mean difference and the preset mean difference-battery state of charge correction mapping table, the battery state of charge correction amount matched with the current vehicle is determined.
7. The method according to claim 6, characterized in that, The preset standard current discrete entropy and the preset standard terminal voltage sequence are determined as follows: Obtain historical operating data from multiple reference vehicles; The historical operating data is divided into multiple data units based on battery health, battery temperature, and calibrated standard charge data. For any data unit, based on the current data of each reference vehicle in the data unit within the first preset time period before the parking time, a corresponding standard current discrete entropy is generated. Based on the terminal voltage data of each reference vehicle in the data unit for a continuous second preset time period from the time of parking, a corresponding standard terminal voltage sequence is generated.
8. A battery state-of-charge correction system, characterized in that, The system includes: The acquisition module is used to acquire the current discrete entropy of the current of the current vehicle within a series of first preset time periods before the parking time. The current discrete entropy is used to quantify the operating condition of the current vehicle. The matching module is used to perform a matching degree analysis on the current discrete entropy and a preset standard current discrete entropy to obtain a matching degree analysis result. If the matching degree analysis result meets a preset condition, the terminal voltage sequence of the current vehicle within a second preset time period from the time of parking is obtained, and the voltage difference sequence between the terminal voltage sequence and the preset standard terminal voltage sequence is determined. The correction module is used to determine a battery state-of-charge correction amount that matches the current vehicle based on the voltage difference sequence.
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 state-of-charge correction 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 the computer to perform the battery state-of-charge correction method according to any one of claims 1 to 7.