Overload determination method, controller, battery management system and battery

Abstract

This application discloses an overload determination method, a controller, a battery management system, and a battery. The overload determination method includes: determining whether an overload event has occurred based on a real-time current sequence and a reference current sequence; when an overload event is determined to have occurred, if the peak value of the sampled current is within a preset current range, then the following steps are performed: determining a first weighting coefficient of the real-time current sequence when updating the reference current sequence based on the peak value and the average value of the sampled current; updating the reference current sequence based on the sum of a first product and a second product, where the first product is the product of the first weighting coefficient and the real-time current sequence, and the second product is the product of the second weighting coefficient and the reference current sequence, and the second weighting coefficient is the difference between 1 and the first weighting coefficient. Through this method, effective overload protection can be achieved.

Description

Technical Field

[0001] This application relates to the field of overload determination technology, and in particular to an overload determination method, controller, battery management system and battery. Background Technology

[0002] In electric vehicles, energy storage systems, and various high-power battery applications, overload protection is a core mechanism for ensuring system safety, preventing thermal runaway, and extending battery life. With the increase in battery energy density and the increasing complexity of operating conditions, higher demands are placed on the real-time performance, accuracy, and adaptability of overload detection. Currently, the most widely adopted overload protection schemes in the industry are mainly based on fixed dual-threshold logic, and their specific implementations can be divided into hardware comparison circuit methods and software timer methods.

[0003] However, existing overload protection strategies based on fixed thresholds have poor accuracy and lack the ability to sense and adaptively adjust to the real-time state of the battery (SOC, temperature, SOH), making it difficult to achieve effective overload protection. Summary of the Invention

[0004] This application provides an overload determination method, a controller, a battery management system, and a battery, which can achieve effective overload protection.

[0005] In a first aspect, embodiments of this application provide an overload determination method, comprising: determining whether an overload event has occurred based on a real-time current sequence and a reference current sequence, wherein the real-time current sequence includes multiple sampled data, each sampled data including a current value in the sampled current and its corresponding sampling time, the reference current sequence includes multiple reference data, each reference data including a reference current value and its corresponding reference time, and the time interval between two adjacent sampling times is equal to the time interval between two adjacent reference times; when an overload event is determined to have occurred, if the peak value of the sampled current is within a preset current range, the following steps are performed: determining a first weighting coefficient of the real-time current sequence for updating the reference current sequence based on the peak value and the mean value of the sampled current; updating the reference current sequence based on the sum of a first product and a second product, wherein the first product is the product of the first weighting coefficient and the real-time current sequence, the second product is the product of the second weighting coefficient and the reference current sequence, and the second weighting coefficient is the difference between 1 and the first weighting coefficient.

[0006] In one or more embodiments, the preset current range is: ,in, This is the current average. This represents the current standard deviation.

[0007] In one or more embodiments, when an overload event is determined to have occurred, if the peak value of the sampled current is within a preset current range, the following steps are further performed: updating the mean value of the sampled current based on the mean value of the sampled current, the peak value of the sampled current, and a first weighting coefficient; updating the standard deviation of the sampled current based on the mean value of the sampled current, the standard deviation of the sampled current, the peak value of the sampled current, and the first weighting coefficient; and updating the preset current range based on the updated mean value of the sampled current and the standard deviation of the sampled current.

[0008] In one or more embodiments, the mean of the sampled current is updated based on the mean of the sampled current, the peak value of the sampled current, and a first weighting coefficient, including: the updated mean of the sampled current is: ,in, The average of the updated sampled current. This is the current average. The current standard deviation, The peak value of the sampled current. This is the first weighting coefficient.

[0009] In one or more embodiments, the standard deviation of the sampled current is updated based on the mean of the sampled current, the standard deviation of the sampled current, the peak value of the sampled current, and a first weighting coefficient, including: the updated standard deviation of the sampled current is: ,in, The standard deviation of the updated sampling current. This is the current average. The current standard deviation, The peak value of the sampled current. This is the first weighting coefficient.

[0010] In one or more embodiments, updating the preset current range based on the updated mean and standard deviation of the sampled current includes: the updated preset current range is: ,in, The average of the updated sampled current. This represents the standard deviation of the updated sampled current.

[0011] In one or more embodiments, determining a first weighting coefficient for the real-time current sequence when updating the reference current sequence based on the peak and mean values ​​of the sampled current includes: determining the first weighting coefficient using the following formula: ,in, As the first weighting coefficient, The peak value of the sampled current. As the benchmark weighting coefficient, This is the current average.

[0012] In one or more embodiments, determining whether an overload event has occurred based on a real-time current sequence and a reference current sequence includes: normalizing the real-time current sequence to obtain a target real-time current sequence, and normalizing the reference current sequence to obtain a target reference current sequence; calculating the distance between the target real-time current sequence and the target reference current sequence; and determining that an overload event has occurred if the ratio of the distance to the sequence length is less than a preset distance threshold, wherein the sequence length is the sum of the length of the target real-time current sequence and the length of the target reference current sequence.

[0013] In one or more embodiments, calculating the distance between a target real-time current sequence and a target reference current sequence includes: generating a local cost matrix based on the Euclidean distance between sampled data in the target real-time current sequence and reference data in the corresponding target reference current sequence; determining a cumulative distance matrix based on the local cost matrix; and determining the distance based on the cumulative distance matrix.

[0014] Secondly, embodiments of this application provide a controller, including: at least one processor and a memory; the memory is coupled to the processor and is used to store instructions or programs, which, when executed by the at least one processor, cause the at least one processor to perform the overload determination method as described in the first aspect.

[0015] Thirdly, embodiments of this application provide a battery management system, including the controller as described in the second aspect.

[0016] Fourthly, embodiments of this application provide a battery, including: a cell module including at least one cell; and a battery management system as described in the third aspect, the battery management system being electrically connected to the cell module, the battery management system being used to determine whether an overload event has occurred in the cell module based on the current of the cell module.

[0017] The beneficial effects of this application are as follows: The overload determination method of this application includes: determining whether an overload event has occurred based on a real-time current sequence and a reference current sequence, wherein the real-time current sequence includes multiple sampled data, each sampled data including a current value in the sampled current and its corresponding sampling time, the reference current sequence includes multiple reference data, each reference data including a reference current value and its corresponding reference time, and the time interval between two adjacent sampling times is equal to the time interval between two adjacent reference times; when an overload event is determined to have occurred, if the peak value of the sampled current is within a preset current range, the following steps are performed: determining a first weighting coefficient of the real-time current sequence when updating the reference current sequence based on the peak value and the mean value of the sampled current; updating the reference current sequence based on the sum of the first product and the second product, wherein the first product is the product of the first weighting coefficient and the real-time current sequence, the second product is the product of the second weighting coefficient and the reference current sequence, and the second weighting coefficient is the difference between 1 and the first weighting coefficient. Thus, on the one hand, the reference current sequence is not fixed but dynamically updated based on the real-time current, enabling the generation of a reference current sequence suitable for the current operating conditions, which is beneficial for achieving effective overload protection. On the other hand, a dynamic weighted update strategy based on the real-time current sequence and the reference current sequence is implemented, ensuring that the reference current sequence can smoothly follow the slow drift of battery characteristics for self-correction, thereby avoiding protection sluggishness (i.e., missed alarms) caused by battery aging or false alarms caused by outdated parameters, significantly improving the system's protection consistency and reliability throughout the entire equipment lifecycle, thus achieving effective overload protection. Furthermore, a preset current range is introduced to separate non-real overloads from real overloads, which can greatly improve the identification accuracy of real overloads, thus facilitating effective overload protection. Attached Figure Description

[0018] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, which are not intended to limit the embodiments, and elements having the same reference numerals in the drawings are designated as similar elements.

[0019] Figure 1 This is the flow chart of the overload determination method provided in the embodiments of this application. Figure 1 ;

[0020] Figure 2 This is the flow chart of the overload determination method provided in the embodiments of this application. Figure 2 ;

[0021] Figure 3 This is the flow chart of the overload determination method provided in the embodiments of this application. Figure 3 ;

[0022] Figure 4This is the flow chart of the overload determination method provided in the embodiments of this application. Figure 4 ;

[0023] Figure 5 This is a schematic diagram of the controller provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be described clearly and in detail below with reference to the accompanying drawings. Obviously, the embodiments in this application are only some embodiments, not all embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0025] It should be noted that when an element is described as "connected" to another element, it can be directly connected to the other element, or there can be one or more intermediate elements between them.

[0026] Furthermore, the technical features involved in the various embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0027] In related technologies, the overload protection schemes used are mainly based on fixed dual threshold logic, and the specific implementation forms can be divided into hardware comparison circuit method and software timer method.

[0028] Hardware Comparison Circuit Method: This scheme uses an analog comparator as the core execution unit. The system compares the voltage drop across the sampling resistor (proportional to the loop current) with a fixed reference voltage (corresponding to the current threshold) in real time. Once the current exceeds the current threshold, a retrievable monostable timer is triggered. If the current fails to fall below another lower fixed threshold within a preset delay time, the system determines that an overload event has occurred and performs protective actions (such as disconnecting the loop).

[0029] Software timer method: This scheme relies on the digital processing capabilities of the microcontroller. The microcontroller acquires the battery circuit current signal in real time through an analog-to-digital converter. Its control logic is as follows: when the sampled current value continuously exceeds a preset fixed threshold and the duration reaches a set value, the software algorithm determines that an overload event has occurred, and then issues a protection command.

[0030] While the above solutions offer some protection under normal operating conditions, they reveal significant technical bottlenecks when faced with the dynamic changes and complex load characteristics throughout the battery's lifecycle.

[0031] (1) In order to ensure that the actual overload risk can still be reliably detected throughout the entire battery life cycle (especially after aging leads to an increase in internal resistance), the threshold must be set relatively low. However, in order to avoid false triggering caused by normal instantaneous large currents such as motor start-up and capacitor charging impact, the threshold must be set high enough to leave a safety margin. This contradiction makes it difficult for the system to find the optimal balance between "not missing real overloads" and "not falsely reporting normal impacts", and often only a compromise can be adopted, sacrificing some protection accuracy, thus making it difficult to achieve effective overload protection.

[0032] (2) The electrochemical characteristics of a battery are not static. Its internal resistance, maximum discharge capacity, and thermal characteristics fluctuate significantly with changes in the state of charge (SOC), ambient temperature, and state of health (SOH). A fixed threshold set at room temperature and high SOC may become an actual overload under low temperature or low SOC conditions due to a significant decrease in battery output capacity. Conversely, under high output capacity conditions, the fixed threshold may be overly sensitive to impact loads, leading to frequent false trips. Furthermore, as the number of battery cycles increases, SOH gradually decreases, battery internal resistance slowly increases, and load-bearing characteristics drift. The fixed threshold and delay parameters calibrated at the factory cannot be dynamically adjusted to keep pace with this slow physical degradation. This results in performance degradation of the protection system in the later stages of the equipment's lifespan: either becoming "sluggish" due to a relatively high threshold, failing to intercept risks early; or becoming "overly sensitive" due to decreased battery tolerance, affecting system availability. It is evident that existing overload protection strategies based on fixed thresholds lack the ability to sense and adaptively adjust the real-time state of the battery (SOC, temperature, SOH), making it difficult to meet the refined protection requirements of the next generation of high-safety, long-life battery systems, and thus difficult to achieve effective overload protection.

[0033] Based on this, this application provides an overload determination method to achieve effective overload protection.

[0034] like Figure 1 As shown, the overload determination method includes the following steps S110 to S130.

[0035] Step S110: Determine whether an overload event has occurred based on the real-time current sequence and the reference current sequence. The real-time current sequence includes multiple sampled data, each of which includes a current value in the sampled current and its corresponding sampling time. The reference current sequence includes multiple reference data, each of which includes a reference current value and its corresponding reference time. The time interval between two adjacent sampling times is equal to the time interval between two adjacent reference times.

[0036] Specifically, the real-time current sequence and the reference current sequence have strictly corresponding sampling points on the time axis. When the real-time current sequence and the reference current sequence meet specific conditions, the system confirms that an overload event has occurred, that is, the system confirms that an overload state has occurred.

[0037] The reference current sequence So is defined as: So = [(reference time Ts1, reference current value Is1), (reference time Ts2, reference current value Is2), ..., (reference time TsK, reference current value IsK)], where K is an integer greater than 1, indicating that the length of the reference current sequence So is K. A reference data set includes a reference current value and its corresponding reference time; for example, (reference time Ts1, reference current value Is1) is a reference data set.

[0038] The reference current sequence So can be set according to the actual application scenario. For example, in a specific embodiment, when an overload event occurs, the current rises relatively slowly and the amplitude is low. The reference current sequence So can be set as: So=[(0,0),(5,200),…,(5(K-1),200(K-1))], where the unit of each reference time is ms (milliseconds) and the unit of each reference current value is A (amperes).

[0039] The real-time current sequence Ro is: Ro = [(sampling time Tr1, sampled current value Ir1), (sampling time Tr2, sampled current value Ir2), ..., (sampling time TrL, sampled current value IrL)], where L is an integer greater than 1, meaning the length of the real-time current sequence Ro is L. A sampled data point includes a current value (i.e., the sampled current value) and its corresponding sampling time. For example, (sampling time Ts1, sampled current value Is1) is a sampled data point.

[0040] The time interval between two adjacent sampling times is equal to the time interval between two adjacent reference times. For example, the time interval between sampling times Tr1 and Tr2 is equal to the time interval between reference times Ts1 and Ts2. By setting the time interval between two adjacent sampling times to be equal to the time interval between two adjacent reference times, time axis alignment can be achieved, thereby reducing potential distance calculation errors.

[0041] The sampling method of the real-time current sequence Ro can be set according to the actual application scenario, as long as the final sequence is aligned with the time axis of the reference current sequence So. For example, in a specific embodiment, the reference current sequence So is set to So=[(0,0),(5,200),…,(5(K-1),200(K-1))]. During sampling, sampling is first performed at a fixed frequency of 1MHz, that is, the current value is sampled once every 1μs to obtain the original sampling sequence. Then, new sequence points are extracted or interpolated at 5ms intervals. For example, an average value is calculated for every 5000 points of the original sampling sequence, so that the real-time current sequence Ro can be obtained as: Ro=[(0, sampled current value Ir1),(5, sampled current value Ir2),…,(5(L-1), sampled current value IrL)].

[0042] In some embodiments, such as Figure 2 As shown, the specific implementation process of step S110 includes the following steps S210 to S230.

[0043] Step S210: Normalize the real-time current sequence to obtain the target real-time current sequence, and normalize the reference current sequence to obtain the target reference current sequence.

[0044] Specifically, by normalizing the real-time current sequence and the reference current sequence, the difference in absolute current values ​​can be avoided from dominating the distance calculation.

[0045] In some embodiments, the normalization process includes Z-score normalization or min-max normalization. Taking min-max normalization as an example, for a sequence X = [x1, x2, ...], the formula for min-max normalization is: .

[0046] In a specific embodiment, the real-time current sequence Ro is assumed to be: Ro = [(0, 15), (5, 650), (10, 850), (15, 920), (20, 950)], where the minimum current value is 15A and the maximum current value is 950A. Substituting these values ​​into the above minimum-maximum normalization formula, the target real-time current sequence Ro_norm is obtained as: Ro_norm = [(0, 0), (5, 0.679), (10, 0.893), (15, 0.968), (20, 1.000)]. The reference current sequence So is: So = [(0, 0), (5, 200), (10, 400), (15, 600), (20, 800)], where the minimum current value is 0A and the maximum value is 800A. Substituting these values ​​into the minimum-maximum normalization formula, the target reference current sequence So_norm is: So_norm = [(0, 0.00), (5, 0.25), (10, 0.50), (15, 0.75), (20, 1.00)]. Thus, after normalization, all current values ​​fall within the [0, 1] interval, which helps to ensure that the distance calculated subsequently better reflects the waveform shape difference rather than the absolute magnitude of the amplitude.

[0047] Step S220: Calculate the distance between the target real-time current sequence and the target reference current sequence.

[0048] Specifically, by calculating the distance between the target real-time current sequence and the target reference current sequence, the similarity between the target real-time current sequence and the target reference current sequence can be determined, thereby determining whether an overload event has occurred.

[0049] In some embodiments, such as Figure 3 As shown, the specific implementation process of step S220 includes the following steps S310 to S330.

[0050] Step S310: Generate a local cost matrix based on the Euclidean distance between the sampled data in the target real-time current sequence and the reference data in the corresponding target reference current sequence.

[0051] Step S320: Determine the cumulative distance matrix based on the local cost matrix.

[0052] Step S330: Determine the distance based on the cumulative distance matrix.

[0053] Elements in the local cost matrix for: ,in, For the nth data in the target real-time current sequence Ro_norm, according to the previous embodiment, the length of the real-time current sequence Ro is L, then the length of the target real-time current sequence Ro_norm is L, that is, n=1, 2, ...L; Let So_norm be the m-th data in the target reference current sequence. According to the previous embodiment, the length of the reference current sequence So is K, so the length of the target reference current sequence So_norm is K, that is, m=1, 2, ...K.

[0054] Taking Ro_norm=[(0, 0), (5, 0.679), (10, 0.893), (15, 0.968), (20, 1.000)] and So_norm=[(0, 0.00), (5, 0.25), (10, 0.50), (15, 0.75), (20, 1.00)] as an example, the calculated values ​​are... As shown in Table 1 below:

[0055] Table 1

[0056]

[0057] Then, based on the local cost matrix, the elements of the following cumulative distance matrix are obtained. :

[0058]

[0059] Based on Table 1, the calculations obtained are as follows: As shown in Table 2 below:

[0060] Table 2

[0061]

[0062] The element D(5, 5) = 0.1121 in the lower right corner of Table 2 represents the distance between the target real-time current sequence Ro_norm and the target reference current sequence So_norm, indicating the cumulative difference under optimal time planning. The distance between Ro_norm and So_norm is denoted as D(L, K). The smaller the distance D(L, K), the more similar the waveforms of Ro_norm and So_norm are.

[0063] Step S230: If the ratio of distance to sequence length is less than a preset distance threshold, an overload event is determined to have occurred, wherein the sequence length is the sum of the length of the target real-time current sequence and the length of the target reference current sequence.

[0064] Specifically, firstly, the distance D(L, K) is divided by the sum of the lengths of the target real-time current sequence (denoted as L according to the aforementioned embodiment) and the target reference current sequence (denoted as K according to the aforementioned embodiment) (i.e., the sequence length, which is equal to L+K) to obtain the normalized distance do, i.e., do=D(L, K) / (L+K), to eliminate the influence of the sequence length on the absolute value of the distance. Then, the normalized distance do is compared with a preset distance threshold. If it is less than the preset distance threshold, it is determined that the waveforms of the target real-time current sequence Ro_norm and the target reference current sequence So_norm are relatively similar, thus indicating that an overload event has occurred; of course, if it is greater than or equal to the preset distance threshold, it is determined that no overload event has occurred.

[0065] Subsequently, when an overload event is determined to have occurred, if the peak value of the sampled current is within a preset current range, then steps S120 to S130 are executed.

[0066] Specifically, after determining that an overload event has occurred, a key characteristic value is first detected (in this embodiment, the peak value of the sampled current is used as the key characteristic value). If the key characteristic value meets a preset condition, i.e., the key characteristic value is within a preset current range, then steps S120 to S130 are allowed to be executed to update the reference current sequence. If the key characteristic value does not meet the preset condition, i.e., the key characteristic value is not within the preset current range, then steps S120 to S130 are not allowed to be executed, i.e., updating the reference current sequence based on this overload event is not allowed. In this way, by introducing a preset current range, non-real overloads and real overloads can be separated, thereby greatly improving the identification accuracy of real overloads and facilitating effective overload protection. True overload refers to the statistically consistent overcurrent phenomenon that occurs within the normal operating life cycle of an electrical system due to increased load, environmental changes, or normal equipment aging. This corresponds to application scenarios where the key characteristic values ​​are within the preset current range. Non-true overload refers to overcurrent events that deviate from the historical statistical patterns of the system, are not representative, or are caused by abnormal factors. This corresponds to application scenarios where the key characteristic values ​​are not within the preset current range. For example, an application scenario where mechanical jamming causes an abnormally high peak starting current, resulting in an excessively large current and leading to the determination of an overload event.

[0067] The preset current range is a pre-set current range that can be set based on the actual application scenario. For example, in a specific embodiment, the preset current range is... ,in, This is the current mean (i.e., the mean currently in use). This represents the current standard deviation (i.e., the standard deviation currently in use). According to the normal distribution theory, the probability of data exceeding the above-mentioned preset current range is only 0.27%. In engineering practice, these extremely low probability events usually do not represent the normal operating state of the system, but are caused by non-real operating conditions such as transient interference in the sampling link (e.g., ADC sampling noise, abnormal signal transmission, etc., causing the software to collect abnormal peak currents that do not match the actual current of the circuit, thus mistakenly determining that an overload event has occurred). Based on this, when the key characteristic value is within the preset current range, it can be determined as a real overload; conversely, when the key characteristic value is not within the preset current range, it can be determined as a non-real overload.

[0068] Step S120: Determine the first weighting coefficient of the real-time current sequence when updating the reference current sequence based on the peak value and mean value of the sampled current.

[0069] The first weighting coefficient is a dynamic scalar between [0, 1] used to adjust the contribution ratio of the real-time current sequence when fused to the reference current sequence. The first weighting coefficient is not a fixed value, but a function determined by two key statistics (peak current and mean current) within the current sampling window. This ensures that the reference current sequence only absorbs real load changes that have both a certain intensity (peak) and continuity (mean), filtering out occasional spurious signals. This helps ensure that the reference current sequence evolves only under real, reliable, and typical operating conditions, thus constructing a sensitive yet robust overload protection model.

[0070] In some embodiments, the specific implementation process of step S120 includes the following steps: determining the first weighting coefficient using the following formula: ,in, As the first weighting coefficient, The peak value of the sampled current. As the benchmark weighting coefficient, This is the current average.

[0071] Among them, settings Used to evaluate new data (i.e., data related to the newly sampled current) (representative) and the current model state (in) The degree of deviation (represented by). Then, according to The inverse proportional function is used to determine the first weighting coefficient. When diff approaches 0, it means that the new data is highly consistent with the model. That is, the first weighting coefficient When the learning rate is close to the baseline, the model quickly absorbs new data; when the diff approaches infinity (∞), it means that the new data deviates abnormally, the denominator becomes larger, and the first weight coefficient... When the value approaches 0, the model almost refuses to update, demonstrating strong inertia and anti-interference ability.

[0072] In some embodiments, a first weighting coefficient is set. The coefficient threshold is less than or equal to a preset threshold to prevent excessively large single updates. This ensures that only repeated observations of the same trend will result in significant drift, effectively filtering out random noise and achieving smooth evolution. The preset threshold is less than the baseline weight coefficient. The preset coefficient threshold can be set according to the actual application scenario. For example, in a specific embodiment, the benchmark weight coefficient... Set to 0.1, and set the preset coefficient threshold to 0.05.

[0073] Step S130: Update the reference current sequence according to the sum of the first product and the second product, wherein the first product is the product of the first weighting coefficient and the real-time current sequence, the second product is the product of the second weighting coefficient and the reference current sequence, and the second weighting coefficient is the difference between 1 and the first weighting coefficient.

[0074] Specifically, the reference current sequence is updated according to the following formula: So_new[i]=(1-α1)*So[i]+α1*Ro[i], where So[i] is the i-th data in the reference current sequence So, Ro[i] is the i-th data in the real-time current sequence Ro, and So_new[i] is the i-th data in the updated reference current sequence.

[0075] Thus, on the one hand, the reference current sequence can be dynamically updated according to the real-time current, thereby obtaining a reference current sequence applicable to the current operating conditions in real time, which is conducive to achieving effective overload protection; on the other hand, a dynamic weighted update strategy based on the real-time current sequence and the reference current sequence is implemented, ensuring that the reference current sequence can smoothly follow the slow drift of battery characteristics to self-correct, thereby avoiding missed alarms due to battery aging or false alarms due to outdated parameters, significantly improving the system's protection consistency and reliability throughout the entire equipment life cycle, so as to achieve effective overload protection.

[0076] In some embodiments, such as Figure 4 As shown, when an overload event is determined to have occurred, if the peak value of the sampled current is within a preset current range, then steps S410 to S430 are further executed. Wherein, Figure 4 The example shown is that steps S410 to S430 are executed after step S130. In other embodiments, steps S410 to S430 may also be executed before step S130, or steps S410 to S430 may be executed simultaneously with step S130.

[0077] Step S410: Update the mean of the sampled current based on the mean of the sampled current, the peak value of the sampled current, and the first weighting coefficient.

[0078] Step S410 is used to make the new mean derived from the historical mean (i.e., the current mean). (the historical average relative to the new sampling current) and the new observation (i.e., the peak value of the new sampling current). (This is obtained by combining the two.)

[0079] In a specific embodiment, step S410 is implemented by the following steps: the average value of the updated sampled current is: ,in, The average of the updated sampled current. This is the current average. The current standard deviation, This represents the peak value of the sampled current.

[0080] Specifically, this embodiment achieves a new mean obtained by a weighted average of the historical mean and the new observation. The first weighting coefficient... Used to control the update magnitude.

[0081] Step S420: Update the standard deviation of the sampled current based on the mean of the sampled current, the standard deviation of the sampled current, the peak value of the sampled current, and the first weighting coefficient.

[0082] Specifically, by changing the new standard deviation from the historical standard deviation (i.e., the current standard deviation) (the historical standard deviation relative to the new sampling current) and the new observation (i.e., the peak value of the new sampling current). This combination ensures that the core parameter describing system volatility (i.e., standard deviation) is not disturbed by accidental, malicious, or abnormal spikes in data.

[0083] In a specific embodiment, step S420 is implemented by the following steps: the standard deviation of the updated sampling current is: ,in, The standard deviation of the updated sampling current. This is the current average. The current standard deviation, This represents the peak value of the sampled current.

[0084] In this way, the new standard deviation integrates the historical standard deviation and the new bias (i.e., the peak value of the new sampling current). Compared with the current standard deviation The difference between the two values ​​(the difference between the two values) helps to achieve smooth updates and avoid single-point data disturbances.

[0085] Step S430: Update the preset current range based on the updated mean and standard deviation of the sampled current.

[0086] The updated mean of the sampled current is used to characterize the normal load centerline currently identified by the system. It has been filtered out for noise spikes by the first weighting coefficient, retaining only the true load trend. The updated standard deviation of the sampled current is used to characterize the normal fluctuation range currently identified by the system. It has eliminated the interference of outliers on the standard deviation estimation and truly reflects the operating jitter characteristics of the equipment.

[0087] In a specific embodiment, step S430 is implemented by the following steps: the updated preset current range is: ,in, The average of the updated sampled current. This represents the standard deviation of the updated sampled current.

[0088] Thus, when the load naturally increases (such as when a motor completes a heavy-load start and enters a steady state), the average value of the updated sampled current rises accordingly, and the preset current range shifts upward, avoiding misjudging the new normal as an overload. Secondly, when the ambient noise is high or the load itself fluctuates drastically, the standard deviation of the updated sampled current automatically increases, the preset current range widens, encompassing normal fluctuations and eliminating false alarms; when the operation is stable, the standard deviation of the updated sampled current converges and decreases, the range narrows, keenly capturing minute anomalies and preventing missed alarms, thereby ensuring that the system is always in a state of optimal detection sensitivity.

[0089] The following is an illustration using a specific example.

[0090] Assuming the current (i.e., before the update) mean Current standard deviation The current preset current range is [(810-3×50)A,(810+3×50)A]=[660A,960A].

[0091] Assuming the peak value of the newly sampled current The current rating is 950A, which falls within the range of [660A, 960A]. This allows for updating the preset current range and the reference current sequence. The reference weighting coefficient is set... Set it to 0.1, and the preset coefficient threshold is set to 0.05. Then... Since the preset coefficient threshold is set to 0.05, the first weight coefficient Take 0.05.

[0092] Therefore, we can conclude that: .

[0093] At the same time, we can obtain: .

[0094] The updated preset current range is: [(817-3×57.50)A, (817+3×57.50)A]=[644.5A, 989.5A].

[0095] Assuming the original reference current sequence So is: So=[(0,0),(5,200),(10,400),(15,600),(20,800)], and the original real-time current sequence Ro is: Ro=[(0,15),(5,650),(10,850),(15,920),(20,950)], then the updated reference current sequence So_new[i] is as follows:

[0096] So_new[1]=(1-0.05)×0+0.05×15=0.75;So_new[2]=(1-0.05)×200+0.05×650=222.5;So_new[3]=(1-0.05)×400+0.05×850=422.5;So_new[4]=(1-0.05)×600+0.05×920=616;So_new[5]=(1-0.05)×800+0.05×950=807.5。 Therefore, the reference current sequence So_new is: So_new=[(0,0.75),(5,222.5),(10,422.5),(15,616),(20,807.5)]. As can be seen, the center of the interval has moved from 810A to 817A, and the interval itself has also been adjusted, indicating that the system can recognize that the current is increasing.

[0097] Please refer to Figure 5 , Figure 5 This is a schematic diagram of a controller provided in an embodiment of this application. The controller 500 can be a microcontroller unit (MCU) or a digital signal processing (DSP) controller, etc.

[0098] The controller 500 includes at least one processor 510 and a memory 520. The memory 520 can be built into the controller 500 or external to the controller 500. The memory 520 can also be a remotely configured memory connected to the controller 500 via a network.

[0099] Memory 520, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 520 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal, etc. Furthermore, memory 520 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 520 may optionally include memory remotely located relative to processor 510, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0100] The processor 510 performs various functions of the terminal and processes data by running or executing software programs and / or modules stored in the memory 520 and calling data stored in the memory 520, thereby performing overall monitoring of the terminal, such as implementing the overload determination method described in any embodiment of this application.

[0101] There can be one or more processors 510. Figure 5 The example provided uses a processor 510. The processor 510 and memory 520 can be connected via a bus or other means. The processor 510 may include a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a controller, a field-programmable gate array (FPGA) device, etc. The processor 510 can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0102] This application also provides a battery management system, which includes the controller 500 in any embodiment of this application.

[0103] This application also provides a battery, which includes a cell module and a battery management system as described in any embodiment of this application. The cell module includes at least one cell. When the cell module includes multiple cells, the cells are connected in parallel, series, or mixed connections, wherein mixed connections include both series and parallel connections. The cell module is used to store and provide electrical energy. The battery management system is electrically connected to the cell module and is used to determine whether an overload event has occurred in the cell module based on the current of the cell module.

[0104] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0105] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, and the steps can be implemented in any order. Those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. An overload determination method, characterized by, include: Based on the real-time current sequence and the reference current sequence, it is determined whether an overload event has occurred. The real-time current sequence includes multiple sampled data, each of which includes a current value and its corresponding sampling time. The reference current sequence includes multiple reference data, each of which includes a reference current value and its corresponding reference time. The time interval between two adjacent sampling times is equal to the time interval between two adjacent reference times. When an overload event is determined to have occurred, if the peak value of the sampled current is within a preset current range, the following steps are performed: Based on the peak value and mean value of the sampled current, determine the first weighting coefficient of the real-time current sequence when updating the reference current sequence; The reference current sequence is updated based on the sum of the first product and the second product, wherein the first product is the product of the first weighting coefficient and the real-time current sequence, the second product is the product of the second weighting coefficient and the reference current sequence, and the second weighting coefficient is the difference between 1 and the first weighting coefficient.

2. The method according to claim 1, characterized in that, The preset current interval is wherein, is the current mean, is the current standard deviation.

3. The method according to claim 1 or 2, characterized in that, When an overload event is determined to have occurred, if the peak value of the sampled current is within a preset current range, the following steps are also performed: The average value of the sampled current is updated based on the average value of the sampled current, the peak value of the sampled current, and the first weighting coefficient. The standard deviation of the sampled current is updated based on the mean of the sampled current, the standard deviation of the sampled current, the peak value of the sampled current, and the first weighting coefficient. The preset current range is updated based on the updated mean and standard deviation of the sampled current.

4. The method of claim 3, wherein, The step of updating the mean of the sampled current based on the mean of the sampled current, the peak value of the sampled current, and the first weighting coefficient includes: the updated mean of the sampled current is: wherein, is the updated mean of the sampled current, is the current mean, is the current standard deviation, is the peak of the sampled current, is the first weight coefficient.

5. The method according to claim 3, characterized in that, The step of updating the standard deviation of the sampled current based on the mean of the sampled current, the standard deviation of the sampled current, the peak value of the sampled current, and the first weighting coefficient includes: The updated standard deviation of the sampled current is: ,in, The standard deviation of the updated sampled current, This is the current average. The current standard deviation, The peak value of the sampled current. This is the first weighting coefficient.

6. The method according to claim 3, characterized in that, The step of updating the preset current range based on the updated mean and standard deviation of the sampled current includes: The updated preset current range is as follows: ,in, The average value of the updated sampled current. This is the standard deviation of the updated sampled current.

7. The method according to claim 1 or 2, characterized in that, The step of determining the first weighting coefficient of the real-time current sequence when updating the reference current sequence based on the peak value and mean value of the sampled current includes: The first weighting coefficient is determined using the following formula: ,in, The first weighting coefficient, The peak value of the sampled current. As the benchmark weighting coefficient, This is the current average.

8. The method according to claim 1, characterized in that, The step of determining whether an overload event has occurred based on the real-time current sequence and the reference current sequence includes: The real-time current sequence is normalized to obtain a target real-time current sequence, and the reference current sequence is normalized to obtain a target reference current sequence. Calculate the distance between the target real-time current sequence and the target reference current sequence; If the ratio of the distance to the sequence length is less than a preset distance threshold, an overload event is determined to have occurred, wherein the sequence length is the sum of the length of the target real-time current sequence and the length of the target reference current sequence.

9. The method according to claim 8, characterized in that, The calculation of the distance between the target real-time current sequence and the target reference current sequence includes: A local cost matrix is ​​generated based on the Euclidean distance between the sampled data in the target real-time current sequence and the corresponding reference data in the target reference current sequence; Based on the local cost matrix, determine the cumulative distance matrix; The distance is determined based on the cumulative distance matrix.

10. A controller, characterized in that, include: At least one processor and memory; The memory is coupled to the processor and is used to store instructions or programs that, when executed by the at least one processor, cause the at least one processor to perform the overload determination method as described in any one of claims 1-9.

11. A battery management system, characterized in that, Includes the controller as described in claim 10.

12. A battery, characterized in that, include: A battery cell module, comprising at least one battery cell; And, as described in claim 11, the battery management system is electrically connected to the cell module, and the battery management system is used to determine whether an overload event has occurred in the cell module based on the current of the cell module.

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