CN117382415A

CN117382415A - Management method for battery pack, electronic device and vehicle

Info

Publication number: CN117382415A
Application number: CN202210794764.7A
Authority: CN
Inventors: 孙景宝; 袁天执; 陈永健; 王南
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2022-07-05
Filing date: 2022-07-05
Publication date: 2024-01-12

Abstract

Embodiments of the present disclosure provide a management method for a battery pack, an electronic device, and a vehicle. The management method comprises the following steps: acquiring a first data set associated with a plurality of battery cells connected to each other, the first data set including electrical data of the plurality of battery cells in a predetermined period of time; selecting, from the first data set, electrical data for at least a portion of the predetermined time period based on states of charge of the plurality of battery cells for the predetermined time period to generate a second data set; determining at least one set of characteristic values of the plurality of battery cells based on the second data set, the at least one set of characteristic values comprising a set of characteristic values associated with internal resistances of the plurality of battery cells; and determining whether an abnormality exists in the plurality of battery cells based on the at least one set of characteristic values. The scheme of the disclosure can effectively improve the accuracy of battery fault early warning with lower cost.

Description

Management method for battery pack, electronic device and vehicle

Technical Field

Embodiments of the present disclosure relate to the field of battery management technology, and more particularly, to a management method for a battery pack, an electronic device, a vehicle including the electronic device, and a computer-readable medium.

Background

In order to mitigate the influence of fossil energy on climate and environment, new energy industries have been rapidly developed, and particularly, energy storage batteries such as lithium batteries are widely used in various fields. For example, energy storage power sources for new energy vehicles as well as wind power and photovoltaic power stations require the use of a large number of energy storage batteries. With the great use of energy storage batteries such as lithium batteries, fires caused by spontaneous combustion of the batteries frequently occur, which makes the safety problem of the energy storage batteries more and more interesting.

Currently, in order to secure the safety of the energy storage battery, a fault diagnosis function may be provided in the battery system. However, many fault diagnosis schemes can only find a fault after the fault has occurred, and thus, possible losses due to the fault cannot be avoided. Therefore, some fault diagnosis schemes propose early warning before the battery fails, so that effective measures can be taken to eliminate potential safety hazards before the fault occurs. However, the existing fault early warning scheme has the problems of low accuracy and poor practicability. Frequent false positives not only degrade the user experience and increase the cost of battery maintenance, but may also lead to missing real anomalies or malfunctions.

Disclosure of Invention

Based on the above-described problems, according to example embodiments of the present disclosure, there are provided a management method for a battery pack, an electronic device, a vehicle, and a computer-readable medium.

In a first aspect of the present disclosure, there is provided a management method for a battery pack, the management method comprising: acquiring a first data set associated with a plurality of battery cells connected to each other, the first data set including electrical data of the plurality of battery cells in a predetermined period of time; selecting, from the first data set, electrical data for at least a portion of the predetermined time period based on states of charge of the plurality of battery cells for the predetermined time period to generate a second data set; determining at least one set of characteristic values of the plurality of battery cells based on the second data set, the at least one set of characteristic values comprising a set of characteristic values associated with internal resistances of the plurality of battery cells; and determining whether an abnormality exists in the plurality of battery cells based on the at least one set of characteristic values.

In a second aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the device to perform actions comprising: acquiring a first data set associated with a plurality of battery cells connected to each other, the first data set including electrical data of the plurality of battery cells in a predetermined period of time; selecting, from the first data set, electrical data for at least a portion of the predetermined time period based on states of charge of the plurality of battery cells for the predetermined time period to generate a second data set; determining at least one set of characteristic values of the plurality of battery cells based on the second data set, the at least one set of characteristic values comprising a set of characteristic values associated with internal resistances of the plurality of battery cells; and determining whether an abnormality exists in the plurality of battery cells based on the at least one set of characteristic values.

In a third aspect of the present disclosure, there is provided a vehicle comprising: a battery pack including a plurality of battery cells connected to each other; and an electronic device according to the second aspect.

In a fourth aspect of the present disclosure, there is provided a computer readable medium having computer readable instructions stored thereon, which when executed by a processing unit, cause the processing unit to perform the management method according to the first aspect.

It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor is it intended to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:

FIG. 1 shows a schematic diagram of an example scenario in which embodiments of the present disclosure may be implemented.

Fig. 2 shows a schematic flow chart of a management method for a battery pack according to an embodiment of the present disclosure.

Fig. 3 shows a schematic flow chart of an example process of acquiring a first data set according to an embodiment of the disclosure.

Fig. 4 shows a schematic flow chart of an example process of generating a second data set according to an embodiment of the disclosure.

Fig. 5A shows a graph of open circuit voltage versus state of charge for a ternary lithium battery.

Fig. 5B shows a graph of the relationship between the internal resistance and the state of charge of a ternary lithium battery.

Fig. 6 shows a schematic flow chart of an example process of determining a set of feature values based on a second data set according to an embodiment of the disclosure.

Fig. 7 shows a schematic flow chart of an example process of determining a set of feature values based on a second data set according to an embodiment of the disclosure.

Fig. 8 shows a schematic flow chart of an example process of determining whether a plurality of battery cells are abnormal based on at least one set of characteristic values, according to an embodiment of the disclosure.

FIG. 9 shows a schematic block diagram of an example device that may be used to implement embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

In describing embodiments of the present disclosure, the term "comprising" and its like should be taken to be open-ended, i.e., including, but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment/implementation" or "this embodiment/implementation" should be understood as "at least one embodiment/implementation". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.

FIG. 1 illustrates a schematic diagram of an example scenario 1000 in which embodiments of the present disclosure may be implemented. It should be appreciated that the devices or apparatus shown in this example scenario 1000 are merely examples, and that devices or apparatus that may appear in different application scenarios will vary depending on the actual situation. The scope of the present disclosure is not limited in this respect.

As shown in fig. 1, a vehicle 100 is included in a scene 1000. As an example, the vehicle 100 may be an electric vehicle, a hybrid vehicle, or other type of vehicle provided with an energy storage battery. The vehicle 100 includes an energy storage device 110, and the energy storage device 110 includes a battery pack 111. The battery pack 111 may include a plurality of battery cells 1111 connected to each other. For example, a plurality of battery cells 1111 may be connected together in series and/or parallel. It is to be noted that the number of battery cells shown in fig. 1 is merely exemplary, and the battery pack 111 may include any number of battery cells 111 as needed. For example, the battery pack 111 for a vehicle may include hundreds or thousands of battery cells.

The vehicle 100 may also include a control platform 120 for implementing monitoring and management of the vehicle 100, as well as implementing other vehicle-related control functions. In some embodiments, the energy storage device 110 of the vehicle 100 may include a battery management system (Battery Management System, BMS) 112 for monitoring, managing, and maintaining the battery pack 111, such as measuring and collecting various physical and electrical real-time parameter data of the battery, battery state estimation, equalization management, thermal management, charge-discharge control, and the like. It is understood that although the BMS 112 is provided separately from the control platform 120 herein, the BMS 112 may be integrated into the control platform 120 as well, which may accomplish the objects of the present disclosure. Thus, the control platform 120 or the BMS 112 may acquire measurement or sampling data associated with the battery pack 111 of the vehicle 100 and its battery cells. The control platform 120 or the BMS 112 may store the data and analyze and diagnose the battery state based on the data in real time or at some appropriate time after collecting the data to determine whether an abnormal condition, which may cause a battery failure and a safety accident, exists in each of the battery cells 1111 in the battery pack 111, so that early warning can be performed before the battery failure occurs.

In some embodiments, scene 1000 may include cloud 200. For example, the cloud 200 may communicate with the vehicle 100 or its energy storage device 110 in a wired or wireless communication manner to interact data with the vehicle 100 or its energy storage device 110. In addition, the cloud 200 may also communicate with other vehicles or related devices through a wireless or wired communication network to implement data interaction. By virtue of the cloud 200, large amounts of measured or sampled data (e.g., long-term data of days or weeks) from the vehicle 100 or other vehicles may be stored at the cloud 200, which avoids the need for the vehicle itself to provide mass storage devices to store the data, reduces vehicle cost and space occupation, and may also avoid data discarding due to insufficient data storage capacity of the vehicle.

In some embodiments, the scenario 1000 may include the computing device 300, and the computing device 300 may be a remote computing platform external to the vehicle 100. The computing device 300 may obtain measurement or sampling data from the cloud 200 relating to the vehicle 100 or other vehicle and process the data to analyze and diagnose battery pack status in the vehicle 100 or other vehicle for battery fault pre-warning for these vehicles. However, it is understood that the control platform 120 or the BMS 112 of the vehicle 100 may also acquire data from the cloud 200 when needed, so that the control platform 120 or the BMS 112 of the vehicle 100 is directly utilized to process the data, thereby realizing the battery fault early warning by the calculation and control device of the vehicle 100 itself.

From the above, it is appreciated that in the scenario 1000, measurement or sampling data for the battery pack 111 may be stored in the storage device of the vehicle 100 and/or in the cloud 200, and may be acquired by the control platform 120 of the vehicle 100 and/or the BMS 112 and/or the computing device 300 external to the vehicle 100 for processing to enable fault pre-warning analysis for the battery pack 111.

Some conventional battery fault warning schemes propose to detect anomalies in the battery using cell voltage fluctuation anomalies in the battery pack. However, this approach may be affected by factors such as data quality, environment, and battery system non-linear characteristics. For example, the battery cell voltage changes drastically with the change of the State of Charge (SOC) when the SOC is high or low, and thus the battery cell itself, in which the voltage fluctuates significantly, does not necessarily have an abnormality. Further, when the SOC varies with time (e.g., gradually decreases upon discharge or gradually increases upon charge), the cell voltage itself varies with the SOC, so that there is necessarily a variation in the cell voltage over a certain period of time, and it is difficult to distinguish whether the voltage fluctuation exhibited by a certain cell with respect to other cells is caused by the SOC variation or the abnormality of the cell itself, taking into account the fact that the SOCs of the individual cells within the battery pack are not uniform with each other. These problems lead to a high false alarm rate for battery fault pre-warning.

Embodiments of the present disclosure propose an improved management method for a battery pack. By acquiring electrical data of the battery for a predetermined period of time and screening the data based on the state of charge, the influence of the SOC on the electrical data, particularly the internal resistance of the battery, can be eliminated or reduced, thereby improving the accuracy of fault early warning.

Fig. 2 shows a schematic flow chart of a management method 2000 for a battery pack according to an embodiment of the present disclosure. The method 2000 may be implemented in the scene 1000 and executed by the computing device 300 or by the control platform 120 or BMS 112 of the vehicle 100. It will be appreciated that the implementation of the method 2000 is not so limited, but may be implemented in other scenarios where management of a battery pack is desired, such as an energy storage power supply system of a photovoltaic power plant. For discussion purposes, the method 2000 will be described in connection with fig. 1 and is assumed to be performed by the computing device 300.

At block 2001, computing device 300 obtains a first data set associated with a plurality of battery cells connected to each other, the first data set including electrical data of the plurality of battery cells 1111 over a predetermined period of time. As an example, the electrical data included in the first data set may be electrical data, such as voltage and current, of each battery cell in the battery pack 111 during a predetermined period of time, which may be acquired by means of measurement or sampling. The electrical data may also include data obtained after a certain process, such as data of the battery charge during a predetermined period of time, which may be obtained by calculating measured or sampled current data.

At block 2002, the computing device 300 selects electrical data for at least a portion of a predetermined period of time from the first data set based on the states of charge of the plurality of battery cells 1111 for the predetermined period of time to generate a second data set. As an example, the state of charge SOC of each battery cell in the resistor pool 111 reflects the remaining amount of each battery cell, e.g., if the SOC is 100% or 1, it indicates that the battery cell is fully charged, and if the SOC is 0% or 0, it indicates that the battery cell is fully discharged. The SOC of each battery cell may be obtained, for example, by integrating the discharge current of the battery cell or by other suitable means. The change in SOC may have an effect on some characteristics of the battery cell, such as the internal resistance of the battery and the voltage fluctuation. For example, when the SOC is high or low, both the internal resistance and the voltage fluctuation of the battery cell may be very sensitive and exhibit nonlinear characteristics. Thus, the first data set may be screened according to the state of charge of the battery cells over the predetermined period of time and, for example, portions of the data having non-linear characteristics are removed.

At block 2003, the computing device 300 determines at least one set of characteristic values for the plurality of battery cells based on the second data set, the at least one set of characteristic values comprising a set of characteristic values associated with internal resistances of the plurality of battery cells 1111. As an example, each characteristic value in a set of characteristic values corresponds to one battery cell, and each characteristic value may reflect whether or not an abnormality occurs in a certain characteristic of the battery cell corresponding to the characteristic value, for example, whether or not a deviation occurs with respect to an average characteristic of the whole inside the battery pack. A set of characteristic values associated with the internal resistances of the plurality of battery cells 1111 may reflect whether the internal resistances of the battery cells are abnormal. For battery packs in applications such as vehicles, battery manufacturers typically screen the cells so that the internal resistance values of the cells within the same battery pack are close and have good uniformity. Since the initial internal resistances of the battery cells are close, the environment in which they are located is also close, and thus the uniformity of the resistances of the battery cells within the battery pack may be relatively good when the battery does not reach the retired aged degree. As the battery ages, when the battery cell experiences abnormal conditions such as micro-short circuit, leakage, etc., the internal resistance value of the battery cell will be affected, in other words, the internal resistance of the battery cell may reflect the mechanism or state change inside the battery to some extent. Therefore, it is possible to determine whether an abnormal cell in the battery pack has occurred with a relatively high accuracy by determining a set of characteristic values associated with the internal resistances of the plurality of cells in the resistor pack.

At block 2004, computing device 300 determines whether there is an anomaly for plurality of battery cells 1111 based on at least one set of characteristic values. As an example, computing device 300 may process at least one set of characteristics and determine whether an abnormal cell of plurality of cells 1111 exists, such that effective measures may be taken in time, such as replacement or maintenance of battery pack 111 to eliminate safety hazards in advance, before the battery fails or an accident (e.g., spontaneous ignition of the battery) occurs.

Fig. 3 shows a schematic flow diagram of an example process 3000 of acquiring a first data set. The process 3000 illustrated in fig. 3 may be implemented at block 2001 of fig. 2.

At block 3001, computing device 300 receives a sequence of sampled data associated with a plurality of battery cells 1111. As an example, the computing device 300 may receive a sequence of sampled data related to the battery pack 111 of the vehicle 100 from the cloud 200 or via the internet of vehicles. The sampled data sequence may be acquired by the BMS 112 or other measurement device of the vehicle 100 and sent to the cloud 200 or the internet of vehicles, and may be measured or sampled data for the vehicle battery over a relatively long period of time.

At block 3002, computing device 300 calculates a current average I based on current values in the sequence of sampled data having absolute values greater than a current threshold _avg Wherein the current threshold is greater than zero. As an example, the current threshold may be 0.5A, i.e., the computing device 300 may select current data greater than 0.5A or less than-0.5A in the sampled data sequence and calculate an average of the current values for each cell. The data of the current approaching zero may be data in the case where the battery pack is not charged or discharged, for example, the vehicle is in a standby scene, such data cannot be used to evaluate the state of the battery, and thus can be excluded. Thus, the calculated current average value I _avg The average charge-discharge current of the battery cells may be represented. It should be noted that, for the sake of brevity, it is assumed here that a plurality of battery cells within the battery pack are connected in series, and thus each battery cell 1111 has the same current average value as the battery pack 111 as a whole.

At block 3003, computing device 300 calculates a current average value I based on the calculated current average value I _avg And calculating the duration of the predetermined time period according to the specified change rate of the state of charge. Specifically, by determining the duration of the predetermined time period, the length of the sliding time window (i.e., the predetermined time period) corresponding to the first data set may be determined. This length of time will affect the choice of electrical data for analysis and diagnosis and thus the outcome of the battery fault pre-alarm analysis and diagnosis. For example, if the duration of the predetermined period is too short, the data amount of the first data set corresponding to the predetermined period is small and the characteristics such as the internal resistance and the voltage fluctuation are unstable, whereas if the duration of the predetermined period is too long, the SOC variation is large and the characteristics such as the voltage fluctuation are more normal fluctuations due to the SOC variation, at which time the state of the battery cell cannot be analyzed with the characteristics such as the voltage fluctuation. Therefore, consideration of electricity is required Pool capacity, occupancy, etc., to select an appropriate length of time for a predetermined period of time or sliding time window. As an example, the amount of change Δsoc of the state of charge SOC of the battery cell, for example, 1 to 5%, may be determined or specified to ensure that the SOC variation within a predetermined period of time or sliding time window is small without causing excessive voltage variation by the SOC variation. Based on the specified amount of change Δsoc and the current average calculated at block 3002, the duration of the predetermined time period or sliding time window may be calculated using the following equation:

where t1 is the duration of a predetermined period of time or sliding time window and Ca is the rated capacity of the battery pack 111 or the battery cell 1111.

At block 3004, computing device 300 pre-processes sample data in a sequence of sample data that is in a predetermined period of time to generate a first data set. In some embodiments, computing device 300 obtains a set of voltage values and a set of current values for plurality of battery cells 1111 over a predetermined period of time, calculates a set of current values for plurality of battery cells 1111 based on the set of current values, and generates the first data set based on the set of voltage values, the set of current values, and the set of power values.

As an example, the voltage and current data of each battery cell may be obtained from a sequence of sampled data according to a predetermined period of time or the duration t1 of the sliding time window. The voltages of the plurality of battery cells 1111 may be expressed asWhere x in the matrix is the number of samples in the predetermined period of time and y is the number of cells 1111 in the battery pack 110. For the sake of brevity and clarity, it is assumed herein that a plurality of battery cells 1111 are connected together in series, or that battery cells having a parallel relationship in the battery pack 111 are regarded as one battery cell, such that the battery pack is regarded as being formed of a plurality of battery cells connected in series. Thereby, in the battery packThe plurality of battery cells of (a) may have the same current magnitude at each sampling instant, and the currents of the plurality of battery cells may be represented as [ I ] ₁ I ₂ … I _x ]Where the subscript x denotes the number of samples. Then, based on the sampling data in the predetermined period of time, the accumulated electric quantity in the predetermined period of time can be calculated using the following equation (i.e., ampere-hour integral formula):

the calculated amounts of electricity of the plurality of battery cells may be expressed as [ Q ] ₁ Q ₂ … Q _x ]。

Fig. 4 shows a schematic flow diagram of an example process 4000 of generating a second data set, fig. 5A shows a graph of open circuit voltage versus state of charge SOC of a ternary lithium battery, and fig. 5B shows a graph of internal resistance versus state of charge SOC of a ternary lithium battery. The process 4000 illustrated in fig. 4 may be implemented at block 2002 of fig. 2.

At block 4001, the computing device 300 obtains a state of charge of each of a plurality of battery cells over a predetermined period of time.

At block 4002, the computing device 300 selects, from the first data set, electrical data for a period of time in which the state of charge, SOC, of the plurality of battery cells are all within a predetermined range to generate a second data set.

Specifically, as shown in fig. 5A and 5B, when the SOC is in a predetermined range (for example, the middle section of the SOC, that is, about 30 to 80%), the internal resistance of the battery tends to be stable, and the open circuit voltage is nearly linear with the SOC, and the voltage fluctuation caused when the SOC is changed is relatively small. However, when the SOC is high or low, the battery internal resistance and the open circuit voltage will exhibit nonlinearity and vary drastically with the SOC variation. That is, when the SOC of the battery pack such as a lithium battery is in the predetermined range or interval described above, battery characteristics such as the internal resistance of the battery and the voltage fluctuation may be in a substantially stable state. Therefore, in determining battery abnormality by analyzing battery characteristics (e.g., battery internal resistance and voltage fluctuation), selecting electrical data corresponding to the above-described SOC predetermined range or interval in the first data set will contribute to improvement of the accuracy of battery abnormality determination.

In some embodiments of the present disclosure, the predetermined range of SOC is a range from 35% to 75% of state of charge. In particular, for lithium batteries, battery characteristics such as internal resistance and voltage fluctuation are smoother in the SOC range of 30% to 80%. Meanwhile, considering that 5% of errors exist in estimation or calculation of the SOC, a 35% -75% SOC range is preferable.

In some embodiments, the computing device 300 selects the electrical data for at least a portion of the predetermined time period from the first data set based on additional conditions, the additional conditions including at least one of: the voltage value and the current value of the plurality of battery cells 1111 at the selected period of time are not empty; and the absolute value of the current value of the plurality of battery cells 1111 in the selected period is greater than zero. Specifically, in addition to selecting the electrical data for a period in which the SOC is within a predetermined range, the data may be further screened. For example, failure to collect data for a portion of the cells at a sampling time may result in the data for the portion of the cells at that sampling time being empty, at which point all electrical data at that sampling time may be removed from the first data set. In addition, the electric vehicle has an application scene of long standby, the battery current of the vehicle is small and stable, and the data is not suitable for analysis of battery abnormality. Thus, if the current at some of the sampling instants is zero or near zero, it is indicated that the vehicle and battery may be in a standby scenario, and thus the electrical data corresponding to these sampling instants may be discarded without the option of putting into the second data set.

The voltages in the second data set obtained after screening can be expressed, for example, asWherein p in the matrix is the number of samples after screening, so p<x. Electric in the second data setThe stream may be represented as [ I ] ₁ I ₂ … I _p ]And the charge can be expressed as Q ₁ Q ₂ … Q _p ]。

The computing device 300 may determine the validity of the second data set prior to further processing of the second data set. In some embodiments of the present disclosure, the computing device 300 may further determine a maximum amount of change in the amount of data of the second data set and/or the current value in the second data set, and if the amount of data is below the amount of data threshold and/or the maximum amount of change is below the amount of change threshold, the computing device 300 determines that the second data set is invalid and discards the second data set, and if the amount of data exceeds the amount of data threshold and/or the maximum amount of change exceeds the amount of change threshold, the computing device 300 determines that the second data set is valid.

For example, a current matrix [ I ] may be provided in the second data set ₁ I ₂ … I _p ]Find the maximum I _max And minimum value I _min And the amount of data num in the second data set may be counted, wherein the theoretical amount of data num_total=t1/Δt in the predetermined period t1, Δt is the sampling time interval. Further, a current variation threshold, for example, 10A, may be set. Thus, when the maximum variation I of the current value in the second data set _max -I _min Below the current variation threshold 10A, it may be determined that the battery current within the time period corresponding to the present second data set lacks a sufficient variation. Further, the data amount threshold may be set, for example, to 100, or to a certain proportion of the theoretical data amount num_total, for example, 70% of num_total. When the amount of data in the second data set is below the data amount threshold, it may be determined that the second data set obtained after filtering the first data set lacks sufficient valid data. The lack of sufficient valid data and insufficient current variation can lead to increased occasional errors, thereby affecting fault warning analysis and even leading to erroneous analysis results. Therefore, the second data set can be discarded when it is determined that the second data set has at least one of an insufficient amount of data and an insufficient amount of current variation, andreturning to block 2001 or block 3001, the data is retrieved.

Fig. 6 shows a schematic flow chart of an example process 6000 for determining a set of characteristic values associated with internal resistance of a battery based on a second data set. The process 6000 illustrated in fig. 6 may be implemented at block 2003 of fig. 2.

At block 6001, for each battery cell, the computing device 300 performs a linear fit based on the voltage value, the current value, and the electrical value of the respective battery cell in the second data set to obtain an internal resistance value of the respective battery cell.

As an example, the voltage matrix of the second data set may be derived fromIs selected from the voltage data V of the nth cell _n,1 V _n,1 … V _n,p ]Wherein n is more than or equal to 1 and y is more than or equal to y. In addition, in the case where a plurality of battery cells are connected in series, the current data and the charge data of the nth battery cell are [ I ] ₁ I ₂ … I _p ]And [ Q ] ₁ Q ₂ … Q _p ]. Then, a multidimensional linear fit was performed with voltage as a dependent variable and current and power as independent variables, and the fitting equation was as follows:

V _n ＝a*I+b*Q+c (3)

a, b and c in equation (3) are parameters of fitting, where parameter a is the internal resistance value r of the nth battery cell during the sliding time window or the predetermined period of time _n . Note that in equation (3), in addition to the current as an argument, the influence of the amount of electricity on the voltage is taken into consideration (fig. 5A exemplarily shows the relation of the amount of electricity or SOC to the voltage), and thus the amount of electricity is further introduced into the linear fitting, which improves the fitting accuracy and thus the accuracy of the obtained internal resistance value.

The above steps are repeated, and the internal resistance value of the other battery cell during the predetermined period of time or the sliding time window can be obtained. A set of internal resistance values of the plurality of battery cells 1111 (i.e., y battery cells) of the battery pack 111 may be expressed as [ r ] ₁ r ₂ … r _y ]。

In the conventional method of obtaining the internal resistance of the lithium battery, it is necessary to perform a hybrid power pulse characteristic (Hybrid Pulse Power Characteristic, HPPC) test on the battery in a laboratory environment and calculate the internal resistance of the lithium battery using the ratio of the voltage variation amount and the current variation amount at different periods when the current is varied. The conventional method can only be carried out in a laboratory environment, and requires high-acquisition-precision charge and discharge peripheral equipment, so that the cost is too high. In the improved scheme for online calculation of the internal resistance of the battery, the internal resistance of the battery can be accurately calculated by screening proper data segments and adopting a multiple regression analysis mode, the calculated amount of the internal resistance is reduced, and high-precision voltage signals and additional high-cost acquisition equipment are not needed.

At block 6002, for each battery cell, computing device 300 is based on a set of internal resistance values [ r ] for the plurality of battery cells ₁ r ₂ … r _y ]The internal resistance Z fraction of the corresponding battery cell is determined.

As an example, based on the internal resistances [ r ] of the plurality of battery cells 1111 ₁ r ₂ … r _y ]The standard deviation r_std and the average value r_avg of the internal resistance sequence may be calculated. Thereby, the internal resistance Z fraction Z of the nth battery cell _r，n The calculation can be made by the following equation:

the internal resistance Z fraction calculated by equation (4) is a dimensionless value and the SOC, temperature, and aging degree within the same battery pack 111 are not greatly different, so that the internal resistance Z fraction obtained by comparing the battery cells within the battery pack 111 with each other will be substantially independent of the SOC, temperature, and aging degree, and thus can be regarded as an internal resistance uniformity characteristic.

At block 6003, a set of internal resistance Z scores for the plurality of battery cells is determined as a set of characteristic values of the at least one set of characteristic values. Specifically, the calculation step of equation (4) is repeated for other battery cells, and the internal resistance Z-score of each battery cell in the battery pack 111 can be calculatedAnd counted to obtain a set of internal resistance Z scores (or referred to as an internal resistance Z score sequence or matrix) [ Z ] of the plurality of battery cells 1111 _r,1 Z _r,2 … Z _r,y ]And is taken as one of the at least one set of eigenvalues mentioned in block 2003.

In some embodiments of the present disclosure, at least one set of characteristic values in block 2003 may also include a set of characteristic values associated with the voltage fluctuation of the plurality of battery cells. Specifically, in addition to the characteristic value associated with the internal resistance of the battery, the characteristic value associated with the voltage fluctuation degrees of the plurality of battery cells may be further introduced, which may increase the accuracy of the battery fault early warning analysis.

Fig. 7 shows a schematic flow chart of an example process 7000 of determining a set of characteristic values associated with the voltage fluctuation degrees of the plurality of battery cells based on the second data set.

At block 7001, for each battery cell, the computing device 300 obtains a voltage entropy of the respective battery cell based on the voltage value of the respective battery cell in the second data set.

As an example, a voltage matrix in the second data setIn determining the maximum voltage V of the nth battery cell _max Minimum voltage V _min And the total data amount is N. According to maximum voltage V _max And a minimum voltage V _min Dividing the voltage data into m voltage intervals with equal width, wherein the width of the voltage interval is +.>Thereby, m voltage intervals (V _min ,V _min +ΔV]、(V _min +ΔV,V _min +2ΔV]、……、(V _min +(m-1)ΔV,V _max ]. Then, the number N of the nth battery cells in different voltage intervals can be calculated _i Statistics are performed, and the probability density distribution P of the nth battery cell in the m voltage intervals is calculated according to the following equation _i ：

In this way, the distribution frequency or the distribution probability of the voltage of the nth battery cell in different voltage intervals can be calculated. In dividing the voltage intervals, the number of intervals cannot be excessively large, and for example, m may be selected to be 5 to 10.

Based on the distribution probability of the nth battery cell in the m voltage intervals, the voltage entropy E of the nth battery cell may be calculated using the following equation similar to the information entropy calculation equation _n ：

Repeating the above steps may determine the voltage entropy of each cell in the battery pack 111 over a predetermined period of time, and obtain a set of voltage entropies (or voltage entropy sequence or matrix) of the plurality of cells of the battery pack 111 [ E ₁ E ₂ … E _y ]。

At block 7002, for each battery cell, the computing device 300 is based on a set of voltage entropies [ E ] for the plurality of battery cells ₁ E ₂ … E _y ]The voltage entropy Z fraction of the corresponding battery cell is determined.

As an example, voltage entropy [ E ] based on the plurality of battery cells 1111 ₁ E ₂ … E _y ]The standard deviation e_std and the average value e_avg of the voltage entropy sequence may be calculated. Thus, the voltage entropy Z of the nth battery cell is fractional Z _E,n The calculation can be made by the following equation:

at block 7003, a set of voltage entropy Z scores for a plurality of battery cells is determined as a set of characteristic values of at least one set of characteristic values.

SpecificallyThe calculation step of equation (7) is repeated for the other battery cells in the battery pack 111, and the voltage entropy Z-scores of all the battery cells in the battery pack 111 can be calculated, thereby obtaining a set of voltage entropy Z-scores (or referred to as a voltage entropy Z-score sequence or matrix) [ Z ] of the plurality of battery cells 1111 _E,1 Z _E,2 … Z _E,y ]And is taken as one of the at least one set of eigenvalues mentioned in block 2003.

It can be seen that the computing device 300 can determine a set of characteristic values associated with the internal resistance of the battery based at least on the second data set. Alternatively, the computing device 300 may determine a first set of characteristic values associated with the internal resistance of the battery and a second set of characteristic values associated with the degree of fluctuation of the battery voltage (e.g., voltage entropy) based on the second data set. It will be appreciated that embodiments of the present disclosure are not so limited, and that feature values of other characteristics may also be introduced to further improve the analysis and diagnostic process, depending on the actual needs of the battery fault pre-warning diagnosis.

Fig. 8 shows a schematic flow diagram of an example process 8000 for determining whether a plurality of battery cells are abnormal based on at least one set of characteristic values. The process 8000 illustrated in fig. 8 may be implemented at block 2004 of fig. 2.

At block 8001, computing device 300 utilizes an outlier algorithm to determine at least one eigenvalue threshold corresponding to at least one set of eigenvalues, respectively. As an example, the computing device 300 may determine the threshold TH1 of the battery internal resistance Z score using an outlier algorithm, such as a 3σ criterion or a fourth difference. Further, in the case where the voltage entropy of the battery cell is provided, a similar outlier algorithm may also be utilized to determine the threshold TH2 of the voltage entropy Z-score. Alternatively, TH1 and/or TH2 may also be determined by means of empirical values.

At block 8002, computing device 300 determines whether at least one set of feature values all have feature values that exceed a respective feature value threshold. For example, the internal resistance Z fractions [ Z ] of the plurality of battery cells are sequentially determined _r,1 Z _r,2 … Z _r,y ]Whether the internal resistance Z fraction of each battery cell exceeds a threshold TH1. If the internal resistance Z fraction of one or more battery cells exceeds a threshold TH1, a tableThe presence of an outlier cell in the battery pack 111 indicates that no outlier cell is present in the battery pack 111. In addition, in the case where the voltage entropy of the battery cells is provided, it is also possible to sequentially judge the voltage entropy Z fractions [ Z ] of the plurality of battery cells _E,1 Z _E,2 … Z _E,y ]Whether the voltage entropy Z fraction of each battery cell exceeds a threshold TH2. If the internal resistance Z fraction of one or more cells exceeds the threshold TH1 and the voltage entropy Z fraction exceeds TH2, it is indicative of an outlier of the battery pack 111, otherwise it is indicative of no outlier of the battery pack 111. Meanwhile, the battery units of the outlier are judged by utilizing the internal resistance Z fraction and the voltage entropy Z fraction, so that the interference and influence of the data quality difference on the outlier judgment can be reduced, and the judgment accuracy is further improved.

At block 8003, if at least one set of feature values each have a feature value that exceeds the corresponding feature value threshold, the computing device 300 increments a counter. As an example, the computing device 300 may provide an indicator speed_fault and a counter cumNum. When it is determined that there are outlier battery cells, the battery_fault may be set to 1, otherwise, the battery_fault is set to 0. Further, when the battery_fault=1, the counter cuminum=cuminum+battery_fault, and when the battery_fault=0, the counter cuminum=0. That is, when it is determined that there is an outlier cell of the battery pack 111 based on the current data, the counter cumNum may be incremented by one, and if there is no outlier cell, the counter cumNum may be cleared.

At block 8004, computing device 300 determines whether the count of the counter exceeds a count threshold. At block 8005, if the count of the counter exceeds the count threshold, the computing device 300 determines that an abnormality exists in the battery pack or plurality of battery cells. Specifically, the previous steps may be repeated to repeatedly determine whether an outlier cell exists based on the newly acquired data. Thus, when it is determined that the number of times that an outlier cell exists exceeds a certain count threshold TH3 (for example, 2 to 5), it is determined that an abnormality does exist in the battery pack 111 or a plurality of cells thereof. By the method, influence caused by accidental errors in the data can be reduced, and the accuracy of battery abnormality judgment is further improved.

The uniformity characteristics of the battery cells may include characteristics of SOC, voltage, and internal resistance, where SOC uniformity is merely indicative of non-uniformity of the battery cell charge, regardless of safety. Both the internal resistance of the battery and the voltage ripple characterized by the voltage entropy may reflect to some extent the mechanism or state change inside the battery. However, both the internal resistance of the battery and the voltage entropy are affected by the SOC, for example, a nonlinear change is exhibited when the SOC is high or low, and thus a high false alarm rate may occur if the battery abnormality is analyzed and diagnosed simply based on the internal resistance (and the voltage entropy) of the battery. In the embodiments of the present disclosure, the data is filtered based on the SOC, and the state of the internal resistance (and voltage entropy) of the battery is determined based on the filtered data, so that the diagnosis of battery abnormality can be achieved with higher accuracy. In addition, the internal resistance of the battery is calculated by adopting an improved algorithm, so that the internal resistance calculation accuracy can be improved, and the calculated amount and the overall cost are reduced.

FIG. 9 shows a schematic block diagram of an example device 9000 that may be used to implement embodiments of the present disclosure. The device 9000 may be implemented as the computing device 300 of fig. 1, or the control platform 120 or BMS 112 of the vehicle 100. The apparatus 9000 may be used to implement the methods of figures 2-4 and 6-8.

As shown, the device 9000 includes a Central Processing Unit (CPU) 9001 that can perform various suitable actions and processes according to computer program instructions stored in a Read Only Memory (ROM) 9002 or computer program instructions loaded from a storage unit 9008 into a Random Access Memory (RAM) 9003. In the RAM 9003, various programs and data required for the operation of the device 9000 can also be stored, for example, the above-mentioned measurement data can be stored. The CPU 9001, ROM 9002, and RAM 9003 are connected to each other by a bus 9004. An input/output (I/O) interface 9005 is also connected to bus 9004.

Various components in the device 9000 are connected to an I/O interface 9005, including: an input unit 9006 such as a keyboard, a mouse, or the like; an output unit 9007 such as various types of displays, speakers, and the like; a storage unit 9008 such as a magnetic disk, an optical disk, or the like; and a communication unit 9009 such as a network card, modem, wireless communication transceiver, or the like. The communication unit 9009 allows the device 9000 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processing unit 9001 performs the methods or processes described above, such as method 2000. For example, in some embodiments, the method 2000 may be implemented as a computer software program or a computer program product tangibly embodied on a machine-readable medium, such as a non-transitory computer-readable medium, such as the storage unit 9008. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 9000 via the ROM 9002 and/or communication unit 9009. When the computer program is loaded into RAM 9003 and executed by CPU 9001, one or more steps of method 2000 described above may be performed. Alternatively, in other embodiments, the CPU 9001 may be configured to perform the method 2000 by any other suitable means (e.g., by means of firmware).

It will be appreciated by those skilled in the art that the various steps of the methods of the present disclosure described above may be implemented by general purpose computing devices, they may be concentrated on a single computing device, or distributed across a network of computing devices, or alternatively, they may be implemented in program code executable by computing devices, such that they may be stored in storage devices for execution by computing devices, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. As such, the present disclosure is not limited to any specific combination of hardware and software.

It should be understood that while several devices or sub-devices of the apparatus are mentioned in the detailed description above, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the devices described above may be embodied in one device in accordance with embodiments of the present disclosure. Conversely, the features and functions of one device described above may be further divided into multiple devices to be embodied.

The foregoing is merely an alternative embodiment of the present disclosure, and is not intended to limit the present disclosure, and various modifications and variations will be apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. that fall within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A management method for a battery pack, comprising:

acquiring a first data set associated with a plurality of battery cells connected to each other, the first data set including electrical data of the plurality of battery cells in a predetermined period of time;

selecting electrical data for at least a portion of the predetermined time period from the first data set based on the state of charge of the plurality of battery cells for the predetermined time period to generate a second data set;

determining at least one set of characteristic values of the plurality of battery cells based on the second data set, the at least one set of characteristic values comprising a set of characteristic values associated with internal resistances of the plurality of battery cells; and

based on the at least one set of characteristic values, it is determined whether an abnormality exists in the plurality of battery cells.

2. The management method of claim 1, wherein determining at least one set of characteristic values for the plurality of battery cells based on the second data set comprises:

for each of the battery cells,

performing a linear fit based on the voltage, current and current values of the respective battery cells in the second data set to obtain internal resistance values of the respective battery cells, an

Determining an internal resistance Z-score for the respective battery cell based on a set of internal resistance values for the plurality of battery cells; and

a set of internal resistance Z scores for the plurality of battery cells is determined as a set of characteristic values of the at least one set of characteristic values.

3. The management method of claim 2, wherein the at least one set of characteristic values further comprises a set of characteristic values associated with voltage fluctuation degrees of the plurality of battery cells, and

wherein determining at least one set of characteristic values for the plurality of battery cells based on the second data set further comprises:

for each of the battery cells,

acquiring voltage entropy of the corresponding battery unit based on the voltage value of the corresponding battery unit in the second data set, and

determining a voltage entropy Z-score for the respective battery cell based on a set of voltage entropies for the plurality of battery cells; and

a set of voltage entropy Z scores for the plurality of battery cells is determined as a set of characteristic values of the at least one set of characteristic values.

4. The management method of claim 1, wherein selecting electrical data for at least a portion of the predetermined time period from the first data set based on the state of charge of the plurality of battery cells for the predetermined time period comprises:

Acquiring the charge states of each battery unit in the plurality of battery units in the preset time period; and

electrical data for a period of time for which the states of charge of the plurality of battery cells are all within a predetermined range is selected from the first data set to generate a second data set.

5. The management method according to claim 4, wherein the predetermined range is a range from 35% to 75% of the state of charge.

6. The management method of claim 4, wherein selecting electrical data for at least a portion of the predetermined time period from the first data set based on the state of charge of the plurality of battery cells for the predetermined time period further comprises:

selecting electrical data for the at least a portion of the predetermined time period from the first data set based on additional conditions, the additional conditions including at least one of:

the voltage and current values of the plurality of battery cells at the selected time period are not empty; and

the absolute value of the current values of the plurality of battery cells over the selected period of time is greater than zero.

7. The management method of claim 1, further comprising, prior to determining at least one set of characteristic values for the plurality of battery cells based on the second data set:

Determining the data amount of the second data set and/or the maximum variation of the current value in the second data set;

determining that the second data set is invalid and discarding the second data set if the amount of data is below a data amount threshold and/or the maximum amount of change is below a change amount threshold; and

and if the data amount exceeds a data amount threshold and/or the maximum variation exceeds a variation threshold, determining that the second data set is valid.

8. The management method of claim 1, wherein obtaining a first set of data associated with a plurality of battery cells connected to each other comprises:

receiving a sequence of sampled data associated with the plurality of battery cells;

calculating a current average based on current values in the sampled data sequence having absolute values greater than a current threshold, wherein the current threshold is greater than zero;

calculating a duration of the predetermined period of time based on the calculated current average value and a specified rate of change of state of charge; and

preprocessing sample data in the sequence of sample data that is in the predetermined time period to generate the first data set.

9. The management method of claim 8, wherein preprocessing sample data in the sequence of sample data that is in the predetermined time period to generate the first data set comprises:

Acquiring a set of voltage values of the plurality of battery cells in the predetermined period of time;

acquiring a set of current values of the plurality of battery cells in the predetermined period of time;

calculating a set of electrical values for the plurality of battery cells based on the set of electrical current values; and

the first data set is generated based on the set of voltage values, the set of current values, and the set of charge values.

10. The management method of claim 1, wherein determining whether an abnormality exists for the plurality of battery cells based on the at least one set of eigenvalues comprises:

determining at least one eigenvalue threshold value corresponding to the at least one set of eigenvalues respectively using an outlier algorithm;

if the at least one group of characteristic values have characteristic values exceeding the corresponding characteristic value threshold value, incrementing a counter; and

and if the count of the counter exceeds a count threshold, determining that the plurality of battery units are abnormal.

11. An electronic device, comprising:

a processor; and

a memory coupled with the processor, the memory having instructions stored therein, which when executed by the processor, cause the device to perform actions comprising:

12. The electronic device of claim 11, wherein determining at least one set of characteristic values for the plurality of battery cells based on the second data set comprises:

for each of the battery cells,

13. The electronic device of claim 12, wherein the at least one set of characteristic values further comprises a set of characteristic values associated with voltage fluctuation of the plurality of battery cells, and

for each of the battery cells,

14. The electronic device of claim 11, wherein selecting electrical data for at least a portion of the predetermined time period from the first data set based on the state of charge of the plurality of battery cells for the predetermined time period comprises:

15. The electronic device of claim 14, wherein the predetermined range is a range from 35% to 75% of the state of charge.

16. The electronic device of claim 14, wherein selecting electrical data for at least a portion of the predetermined time period from the first data set based on the state of charge of the plurality of battery cells for the predetermined time period further comprises:

17. The electronic device of claim 11, prior to determining at least one set of characteristic values for the plurality of battery cells based on the second data set, further comprising:

18. The electronic device of claim 11, wherein obtaining a first set of data associated with a plurality of battery cells connected to each other comprises:

19. The electronic device of claim 18, wherein preprocessing sample data in the sequence of sample data that is in the predetermined time period to generate the first data set comprises:

determining a set of electrical values for the plurality of battery cells based on the set of electrical current values; and

20. The electronic device of claim 11, wherein determining whether an anomaly exists for the plurality of battery cells based on the at least one set of eigenvalues comprises:

21. A vehicle, comprising:

a battery pack including a plurality of battery cells connected to each other; and

the electronic device of any of claims 11-20.

22. A computer readable medium having computer readable instructions stored thereon, which when executed by a processing unit, cause the processing unit to perform the management method according to any of claims 1-10.