Internal short-circuit prediction generation

Abstract

Techniques for generating an internal short-circuit prediction of a battery cell are provided. In one embodiment, the techniques involve receiving characteristic measurements for a plurality of battery cells, determining a snapshot moving average for the characteristic measurements for each of the plurality of battery cells based on a corresponding measurement window, determining a health indicator for each of the plurality of battery cells based on the corresponding snapshot moving average, and determining a health indicator for each of the plurality of battery cells based on the determined health indicator. The plurality of health indicators are ranked by amplitude, determining a short-circuit threshold based on a second ranked health indicator, and upon determining that a first ranked health indicator exceeds the short-circuit threshold, determining that the first ranked health indicator is greater than the short-circuit threshold, and if the first ranked health indicator is greater than the short-circuit threshold, determining that the first ranked health indicator is greater than the short-circuit threshold. A prediction of a short circuit in the battery cell corresponding to the first graded health indicator is generated.

Description

Technical Field

[0001] The present disclosure relates to battery cell prognostics, and more particularly, to predicting an internal short circuit of a battery cell. Background Art

[0002] Electric vehicles or hybrid electric vehicles use battery packs to power motors and auxiliary electrical systems. Battery packs include multiple interconnected battery cells that can develop internal short circuits. Internal short circuits can bypass or reduce the designed resistance of the battery and cause a sudden increase in current through the short circuit electrical path, which can cause performance degradation or battery failure. Summary of the invention

[0003] In one exemplary embodiment, a method for predicting a circuit condition of an electrical system of a vehicle is provided. The method includes receiving characteristic measurements of a plurality of battery cells of the electrical system, determining a snapshot moving average of the characteristic measurements of each of the plurality of battery cells based on a corresponding measurement window, determining a health indicator of each of the plurality of battery cells based on the corresponding snapshot moving average, grading a plurality of health indicators of the corresponding plurality of battery cells, wherein the plurality of health indicators are graded by magnitude, determining a short circuit threshold based on the health indicators of a second grade, and generating a prediction of a short circuit in the battery cell corresponding to the health indicator of the first grade when determining that the health indicator of the first grade exceeds the short circuit threshold.

[0004] In addition to one or more characteristics described herein, the characteristic measurement includes voltage, current, temperature, pressure measurement, or gas emission value; and wherein the health status indicator includes voltage drop, estimated internal short circuit resistance, solid electrolyte interface thickness, state of charge, battery cell capacity, or lithium plating index.

[0005] In addition to one or more features described herein, a size of the measurement window is based on an expected short circuit resistance of the plurality of battery cells.

[0006] In addition to one or more features described herein, the size of the measurement window is constant, wherein data for the measurement window is input into a parallel interleaved data buffer, and wherein the data buffer is processed at intervals based on expected short circuit resistances of the plurality of battery cells.

[0007] In addition to one or more features described herein, characteristic measurements are determined during relaxation periods of the plurality of cells.

[0008] In addition to one or more of the features described herein, a characteristic measurement is determined from a cell-balanced battery cell, and wherein the characteristic measurement is restored to a non-cell-balanced value via a voltage-to-state-of-charge conversion

[0009] In addition to one or more of the features described herein, the multiple health indicators are graded in ascending or descending order, wherein the health indicators of a first grade indicate a potential short circuit of a battery cell corresponding to the health indicator of the first grade, and wherein the health indicators of a second grade indicate the absence of a short circuit of a battery cell corresponding to the health indicator of the second grade.

[0010] In another exemplary embodiment, a system for predicting a circuit condition of an electrical system of a vehicle is provided. The system includes a processor, and a memory or storage device including an algorithm or computer instructions that, when executed by the processor, performs operations including receiving characteristic measurements of a plurality of battery cells of the electrical system, determining a snapshot moving average of the characteristic measurements of each of the plurality of battery cells based on a corresponding measurement window, determining a health indicator for each of the plurality of battery cells based on the corresponding snapshot moving average, grading a plurality of health indicators of the corresponding plurality of battery cells, wherein the plurality of health indicators are graded by magnitude, determining a short circuit threshold based on a second graded health indicator, and upon determining that a first graded health indicator exceeds the short circuit threshold, generating a prediction of a short circuit in the battery cell corresponding to the first graded health indicator.

[0011] In addition to one or more characteristics described herein, the characteristic measurement includes voltage, current, temperature, pressure measurement, or gas emission value; and wherein the health status indicator includes voltage drop, estimated internal short circuit resistance, solid electrolyte interface thickness, state of charge, battery cell capacity, or lithium plating index.

[0012] In addition to one or more features described herein, a size of the measurement window is based on an expected short circuit resistance of the plurality of battery cells.

[0013] In addition to one or more features described herein, the size of the measurement window is constant, wherein data for the measurement window is input into a parallel interleaved data buffer, and wherein the data buffer is processed at intervals based on expected short circuit resistances of the plurality of battery cells.

[0014] In addition to one or more features described herein, characteristic measurements are determined during relaxation periods of the plurality of battery cells.

[0015] In addition to one or more of the features described herein, a characteristic measurement is determined from a cell-balanced battery cell, and wherein the characteristic measurement is restored to a non-cell-balanced value via a voltage-to-state-of-charge conversion.

[0016] In addition to one or more of the features described herein, the multiple health indicators are graded in ascending or descending order, wherein the health indicators of a first grade indicate a potential short circuit of a battery cell corresponding to the health indicator of the first grade, and wherein the health indicators of a second grade indicate the absence of a short circuit of a battery cell corresponding to the health indicator of the second grade.

[0017] In yet another exemplary embodiment, a computer readable storage medium is provided having computer readable program code embodied therewith to predict a circuit condition of an electrical system of a vehicle. The computer readable program code is executable by one or more computer processors to perform operations including receiving characteristic measurements of a plurality of battery cells of the electrical system, determining a snapshot moving average of the characteristic measurements of each of the plurality of battery cells based on a corresponding measurement window, determining a health indicator of each of the plurality of battery cells based on the corresponding snapshot moving average, grading a plurality of health indicators of the corresponding plurality of battery cells, wherein the plurality of health indicators are graded by magnitude, determining a short circuit threshold based on a second graded health indicator, and upon determining that a first graded health indicator exceeds the short circuit threshold, generating a prediction of a short circuit in the battery cell corresponding to the first graded health indicator.

[0018] In addition to one or more characteristics described herein, the characteristic measurements include voltage, current, temperature, pressure measurements, or gas emission values; and wherein the health status indicator includes voltage drop, estimated internal short circuit resistance, solid electrolyte interface thickness, state of charge, battery cell capacity, or lithium plating index.

[0019] In addition to one or more features described herein, a size of the measurement window is based on an expected short circuit resistance of the plurality of battery cells.

[0020] In addition to one or more features described herein, the size of the measurement window is constant, wherein data for the measurement window is input into a parallel interleaved data buffer, and wherein the data buffer is processed at intervals based on expected short circuit resistances of the plurality of battery cells.

[0021] In addition to one or more of the features described herein, a characteristic measurement is determined from a cell-balanced battery cell, and wherein the characteristic measurement is restored to a non-cell-balanced value via a voltage-to-state-of-charge conversion.

[0022] In addition to one or more of the features described herein, the multiple health indicators are graded in ascending or descending order, wherein the health indicators of a first grade indicate a potential short circuit of a battery cell corresponding to the health indicator of the first grade, and wherein the health indicators of a second grade indicate the absence of a short circuit of a battery cell corresponding to the health indicator of the second grade.

[0023] The above features and advantages and other features and advantages of the present disclosure are apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Other features, advantages and details appear, by way of example only, from the following detailed description, which refers to the accompanying drawings, in which:

[0025] Figure 1 A vehicle according to an embodiment is shown;

[0026] Figure 2 shows a computing environment according to an embodiment;

[0027] Figure 3 A flowchart illustrating a method of determining a health indicator according to an embodiment; and

[0028] Figure 4 A flow chart of a method for predicting an internal short circuit based on a health indicator according to an embodiment is illustrated. DETAILED DESCRIPTION

[0029] The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application or use. It should be understood that throughout the drawings, corresponding reference numerals represent identical or corresponding components and features. As used herein, the term "module" may refer to one or more algorithms, instruction sets, software applications or other computer-readable program codes that can be executed by a processor to perform the functions, operations or processes described herein.

[0030] Embodiments of the present invention improve battery systems by predicting internal short circuits in battery cells and taking preventive actions to mitigate potential damage caused by internal short circuits. In one embodiment, the prediction module uses a snapshot of the moving average of characteristic measurements of the battery cell (e.g., voltage, current, temperature, pressure measurements, gas emission values, etc.) to determine the health indicators of the battery cell (e.g., voltage drop, estimated internal short circuit resistance, solid electrolyte interface (SEI) thickness, state of charge (SOC), battery cell capacity, etc.). The prediction module can grade the health indicators and determine the short circuit threshold based on the grading. When the health indicator of the battery cell exceeds the short circuit threshold, the prediction module can predict the presence or development of a short circuit in the battery cell. The prediction module can then notify the vehicle user of the short circuit, or control the operation of the vehicle to mitigate the impact of the short circuit on the battery cell.

[0031] One benefit of the disclosed embodiments is an improved signal-to-noise ratio for determining health indicators for battery cells in electrically noisy environments, which can help diagnose or predict problems with the battery cells. In addition, embodiments of the present disclosure can prevent or mitigate the effects of using shorted battery cells, which can cause performance degradation or failure of the battery cells.

[0032] Figure 1 A vehicle 100 is shown according to an embodiment. The vehicle 100 includes a body 102 that may support a power system 104, a sensor system 106, a propulsion system 120, a controller 140, and other systems of the vehicle 100 described herein.

[0033] In one embodiment, the vehicle 100 is an internal combustion engine (ICE) vehicle, an electric vehicle (EV), or a hybrid electric vehicle (HEV). In the illustrated embodiment, the vehicle 100 is a HEV that is partially powered by a power system 104, which includes a plurality of interconnected battery cells. The power system 104 can be electrically coupled to at least one motor assembly of a propulsion system 120. In one embodiment, the power system 104 is electrically coupled to a direct current (DC) converter module 108 (e.g., a DC-DC converter) and an inverter module 110 (e.g., a traction power inverter unit). The inverter module 110 may include a plurality of inverters that convert a DC signal from the power system 104 into a three-phase alternating current (AC) signal to drive the motor of the propulsion system 120. The power system 104 may also be electrically coupled to vehicle electronic systems, such as an audio system, a display system, a navigation system, a temperature control system, and the like.

[0034] The sensor system 106 includes various sensors disposed on or integrated with various components of the vehicle 100. In one embodiment, the sensor system 106 is communicatively coupled to the power system and the controller 140 to transmit measurements of the power system 104 to the controller 140. The sensor system 106 may include current sensors, voltage sensors, temperature sensors, etc.

[0035] The propulsion system 120 may include an ICE system 122 and at least one motor assembly (e.g., a first motor 124 and a second motor 126). Each component of the propulsion system 120 may be configured to drive at least one wheel 130 of the vehicle 100 via a drivetrain coupled to a front axle or a rear axle coupled to a respective front set of wheels 130 and a rear set of wheels 130.

[0036] In one embodiment, controller 140 is configured to analyze the output of power system 104 and generate a prediction of an internal short circuit of power system 104. Figure 2 Controllers are discussed in more detail in . Figure 3 Techniques for analyzing the output of power system 104 are discussed in Figure 4 Techniques for generating short-circuit predictions are discussed in .

[0037] Figure 2 A computing environment 200 is shown according to an embodiment. In the illustrated embodiment, the computing environment 200 includes a controller 140, a network 230, and a unit 240.

[0038] In one embodiment, the controller 140 includes a processor 202, which obtains instructions and data from a memory 204 or a storage device 208 via a bus 222. Not all components of the controller 140 are shown. The controller 140 is typically under the control of an operating system (OS) suitable for performing or supporting the functions or processes disclosed herein. The processor 202 is a programmable logic device that performs instructions, logic, and mathematical processing, and may represent one or more CPUs. The processor may execute one or more algorithms, instruction sets, or applications in the memory 204 or the storage device 208 to perform the functions or processes described herein.

[0039] The memory 204 and storage 208 may represent hard disk drives, solid state drives, flash memory devices, optical media, etc. The storage 208 may also include structured storage (e.g., a database). In addition, the memory 204 and storage 208 may be considered to include storage physically located elsewhere. For example, the memory 204 and storage 208 may be physically located on another computer that is communicatively coupled to the controller 140 via the bus 222 or the network 230.

[0040] The controller 140 may be connected to other computers (e.g., controllers, distributed databases, servers, or network hosts) or units 240 via a network interface 220 and a network 230. Examples of a network 230 include a controller area network (CAN), a transmission control protocol (TCP) bus, an electrical bus, a physical transmission cable, an optical transmission fiber, a wireless transmission medium, a router, a firewall, a switch, a gateway computer, an edge server, a local area network, a wide area network, a wireless network, etc. The network interface 220 may be any type of network communication device that allows the controller 140 to communicate with computers and other components of the computing environment 200 via the network 230.

[0041] In the illustrated embodiment, the memory 204 includes a prediction module 206. The prediction module 206 represents one or more algorithms, sets of instructions, software applications, or other computer-readable program codes that may be executed by the processor 202 to perform the functions, operations, or processes described herein.

[0042] In one embodiment, the prediction module 206 receives characteristic measurements of the power system 104 from the sensor system 106 coupled to the power system 104. Examples of characteristic measurements include voltage, current, temperature, resistance, pressure, gas emission values, etc. The prediction module 206 can store the characteristic measurements as battery cell data 212 in the storage device 208.

[0043] In the illustrated embodiment, battery cell 240 is one of the battery cells included in power system 104. The circuit configuration depicted in battery cell 240 represents one example of a battery cell equivalent circuit model common to the cells of power system 104. In the illustrated embodiment, battery cell 240 includes a voltage source 242 connected to a first terminal 244 of a first internal resistor 246. In one embodiment, voltage source 242 provides an open circuit voltage (V OC ). A second end 248 of the first internal resistor 246 is connected to a measurement point 250, a load (not shown), and a first connection point 252 of a short-circuit resistor 254. A second connection point 256 of the short-circuit resistor is connected to the voltage source 242 and the load.

[0044] The sensor system can use the measurement point 250 as one connection to measure a characteristic (e.g., a voltage drop or resistance) corresponding to the short-circuit resistor 254. In one embodiment, the short circuit created in the battery cell 240 reduces the resistance of the short-circuit resistor 254. When the resistance of the short-circuit resistor 254 approaches 0 ohms, more excess current can flow in the circuit path including the short-circuit resistor 254.

[0045] In one embodiment, the storage device 208 includes V OC 2SOC data 210, such as open circuit voltage (V OC) to a state of charge (SOC) table, chart, or graph, which can be used to convert the voltage or current at the measurement point 250 to an SOC value, or vice versa. The prediction module 206 can use characteristic measurements or V OC 2SOC data 210 to determine a health indicator of the power system 104, and predict an internal short circuit based on the health indicator. Figure 3 and Figure 4 This process is discussed further in .

[0046] Figure 3 A flow chart of a method 300 of determining health indicators according to an embodiment is illustrated. The method 300 begins at block 302 .

[0047] At block 304, the prediction module 206 receives characteristic measurements of the plurality of battery cells. As previously described, the sensor system 106 may be coupled to the power system 104 to measure characteristics of the battery cells of the power system 104. Examples of characteristic measurements include: voltage, current, temperature, pressure, gas emission values, etc. In one embodiment, the sensor system 106 measures the characteristics at the measurement point 250.

[0048] At block 306, the prediction module 206 determines a snapshot moving average of the characteristic measurements of each of the plurality of battery cells based on the corresponding measurement window. In one embodiment, the measurements of the sensor system 106 include electrical noise that can affect the measured output signal at different times. For example, a voltage signal may have an average measured value of 5V, but sampling the signal at a first time (e.g., at 0.01 seconds) may produce a measured value of 4.9V, while sampling the signal at a second time (e.g., at 0.02 seconds) may produce a measured value of 5.1V.

[0049] Therefore, in order to accurately determine the characteristic measurement value of the battery cell, the prediction module 206 can take a moving average of the characteristic measurement value over the snapshot duration (i.e., the measurement window), which can be centered at a periodic time interval. For example, assuming that the characteristic measurement is the voltage output of the cell 240 (denoted as V(i), where i represents the iteration count corresponding to one time interval), and assuming that the time interval is 5 seconds, and assuming that the measurement window size spans 1 second, then after the first 5 seconds of time elapses in the signal output, the prediction module 206 determines V(1) as the moving average of V(i) from 4.5 seconds to 5.5 seconds. At the next time interval, the prediction module 206 determines V(2) as the moving average of V(i) from 9.5 seconds to 10.5 seconds, and so on.

[0050] In one embodiment, the prediction module 206 determines the size of the measurement window to be applied to the feature measurement based on the expected short circuit resistance of the short circuit resistor 254. As previously described, as the resistance of the short circuit resistor 254 approaches 0 ohms, more excess current may flow in the circuit path including the short circuit resistor 254. Therefore, as the resistance of the short circuit resistor 254 decreases, the prediction module 206 may use a smaller measurement window duration to more quickly identify the presence or development of a short circuit.

[0051] In another embodiment, the prediction module 206 is configured to use a predetermined measurement window size according to the following calibration guidelines.

[0052]

[0053] In yet another embodiment, the measurement window size is kept constant and the characteristic measurements covered by the measurement window are input into a parallel interleaved data buffer. In this way, the prediction module 206 can identify low short circuit resistance values ​​even with a large measurement window size (e.g., 600 seconds) as long as the buffer is interleaved at time intervals corresponding to the expected range of measurement window sizes for short circuit resistance (e.g., 0-200 seconds for Rs<10 ohms).

[0054] At block 308, the prediction module 206 determines a health indicator for each of the plurality of battery cells based on the corresponding snapshot moving average. Examples of health indicators include voltage drop, estimated internal short circuit resistance, solid electrolyte interface (SEI) thickness, state of charge, battery cell capacity, lithium plating index, etc.

[0055] In one embodiment, the prediction module 206 determines the health indicator as the difference between two snapshot moving averages of consecutive time intervals. For example, continuing the example at block 306, the voltage drop health indicator may be determined as V(2)-V(1).

[0056] In one embodiment, when the state of charge (SOC) of the battery cell 240 reaches 35%-45%, it may be difficult to detect changes in the characteristic measurements. Therefore, the prediction module 206 can determine the difference in the health indicator by first stopping the external charging or discharging of the battery cell 240. In the absence of external charging or discharging, the battery cell 240 enters a relaxation period, in which the chemical reactions, ion migration, polarization effects, etc. of the battery cell 240 cause transient effects until the battery cell 240 reaches equilibrium. During the relaxation period, the prediction module 206 can use the ongoing characteristic measurements to determine the health indicator. The relaxation time of the battery cell can also be used as a health status indicator to predict a short circuit of the battery cell 240.

[0057] In one embodiment, the prediction module 206 may use VOC 2SOC data 210 to determine the health indicator. For example, after determining the snapshot moving average of the characteristic measurement, the prediction module 206 can use V(1) or V(2) as an index from V OC 2SOC table, chart or graph identifying the SOC of the battery cell 240.

[0058] In one embodiment, when the power system 104 is subjected to cell balancing, the prediction module 206 determines an unbalanced characteristic measurement to determine a health indicator. The prediction module 206 can restore the balanced characteristic measurement to an unbalanced characteristic measurement by bypassing the resistance of the balancing cell to eliminate the effect of the current discharge on the characteristic measurement (e.g., voltage or SOC) of the balancing cell. The prediction module 206 can then measure the unbalanced characteristic via the sensor system 106. Thereafter, the prediction module 206 can use V OC The 2SOC conversion compares the non-balanced characteristics with the cell balanced characteristics.

[0059] For example, after discharging the battery cell, the prediction module 206 may determine the balanced SOC of the battery cell 240 as follows:

[0060] SOC B (i) = V OC 2SOC(V B (i)+p), (Equation 1)

[0061] Among them, SOC B (i) represents the equilibrium SOC of the unit 240 at the i-th time interval, V OC 2SOC means based on V OC 2SOC data 210, VB(i) represents the measured cell balance voltage of the battery cell 240, and p represents the polarization correction (eg, the voltage drop across the first internal resistor 246).

[0062] Thereafter, the prediction module 206 may use the balanced SOC of the battery cell 240 to determine the unbalanced SOC as follows:

[0063] SOC(i)=SOC B (i)-ΔSOC B (i), (Equation 2)

[0064] Wherein, SOC(i) represents the unbalanced SOC of the battery cell 240, and SOC B (i) Determined in Equation 1. ΔSOC B (i) represents the change in the balanced SOC of the battery cell 240 at the i-th time interval. In one embodiment, ΔSOC B(i) may be determined as the measured balancing amperes per hour (“A(i) / hr”) of the battery cell 240 divided by the capacity of the battery cell 240 .

[0065] Finally, the prediction module 206 may use the unbalanced SOC of the battery cell 240 to determine an unbalanced characteristic measure of the battery cell 240 as follows:

[0066] V(i)=V OC 2SOC(SOC(i))–p(Equation 3)

[0067] Where V(i) represents the unbalanced voltage measurement of cell 240, SOC(i) is determined in Equation 2, V OC 2SOC means based on V OC 2SOC data 210, and p represents the polarization correction.

[0068] The prediction module 206 may then use V(i) to determine a health indicator as previously described.

[0069] In another embodiment, the prediction module 206 may adjust the short circuit threshold to take into account the battery cell balancing voltage, such as Figure 4 The method 300 ends at block 310 .

[0070] Figure 4 A flow chart of a method 400 for predicting an internal short circuit based on a health indicator according to an embodiment is illustrated. The method 400 begins at block 402 .

[0071] At block 404, the prediction module 206 ranks the plurality of health indicators of the corresponding plurality of battery cells, wherein the plurality of health indicators are ranked by magnitude. The health indicators may be ranked in ascending or descending order. As previously described, examples of health indicators include voltage drop, estimated internal short circuit resistance, SEI thickness, state of charge, battery cell capacity, lithium plating index, etc.

[0072] In one embodiment, the n-level indicator represents the health indicator having the nth largest magnitude in the level and corresponds to the nth battery cell in the plurality of battery cells. For example, the health indicator of the first level may represent the largest voltage drop, which corresponds to the first battery cell; and the indicator of the second level may represent the second largest voltage drop, which corresponds to the second battery cell.

[0073] At block 406, the prediction module 206 determines a short circuit threshold based on the second-level health indicator. In one embodiment, the prediction module 206 assumes that while the first-level health indicator may indicate an internal short circuit in the first battery cell, the second-level health indicator indicates that there is no internal short circuit in the second battery cell. In another embodiment, the first-level health indicator and the second-level health indicator may simultaneously indicate an internal short circuit in the first battery cell and the second battery cell, while the third-level health indicator indicates that there is no internal short circuit in the third battery cell.

[0074] In another embodiment, the prediction module 206 may determine whether the n-level health indicator deviates from an average value of the lower magnitude health indicator magnitude. When the n-level health indicator deviates from the average value, the prediction module 206 determines that the n-level health indicator may indicate an internal short circuit of the n-level battery cell, and the lower magnitude health indicator indicates that the corresponding battery cell does not have an internal short circuit. In this case, the prediction module 206 may determine the short circuit threshold based on the (n+1)-level health indicator.

[0075] In one embodiment, the prediction module 206 determines the short circuit threshold based on the empirical evidence shown to minimize false alarms. For example, the short threshold can be set to 1.6*the health status indicator of the second level (or the indicator of the (n+1) level), which can reduce the false positive of the short circuit prediction of the battery cell 240.

[0076] As previously described, in one embodiment, the prediction module 206 can adjust the short circuit threshold to take into account the situation when multiple batteries are subjected to battery balancing. In this case, the prediction module 206 can adjust the threshold by reducing the short circuit threshold by the difference between the unbalanced voltage (V(i)) and the balanced voltage (VB(i)).

[0077] At block 408, the prediction module 206 determines whether the first level health indicator exceeds the short-circuit threshold. Upon determining that the first level health indicator does not exceed the short-circuit threshold, the method 400 proceeds to block 404, where the method 400 is performed as described above. However, upon determining that the first level health indicator does exceed the short-circuit threshold, the method 400 proceeds to block 410.

[0078] At box 410, the prediction module 206 generates a prediction of a short circuit in the battery cell corresponding to the health indicator of the first level. When the health indicator of the first level does not exceed the short circuit value threshold, the prediction module 206 determines that there is no short circuit in the first cell. However, when the first level health indicator does exceed the short circuit threshold, the prediction module 206 determines that there is a short circuit or a short circuit is developing in the first battery cell. In one embodiment, the prediction module 206 can also predict that the capacity of the battery cell 240 is reduced when the voltage-based health indicator of the battery cell 240 is greater than the voltage-based health indicators of other battery cells of the power system 104.

[0079] When generating a prediction of a short circuit in the first battery cell, the prediction module 206 can take action to mitigate the effects of the short circuit, or prevent the short circuit from developing further. For example, the prediction module 206 can generate an alert of the short circuit and send it to a user of the vehicle 100 or a repair facility so that the battery cell of concern can be repaired or replaced. The prediction module 206 can also control the operation of the vehicle 100 to mitigate the effects of the short circuit on the battery (e.g., to prevent the battery from overheating). The method 400 ends at box 412.

[0080] The terms "one" and "an" do not indicate a limitation of quantity, but rather indicate the presence of at least one of the referenced item. Unless the context clearly indicates otherwise, the term "or" means "and / or". References to "aspects" throughout the specification mean that a particular element (e.g., a feature, structure, step, or characteristic) described in conjunction with that aspect is included in at least one aspect described herein, and may or may not be present in other aspects. In addition, it should be understood that the described elements may be combined in any suitable manner in the various aspects.

[0081] When an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. In contrast, when an element is referred to as being "directly on" another element, there are no intervening elements present.

[0082] Unless otherwise indicated herein, all test standards are the most current standards in effect as of the filing date of this application or, if priority is claimed, the filing date of the earliest priority application in which the test standards appear.

[0083] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0084] Although the above disclosure has been described with reference to exemplary embodiments, it will be appreciated by those skilled in the art that various changes may be made and equivalents may be substituted for its elements without departing from its scope. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the disclosure without departing from the basic scope of the disclosure. Therefore, it is intended that the disclosure is not limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A system for predicting a circuit condition of an electric power system of a vehicle, comprising: processor; as well as a memory or storage device including an algorithm or computer instructions that, when executed by the processor, perform operations comprising: receiving characteristic measurements of a plurality of battery cells of the power system; determining a snapshot moving average of the characteristic measurement for each battery cell in the plurality of battery cells based on a corresponding measurement window; determining a health indicator for each battery cell of the plurality of battery cells based on the corresponding snapshot moving average; grading a plurality of health indicators of the corresponding plurality of battery cells, wherein the plurality of health indicators are graded by magnitude; determining a short circuit threshold based on the health indicator of the second level; and Upon determining that a first-level health indicator exceeds the short-circuit threshold, a prediction of a short circuit in a battery cell corresponding to the first-level health indicator is generated.

2. The system according to claim 1, wherein: The characteristic measurements include voltage, current, temperature, pressure measurements or gas emission values; and wherein the multiple health status indicators include voltage drop, estimated internal short circuit resistance, solid electrolyte interface thickness, charge state, battery cell capacity or lithium plating index.

3. The system according to claim 1, wherein: The size of the measurement window is based on an expected short circuit resistance of the plurality of battery cells.

4. The system according to claim 1, wherein: The size of the measurement window is constant, wherein data of the measurement window is input into a parallel interleaved data buffer, and wherein the data buffer is processed at intervals based on expected short circuit resistances of the plurality of battery cells. The system of claim 1 , wherein the characteristic measurements are determined during a relaxation period of the plurality of battery cells.

6. The system according to claim 1, wherein: The characteristic measurement is determined from a cell-balanced battery cell, and wherein the characteristic measurement is restored to a non-cell-balanced value via a voltage-to-state-of-charge conversion.

7. The system according to claim 1, wherein: The plurality of health indicators are ranked in ascending or descending order, wherein the first-ranked health indicator indicates a potential short circuit of a battery cell corresponding to the first-ranked health indicator, and wherein the second-ranked health indicator indicates the absence of a short circuit of a battery cell corresponding to the second-ranked health indicator.

8. A computer readable storage medium having computer readable program code embodied therewith, the computer readable program code for predicting a circuit condition of a power system of a vehicle, the computer readable program code executable by one or more computer processors to perform operations comprising: receiving characteristic measurements of a plurality of battery cells of the power system; determining a snapshot moving average of the characteristic measurement for each battery cell of the plurality of battery cells based on a corresponding measurement window; determining a health indicator for each battery cell of the plurality of battery cells based on the corresponding snapshot moving average; grading a plurality of health indicators of the corresponding plurality of battery cells, wherein the plurality of health indicators are graded by magnitude; determining a short threshold based on the health indicator of the second level; and Upon determining that a first-level health indicator exceeds the short threshold, a prediction of a short circuit in a battery cell corresponding to the first-level health indicator is generated.

9. The computer-readable storage medium of claim 8, wherein: The characteristic measurements include voltage, current, temperature, pressure measurements or gas emission values; and wherein the multiple health status indicators include voltage drop, estimated internal short circuit resistance, solid electrolyte interface thickness, charge state, battery cell capacity or lithium plating index.

10. The computer-readable storage medium of claim 8, wherein: The plurality of health indicators are ranked in ascending or descending order, wherein the first-ranked health indicator indicates a potential short circuit of a battery cell corresponding to the first-ranked health indicator, and wherein the second-ranked health indicator indicates the absence of a short circuit of a battery cell corresponding to the second-ranked health indicator.

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