Electrolyte failure prediction for lithium ion batteries

Abstract

A system includes a measurement module, a health indicator module, a normalization module, and a fault detection module. The measurement module is configured to measure a plurality of parameters associated with a battery including a cell, the cell including an electrolyte. The health indicator module is configured to generate a plurality of health indicators based on the measured parameters. The normalization module is configured to: normalize the health indicator; and combining the normalized health indicators into different sets to detect different types of faults associated with the electrolyte. The fault detection module is configured to detect one or more of faults associated with the electrolyte based on one or more of the normalized health indicators in one or more of the sets.

Description

Background Art

[0001] The information provided in this section is for the purpose of generally presenting the context of the present disclosure. It is neither expressly nor impliedly admitted that the work of the presently named inventors is prior art with respect to the present disclosure with respect to aspects of the description that may not have originally qualified as prior art at the time of filing.

[0002] The present disclosure relates generally to lithium-ion batteries, and more particularly to electrolyte failure prediction for lithium-ion batteries.

[0003] Lithium-ion batteries can be used in a wide variety of applications. For example, lithium-ion batteries can be used to power computing devices such as laptops, handheld devices (e.g., smartphones and tablets), and the like. Lithium-ion batteries can also be used to power vehicles such as electric vehicles (EVs) and other equipment such as lawn care equipment, such as lawn mowers, snow blowers, trimmers, and the like. Summary of the Invention

[0004] A system includes a measurement module, a health indicator module, a normalization module, and a fault detection module. The measurement module is configured to measure a plurality of parameters associated with a battery including a cell, the cell including an electrolyte. The health indicator module is configured to generate a plurality of health indicators based on the measured parameters. The normalization module is configured to normalize the health indicators and combine the normalized health indicators into different sets to detect different types of faults associated with the electrolyte. The fault detection module is configured to detect one or more faults associated with the electrolyte based on one or more of the normalized health indicators in one or more of the sets.

[0005] In other features, the types of failures associated with the electrolyte include: a first failure due to leakage of the electrolyte from one or more of the cells; a second failure due to moisture penetration into one or more of the cells; and a third failure due to aging of the electrolyte in one or more of the cells.

[0006] In other features, the battery includes a plurality of modules, each module including a plurality of cell groups, and each group including one or more of the cells. The fault detection module is configured to detect one or more faults associated with an electrolyte in one of the cell groups.

[0007] In other features, the battery includes a plurality of modules, each module including a group of cells, and each group including one or more of the cells. The normalization module is configured to normalize one of the health indicators for one of the cell groups in one of the modules by subtracting a median value of one of the health indicators for one of the cell groups in the module from one of the health indicators for the one of the cell groups in the module. The health indicator module is configured to generate the one of the health indicators and the median value of the one of the health indicators based on parameters measured during the same charge / discharge cycle of the battery.

[0008] In other features, the fault detection module is configured to: detect one of the faults based on one or more of the normalized health indicators in one of the sets exceeding respective predetermined thresholds; and generate an alert upon detecting the one of the faults.

[0009] In other features, the fault detection module is configured to determine the health of the battery based on which of the faults is detected and which of the normalized health indicators exceed corresponding predetermined thresholds.

[0010] In other features, the battery includes a plurality of modules, each module including cell groups, and each group including one or more cells. The measurement module is configured to measure parameters including current through the battery, voltage across each cell group, temperature of each cell group, and state of charge of the battery. The health indicator module is configured to generate a health indicator for each cell group. The normalization module is configured to normalize each of the health indicators for one of the cell groups based on a median value of each of the health indicators for the one of the cell groups.

[0011] In other features, one of a set of normalized health indicators for detecting a fault due to leakage of the electrolyte from one or more of the cells includes: (i) the static resistance of the battery during a discharge cycle of the battery; (ii) a change in capacity of the battery during constant current charging of the battery; (iii) a location of a peak in dQ / dV relative to a voltage V of the battery during constant current charging of the battery; (iv) a difference in energy between charge and discharge cycles of the battery; and (v) an ohmic internal resistance of the battery during charge and discharge of the battery.

[0012] In other features, one of a set of normalized health indicators for detecting failures due to moisture penetration into one or more of the cells includes: (i) a change in capacity of the battery during constant current charging of the battery; (ii) a difference in energy between charge and discharge cycles of the battery; (iii) a discharge duration for the battery; (iv) a sum of the voltages of the battery during constant current charging of the battery; (v) a peak value of dQ / dV relative to the voltage V of the battery during constant current charging of the battery; and (vi) polarization resistance during charge and discharge of the battery.

[0013] In other features, one of a set of normalized health indicators for detecting failure due to aging of the electrolyte in one or more of the cells includes: (i) a change in capacity of the battery during constant current charging of the battery; (ii) a discharge duration for the battery; (iii) a sum of the voltages of the battery during constant current charging of the battery; (iv) a difference in energy between charge and discharge cycles of the battery; (v) a difference in capacity of the battery during charge and discharge of the battery; (vi) a rate of change of voltage of the battery during constant current charging of the battery; (vii) a logarithmic rate of change of current during constant voltage charging of the battery; (viii) a loss of capacity of the anode during constant current charging of the battery; (ix) a loss of capacity of the cathode during constant current charging of the battery; (x) a loss of lithium inventory during constant current charging of the battery; and (xi) an ohmic internal resistance of the battery during charge and discharge of the battery.

[0014] In other features, a vehicle includes the battery and the system.The fault detection module is configured to output an indication of the one or more faults associated with the electrolyte to control power supplied from the battery to one or more subsystems of the vehicle.

[0015] In still other features, a method includes measuring a plurality of parameters associated with a battery including a cell, the cell including an electrolyte, and generating a plurality of health indicators based on the measured parameters. The method includes normalizing the health indicators, combining the normalized health indicators into different sets to detect different types of faults associated with the electrolyte, and detecting one or more of the faults associated with the electrolyte based on one or more of the normalized health indicators in one or more of the sets.

[0016] In other features, the types of failures associated with the electrolyte include: a first failure due to leakage of the electrolyte from one or more of the cells; a second failure due to moisture penetration into one or more of the cells; and a third failure due to aging of the electrolyte in one or more of the cells.

[0017] In other features, the battery includes a plurality of modules, each module including a plurality of cell groups, and each group including one or more of the cells. The method further includes detecting one or more faults associated with an electrolyte in one of the cell groups.

[0018] In other features, the battery includes a plurality of modules, each module including groups of cells, and each group including one or more of the cells. The method further includes normalizing one of the health indicators for one of the groups of cells in one of the modules by subtracting a median value of one of the health indicators for one of the groups of cells in the module from one of the health indicators for the one of the groups of cells in the module.

[0019] In other features, the method further comprises generating the one of the health indicators and a median of the one of the health indicators based on parameters measured during a same charge / discharge cycle of the battery.

[0020] In other features, the method further comprises detecting one of the faults based on one or more of the normalized health indicators in one of the sets exceeding respective predetermined thresholds; and generating an alert upon detecting the one of the faults.

[0021] In other features, the method further comprises determining a health of the battery based on which of the faults are detected and which of the normalized health indicators exceed corresponding predetermined thresholds.

[0022] In other features, the battery includes a plurality of modules, each module including cell groups, and each group including one or more cells. The method further includes measuring parameters including current through the battery, voltage across each cell group, temperature of each cell group, and state of charge of the battery. The method further includes generating a health indicator for each cell group; and normalizing each of the health indicators for one of the cell groups based on a median value of each of the health indicators for the one of the cell groups.

[0023] Further areas of applicability of the present disclosure will become apparent from the detailed description, claims, and accompanying drawings.The detailed description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present disclosure will become more fully understood from the detailed description and accompanying drawings, in which: Figure 1A An example of a lithium-ion battery is shown; Figure 1B Shown Figure 1A An example of a cell of a lithium-ion battery; Figure 2 An example of a system including a vehicle using Figure 1A lithium-ion batteries, and using the prediction method disclosed herein to detect Figure 1A Failures in the electrolytes used in lithium-ion batteries; Figure 3 shows the use of prediction methods to detect Figure 1A Failure of the electrolyte used in the cells of lithium-ion batteries Figure 2 An example of a control module for a vehicle; Figure 4 Shown by the detection in Figure 1A Failure of the electrolyte used in the cells of lithium-ion batteries Figure 2 An example of a prediction method performed by a control module of a vehicle; Figure 5 Shown in Figure 1A Examples of graphs of current versus time during charge and discharge cycles of a lithium-ion battery; Figure 6 Shown Figure 1A Example of an equivalent circuit model (ECM) for a lithium-ion battery; Figure 7 shows the use of the prediction method to Figure 1A The health indicators of the lithium-ion battery cells are normalized to detect Figure 1A An example of a method for normalizing failures in an electrolyte used in a cell of a lithium-ion battery; and Figure 8 shows the use by the prediction method to declare Figure 1A Examples of the methods of malfunction and failure modes of electrolytes used in lithium-ion battery cells.

[0025] Among the drawings, reference numbers may be reused to identify similar and / or identical elements. DETAILED DESCRIPTION

[0026] The present disclosure provides a prediction method for detecting different faults (failure modes) of electrolytes in lithium-ion batteries. The prediction method uses various health indicators derived from the current, voltage, temperature and state of charge (SOC) measurements of the battery, as described in detail below. First, before describing the prediction method, the following reference Figure 1A and 1B Examples of batteries and battery cells are shown and described.

[0027] Figure 1A and 1B Schematically shows an example of a lithium-ion battery 10 and a cell 12 of the lithium-ion battery 10, respectively. Figure 1A In, for example, a lithium-ion battery 10 (hereinafter referred to as battery 10) includes a package that includes a plurality of modules, wherein each module includes a plurality of cell groups. For example, in a cell group, one or more cells C1, C2, ..., Cc (collectively referred to as cells 12) identified at 12-1, 12-2, ..., 12-c, respectively, are connected in parallel to each other, where c is an integer greater than or equal to 1. In a module, a plurality of cell groups 12G1, G2, ..., Gg (collectively referred to as groups 14 or cell groups 14) identified at 14-1, 14-2, ..., 14-g, respectively, are connected in series to each other, where g is an integer greater than 1. In a package referred to as P and identified at 18, a plurality of modules M1, M2, ..., Mm (collectively referred to as modules 16 or cell modules 16) identified at 16-1, 16-2, ..., 16-m, are connected in series to each other, where m is an integer greater than 1. In each group 14 , a temperature sensor T, identified at 13 , may be provided. Alternatively or additionally, although not shown, one or more temperature sensors may also be provided in each module 16 .

[0028] In some batteries, the pack 18 may not include multiple modules 16 in the pack 18. Instead, the groups 14 of cells 12 are connected in series to each other and arranged in the pack 18 rather than being arranged in multiple modules 16. Therefore, in these batteries, the pack 18 is the same as a single module 16, and m=1. In the following description, the prediction method calculates the median value of the health indicator for each module 16 to normalize the health indicator for each group 14 in the corresponding module 16. Alternatively, when the pack 18 includes the groups 14 and functions as a single module 16, the median value of the health indicator can be calculated for the pack 18 just as the median value is calculated for the module 16. The median value of the health indicator calculated for the pack 18 can then be used to normalize the health indicator for each group 14 in the pack 18.

[0029] Although not shown, in some batteries, multiple packs such as pack 18 may be connected in series or in parallel with each other, or using a combination of series and parallel connections, which may be switched between series and parallel connections depending on the power requirements of one or more loads. For simplicity of discussion, battery 10 is assumed to include pack 18, although the present disclosure is not limited thereto and may be extended to batteries including multiple packs.

[0030] The measurement module 50 can be connected across the pack 18 (i.e., across the battery 10) to measure current, voltage, temperature, and estimate the SOC of the battery 10. For example, the measurement module 50 can be implemented in a control module of a vehicle or in test equipment at a manufacturing plant, laboratory, or service facility. The measurement module 50 includes a current measurement circuit 52, multiple voltage measurement circuits 54, multiple temperature measurement circuits, and an SOC estimation circuit 58.

[0031] In some examples, measurement module 50 may not be a single module. For example, each module 16 and package 18 may include a measurement module similar to measurement module 50.

[0032] The current measurement circuit 52 can measure the current I passing through the pack 18 (i.e., through the battery 10). The same current I flows through all cells 12 in the pack 18. The voltage measurement circuit 54 can measure the voltage across each group 14 of cells 12. The temperature measurement circuit 56 can measure the temperature of each group 14 of cells 12. The SOC estimation circuit 58 can estimate the SOC of the battery 10, which is expressed as the available capacity of the battery 10 as a function of the rated capacity of the battery 10. For example, the SOC can be estimated using the open circuit voltage of the battery 10, or can be estimated using a coulomb counting method.

[0033] In some examples, although not shown, an onboard battery management system (BMS) in the vehicle can be used to estimate SOC and control the charge / discharge voltage / current of the battery. Further, SOC can be estimated for the pack 18 and / or the group 14 of cells 12.

[0034] The prediction method of the present disclosure uses these measurements to calculate various health indicators for each group 14 of cells 12. The prediction method also calculates a median value for each health indicator for each module 16. The health indicators for each group 14 are normalized based on the median value of the health indicators for the modules 16 that comprise the group 14. The normalized health indicators are then used to detect faults (failure modes) in the electrolytes of the cells 12 in each group 14, as described in detail below.

[0035] Figure 1BAn example of a cell 12 is schematically shown. Cell 12 includes a cathode (K+) 102, an anode (A-) 104, a separator 106, an electrolyte 108, and current collectors 110 and 112. Cathode 102 is a positive electrode. Anode 104 is a negative electrode. During charging of battery 10, lithium ions flow from cathode 102 to anode 104 in each cell 12 through separator 106 and electrolyte 108, as indicated by arrow 120. During discharge (i.e., when power from lithium-ion battery 10 is supplied to a load such as a subsystem of a vehicle), lithium ions flow from anode 104 to cathode 102 through separator 106 and electrolyte 108, as indicated by arrow 122.

[0036] The electrolyte 108 conducts ion movement between the electrodes within the battery 10. The electrolyte 108 may fail for various reasons. Failure may occur during the manufacture of the battery 10, during storage of the battery 10 (e.g., in a storage facility after manufacture and before shipping the battery 10, in a parked vehicle, etc.), and during use of the battery 10 in a vehicle for an extended period of time.

[0037] Broadly, electrolyte failures are categorized as failures due to low capacity of the electrolyte 108 (e.g., due to leakage) (e.g., due to physical damage to the battery 10, which causes leakage), moisture penetration into the electrolyte 108 (e.g., due to physical damage to the battery), and aging of the electrolyte 108. Throughout this disclosure, these three types of failures of the electrolyte 108 are also referred to as failure modes of the electrolyte 108: low capacity failure or low capacity failure mode, moisture failure or moisture failure mode, and aging failure or aging failure mode.

[0038] If a relatively small amount of electrolyte 108 is added to the battery 10 before the battery 10 is sealed, a low capacity failure may occur during the manufacture of the battery 10. During storage or use of the battery 10, if the electrolyte 108 leaks due to physical damage to the battery 10 while in storage or in a vehicle, a low capacity failure may occur. If the electrolyte 108 is exposed to the environment before it is added to the battery 10 during manufacture, a moisture failure may occur. During storage or use, if moisture penetrates the electrolyte 108 through openings (e.g., pores) in the battery 10 created by physical damage to the battery 10 while in storage or in a vehicle, a moisture failure may occur. If aged electrolyte 108 is added to the battery 10 during manufacture, an aging failure may occur during manufacture. During storage or use, if the battery 10 is stored and remains unused for an extended period of time before or during use of the battery 10 in a vehicle, an aging failure may occur.

[0039] Electrolyte failure may result in performance degradation or even ignition or explosion due to the corrosive nature of electrolyte 108. Electrolyte failure can be detected offline (i.e., with battery 10 outside the vehicle, for example, at a service facility, in a laboratory, etc.). Offline methods for detecting electrolyte failure involve using a test device, such as a spectrometer or instrument, for measuring the conductivity of electrolyte 108. Offline fault detection methods are difficult to use in a vehicle.

[0040] Furthermore, using the same criteria as those used in the offline fault detection method to predict different electrolyte failures can be challenging due to the various electrolyte failure modes. For example, the voltage-time scatter plots for the healthy electrolyte and the three failure modes of the electrolyte are indistinguishably close to each other. Therefore, it can be challenging to distinguish a faulty electrolyte from a healthy electrolyte and isolate the type of failure (failure mode). Although there are several health indicators for determining the health of the battery 10, the performance of these health indicators for detecting electrolyte failure and different failure modes is well established and largely unknown in the art.

[0041] Furthermore, if only one cell 12 in a group 14 or module 16 fails, the health indicators used to determine the health of the battery 10 are not significantly deviated. Instead, to identify the failed cell or cell group, the type of electrolyte failure, and the severity of the electrolyte failure, different health indicators need to be combined, normalized to a median value for the health indicator for each module 16, and compared to corresponding thresholds. Thereafter, to declare a failure based on the comparison, different types of logic need to be used (e.g., conservative logic, tolerant logic, or a combination thereof).

[0042] This disclosure provides a prognostic method that uses a combination of various health indicators to detect three types of electrolyte faults (failure modes): low capacity (leakage), moisture, and aging. The prognostic method uses three sets of health indicators, combined with thresholding and decision tree logic, to detect three failure modes (failures). Some of the health indicators used are common to two or all three sets, but have different effects on the detection of the corresponding fault type when used in combination with other health indicators in each set.

[0043] Fault detection using the prediction method described below is performed for each group 14 of cells 12. That is, the prediction method of the present disclosure detects electrolyte faults at the cell group level, rather than for the battery as a whole. Therefore, for each group 14 of cells 12, the current through the battery 10 is measured, the voltage across the cells 12 in the group 14 is measured, the temperature of the group 14 of cells 12 is measured, and the state of charge (SOC) of the battery 10 is measured. The measurements for each group 14 are then used to generate various health indicators. The health indicators are grouped into three sets for use in detecting three types of electrolyte faults.

[0044] Within each set, the health indicators are normalized based on the median value of the health indicators for the modules 16 of the group 14 comprising the cells 12. The median value is not predetermined or pre-calibrated. Rather, the median value is calculated in real time as the health indicators are calculated. Because the health indicators and the median value are calculated based on measurements of the battery 10 taken at the same time (e.g., during the same charge / discharge cycle), the health indicators and the median value reflect the same aging and other environmental effects that affect the battery 10. After normalization, the health indicators are compared to corresponding thresholds. Thereafter, different types of logic are used to declare a fault.

[0045] To detect electrolyte failures at the pack level rather than the cell level, the health indicators and thresholds selected are not simply design choices. Rather, they are empirically selected by analyzing the impact of each type of electrolyte failure on each health indicator and different combinations of health indicators, and selecting specific health indicators and combinations based on that analysis. Other approaches to selection include using machine learning techniques such as random forests, support vector machines (SVMs), neural networks, deep neural networks, and the like.

[0046] One of the electrical parameters of the battery 10 that is affected by any of the three failure modes of the electrolyte 108 is the internal resistance of the battery 10 given by the following equation: Among them C e is the concentration of the electrolyte 108, L is the length of the electrolyte 108 path (ie, the thickness or width of the battery), K eff is the conductivity of the electrolyte 108, and L s is the thickness of the solid phase (electrode).

[0047] The electrolyte typically includes a lithium salt solution, such as a lithium salt and a solvent. One or more failure modes may affect one or more parameters of the above equation, which in turn changes the internal resistance of the battery 10. For example, as the electrolyte 108 ages, the solvent used in the electrolyte 108 may deteriorate and decompose, which increases the concentration C of the lithium salt in the electrolyte 108. e The low capacity of the electrolyte 108 may reduce L, and the penetration of water into the electrolyte 108 may change the concentration C of the electrolyte 108. e The length (L) affects the cell resistance, capacitance, and pack isolation resistance of the battery 10. The concentration (C e )Change the resistance and capacity of battery 10.

[0048] Changes in the internal resistance of battery 10 are reflected in the current and voltage measurements of battery 10. Furthermore, environmental conditions such as ambient temperature and the temperature of battery 10 may also change the internal resistance of battery 10. However, measuring only these electrical parameters of battery 10 may not indicate an electrolyte failure, because changes in these electrical parameters due to an electrolyte failure in one or a few cells among a large number of cells in battery 10 do not result in a measurable deviation in these electrical parameters of battery 10. Instead, by using the prediction method of the present disclosure, different health parameters may be normalized and used to detect electrolyte failure as follows.

[0049] Figure 2 A system 200 is shown that uses the predictive methods of the present disclosure to detect electrolyte failures in cells of a lithium-ion battery in a vehicle 202. The system 200 includes the vehicle 202, a distributed communication system 204, a server 206, a service facility 208-1 (e.g., a car dealership or service station), and a manufacturing facility 208-2 (e.g., a battery manufacturing plant, a vehicle assembly plant, a battery testing laboratory, etc.). The service facility 208-1 and the manufacturing facility 208-2 may be collectively referred to as external facilities 208 or simply as facilities 208.

[0050] The prediction method of the present disclosure described below can be executed in the vehicle 202 or at an external facility 208 (e.g., on a laptop or handheld device). The prediction method can be executed at least in part in the server 206 (also referred to as a remote server or a server in the cloud). For example, data from the battery 210 (e.g., current, voltage, temperature, and SOC measurements) can be transmitted from the vehicle 202 and from the external facility 208 to the server 206, which analyzes the data and uses the prediction method of the present disclosure to provide a prediction.

[0051] Vehicle 202 includes a lithium-ion battery (battery) 210, a plurality of vehicle subsystems 212, and a control module 220. Battery 210 is similar to the battery described above with reference to FIG. Figure 1A and 1B The battery 10 is shown and described. The battery 210 supplies power to various vehicle subsystems 212 of the vehicle 202. The vehicle subsystems 212 include various electrical, mechanical, and electromechanical electronic systems of the vehicle 202. Non-limiting examples of the vehicle subsystems 212 include a propulsion subsystem, including one or more electric motors for propelling the vehicle 202, a steering subsystem, a braking subsystem, a suspension subsystem, an infotainment subsystem, a heating, ventilation, and cooling (HVAC) subsystem, and the like. The control module 220 communicates with the battery 210 and the vehicle subsystems 212 and controls the vehicle subsystems 212. Figure 3 The control module 220 is shown and described in further detail.

[0052] The distributed communication system 204 includes one or more networks (wired and / or wireless), such as the Internet, a local area network, and / or a wide area network, etc. The vehicle 202 (e.g., the control module 220) communicates with the server 206 via the distributed communication system 204. A computing device at an external facility 208, such as a laptop or handheld device, communicates with the server 206 via the distributed communication system 204.

[0053] Although not shown, a handheld device such as a smartphone can also be used in the vehicle 202. For example, the handheld device in the vehicle 202 can communicate with the control module 220 via Bluetooth. The handheld device in the vehicle 202 can also communicate with the server 206 and the external facility 208 via the distributed communication system 204.

[0054] Figure 3 The control module 220 of the vehicle 202 is shown. The control module 220 includes a measurement module 222 and a prediction module 230. The measurement module 222 is similar to the above reference Figure 1B The measurement module 50 is shown and described. The prediction module 230 includes a health indicator calculation module (HI module) 232, a normalization module 234, and a fault detection module 236.

[0055] The measurement module 222 measures (e.g., senses) various parameters of the battery 210 (e.g., current, voltage, temperature, and SOC measurements). The HI module 232 calculates various health indicators of the battery 210 (described below) based on the parameters measured by the measurement module 222. The normalization module 234 normalizes the health indicators of the battery 210, as described below. The fault detection module 236 detects whether the battery 210 exhibits any of the three electrolyte failure modes described above. The operation of the prediction module 230, including the calculation and grouping of health indicators, normalization of health indicators, and detection of failure modes, is described in detail below.

[0056] Prediction module 230 may also be implemented in a computing device such as a laptop or handheld device at external facility 208 and in server 206. Thus, the prediction method of the present disclosure may be performed entirely in vehicle 200 and entirely at external facility 208. The prediction method of the present disclosure may also be performed partially in vehicle 200 and partially at external facility 208 in conjunction with server 206. For example, measurements may be made in vehicle 202 or at external facility 208 and sent to server 206 for analysis. Server 206 may send the results of the analysis (such as whether battery 210 is faulty and needs to be repaired or replaced) to vehicle 202 or external facility 208. The results may be displayed on the dashboard of vehicle 202 or on a handheld device in the vehicle. The results may also be displayed on a handheld device or test equipment at external facility 208. A person or maintenance technician at external facility 208 may decide whether to repair or discard battery 210.

[0057] Figure 4 A method 300 is shown that is performed by the control module 220. At 302, the measurement module 222 measures (e.g., senses) various parameters (e.g., voltage, current, temperature, and SOC) of the battery 210. At 304, the HI module 232 calculates various health indicators of the battery 210 based on the parameters measured by the measurement module 222. The health indicators are described below. At 306, the normalization module 234 normalizes the health indicators of the battery 210, as described below with reference to Figure 7 As shown and described.

[0058] At 308, the fault detection module 236 groups the normalized health indicators into three sets to respectively detect the three failure modes of the electrolyte 208. In some examples, the health indicators may be grouped prior to normalization. At 310, in each set, the fault detection module 236 compares the normalized health indicators to a corresponding threshold value selected for the type of electrolyte failure to be detected by the corresponding set of normalized health indicators. At 312, the fault detection module 236 detects whether the battery 210 exhibits any of the three failure modes described above (i.e., detects one or more types of failures in the electrolyte 208 based on the comparison). Figure 8 Steps 310 and 312 are shown and described in further detail.

[0059] Before describing the health indicators in detail, refer to Figure 5 The various time periods during the charge and discharge cycles of the battery 210 are shown and described. Some of the health indicators are measured during these time periods. Figure 6 To illustrate and describe the equivalent circuit model (ECM) of the battery 210. Figure 6 Various battery parameters used to derive some of the health indicators are shown and described.

[0060] Figure 5 A graph of current versus time is shown during a charge and discharge cycle of the battery 210, based on which the HI module 232 calculates various health indicators for the cells 12 of the battery 210. From time t0 to t1, the battery 210 is charged with a constant current. Thus, time t0 to t1 is a constant current charging period for the battery 210, during which the voltage of the battery 210 increases. From time t1 to t2, the battery 210 is charged at a constant voltage. Thus, time t1 to t2 is a constant voltage charging period for the battery 210, during which the current of the battery 210 decreases. From time t3 to t4, the battery 210 is at rest (i.e., not connected to any load; neither charging nor discharging).

[0061] From time t5 to t6, battery 210 is discharged while supplying a constant current to a load (e.g., an electric motor of the propulsion subsystem of vehicle 202). The voltage of battery 210 decreases during time t5 to t6. From time t7 to t8, battery 210 is at rest (i.e., not connected to any load; neither charging nor discharging). The time periods t2 to t3 and t6 to t7 are referred to as transition periods to the resting state of battery 210.

[0062] Figure 5The battery current during the charge and discharge cycles for a healthy battery is shown. As battery 210 degrades (e.g., due to one or more electrolyte failures), the battery voltage and current change during the charge and discharge cycles. The current level is controlled during constant current charging and during discharge. The current changes during contact voltage charging. Times t1-t7 are shifted. These changes and shifts, reflecting one or more electrolyte failures, are captured by the various health indicators described below.

[0063] Figure 6 An equivalent circuit model (ECM) of battery 210 is shown. In the ECM, battery 210 is shown as having an open circuit voltage (OCV), which is the voltage across the cathode and anode of battery 210 when no load is connected to battery 210. R0 (also referred to as ECM_R0) is the ohmic internal resistance of battery 210. R1 (also referred to as ECM_R1) is the polarization internal resistance of battery 210. C1 is the polarization capacitance of battery 210. V1 is the polarization voltage of battery 210. I is the discharge current of battery 210. And Vd is the terminal voltage of battery 210. As battery 210 degrades (e.g., due to one or more electrolyte failures), these ECM parameters of battery 210 also change. Changes in these ECM parameters of battery 210 that reflect one or more electrolyte failures are captured by various health indicators described below.

[0064] The fault detection module 236 uses a first set of health indicators (HI Set 1) to detect a low capacity fault or low capacity failure mode of the electrolyte 208. The fault detection module 236 uses a second set of health indicators (HI Set 2) to detect a moisture fault or moisture failure mode of the electrolyte 208. The fault detection module 236 uses a third set of health indicators (HI Set 3) to detect an aging fault or aging failure mode of the electrolyte 208. The values ​​of these health indicators change due to any of the failure modes of the electrolyte 108.

[0065] The Hi module 232 calculates the health indicators in the first set of health indicators (HI Set 1) based on the measurement results made by the measurement module 222 as follows. HI Set 1 includes the following five health indicators: (i) A health indicator, referred to as the static resistance Rs of the battery 210 during a discharge cycle of the battery 210, is denoted by Rs_dchg. The health indicator Rs_dchg is calculated using the following formula: Static resistance (ii) A health indicator, referred to as a change in the capacity of the battery 210 measured in terms of charge dQ required to increase the voltage of the battery 210 from a first value to a second value during constant current charging, is denoted by dQ_cc_chg. The first and second voltage values ​​are selected between time t0 and t1 during constant current charging. The health indicator dQ_cc_chg is calculated using the following formula: If the capacity of battery 210 has decreased, the voltage of battery 210 will rise from a first value to a second value with a smaller amount of charge than would be required to raise the voltage of battery 210 from the first value to the second value when the battery is at rated capacity. Thus, a lower value of dQ_cc_chg may indicate a fault. (iii) A health indicator, which is referred to as the position of the peak value of the rate of change of charge Q with respect to voltage (dQ / dV) of the battery 210 relative to the voltage V of the battery 210, is denoted by dQ / dV_cc_chg_peak_position. The health indicator dQ / dV_cc_chg_peak_position indicates the peak position (i.e., the position of the peak value) of dQ / dV with respect to the voltage V of the battery 210 during the constant current charging period t0 to t1. The health indicator dQ / dV_cc_chg_peak_position is measured by plotting dQ / dV on the x-axis and the voltage V on the y-axis during the constant current charging period t0 to t1. The health indicator dQ / dV_cc_chg_peak_position indicates the position or value of V at which the peak value of dQ / dV occurs during the constant current charging period t0 to t1. The health indicator dQ / dV_cc_chg_peak_position is calculated using the following formula: A shift to the left or right along the x-axis in dQ / dV_cc_chg_peak_position (ie, the voltage V at which the dQ / dV peak occurs) compared to when the peak is for a healthy battery may indicate a fault. (iv) The health indicator, referred to as the difference in energy between charge and discharge cycles of the battery 210 at a selected voltage range, is denoted by dE. The health indicator energy difference (also referred to as energy loss), dE, is calculated using the following formula: (v) A health indicator called the ohmic internal resistance R0 of the battery 210 is denoted by ECM_R0. The health indicator ECM_R0 is a function of the internal resistance R0 of the battery 210 during charging and discharging. Figure 6 The ECM shown in is calculated.

[0066] The Hi module 232 calculates the health indicators in the second set of health indicators (HI Set 2) based on the measurements made by the measurement module 222 as follows. HI Set 2 includes the following six health indicators: The health indicators dQ_cc_chg and energy loss dE in HI Set 2 are the same as in HI Set 1. In addition, HI Set 2 includes the following four health indicators: (i) A health indicator, referred to as the discharge duration for the battery 210 within the selected voltage range during the discharge cycle, is denoted by dT_cc_dchg. The health indicator dT_cc_dchg is calculated using the following formula: The discharge duration within the selected voltage range of dT_cc_dchg=t6-t5 will vary depending on the health of the battery 210. (ii) A health indicator, referred to as the sum of the voltages of the battery 210 during the constant current charge duration (from time t0 to t1) of the charge cycle of the battery 210, is denoted by Vsum_cc. The health indicator Vsum_cc is calculated using the following formula: (iii) A health indicator, which is the rate of change of charge Q with respect to voltage (dQ / dV) of the battery 210 relative to the peak value of the voltage V of the battery 210, is denoted by dQ / dV_cc_chg_peak_value. The health indicator dQ / dV_cc_chg_peak_value indicates the peak value of dQ / dV with respect to the voltage V of the battery 210 during the constant current charging period t0 to t1. The health indicator dQ / dV_cc_chg_peak_value is measured by plotting dQ / dV on the x-axis and the voltage V on the y-axis during the constant current charging period t0 to t1. The health indicator dQ / dV_cc_chg_peak_value indicates the peak value of dQ / dV during the constant current charging period t0 to t1. The health indicator dQ / dV_cc_chg_peak_value is calculated using the following formula: (iv) The health indicator of the polarization resistance R1 of the battery 210 is denoted by ECM_R1. The health indicator ECM_R1 is a function of the polarization resistance R1 of the battery 210 during charging and discharging. Figure 6 The ECM shown in is calculated.

[0067] The Hi module 232 calculates the health indicators in the third set of health indicators (HI Set 3) based on the measurement results made by the measurement module 222 as follows. HI Set 3 includes the following eleven health indicators: The five health indicators dQ_cc_chg, dT_cc_chg, energy loss dE, Vsum_cc, and ECM_R0 in HI Set 3 are the same as in HI Sets 1 and 2. In addition, HI Set 3 includes the following six health indicators: (i) A health indicator, referred to as the loss in capacity (also referred to as energy loss) of the battery 210 due to aging of the battery 210, is denoted by dC. The health indicator dC is the difference in capacity of the battery 210 during charge and discharge within a selected voltage range. The health indicator dC is calculated using the following formula: (ii) A health indicator, which is a change in (the rate of change of) the voltage V of the battery 210, is denoted by dV / dt. The health indicator dV / dt is measured during constant current charging of the battery 210 within a selected voltage range. The health indicator dV / dt is calculated using the following formula: (iii) A health indicator referred to as the logarithmic rate of change of current during the constant voltage charging duration of the battery 210 is denoted by dln(I) / dt. The health indicator dln(I) / dt is calculated using the following formula. During constant voltage charging of the battery (from t2 to t3), the current through the battery 210 follows the equation: ln(I)=at+ln(I(0)), where a is the slope of ln(I), i.e., a=dln(I) / dt, which is a health indicator. Let [a,ln(I(0))] T =A,[t,1]=X ln(I)=X*A A can be calculated by the least squares method as follows: X T ln(I)=X T X*A (X T X) -1 X T ln(I)=(X T X)- 1 X T X*A (X T X) -1 X T ln(I)=A A[0] is a; It is a health indicator. The slope a changes due to a failure of one or more electrolytes. (iv-vi) For the battery 210, the SOC curves plotted at two different charging rates can be used to mathematically derive the relationship between the charge Q and the open circuit electrode potential (EP) of the anode and cathode (OCPan and OCPca) during constant current charging of the battery 210. The electrode potential is the potential of the cathode and anode electrodes relative to a third reference electrode, not relative to each other. The electrode potential relative to the third reference electrode is mathematically derived without using the third reference electrode and without measuring the electrode potential relative to the third reference electrode.

[0068] The electrode potential (EP) is calculated by the HI module 232 based on a mathematical analysis of the measurements made by the measurement module 222. The electrode potential is affected by one or more electrolyte faults. Based on the electrode potential, three EP-based health indicators are mathematically derived by the HI module 232: capacity loss (anode) indicated by EP_LoCan, capacity loss (cathode) indicated by EP_LoCca, and lithium inventory loss indicated by EP_LLI. These three health indicators change due to one or more electrolyte faults. Therefore, changes in these three health indicators reflect one or more electrolyte faults.

[0069] The HI module 232 calculates all of the above health indicators for each cell group 14 in the battery 210. To calculate the above health indicators, the measurement module 222 measures the current through the battery 210, which is the same current flowing through all cell groups 14 in the battery 210. The measurement module 222 measures the voltage across each cell group 14 in each module 16 in the battery 210. Therefore, in the battery 210, if each of the M modules 16 includes G cell groups 14, the measurement module 222 measures M×G voltages across the M×G cell groups 14. The measurement module 222 also measures the temperature of each of the M×G cell groups 14. The measurement module 222 measures the SOC of the battery 210.

[0070] All of these measurements are made during the same charge / discharge cycle of battery 210 when calculating the health indicator. Measurements from one charge / discharge cycle are not used to calculate the health indicator during another charge / discharge cycle. Accordingly, the measurements and the health indicator calculated based on the measurements reflect the same effects due to aging of battery 210 and the same environmental effects on battery 210 (e.g., ambient temperature, temperature of battery 210, etc.).

[0071] Normalization module 234 normalizes each of the three sets of health indicators for each cell group 14 as follows. For each health indicator, HI module 232 also calculates the corresponding median health indicator for module 16 that includes cell group 14. To normalize the health indicators for cell groups 14 in module 16, normalization module 234 subtracts the corresponding median health indicator for module 16 from the health indicators for cell group 14. Normalization is represented by the following equation: Normalized indicator = indicator[i] - baseline[i], where i is the cycle number, i=1, 2, 3, ...

[0072] After each charge / discharge cycle of the battery 210 , the index of the cycle number is increased by 1, and the same index (ie, measurements and HI calculations made in the same charge / discharge cycle) is used for normalization of the health indicator.

[0073] Figure 7 A normalization method 350 is shown ( Figure 4 ), a normalization method 350 is employed by the normalization module 234 to normalize the health indicators in the three sets of health indicators for each cell group 14 in each module 16. At 352, the HI module 232 calculates the health indicator for each cell group 14 in the module 16 during the charge / discharge cycle of the battery 210.

[0074] At 354, the HI module 232 calculates a median value of the health indicator for the module 16 that includes the cell group 14. For example, the HI module 232 calculates the average of the values ​​of the health indicators calculated for all cell groups 14 within the module 16 to provide a median value of the health indicator for the module 16. At 356, the normalization module 234 normalizes the health indicators for the cell groups 14 in the module 16 by subtracting the median value of the health indicator from the value of the health indicator for the cell group 14.

[0075] At 358, the normalization module 356 determines whether all health indicators in the three sets of health indicators for the cell group 14 are normalized. If not all health indicators in the three sets of health indicators for the cell group 14 are normalized, then at 360, the normalization module 356 selects the next health indicator to normalize, and the method 350 returns to 352.

[0076] If all health indicators in the three sets of health indicators for the cell group 14 are normalized, then at 362, the normalization module 356 determines whether all health indicators in the three sets of health indicators are normalized for all cell groups 14 in the module 16. If not all health indicators in the three sets of health indicators are normalized for all cell groups 14 in the module 16, then at 364, the normalization module 356 selects the next cell group 14, and the method 350 returns to 352.

[0077] If all health indicators in the three sets of health indicators are normalized for all cell groups 14 in the module 16, then at 366, the normalization module 356 determines whether all health indicators are normalized for all modules 16 in the battery 210. If not all health indicators are normalized for all modules 16 in the battery 210, then at 368, the normalization module 356 selects the next module 16, and the method 350 returns to 352. If all health indicators are normalized for all modules 16 in the battery 210, then normalization of all health indicators for the battery 210 is complete, and the method 350 ends.

[0078] The fault detection module 236 groups the normalized health indicators into three sets of normalized health indicators for each group 14 of cells 12 in the battery 210. The fault detection module 236 detects one or more of three faults (failure modes) of the electrolyte for each group 14 of cells 12 in the battery 210. In one example, the fault detection module 236 may declare a fault based on a single fault (failure mode) detected in a single group 14 of cells 12. In another example, the fault detection module 236 may declare a fault if two or more faults (failure modes) are detected in a single group 14 of cells 12. In yet another example, the fault detection module 236 may declare a fault only if all three faults (failure modes) are detected in a single group 14 of cells 12.

[0079] When a fault (failure mode) is detected in the group 14 of cells 12, the fault detection module 236 compares each normalized health indicator in the corresponding set of normalized health indicators used to detect the fault with a corresponding threshold value. In one example, if a single normalized health indicator in the corresponding set of normalized health indicators exceeds the corresponding threshold value, the fault detection module 236 may declare a fault (detection of a failure mode) in the group 14 of cells 12. In another example, if two or more normalized health indicators in the corresponding set of normalized health indicators exceed the corresponding threshold value, the fault detection module 236 may declare a fault (detection of a failure mode) in the group 14 of cells 12. In yet another example, if all normalized health indicators in the corresponding set of normalized health indicators exceed the corresponding threshold value, the fault detection module 236 may declare a fault (detection of a failure mode) in the group 14 of cells 12.

[0080] The fault detection module 236 can be configured to declare a fault using any combination of detecting one or more failure modes and one or more normalized health indicators exceeding corresponding thresholds. For example, in a factory, before shipping the battery 210, the fault detection module 236 can be configured to declare a fault after detecting a single failure mode and a single normalized health indicator exceeding a corresponding threshold to detect the failure mode. At a service station, the fault detection module 236 can be configured to indicate to a technician which failure mode(s) were detected and which normalized health indicator(s) exceeded the corresponding threshold. The technician can decide whether to repair or replace the battery 210 based on the normalized health indicator exceeding the threshold and the detected failure mode.

[0081] In the vehicle 202, the fault detection module 236 can be configured to provide different levels of warnings (alerts) on the dashboard of the vehicle 202 (or on a handheld device such as a smartphone) based on which failure mode(s) are detected and which normalized health indicators have exceeded corresponding thresholds. Accordingly, recalls and visits to service stations can be optimized. For example, if the electrolyte in the battery 210 is leaking, the warning can be severe (the highest level) because the electrolyte leak may cause corrosion or fire. If the battery 210 is simply aging and the performance of the battery 210 is slowly degrading, the warning can be less severe.

[0082] Further, within the failure modes, warnings (alarms) may be further graded. For example, if a leak is detected in two or more groups 14 of cells 12 or in two or more modules 16, a warning due to the leakage failure mode may be severe, but if only a single leak is detected, a warning due to the leakage failure mode may be less severe. For example, if moisture penetration increases rapidly based on daily monitoring, a warning due to the moisture failure mode may be severe, but if moisture penetration is slow, a warning due to the moisture failure mode may be less severe. For example, if aging based on daily monitoring indicates that the battery 210 is approaching the end of its life prematurely, a warning due to the aging failure mode may be severe, but if aging is slow, a warning due to the aging failure mode may be less severe, and so on.

[0083] Thus, the declaration of whether the cell stack 14 and / or battery 210 is healthy or faulty is not a binary decision based on a single health indicator or a single electrolyte fault type. Instead, the declaration is graded into multiple severity levels of the detected fault and / or the detected failure of the health indicator. In one example, if a single electrolyte fault is detected based on the failure of a single health indicator, the battery 210 may be declared faulty. In other examples, even if an electrolyte fault based on the failure of a health indicator is detected, the battery 210 may still be declared healthy. The fault detection module 236 determines the health of the battery 210 based on which of the electrolyte faults is detected and which of the normalized health indicators exceed corresponding predetermined thresholds.

[0084] Further, the prediction method is not limited to detecting declared electrolyte failures for maintenance purposes only. The output of the prediction module 230 can also be used to modify vehicle control by modifying battery usage. For example, when an electrolyte failure is detected during battery usage in a vehicle, the control module 220 can reduce the power supplied from the battery 210 to one or more subsystems 212 of the vehicle 202. For example, the control module 220 can maintain the power supply from the battery 210 to the subsystems 212 in a prioritized manner. For example, initially, until the battery 210 is repaired after the electrolyte failure is detected, the power supply to comfort systems such as the HVAC subsystem and the infotainment subsystem can be reduced while maintaining the power supply to the propulsion, steering, and braking subsystems of the vehicle 202.

[0085] While the above-mentioned permutations and many other combinations of health indicators can be used to detect and declare electrolyte failure, Figure 8An example of the most robust (conservative) method 400 for declaring a fault is shown, where a single normalized health indicator from a single set of normalized health indicators results in a declaration that the battery 210 is faulty. In the method 400, the battery 210 is declared healthy (i.e., free of any of the three failure modes) only if all normalized health indicators result in all three sets of normalized health indicators being less than or equal to the corresponding thresholds (i.e., if no single normalized health indicator exceeds its threshold). The thresholds are predetermined at the factory before the battery 210 is shipped in the vehicle 202. The method 400 is executed by the fault detection module 236 upon Figure 4 Steps 310 and 312 of the method 300 shown in FIG.

[0086] exist Figure 8 , at 402, the fault detection module 236 determines whether any normalized health indicator in the first set of normalized health indicators (Set 1) exceeds a corresponding threshold. If any normalized health indicator in the first set of normalized health indicators (Set 1) exceeds a corresponding threshold, then at 404, the fault detection module 236 declares that a first type of fault (low capacity failure mode of the electrolyte 108) is detected. At 406, the fault detection module 236 declares that the battery 210 is faulty.

[0087] If no normalized health indicator in the first set of normalized health indicators (Set 1) exceeds the corresponding threshold value (i.e., if all normalized health indicators in the first set of normalized health indicators (Set 1) are less than or equal to the corresponding threshold value), then at 408, the fault detection module 236 determines whether any normalized health indicator in the second set of normalized health indicators (Set 2) exceeds the corresponding threshold value. If any normalized health indicator in the second set of normalized health indicators (Set 2) exceeds the corresponding threshold value, then at 410, the fault detection module 236 declares that the second type of fault (moisture failure mode of the electrolyte 108) has been detected. At 406, the fault detection module 236 declares that the battery 210 is faulty.

[0088] If no normalized health indicator in the second set of normalized health indicators (Set 2) exceeds the corresponding threshold value (i.e., if all normalized health indicators in the second set of normalized health indicators (Set 2) are less than or equal to the corresponding threshold value), then at 412, the fault detection module 236 determines whether any normalized health indicator in the third set of normalized health indicators (Set 3) exceeds the corresponding threshold value. If any normalized health indicator in the third set of normalized health indicators (Set 3) exceeds the corresponding threshold value, then at 414, the fault detection module 236 declares that the third type of fault (aging failure mode of the electrolyte 108) has been detected. At 406, the fault detection module 236 declares that the battery 210 is faulty.

[0089] If no normalized health indicator in the third set of normalized health indicators (Set 3) exceeds the corresponding threshold (i.e., if all normalized health indicators in the third set of normalized health indicators (Set 3) are less than or equal to the corresponding threshold), then at 416, the fault detection module 236 declares that the battery 210 is healthy.

[0090] The above description is essentially illustrative and is not intended to limit the present disclosure, its application or use. The broad teachings of the present disclosure can be implemented in various forms. Therefore, although the present disclosure includes specific examples, the true scope of the present disclosure should not be so limited, as other modifications will become apparent when studying the drawings, the specification and the appended claims. It should be understood that one or more steps in the method can be performed in a different order (or simultaneously) without changing the principles of the present disclosure.

[0091] Further, although each of the embodiments is described above as having certain features, any one or more of those features described with respect to any embodiment of the present disclosure may be implemented in any of the other embodiments and / or combined with features of any of the other embodiments, even if the combination is not explicitly described. In other words, the described embodiments are not mutually exclusive, and the permutation of one or more embodiments with each other remains within the scope of the present disclosure.

[0092] Various terms are used to describe the spatial and functional relationships between elements (e.g., between modules, circuit elements, semiconductor layers, etc.), including "connected," "engaged," "coupled," "adjacent," "immediately next to," "on top of," "above," "below," and "disposed." Unless explicitly described as "directly," when describing a relationship between a first and a second element in the above disclosure, the relationship can be a direct relationship in which no other intervening elements exist between the first and second elements, but can also be an indirect relationship in which one or more intervening elements exist (either spatially or functionally) between the first and second elements. As used herein, the phrase "at least one of A, B, and C" should be understood to mean a logical (A or B or C) using a non-exclusive logical "OR" and should not be understood to mean "at least one of A, at least one of B, and at least one of C."

[0093] In the accompanying drawings, as indicated by the arrows, the direction of the arrows generally demonstrates the flow of information (such as data or instructions) of interest to the illustration. For example, when element A and element B exchange various types of information, but the information transmitted from element A to element B is relevant to the illustration, an arrow may point from element A to element B. This unidirectional arrow does not imply that other information is not transmitted from element B to element A. Furthermore, for information transmitted from element A to element B, element B may send a request for the information or an acknowledgment of receipt of the information to element A.

[0094] In this application, including the definitions below, the term "circuit" may be used instead of the term "module" or the term "controller". The term "module" may refer to, be part of, or include: an application-specific integrated circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field-programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system on a chip.

[0095] The module may include one or more interface circuits. In some examples, the interface circuit may include a wired or wireless interface connected to a local area network (LAN), the Internet, a wide area network (WAN), or a combination thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules connected via the interface circuit. For example, multiple modules may allow for load balancing. In a further example, a server (also referred to as a remote or cloud) module may perform a certain functionality on behalf of a client module.

[0096] As used above, the term "code" may include software, firmware, and / or microcode, and may refer to programs, routines, functions, classes, data structures, and / or objects. The term "shared processor circuit" encompasses a single processor circuit that executes some or all code from multiple modules. The term "group processor circuit" encompasses a processor circuit that, in combination with additional processor circuits, executes some or all code from one or more modules. References to multiple processor circuits encompass multiple processor circuits on discrete dies, multiple processor circuits on a single die, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or combinations of the above. The term "shared memory circuit" encompasses a single memory circuit that stores some or all code from multiple modules. The term "group memory circuit" encompasses a memory circuit that, in combination with additional memory, stores some or all code from one or more modules.

[0097] The term "memory circuit" is a subset of the term "computer-readable medium". As used herein, the term "computer-readable medium" does not encompass transient electrical or electromagnetic signals propagated through a medium (such as on a carrier wave); the term "computer-readable medium" may thus be considered tangible and non-transient. Non-limiting examples of non-transient tangible computer-readable media are non-volatile memory circuits (such as flash memory circuits, erasable programmable read-only memory circuits, or mask read-only memory circuits), volatile memory circuits (such as static random access memory circuits or dynamic random access memory circuits), magnetic storage media (such as analog or digital magnetic tape or hard drives), and optical storage media (such as CDs, DVDs, or Blu-ray discs).

[0098] The apparatus and methods described in this application may be implemented partially or completely by a special-purpose computer, which is created by configuring a general-purpose computer to perform one or more specific functions embodied in a computer program. The functional blocks, flow chart components, and other elements described above serve as software specifications that can be translated into a computer program by a skilled technician or programmer through routine work.

[0099] The computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program may also include or rely on stored data. The computer program may include a basic input / output system (BIOS) that interacts with the hardware of the special-purpose computer, device drivers that interact with specific devices of the special-purpose computer, one or more operating systems, user applications, background services, background applications, etc.

[0100] A computer program may include: (i) descriptive text to be parsed, such as HTML (Hypertext Markup Language), XML (Extensible Markup Language), or JSON (JavaScript Object Notation); (ii) assembly code; (iii) object code generated by a compiler from source code; (iv) source code for execution by an interpreter; (v) source code for compilation and execution by a just-in-time compiler; and so on. By way of example only, source code may be written using syntax from languages ​​including: C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Fortran, Perl, Pascal, Curl, OCaml, HTML5 (Hypertext Markup Language 5th Revision), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Lua, MATLAB, SIMULINK, and

Claims

1. A system comprising: a measurement module configured to: measure a plurality of parameters associated with a battery comprising a cell, the cell comprising an electrolyte; a health indicator module configured to: generate a plurality of health indicators based on the measured parameters; a normalization module, configured to: normalize the health indicator; and combining the normalized health indicators into different sets to detect different types of faults associated with the electrolyte; as well as A fault detection module is configured to detect one or more faults associated with the electrolyte based on one or more of the normalized health indicators in one or more of the sets.

2. The system of claim 1 , wherein the types of faults associated with the electrolyte include: a first fault due to leakage of said electrolyte from one or more of said cells; a second fault, caused by moisture penetrating into one or more of the units; and a third failure due to aging of the electrolyte in one or more of the cells.

3. The system of claim 1 , wherein: The battery comprises a plurality of modules, each module comprising a plurality of cell groups, and each group comprising one or more of the cells; and The fault detection module is configured to detect one or more faults associated with an electrolyte in one of the cell groups.

4. The system of claim 1 , wherein: The battery comprises a plurality of modules, each module comprising groups of cells, and each group comprising one or more of the cells; and The normalization module is configured to normalize the one of the health indicators for the one of the groups of cells in one of the modules by subtracting a median value of the one of the health indicators for the one of the modules from the one of the health indicators for the one of the groups of cells in the modules, The health indicator module is configured to generate the one of the health indicators and a median of the one of the health indicators based on parameters measured during a same charge / discharge cycle of the battery.

5. The system of claim 1 , wherein the fault detection module is configured to: detect one of the faults based on one or more of the normalized health indicators in one of the sets exceeding a corresponding predetermined threshold; and An alarm is generated upon detection of said one of said faults. 6 . The system of claim 1 , wherein the fault detection module is configured to determine the health of the battery based on which of the faults is detected and which of the normalized health indicators exceed corresponding predetermined thresholds.

7. The system of claim 1 , wherein: The battery comprises a plurality of modules, each module comprising groups of cells, and each group comprising one or more cells; The measurement module is configured to: measure parameters including current through the battery, voltage across each cell group, temperature of each cell group, and state of charge of the battery; The health indicator module is configured to: generate a health indicator for each cell group; and The normalization module is configured to normalize each of the health indicators for the one of the groups of cells based on a median value of each of the health indicators for the one of the groups of cells.

8. The system of claim 1 , wherein one of a set of normalized health indicators for detecting a fault due to leakage of the electrolyte from one or more of the cells comprises: (i) the static resistance of the battery during a discharge cycle of the battery; (ii) the change in capacity of the battery during constant current charging of the battery; (iii) the location of the peak of dQ / dV relative to the voltage V of the battery during constant current charging of the battery; (iv) the difference in energy between charge and discharge cycles of the battery; and (v) the ohmic internal resistance of the battery during charge and discharge of the battery.

9. The system of claim 1 , wherein one of the set of normalized health indicators for detecting a failure due to moisture penetration into one or more of the units comprises: (i) a change in capacity of the battery during constant current charging of the battery; (ii) the difference in energy between charge and discharge cycles of the battery; (iii) the duration of discharge for the battery; (iv) the sum of the voltages of the battery during constant current charging of the battery; (v) the peak value of dQ / dV relative to the voltage V of the battery during constant current charging of the battery; and (vi) Polarization resistance during charge and discharge of the battery.

10. The system of claim 1 , wherein one of a set of normalized health indicators for detecting failure due to aging of the electrolyte in one or more of the cells comprises: (i) a change in capacity of the battery during constant current charging of the battery; (ii) a discharge duration for the battery; (iii) the sum of the voltages of the battery during constant current charging of the battery; (iv) the difference in energy between charge and discharge cycles of the battery; (v) the difference in capacity of the battery during charge and discharge of the battery; (vi) the rate of change of voltage of the battery during constant current charging of the battery; (vii) the logarithmic rate of change of current during constant voltage charging of the battery; (viii) the loss of capacity of the anode during constant current charging of the battery; (ix) loss of capacity of the cathode during constant current charging of the battery; (x) loss of lithium inventory during constant current charging of the battery; and (xi) the ohmic internal resistance of the battery during charging and discharging of the battery.

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