ELECTROLYTE ERROR PREDICTION SYSTEM

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 comprising cells having 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 of the faults associated with the electrolyte based on one or more of the normalized health indicators in one or more of the sets.

Description

INTRODUCTION

[0001] The information in this section is intended to provide a general context for the disclosure. Work by the presently named inventors, to the extent described in this section, as well as aspects of the description that may not be prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.

[0002] The present disclosure generally relates to systems for electrolyte fault prediction, in particular for lithium-ion batteries. Reference is made to US 2023 / 0 280 411 A1 as prior art according to the preamble of the appended main claim. Further prior art reference is made to DE 10 2018 216 518 A1, WO 2022 / 169 652 A1, and DE 10 2018 216 517 A1.

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

[0004] One object of the present invention is the further development of known systems for electrolyte fault prediction, particularly in lithium-ion batteries. This object is achieved by a system according to claim 1. SUMMARY

[0005] The present invention is defined by the features of the appended independent claim 1. Advantageous further developments are specified in the following description and in the dependent claims.

[0006] According to the invention, the system comprises 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 comprising cells having 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 electrolyte-related faults. The fault detection module is configured to detect one or more of the electrolyte-related faults based on one or more of the normalized health indicators in one or more of the sets.

[0007] In other features, the failure modes 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 ingress of moisture into one or more of the cells, and a third failure due to aging of the electrolyte in one or more of the cells.

[0008] In other features, the battery comprises a plurality of modules, each module comprising a plurality of cell groups, each group comprising one or more of the cells. The fault detection module is configured to detect one or more electrolyte-related faults in one of the cell groups.

[0009] According to the invention, the battery comprises a plurality of modules, each module comprising cell groups and each group comprising 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 the one of the health indicators for the one of the modules from the one of the health indicators for the one of the cell groups. 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 the parameters measured in the same charge / discharge cycle of the battery.

[0010] 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 a respective predetermined threshold, and to generate an alert upon detecting the one of the faults.

[0011] 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.

[0012] In other features, the measurement module is configured to measure parameters including the current through the battery, the voltages across each cell group, the temperatures of each cell group, and the battery state of charge. The health indicator module is configured to generate health indicators for each cell group. The normalization module is configured to normalize each of the health indicators for one of the cell groups based on median values ​​of each of the health indicators for that one of the cell groups.

[0013] In other features, one of the sets of normalized health indicators for detecting a failure due to electrolyte leakage from one or more of the cells comprises: (i) a static resistance of the battery during a discharge cycle of the battery, (ii) a capacity change of the battery during constant current charging of the battery, (iii) a position of a peak value of dQ / dV relative to the voltage V of the battery during constant current charging of the battery, (iv) an energy difference between charge and discharge cycles of the battery, and (v) an internal ohmic resistance of the battery during charge and discharge of the battery.

[0014] In other features, one of the sets of normalized health indicators for detecting a failure due to moisture ingress into one or more of the cells comprises: (i) a capacity change of the battery during constant current charging of the battery, (ii) an energy difference between charge and discharge cycles of the battery, (iii) a discharge duration for the battery, (iv) a sum of 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) a polarization resistance during charge and discharge of the battery.

[0015] In other features, one of the sets of normalized health indicators for detecting a failure due to electrolyte aging in one or more of the cells comprises: (i) a capacity change of the battery during constant current charging of the battery, (ii) a discharge duration of the battery, (iii) a sum of voltages of the battery during constant current charging of the battery, (iv) an energy difference between charge and discharge cycles of the battery, (v) a capacity difference of the battery during charge and discharge of the battery, (vi) a voltage change rate 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) anode capacity loss during constant current charging of the battery, (ix) cathode capacity loss during constant current charging of the battery,(x) loss of lithium content during charging of the battery with constant current and (xi) an ohmic internal resistance of the battery during charging and discharging of the battery.

[0016] In other features, a vehicle includes the battery and the system. The fault detection module is configured to provide an indication of one or more electrolyte-related faults to control the power supply from the battery to one or more subsystems of the vehicle.

[0017] In still other features, a method comprises measuring a plurality of parameters associated with a battery comprising cells including an electrolyte, and generating a plurality of health indicators based on the measured parameters. The method comprises normalizing the health indicators and combining the normalized health indicators into different sets to detect different types of electrolyte-related faults, and detecting one or more of the electrolyte-related faults based on one or more of the normalized health indicators in one or more of the sets.

[0018] In other features, the failure modes 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 ingress of moisture into one or more of the cells, and a third failure due to aging of the electrolyte in one or more of the cells.

[0019] In other features, the battery comprises a plurality of modules, each module comprising a plurality of cell groups, each group comprising one or more of the cells. The method further comprises detecting one or more electrolyte-related faults in one of the cell groups.

[0020] In other features, the battery comprises a plurality of modules, each module comprising cell groups, each group comprising one or more of the cells. The method further comprises normalizing one of the health indicators for one of the cell groups in one of the modules by subtracting a median value of the one health indicator for the one of the modules from the one of the health indicators for the one of the cell groups.

[0021] In other features, the method further comprises generating the one of the health indicators and the median value of the one of the health indicators based on the parameters measured in the same charge / discharge cycle of the battery.

[0022] 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 a respective predetermined threshold, and generating an alert upon detecting the one of the faults.

[0023] In other features, the method further includes determining the health of the battery based on which of the faults is detected and which of the normalized health indicators exceed the respective predetermined thresholds.

[0024] In other features, the battery comprises a plurality of modules, each module comprising cell groups, and each group containing one or more cells. The method further includes measuring parameters including the current through the battery, the voltages across each cell group, the temperatures of each cell group, and the state of charge of the battery. The method further includes generating the health indicators for each cell group and normalizing each of the health indicators for one of the cell groups based on median values ​​of each of the health indicators for the one of the cell groups.

[0025] Further areas of applicability of the present disclosure will become apparent from the detailed description, claims, and drawings. The detailed description and specific examples are provided for purposes of illustration only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] This disclosure will become more fully apparent from the detailed description and the accompanying drawings, in which: Fig. 1A shows an example of a lithium-ion battery; Fig. 1B shows an example of a cell of the lithium-ion battery from Fig. 1A; Fig. Figure 2 shows an example of a system that includes a vehicle that uses the lithium-ion battery of Fig. 1A and which uses a prediction method of the present disclosure to detect defects in the electrolyte used in the lithium-ion battery of Fig. 1A is used; Fig. 3 shows an example of a control module of the vehicle of Fig. 2, which describes the prognosis method for detecting electrolyte defects in the cells of the lithium-ion battery of Fig. 1A used; Fig. Figure 4 shows an example of a prediction method used by the vehicle control module of Fig. 2 is carried out to detect defects in the electrolyte used in the cells of the lithium-ion battery of Fig. 1A is used; Fig. Figure 5 shows an example of a graph of current versus time during the charging and discharging cycles of the lithium-ion battery from Fig. 1A; Fig. 6 shows an example of an equivalent circuit diagram (ECM) of the lithium-ion battery from Fig. 1A; Fig. Figure 7 shows an example of a normalization method used by the prediction method to calculate health indicators of the cells of the lithium-ion battery of Fig. 1A to detect faults in the electrolyte used in the cells of the lithium-ion battery of Fig. 1A is used; and Fig. Figure 8 shows an example of a method used by the prediction method to predict a failure and a failure mode of the system present in the cells of the lithium-ion battery of Fig. 1A electrolytes used must be declared.

[0027] Reference numbers may be reused in the drawings to identify similar and / or identical elements. DETAILED DESCRIPTION

[0028] The present disclosure provides a prognostic method for detecting various electrolyte failure modes in lithium-ion batteries. The prognostic method uses various health indicators derived from measurements of current, voltage, temperature, and state of charge (SOC) of the battery, as described in detail below. Before describing the prognostic method, an example of a battery and a cell of the battery are first shown and described below with reference to Fig. 1A and Fig. 1B.

[0029] Fig. 1A and Fig. 1B schematically show an example of a lithium-ion battery 10 or a cell 12 of the lithium-ion battery 10. In Fig. 1A, for example, the lithium-ion battery 10 (hereinafter referred to as battery 10) comprises a pack including a plurality of modules, each module comprising a plurality of cell groups. For example, in a cell group, one or more cells C1, C2, ..., Cc, each designated 12-1, 12-2, ..., 12-c, where c is an integer greater than or equal to 1 (collectively referred to as the cells 12), are connected in parallel to each other. In a module, a plurality of cell groups 14 G1, G2, ..., Gg, each designated 14-1, 14-2, ..., 14-g, where g is an integer greater than 1 (collectively referred to as the groups 14 or the cell groups 14), are connected in series to each other. In a pack called P and designated 18, several modules M1, M2, ..., Mm, designated 16-1, 16-2, ..., 16-m, where m is an integer greater than 1 (collectively called the modules 16 or the cell modules 16), are connected in series.A temperature sensor T, designated 13, can be arranged in each group 14. Alternatively or additionally, one or more temperature sensors can also be arranged in each module 16 (not shown).

[0030] In some batteries, the pack 18 cannot contain multiple modules 16 within the pack 18. Instead, the groups 14 of cells 12 are connected in series and arranged within the pack 18 rather than in multiple modules 16. For these batteries, the pack 18 therefore corresponds to a single module 16, and m = 1. In the following description, the forecasting method calculates median values ​​of health indicators for each module 16 to normalize the health indicators for each group 14 in the corresponding module 16. If, instead, the pack 18 comprises the groups 14 and functions like a single module 16, the median values ​​of the health indicators for the pack 18 can be calculated in the same way as the median values ​​for a module 16. Then, the health indicators for each group 14 in the pack 18 can be normalized using the median values ​​of the health indicators calculated for the pack 18.

[0031] Although not shown, some batteries may connect multiple packs, such as pack 18, in series or parallel, or use a combination of series and parallel connections, with connections that can be switched between series and parallel connections depending on the power requirements of one or more loads. For simplicity, battery 10 is assumed to include pack 18, although the present disclosure is not so limited and may be extended to batteries with multiple packs.

[0032] A measurement module 50 can be connected across pack 18 (i.e., across battery 10) to measure current, voltage, and temperature and to estimate the SOC of battery 10. Measurement module 50 can be implemented, for example, in a control module of a vehicle or in a test facility in a manufacturing facility, laboratory, or service facility. Measurement module 50 includes a current measurement circuit 52, a plurality of voltage measurement circuits 54, a plurality of temperature measurement circuits, and an SOC estimation circuit 58.

[0033] In some examples, the measurement module 50 need not necessarily be a single module. For example, each module 16 and pack 18 may contain a measurement module similar to the measurement module 50.

[0034] The current measuring circuit 52 can measure a current I 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 measuring circuits 54 can measure voltages at each group 14 of cells 12. The temperature measuring circuits 56 can measure the temperatures 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 a nominal capacity of the battery 10. The SOC can be estimated, for example, from the open circuit voltage of the battery 10 or using a Coulomb counting method.

[0035] In some examples not shown, a vehicle-mounted battery management system (BMS) may be used to estimate the SOC and control the battery's charge / discharge voltages / currents. In addition, the SOC may be estimated for the pack 18 and / or the groups 14 of cells 12.

[0036] 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 of each health indicator for each module 16. The health indicators for each group 14 are normalized based on the median values ​​of the health indicators for the module 16 comprising the groups 14. The normalized health indicators are then used to detect failures (failure modes) of an electrolyte in the cells 12 of each group 14, as described in detail below.

[0037] Fig. 1B schematically shows an example of cell 12. Cell 12 includes a cathode (K+) 102, an anode (A-) 104, a separator 106, an electrolyte 108, and current collectors 110, 112. Cathode 102 is the positive electrode. Anode 104 is the negative electrode. During charging of battery 10, in each cell 12, lithium ions flow from cathode 102 to anode 104 through separator 106 and electrolyte 108, as shown by arrow 120. During discharging (i.e., when lithium-ion battery 10 is powering a load, e.g., a vehicle subsystem), lithium ions flow from anode 104 to cathode 102 through separator 106 and electrolyte 108, as shown by arrow 122.

[0038] The electrolyte 108 conducts the movement of ions between the electrodes inside the battery 10. The electrolyte 108 can be damaged for various reasons. The failure can occur during the manufacturing of the battery 10, the storage of the battery 10 (e.g., in storage facilities after manufacturing and before shipping the battery 10, in parked vehicles, etc.), and the use of the battery 10 in vehicles for extended periods.

[0039] Generally, electrolyte failures are categorized as failures due to low volume (e.g., leakage) of the electrolyte 108 (e.g., due to physical damage to the battery 10 causing the leakage), moisture ingress into the electrolyte 108 (e.g., due to physical damage to the battery), and aging of the electrolyte 108. In the present disclosure, these three types of failures of the electrolyte 108 are also referred to as failure modes of the electrolyte 108: low volume failure or low volume failure mode, moisture failure or moisture failure mode, and aging failure or aging failure mode.

[0040] The low volume defect may occur during manufacturing of the battery 10 when a smaller amount of electrolyte 108 is added to the battery 10 before sealing the battery 10. During storage or use of the battery 10, the low volume defect may occur if the electrolyte 108 leaks due to physical damage to the battery 10 during storage or in the vehicle. The moisture defect may occur when the electrolyte 108 is exposed to the environment before the electrolyte 108 is introduced into the battery 10 during manufacturing. During storage or use, the moisture defect may occur when moisture enters the electrolyte 108 through openings (e.g., holes) in the battery 10 that were caused by physical damage to the battery 10 during storage or in the vehicle.The aging defect may occur during manufacturing if aged electrolyte 108 is added to the battery 10 during manufacturing. During storage or use, the aging defect may occur if the battery 10 is stored and remains unused for an extended period of time before or during use of the battery 10 in the vehicle.

[0041] Electrolyte defects can lead to performance degradation or even ignition or explosion due to the corrosive nature of the electrolyte 108. Electrolyte defects can be detected offline (i.e., with the battery 10 outside the vehicle, e.g., in a service facility, a laboratory, etc.). Offline methods for detecting electrolyte defects use testing equipment such as spectrometers or devices for measuring the conductivity of the electrolyte 108. Offline defect detection methods are difficult to apply in vehicles.

[0042] Furthermore, using the same standards as offline fault detection methods to predict various electrolyte faults can be challenging due to the different electrolyte fault types. For example, the voltage-time scattergrams for a healthy electrolyte and the three electrolyte fault modes are indistinguishably close to each other. Therefore, it can be difficult to distinguish faulty electrolyte from healthy electrolyte and to isolate the type of fault (fault mode). While there are several health indicators used to determine the condition of battery 10, the performance of these health indicators to detect electrolyte failures and various fault types is not well established and largely unknown in the field.

[0043] If only one cell 12 in a group 14 or one module 16 is defective, the health indicators used to determine the condition of the battery 10 will not differ significantly. Instead, to determine the faulty cell or group of cells, the type of electrolyte failure, and the severity of the electrolyte failure, various health indicators must be combined, normalized to the median value of the health indicators for each module 16, and compared to respective thresholds. Various types of logic (e.g., conservative logic, lenient logic, or a combination thereof) must then be used to declare a failure based on the comparisons.

[0044] The present disclosure provides a prediction method that uses a combination of different health indicators to detect three types of electrolyte failure modes: low volume (leaking), moisture, and aging. The prediction method uses three sets of health indicators in combination with thresholds and decision tree logic to detect each of the three failure modes. Some of the health indicators used are common to two or all three sets, but when combined with other health indicators in each set, have different effects on detecting each failure mode.

[0045] Fault detection using the prediction method described below is performed for each group 14 of cells 12. This means that the prediction method of the present disclosure detects electrolyte faults at the cell group level, not for the battery as a whole. Thus, 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 SOC of the battery 10 is measured. Various health indicators are then created from the measured values ​​of each group 14. The health indicators are divided into three sets to detect the three types of electrolyte faults.

[0046] In each set, the health indicators are normalized based on median values ​​of the health indicators for the module 16, which comprises the group 14 of cells 12. The median values ​​are not predetermined or precalibrated. Rather, the median values ​​are calculated in real time when the health indicators are calculated. Because the health indicators and the median values ​​are calculated based on measurements taken on the battery 10 at the same time (e.g., during the same charge / discharge cycle), the health indicators and the median values ​​reflect the same aging and other environmental influences affecting the battery 10. After normalization, the health indicators are compared to respective threshold values. After that, various types of logic are used to declare a fault.

[0047] To capture electrolyte faults at the cell group level rather than the battery level, the selected health indicators and thresholds are not merely a matter of design. Rather, the health indicators and thresholds are selected empirically by analyzing the impact of each type of electrolyte fault on each health indicator and on various combinations of health indicators, and selecting the specific health indicators and combinations based on the analyses. Other selection approaches include machine learning techniques such as random forest, support vector machine (SVM), neural networks, deep neural networks, and so on.

[0048] One of the electrical parameters of the battery 10 that is affected by each of the three failure modes of the electrolyte 108 is an internal resistance of the battery 10, which is given by the following equation: Gutter=RSEI+RTαF(12aLsSFkCe(Cs,max−Ce,s)Ce,s)+(L2KeffS) where C e the concentration of the electrolyte 108, L the length of the path of the electrolyte 108 (ie thickness or width of the battery), K eff the conductivity of the electrolyte 108 and L s is the thickness of the solid phase (electrode).

[0049] Electrolytes typically contain a lithium salt solution, such as a lithium salt, and a solvent. One or more failure modes can affect one or more parameters of the above equation, which in turn changes the internal resistance of the battery 10. For example, the solvent used in the electrolyte 108 can deteriorate and decompose due to the aging of the electrolyte 108, thereby increasing the concentration C e of the lithium salt in the electrolyte 108. A small volume of the electrolyte 108 can reduce L, and moisture penetrating into the electrolyte 108 can increase the concentration Ce of the electrolyte 108. The length (L) influences the cell resistance, the capacity and the pack insulation resistance of the battery 10. The concentration (C e ) of the electrolyte 108 changes the resistance and capacity of the battery 10.

[0050] Changes in the internal resistance of battery 10 are reflected in the current and voltage measurements of battery 10. Furthermore, environmental conditions such as the ambient temperature and the temperature of battery 10 can also change the internal resistance of battery 10. However, measuring only these electrical parameters of battery 10 may not indicate an electrolyte fault, because changes in these electrical parameters due to an electrolyte fault in one or a few cells among a large number of cells in battery 10 do not cause a measurable deviation in these electrical parameters of battery 10. Instead, using the predictive method of the present disclosure, various health parameters can be calculated, normalized, and used to detect electrolyte faults as follows.

[0051] Fig. 2 shows a system 200 that uses the prediction method of the present disclosure to detect electrolyte defects in the cells of a lithium-ion battery in a vehicle 202. The system 200 includes the vehicle 202, a distributed communications system 204, a server 206, a service facility 208-1 (e.g., a car dealership or gas 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 collectively be referred to as external facilities 208 or simply as facilities 208.

[0052] The forecasting method of the present disclosure described below may be performed in the vehicle 202 or in the external devices 208 (e.g., on a laptop or a handheld device). The forecasting method may be performed at least partially on the server 206 (also called a remote server or server in a cloud). For example, data (e.g., current, voltage, temperature, and SOC measurements) from the battery 210 may be transmitted from the vehicle 202 and from the external devices 208 to the server 206, which analyzes the data and creates the forecast using the forecasting method of the present disclosure.

[0053] The vehicle 202 includes a lithium-ion battery (the battery) 210, a plurality of vehicle subsystems 212, and a control module 220. The battery 210 is similar to the one shown in the Fig. 1A and Fig. 1B. The battery 210 supplies power to various vehicle subsystems 212 of the vehicle 202. The vehicle subsystems 212 include various electrical, mechanical, and electromechanical subsystems of the vehicle 202. Non-limiting examples of the vehicle subsystems 212 include a propulsion subsystem having one or more 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, etc. The control module 220 communicates with the battery 210 and the vehicle subsystems 212 and controls the vehicle subsystems 212. The control module 220 is in Fig. 3 and described in more detail.

[0054] The distributed communications system 204 includes one or more networks (wired and / or wireless) such as the Internet, local and / or wide area networks, and so on. The vehicle 202 (e.g., the control module 220) communicates with the server 206 via the distributed communications system 204. A computer, such as a laptop or handheld device in the external facilities 208, communicates with the server 206 via the distributed communications system 204.

[0055] A handheld device, such as a smartphone (not shown), may also be used in vehicle 202. For example, the handheld device in vehicle 202 may communicate with control module 220 via Bluetooth. The handheld device in vehicle 202 may also communicate with server 206 and external devices 208 via distributed communications system 204.

[0056] Fig. 3 shows the control module 220 of the vehicle 202. The control module 220 includes a measurement module 222 and a prediction module 230. The measurement module 222 is similar to the measurement module 50 described above with reference to Fig. 1B. The prediction module 230 includes a health indicator calculation module (HI module) 232, a normalization module 234, and an error detection module 236.

[0057] The measurement module 222 measures (e.g., captures) various parameters (e.g., current, voltage, temperature, and SOC measurements) of the battery 210. The HI module 232 calculates various health indicators of the battery 210 (see 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 one of the three electrolyte fault modes described above. The operations of the prognostic module 230, including calculating and grouping the health indicators, normalizing the health indicators, and detecting the fault modes, are described in detail below.

[0058] The prediction module 230 can also be implemented in a computing device such as a laptop or a handheld device in the external devices 208 and in the server 206. Thus, the prediction method of the present disclosure can be performed entirely in the vehicle 200 and entirely in the external devices 208. The prediction method of the present disclosure can also be performed partially in the vehicle 200 and partially in the external devices 208 in conjunction with the server 206. The measurements can be performed, for example, in the vehicle 202 or in the external devices 208 and sent to the server 206 for evaluation. The server 206 can send the result of the analysis, e.g., whether the battery 210 is defective and needs to be serviced or replaced, to the vehicle 202 or to the external devices 208. The results can be displayed on a dashboard of the vehicle 202 or on the handheld device in the vehicle.The results can be displayed on the handheld device or the test device at the external facilities 208. A service technician or a person at the external facilities 208 can decide whether the battery 210 should be serviced or discarded.

[0059] Fig. 4 shows a method 300 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 shown in Fig. 7 shown and described.

[0060] At 308, the fault detection module 236 groups the normalized health indicators into three sets to detect each of the three failure modes of the electrolyte 208. In some examples, the health indicators may be grouped prior to normalization. At 310, the fault detection module 236 compares the normalized health indicators in each set to respective thresholds selected for the type of electrolyte failure to be detected by the respective set of normalized health indicators. At 312, the fault detection module 236 detects whether the battery 210 has one of the three failure modes described above (i.e., it detects one or more types of failures in the electrolyte 208 based on the comparisons). Steps 310 and 312 are described below with reference to Fig. 8 is presented and described in more detail.

[0061] Before describing the health indicators in detail, different periods during the charging and discharging cycles of the battery 210 are described using Fig. 5. Some of the health indicators are measured during these periods. In addition, an equivalent circuit model (ECM) of the battery 210 is shown and described with reference to Fig. 6. Various battery parameters used to derive some health indicators are described in Fig. 6 shown and described.

[0062] Fig. Figure 5 shows a graph of current versus time during the charge and discharge cycles 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, the time t0 to t1 is a constant current charging period of the battery 210, during which the voltage of the battery 210 increases. From time t1 to t2, the battery 210 is charged with a constant voltage. Thus, the time t1 to t2 is a constant voltage charging period of the battery 210, during which the current of the battery 210 decreases. From time t3 to t4, the battery 210 is in the idle state (i.e., it is not connected to a load; neither charging nor discharging).

[0063] From time t5 to t6, the battery 210 is discharged when the battery 210 supplies a constant current to a load (e.g., a motor of the propulsion subsystem of the vehicle 202). The voltage of the battery 210 decreases during time t5 to t6. From time t7 to t8, the battery 210 is in the idle state (i.e., it is not connected to a load; neither charging nor discharging). The time periods t2 to t3 and t6 to t7 are referred to as transition periods to the idle state of the battery 210.

[0064] Fig. Figure 5 shows the battery current during charge and discharge cycles for a healthy battery. If the battery 210 degrades (e.g., due to one or more electrolyte defects), the battery voltage and current change during the charge and discharge cycles. The current is controlled during constant current charging and during discharging. The current changes during constant voltage charging. The times t1 - t7 shift. These changes and shifts, which reflect one or more electrolyte defects, are detected by the various health indicators described below.

[0065] Fig. 6 shows an equivalent circuit diagram (ECM) of the battery 210. The ECM specifies an open circuit voltage (OCV) for the battery 210, ie the voltage between the cathode and the anode of the battery 210 when no load is connected to the battery 210. R0 (also called ECM_R0) is the ohmic internal resistance of the battery 210. R1 (also called ECM_R1) is the polarization internal resistance of the battery 210. C1 is the polarization capacitance of the battery 210. V1 is the polarization voltage of the battery 210. I is the discharge current of the battery 210, and Vd is the terminal voltage of the battery 210. As the battery 210 degrades (e.g., due to one or more electrolyte faults), these ECM parameters of the battery 210 also change. The changes in these ECM parameters of the battery 210, which reflect one or more electrolyte faults, are detected by the various health indicators described below.

[0066] The fault detection module 236 uses a first set of health indicators (HI Set 1) to detect the low volume fault or failure mode of the electrolyte 208. The fault detection module 236 uses a second set of health indicators (HI Set 2) to detect the 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 the aging fault or aging failure mode of the electrolyte 208. The values ​​of these health indicators change based on the various failure modes of the electrolyte 208.

[0067] The HI module 232 calculates the health indicators in the first set of health indicators (HI Set 1) based on the measurements performed 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_dchg=Vt7−Vt6It7−It6 (ii) A health indicator, referred to as the capacity change of the battery 210, measured in terms of the 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 during constant current charging between times t0 and t1. The health indicator dQ_cc_chg is calculated using the following formula: dQ_cc_chg=∫t0t1Idt When the capacity of battery 210 has decreased, the voltage of battery 210 increases from the first to the second value with a smaller charge than the amount of charge required to increase the voltage of battery 210 from the first to the second value when the battery is at its nominal capacity. Therefore, a low value of dQ_cc_chg may indicate a fault. (iii) A health indicator, denoted as the position of the peak value of the charge change rate Q relative to the 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 denotes a peak position (i.e., a position of the peak value) of dQ / dV relative 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 where 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: V=V[max(ddV∫t0t1Idt).index] A shift of dQ / dV_cc_chg_peak_position (i.e. the voltage V at which the dQ / dV peak occurs) to the left or right along the x-axis compared to where the peak is in a healthy battery may indicate a fault. (iv) A health indicator indicating the energy difference between charge and discharge cycles of the battery 210 in a selected voltage range is denoted by dE. The health indicator energy difference (also called energy loss) dE is calculated using the following formula: dE=∫t0t2VIdt−∫t5t6VIdt (v) A health indicator, referred to as the ohmic internal resistance R0 of the battery 210, is denoted by ECM_R0. The health indicator ECM_R0 is determined by the Fig. 6 during charging and discharging of the battery 210.

[0068] The HI module 232 calculates the health indicators in the second set of health indicators (HI Set 2) based on the measurements performed 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 over a selected voltage range during a discharge cycle, is specified as dT_cc_dchg. The health indicator dT_cc_dchg is calculated using the following formula: dT_cc_dchg=t6−t5 The discharge time over the selected voltage range depends on the health of the battery 210. (ii) A health indicator, which is defined as the sum of voltages of the battery 210 during a constant current charging period of a charging cycle of the battery 210 (from time t0 to t1), is denoted by Vsum_cc. The health indicator Vsum_cc is calculated according to the following formula: Vbased=∑k=t0t1V(k) (iii) A health indicator, referred to as the peak value of the charge change rate Q with respect to voltage (dQ / dV) of the battery 210 with respect to 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 denotes a peak value of dQ / dV relative 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: dQdVpeak value=max(ddV∫t0t1Idt) (iv) A health indicator, referred to as polarization resistance R1 of the battery 210, is denoted by ECM_R1. The health indicator ECM_R1 is determined by the Fig. 6 during charging and discharging of the battery 210.

[0069] The HI module 232 calculates the health indicators in the third set of health indicators (HI Set 3) based on the measurements performed 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 of capacity (also called capacity loss) of the battery 210 due to aging of the battery 210, is denoted by dC. The health indicator dC is a capacity difference of the battery 210 over a selected voltage range during charging and discharging. The health indicator dC is calculated using the following formula: dC=∫t0t2Idt−∫t6t5Idt (ii) A health indicator, referred to as the change in voltage V of the battery 210, is denoted by dV / dt. The health indicator dV / dt is measured over a selected voltage range during constant current charging of the battery 210. The health indicator dV / dt is calculated using the following formula: dV / dt=Vt1−Vt0t1−t0 (iii) A health indicator, referred to as the logarithmic rate of change of current during a constant voltage charging period of the battery 210, is denoted by dln(I) / dt. The health indicator dln(I) / dt is calculated using the following formulas.

[0070] During charging of the battery at constant voltage (from t2 to t3), the current through the battery 210 follows the equation: In(I) = at + In(I(0)), where a is the slope of In(I), i.e., a = dln(I) / dt, which is the health indicator.

[0071] Let [a, ln(I(0))] T = A, [t, 1] = X ln(I)=X*A

[0072] A can then be calculated using the least squares method as follows: XTln(I)=XTX*A (XTX)−1XTln(I)=(XTX)−1XTX*A (XTX)−1XTln(i)=A A[0] is a; a=A[0]=dln(I)dt, which is the health indicator. The slope a changes due to one or more electrolyte imbalances.

[0073] (iv-vi) For the battery 210, the relationships between the charge Q and the open-circuit electrode potentials (EP) of the anode and cathode (OCPan and OCPca) during constant current charging of the battery 210 can be mathematically derived by recording SOC curves at two different charging rates. The electrode potentials are potentials of the cathode and anode relative to a third reference electrode, not relative to each other. The electrode potentials relative to a third reference electrode are mathematically derived without using a third reference electrode and without measuring the electrode potentials relative to a third reference electrode.

[0074] The electrode potentials (EP) are calculated by the HI module 232 based on mathematical analyses of the measurements performed by the measurement module 222. The electrode potentials are influenced by one or more electrolyte faults. From the electrode potentials, the HI module 232 mathematically derives three EP-based health indicators: capacity loss (anode), denoted EP_LoCan, capacity loss (cathode), denoted EP_LoCca, and loss of lithium supply, denoted EP_LLI. These three health indicators change due to one or more electrolyte faults. Changes in these three health indicators therefore reflect one or more electrolyte faults.

[0075] The HI module 232 calculates all of the above-mentioned health indicators for each cell group 14 in the battery 210. To calculate the above-mentioned health indicators, the measuring module 222 measures the current flowing through the battery 210, i.e., the same current flowing through all cell groups 14 in the battery 210. The measuring module 222 measures the voltages at each cell group 14 in each module 16 of the battery 210. Thus, if in the battery 210, each of the M modules 16 comprises G cell groups 14, the measuring module 222 measures M x G voltages at M x G cell groups 14. The measuring module 222 also measures the temperatures of the individual M x G cell groups 14. The measuring module 222 measures the SOC of the battery 210.

[0076] All of these measurements are taken at the time the health indicators are calculated in the same charge / discharge cycle of the battery 210. Measurements from one charge / discharge cycle are not used to calculate the health indicators in a different charge / discharge cycle. Accordingly, the measurements and the health indicators calculated from the measurements reflect the same effects due to the aging of the battery 210 and the same environmental influences (e.g., the ambient temperature, the temperature of the battery 210, etc.) on the battery 210.

[0077] The normalization module 234 normalizes each health indicator in the three sets of health indicators for each cell group 14 as follows. For each health indicator, the HI module 232 also calculates a corresponding median health indicator value for the module 16 that includes the cell group 14. To normalize a health indicator for a cell group 14 in a module 16, the normalization module 234 subtracts the corresponding mean or median health indicator for the module 16 from the health indicator of the cell group 14. The normalization is represented by the following equation: Normalized indicator = indicator[i] - baseline[i] , where i is the cycle number i = 1,2,3 ...

[0078] After each charge / discharge cycle of the battery 210, the cycle number index is increased by 1, and the same index (i.e., the measurements and HI calculations performed in the same charge / discharge cycle) is used to normalize the health indicators.

[0079] Fig. 7 shows an example of a normalization method 350 (step 306 in Fig. 4) used 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 a health indicator for each cell group 14 in a module 16 during a charge / discharge cycle of the battery 210.

[0080] At 354, the HI module 232 calculates a median health indicator value for the module 16 that includes the cell group 14. For example, the HI module 232 calculates an average of the health indicator values ​​calculated for all cell groups 14 within the module 16 to determine the median health indicator value for the module 16. At 356, the normalization module 234 normalizes the health indicator for a cell group 14 in the module 16 by subtracting the median health indicator value from the health indicator value for the cell group 14.

[0081] At 358, the normalization module 356 determines whether all health indicators in the three sets of health indicators for cell group 14 are normalized. If not all health indicators in the three sets of health indicators for cell group 14 are normalized, the normalization module 356 selects the next health indicator to be normalized at 360, and the method 350 returns to 352.

[0082] If all health indicators in the three sets of health indicators are normalized for cell group 14, the normalization module 356 determines at 362 whether all health indicators in the three sets of health indicators are normalized for all cell groups 14 in module 16. If not all health indicators in the three sets of health indicators are normalized for all cell groups 14 in module 16, the normalization module 356 selects the next cell group 14 at 364, and the method 350 returns to 352.

[0083] When all health indicators in the three sets of health indicators for all cell groups 14 in module 16 are normalized, the normalization module 356 determines at 366 whether all health indicators for all modules 16 in battery 210 are normalized. If not all health indicators for all modules 16 in battery 210 are normalized, the normalization module 356 selects the next module 16 at 368, and the method 350 returns to 352. When all health indicators for all modules 16 in battery 210 are normalized, the normalization of all health indicators for battery 210 is complete, and the method 350 ends.

[0084] The fault detection module 236 groups the normalized health indicators into the 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 the three electrolyte faults (fault modes) 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 (fault mode) detected in a single group 14 of cells 12. In another example, the fault detection module 236 may declare a fault when two or more faults (fault modes) are detected in a single group 14 of cells 12. In another example, the fault detection module 236 may only declare a fault when all three faults (fault modes) are detected in a single group 14 of cells 12.

[0085] Upon detecting a failure (failure mode) in a group 14 of cells 12, the failure detection module 236 compares each normalized health indicator in a corresponding set of normalized health indicators used to detect the failure with a corresponding threshold. In one example, the failure detection module 236 may declare a failure (detection of a failure mode) in a group 14 of cells 12 when a single normalized health indicator in the corresponding set of normalized health indicators exceeds a corresponding threshold. In another example, the failure detection module 236 may declare a failure (detection of a failure mode) in a group 14 of cells 12 when two or more normalized health indicators in the corresponding set of normalized health indicators exceed corresponding thresholds.In another example, the fault detection module 236 may declare a fault (detection of a fault mode) in a group 14 of cells 12 when all normalized health indicators in the corresponding set of normalized health indicators exceed corresponding thresholds.

[0086] The fault detection module 236 may be configured to declare a fault using any combination of detecting one or more failure modes and one or more normalized health indicators that exceed corresponding thresholds. For example, at the factory, prior to shipping the battery 210, the fault detection module 236 may be configured to declare a fault after detecting a single failure mode and a single normalized health indicator that exceeds a corresponding threshold to detect a failure mode. At a service facility, the fault detection module 236 may be configured to indicate to a technician which failure mode(s) were detected and which normalized health indicator(s) exceeded the corresponding thresholds.Based on the normalized health indicators that have exceeded the thresholds and the detected failure modes, the technician can decide whether the battery 210 should be serviced or replaced.

[0087] In the vehicle 202, the fault detection module 236 may be configured to issue different levels of warnings (alerts) on an instrument panel of the vehicle 202 (or on a portable device such as a smartphone) depending on the detected fault mode(s) and the normalized health indicator(s) that have exceeded corresponding thresholds. In this way, recalls and service facility visits can be optimized. For example, if the electrolyte in the battery 210 is leaking, the warning may be severe (the highest level), as the electrolyte leakage could cause corrosion or fire. The warning may be less severe if the battery 210 is simply aging and the performance of the battery 210 is slowly deteriorating.

[0088] Within a failure mode, the alerts can be further graded. For example, a leak warning may be severe if leaks are detected in two or more groups 14 of cells 12 or in two or more modules 16, but less severe if only a single leak is detected. For example, a moisture warning may be severe if moisture ingress is increasing rapidly based on daily monitoring, but less severe if moisture ingress is occurring slowly. For example, an aging warning may be severe if aging based on daily monitoring indicates that the battery 210 is approaching the end of its service life prematurely, but less severe if aging is occurring slowly, etc.

[0089] Thus, the determination that a cell group 14 and / or the battery 210 is healthy or defective is not a binary decision based on a single health indicator or a single type of electrolyte defect. Rather, the determination is broken down into multiple severities of the detected defect and / or the defect of the detected health indicator. In one example, the battery 210 may be declared defective if a single electrolyte defect is detected based on the defect of a single health indicator. In other examples, the battery 210 may be declared healthy even if an electrolyte defect is detected based on the defect of a health indicator. The defect detection module 236 determines the state of the battery 210 based on which of the electrolyte defects is detected and which of the normalized health indicators exceed respective predetermined thresholds.

[0090] Furthermore, the prediction method is not limited to detecting electrolyte faults for service purposes. The output of the prediction module 230 can also be used to modify vehicle control by changing battery usage. For example, if an electrolyte fault is detected during use of the battery in the vehicle, the control module 220 can reduce the power supplied by the battery 210 to one or more subsystems 212 of the vehicle 202. For example, the control module 220 can maintain power from the battery 210 to the subsystems 212 in a prioritized manner. For example, until the battery 210 is serviced after detecting an electrolyte fault, power to convenience systems such as the HVAC subsystem and the infotainment subsystem can be reduced, while power to the propulsion, steering, and braking subsystems of the vehicle 202 is maintained.

[0091] While the above permutations and many other combinations of health indicators can be used to detect and declare electrolyte defects, Fig. 8 shows an example of a highly robust (conservative) method 400 for declaring a fault, in which a single normalized health indicator in 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., without any of the three failure modes) only if all normalized health indicators in all three sets of normalized health indicators are less than or equal to the corresponding threshold values ​​(i.e., if no single normalized health indicator exceeds its threshold value). The threshold values ​​are set at the factory before the battery 210 is delivered in the vehicle 202. The method 400 is implemented by the fault detection module 236 when performing steps 310 and 312 of the method described in Fig. 4 is carried out.

[0092] In Fig.8, the fault detection module 236 determines at 402 whether any normalized health indicator in the first set of normalized health indicators (Set 1) exceeds a corresponding threshold. If any of the normalized health indicators in the first set of normalized health indicators (Set 1) exceeds a corresponding threshold, the fault detection module 236 declares at 404 that the first type of fault (low volume failure mode of electrolyte 108) has been detected. At 406, the fault detection module 236 declares that the battery 210 is defective.

[0093] If none of the normalized health indicators in the first set of normalized health indicators (Set 1) exceeds a corresponding threshold (i.e., if all normalized health indicators in the first set of normalized health indicators (Set 1) are less than or equal to their respective thresholds), the fault detection module 236 determines at 408 whether any normalized health indicator in the second set of normalized health indicators (Set 2) exceeds a corresponding threshold. If any of the normalized health indicators in the second set of normalized health indicators (Set 2) exceeds a corresponding threshold, the fault detection module 236 declares at 410 that the second type of fault (electrolyte 108 moisture fault) has been detected. At 406, the fault detection module 236 declares that the battery 210 is defective.

[0094] If none of the normalized health indicators in the second set of normalized health indicators (Set 2) exceeds a corresponding threshold (i.e., if all normalized health indicators in the second set of normalized health indicators (Set 2) are less than or equal to their respective thresholds), the fault detection module 236 determines at 412 whether any normalized health indicator in the third set of normalized health indicators (Set 3) exceeds a corresponding threshold. If any of the normalized health indicators in the third set of normalized health indicators (Set 3) exceeds a corresponding threshold, the fault detection module 236 declares at 414 that the third type of fault (aging fault mode of the electrolyte 108) has been detected. At 406, the fault detection module 236 declares that the battery 210 is defective.

[0095] If none of the normalized health indicators in the third set of normalized health indicators (Set 3) exceeds a 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 respective thresholds), the fault detection module 236 declares at 416 that the battery 210 is healthy.

[0096] The foregoing description is merely illustrative in nature and is not intended to limit the disclosure, its application, or uses. The broad teachings of the disclosure may be embodied in a variety of forms. Therefore, although this disclosure contains specific examples, the true scope of the disclosure should not be so limited, since other modifications will become apparent upon a study of the drawings, the specification, and the following claims. It is to be understood that one or more steps within a method may be performed in a different order (or simultaneously) without altering the principles of the present disclosure.

[0097] Although each of the embodiments is described above with specific features, any one or more of these features described with respect to any embodiment of the disclosure may be implemented in one of the other embodiments and / or combined with features of another embodiment, even if this combination is not explicitly described. In other words, the described embodiments are not mutually exclusive, and permutations of one or more embodiments with each other remain within the scope of this disclosure.

[0098] Spatial and functional relationships between elements (e.g., between modules, circuit elements, semiconductor layers, etc.) are described using various terms, e.g., "connected," "engaging," "coupled," "adjacent," "beside," "on," "over," "below," and "disposed." When a relationship between a first and a second element is not explicitly described as "direct" in the above disclosure, that relationship may be a direct relationship, with no other intervening elements present between the first and second elements, or it may be an indirect relationship, with one or more intervening elements (either spatial or functional) present between the first and second elements.As used herein, the phrase “at least one of A, B, and C” should be construed as logical (A OR B OR C) using a non-exclusive logical OR, and not as “at least one of A, at least one of B, and at least one of C.”

[0099] In the figures, the direction of an arrow, as indicated by the arrowhead, generally indicates the flow of information (e.g., data or instructions) of interest for the representation. For example, if element A and element B exchange a lot of information, but the information transmitted from element A to element B is relevant for the representation, the arrow may point from element A to element B. This unidirectional arrow does not imply that no further information is transmitted from element B to element A. Also, for information sent from element A to element B, element B may send requests for, or acknowledgments of, the information to element A.

[0100] In this application, including the definitions below, the term "module" or the term "controller" may be replaced by the term "circuit". The term "module" may refer to, be a part of, or contain: 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); processor circuitry (common, dedicated, or group) executing code; memory circuitry (common, dedicated, or group) storing code executed by the processor circuitry; other suitable hardware components providing the described functionality; or a combination of some or all of the above, e.g., in a system-on-chip.

[0101] The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any module of the present disclosure may be distributed among multiple modules connected via interface circuits. For example, multiple modules may enable load balancing. In another example, a server module (also referred to as a remote or cloud module) may perform some functions on behalf of a client module.

[0102] The term code, as used above, 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" includes a single processor circuit that executes some or all of the code from multiple modules. The term "group processor circuit" includes a processor circuit that, in combination with other processor circuits, executes some or all of the code from one or more modules. References to multiple processor circuits include multiple processor circuits on discrete chips, multiple processor circuits on a single chip, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or a combination of the above.The term "shared memory circuit" refers to a single memory circuit that stores some or all of the code from multiple modules. The term "group memory circuit" refers to a memory circuit that, in combination with other memories, stores some or all of the code from one or more modules.

[0103] The term "memory circuit" is a subset of the term "computer-readable medium." The term "computer-readable medium," as used herein, does not include transitory electrical or electromagnetic signals that propagate through a medium (e.g., on a carrier wave); the term "computer-readable medium" can therefore be considered tangible / material and non-transitory. Non-limiting examples of a non-transitory, tangible, computer-readable medium include non-volatile memory circuits (e.g., a flash memory circuit, an erasable, programmable read-only memory circuit, or a mask read-only memory circuit), volatile memory circuits (e.g., a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (e.g., an analog or digital magnetic tape or a hard disk drive), and optical storage media (e.g.,a CD, a DVD or a Blu-ray Disc).

[0104] The devices and methods described in this application may be implemented partially or entirely by a special-purpose computer formed by configuring a general-purpose computer to perform one or more specific functions embodied in computer programs. The functional blocks, flowchart components, and other elements described above serve as software specifications that can be translated into computer programs through the routine work of a skilled technician or programmer.

[0105] The computer programs contain processor-executable instructions stored on at least one non-transitory, tangible, computer-readable medium. The computer programs may also contain or access stored data. The computer programs 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.

[0106] The computer programs may contain: (i) descriptive text to be parsed, e.g. HTML (Hypertext Markup Language), XML (Extensible Markup Language) or JSON (JavaScript Object Notation) (ii) assembly code, (iii) object code generated from the source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. The source code may only contain, for example, the syntax of languages ​​such as C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language 5th revision), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, MATLAB, SIMULINK and Python®. Figure description Fig. 7 and 8: Y Yes N No

Claims

[1] System (200), comprising: a measurement module (222) configured to measure a plurality of parameters associated with a battery (210) comprising cells (12) with an electrolyte (108), the battery (210) comprising a plurality of modules, each module comprising groups of cells, each group comprising one or more of the cells (12); a health indicator module (232) configured to generate a plurality of health indicators based on the measured parameters; a normalization module (234) configured to normalize the health indicators; characterized by , that the normalization module (234) is configured to combine the normalized health indicators into different sets to detect different types of faults associated with the electrolyte (108); wherein the system further comprises a fault detection module (236) configured to detect one or more of the faults associated with the electrolyte (108) based on one or more of the normalized health indicators in one or more of the sets; wherein the normalization module (234) is further 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 the one of the health indicators for the one of the modules from the one of the health indicators for the one of the cell groups, and 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 the parameters measured in the same charge / discharge cycle of the battery (210). [2] The system (200) of claim 1, wherein the types of failures associated with the electrolyte (108) include a first failure due to leakage of the electrolyte (108) from one or more of the cells (12), a second failure due to ingress of moisture into one or more of the cells (12), and a third failure due to aging of the electrolyte (108) in one or more of the cells (12). [3] System (200) according to claim 1, wherein: the battery (210) comprises a plurality of modules, each module comprising a plurality of cell groups, each group comprising one or more of the cells (12); and the fault detection module (236) is configured to detect one or more of the faults associated with the electrolyte (108) in one of the cell groups. [4] The system (200) of claim 1, wherein the fault detection module (236) 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 respective predetermined threshold, and to generate an alert upon detecting the one of the faults. [5] The system (200) of claim 1, wherein the fault detection module is configured to determine the health of the battery (210) based on which of the faults is detected and which of the normalized health indicators exceed corresponding predetermined thresholds. [6] System (200) according to claim 1, wherein: the measurement module (222) is configured to measure the parameters including a current through the battery (210), voltages across each cell group, temperatures of each cell group, and a state of charge (SOC) of the battery (210); the health indicator module (232) is configured to generate the health indicators for each cell group; and the normalization module is configured to normalize each of the health indicators for one of the cell groups based on median values ​​of each of the health indicators for the one of the cell groups. [7] The system (200) of claim 1, wherein one of the sets of normalized health indicators for detecting a fault due to electrolyte leakage from one or more of the cells (12) comprises: (i) a static resistance of the battery (210) during a discharge cycle of the battery (210), (ii) a change in capacity of the battery (210) during constant current charging of the battery (210), (iii) a position of a peak value of dQ / dV relative to the voltage V of the battery (210) during constant current charging of the battery (210), (iv) an energy difference between charge and discharge cycles of the battery (210), and (v) an internal ohmic resistance of the battery (210) during charge and discharge of the battery (210). [8] The system (200) of claim 1, wherein one of the sets of normalized health indicators for detecting a failure due to moisture ingress into one or more of the cells (12) comprises: (i) a change in capacity of the battery (210) during constant current charging of the battery (210), (ii) an energy difference between charge and discharge cycles of the battery (210), (iii) a discharge duration for the battery (210), (iv) a sum of voltages of the battery (210) during constant current charging of the battery (210), (v) a peak value of dQ / dV relative to the voltage V of the battery (210) during constant current charging of the battery (210), and (vi) a polarization resistance during charging and discharging of the battery (210). [9] The system (200) of claim 1, wherein one of the sets of normalized health indicators for detecting a fault due to electrolyte aging in one or more of the cells (12) comprises: (i) a capacity change of the battery (210) during charging of the battery (210) with a constant current, (ii) a discharge duration of the battery (210), (iii) a sum of voltages of the battery (210) during charging of the battery (210) with a constant current, (iv) an energy difference between charge and discharge cycles of the battery (210), (v) a capacity difference of the battery (210) during charging and discharging of the battery (210), (vi) a voltage change rate of the battery (210) during charging of the battery (210) with a constant current, (vii) a logarithmic rate of change of the current during charging of the battery (210) with a constant voltage, (viii) Loss of capacity of the anode during charging of the battery (210) with constant current,(ix) loss of capacity of the cathode (102) during charging of the battery (210) with a constant current, (x) loss of lithium content during charging of the battery (210) with a constant current and (xi) an ohmic internal resistance of the battery (210) during charging and discharging of the battery (210).,

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