Health monitoring method for early fault detection in high voltage battery packs used in electric vehicles

Abstract

A system for monitoring a battery of a vehicle includes a processor and a memory storing instructions that, when executed by the processor, configure the processor to receive first features, statistical data including internal resistances of a plurality of cell groups of a battery bank including the battery, calculate second features of the battery bank based on the first features, determine whether the battery bank is failing based on one or more of the second features, and in response to the battery bank failing, determine whether one or more of the cell groups is failing based on one or more of the first features.

Description

Technical Field

[0001] The information provided in this section is for the purpose of presenting the general context of this disclosure. The work of the currently named inventors within the scope described in this section, as well as aspects of this specification that might not have been prior art at the time of application, are neither expressly nor implicitly acknowledged as prior art to this disclosure.

[0002] This disclosure generally relates to electric vehicles, and more specifically to a health monitoring method for early fault detection and / or predictive failure in high-voltage battery packs used in electric vehicles. Background Technology

[0003] The use of electric vehicles is surging. Electric vehicles are powered by batteries. Battery performance tends to deteriorate over time. Batteries can also experience problems during use. For example, one or more battery cells in a battery pack may experience problems and / or deteriorate faster than other cells in the pack. The internal resistance of a battery changes as it ages. For example, internal resistance increases due to changes in temperature, state of charge, and current drawn from the battery. Internal resistance also changes when one or more battery cells in the battery pack experience problems. Changes in battery internal resistance can indicate the deterioration of battery performance over time and can be used to detect potential problems in the battery during use. Summary of the Invention

[0004] A system for monitoring a vehicle battery includes a processor and a memory storing instructions, which, when executed by the processor, configure the processor to: receive a first characteristic and statistical data on the internal resistance of a plurality of battery cell groups in a battery pack including the battery; calculate a second characteristic of the battery pack based on the first characteristic; determine whether the battery pack is faulty based on one or more of the second characteristics; and, in response to a fault in the battery pack, determine whether one or more of the battery cell groups are faulty based on one or more of the first characteristics.

[0005] In another feature, the instructions further configure the processor to identify, based on one or more of the first features indicating that the battery pack is faulty and the faulty battery cell group, one or more of the second feature and the first feature indicating that the battery pack is faulty, one or more of the faulty battery cell group, one or more of the features that indicate ... features that indicate that the battery cell group is faulty, one or more of the features that indicate that the battery pack is faulty, one or more of the features that indicate that the battery cell group is fault

[0006] In another feature, the instructions further configure the processor to determine a fault severity level in response to a failure in one or more of the battery pack and the group of battery cells.

[0007] In another feature, the instructions further configure the processor to provide an indication of whether one or more of the battery pack and the group of battery cells are faulty and the severity level of the fault.

[0008] In another feature, the internal resistance of the battery cell group includes at least one of the charging and discharging resistances of the battery cell group.

[0009] In another feature, the instructions further configure the processor to determine that the battery pack is faulty in response to one or more of the factors in the second feature (i) during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle gradually deviating from a corresponding normal value to a corresponding threshold.

[0010] In another feature, the instructions further configure the processor to determine that one or more of the battery cell groups are faulty in response to one or more of the first feature (i) gradually deviating from a corresponding normal value to a corresponding threshold during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle.

[0011] In another feature, the first feature includes at least the maximum, minimum, and average internal resistance values ​​of each of the battery cell groups, and the instructions further configure the processor to calculate the second feature using a combination of the maximum, minimum, and average values ​​of the first feature.

[0012] In another feature, the instructions further configure the processor to adjust the first feature based on one or more parameters of the battery before calculating the second feature based on the first feature.

[0013] In another feature, the instructions further configure the processor to normalize the second feature based on one or more parameters of the battery before determining whether the battery pack is faulty based on one or more of the second features.

[0014] Among other features, a method for monitoring a vehicle's battery includes: receiving a first feature, statistical data on the internal resistance of a plurality of battery cell groups in a battery pack including the battery; calculating a second feature of the battery pack based on the first feature; determining whether the battery pack is faulty based on one or more of the second features; and, in response to a fault in the battery pack, determining whether one or more of the battery cell groups is faulty based on one or more of the first features.

[0015] In another feature, the method further includes identifying, based on one or more of the first features indicating that the battery pack is faulty and the faulty battery cell group, one or more of the second feature indicating that the battery pack is faulty and the faulty battery cell group, one or more of the first features, that contribute the most to the faulty battery pack.

[0016] In another feature, the method further includes determining a fault severity level in response to a fault in one or more of the battery pack and the group of battery cells.

[0017] In another feature, the method further includes providing an indication of a fault in one or more of the battery pack and the group of battery cells, and the severity level of the fault.

[0018] In another feature, the method further includes determining at least one of the internal resistance of the battery cell group and the charging and discharging resistance of the battery cell group.

[0019] In another feature, the method further includes determining that the battery pack is faulty in response to one or more of the second feature (i) during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle, gradually deviating from a corresponding normal value to a corresponding threshold.

[0020] In another feature, the method further includes determining that one or more of the battery cells in the battery cell group are faulty in response to one or more of the first feature (i) gradually deviating from a corresponding normal value to a corresponding threshold during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle.

[0021] In another feature, the method further includes calculating the first feature, including at least the maximum, minimum, and average internal resistance values ​​of each of the battery cell groups, and the method further includes calculating the second feature using a combination of the maximum, minimum, and average values ​​of the first feature.

[0022] In another feature, the method further includes adjusting the first feature based on one or more parameters of the battery before calculating the second feature based on the first feature.

[0023] In another feature, the method further includes standardizing the second feature based on one or more parameters of the battery before determining whether the battery pack is faulty based on one or more of the second feature.

[0024] Other applicable areas of this disclosure will become apparent from the detailed description, claims, and drawings. The detailed description and specific examples are intended for illustrative purposes only and are not intended to limit the scope of this disclosure.

[0025] The present invention also includes the following technical solutions.

[0026] Technical Solution 1. A system for monitoring a vehicle's battery, the system comprising:

[0027] Processor; and

[0028] A memory for storing instructions, which, when executed by the processor, configure the processor to:

[0029] Receive a first feature, the first feature including statistical data on the internal resistance of multiple battery cell groups in the battery pack of the battery;

[0030] Calculate the second characteristic of the battery pack based on the first characteristic;

[0031] Determine whether the battery pack is faulty based on one or more of the second features; and

[0032] In response to a fault in the battery pack, it is determined whether one or more of the battery cells in the group are faulty based on one or more of the first features.

[0033] Technical Solution 2. The system according to Technical Solution 1, wherein the instructions further configure the processor to identify, based on one or more of the first features indicating that the battery pack is faulty and the faulty battery cell group, one or more of the second features indicating that the battery pack is faulty and the first features indicating that the battery cell group is faulty, the one or more of which contribute the most to the faulty battery pack.

[0034] Technical Solution 3. The system according to Technical Solution 1, wherein the instructions further configure the processor to determine a fault severity level in response to a fault in one or more of the battery pack and the battery cell group.

[0035] Technical Solution 4. The system according to Technical Solution 3, wherein the instructions further configure the processor to provide an indication of a fault in one or more of the battery pack and the battery cell group and the severity level of the fault.

[0036] Technical Solution 5. The system according to Technical Solution 1, wherein the internal resistance of the battery cell group includes at least one of the charging and discharging resistances of the battery cell group.

[0037] Technical Solution 6. The system according to Technical Solution 1, wherein the instructions further configure the processor to determine that the battery pack is faulty in response to one or more of the second features (i) during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle, by gradually deviating from a corresponding normal value to a corresponding threshold.

[0038] Technical Solution 7. The system according to Technical Solution 1, wherein the instructions further configure the processor to determine that one or more of the battery cell groups are faulty in response to one or more of the first features (i) gradually deviating from a corresponding normal value to a corresponding threshold during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle.

[0039] Technical Solution 8. The system according to Technical Solution 1, wherein the first feature includes at least the maximum, minimum, and average internal resistance values ​​of each of the battery cell groups, and wherein the instructions further configure the processor to calculate the second feature using a combination of the maximum, minimum, and average values ​​of the first feature.

[0040] Technical Solution 9. The system according to Technical Solution 1, wherein the instructions further configure the processor to adjust the first feature based on one or more parameters of the battery before calculating the second feature based on the first feature.

[0041] Technical Solution 10. The system according to Technical Solution 1, wherein the instructions further configure the processor to normalize the second feature based on one or more parameters of the battery before determining whether the battery pack is faulty based on one or more of the second features.

[0042] Technical Solution 11. A method for monitoring a vehicle's battery, the method comprising:

[0043] Receive a first feature, the first feature including statistical data on the internal resistance of multiple battery cell groups in the battery pack of the battery;

[0044] Calculate the second characteristic of the battery pack based on the first characteristic;

[0045] Determine whether the battery pack is faulty based on one or more of the second features; and

[0046] In response to a fault in the battery pack, it is determined whether one or more of the battery cells in the group are faulty based on one or more of the first features.

[0047] Technical Solution 12. The method according to Technical Solution 11 further includes identifying one or more of the faulty battery cells in the faulty battery cell group that contributes the most to the faulty battery pack based on one or more of the second feature indicating that the battery pack is faulty and the first feature of the faulty battery cell group.

[0048] Technical Solution 13. The method according to Technical Solution 11 further includes determining a fault severity level in response to a fault in one or more of the battery pack and the group of battery cells.

[0049] Technical Solution 14. The method according to Technical Solution 13 further includes providing an indication of a fault in one or more of the battery pack and the group of battery cells and the severity level of the fault.

[0050] Technical Solution 15. The method according to Technical Solution 11 further includes determining the internal resistance of the battery cell group, the internal resistance including at least one of the charging and discharging resistances of the battery cell group.

[0051] Technical Solution 16. The method according to Technical Solution 11 further includes determining that the battery pack is faulty in response to one or more of the second features (i) during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle, gradually deviating from a corresponding normal value to a corresponding threshold.

[0052] Technical Solution 17. The method according to Technical Solution 11 further includes determining that one or more of the battery cell groups are faulty in response to one or more of the first features (i) gradually deviating from a corresponding normal value to a corresponding threshold during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle.

[0053] Technical Solution 18. The method according to Technical Solution 11 further includes calculating a first feature, the first feature including at least the maximum, minimum, and average internal resistance values ​​of each of the battery cell groups, the method further including calculating a second feature using a combination of the maximum, minimum, and average values ​​of the first feature.

[0054] Technical Solution 19. The method according to Technical Solution 11 further includes adjusting the first feature based on one or more parameters of the battery before calculating the second feature based on the first feature.

[0055] Technical Solution 20. The method according to Technical Solution 11, further comprising standardizing the second feature based on one or more parameters of the battery before determining whether the battery pack is faulty based on one or more of the second features. Attached Figure Description

[0056] This disclosure will be more fully understood in light of the specific embodiments and accompanying drawings, wherein:

[0057] Figure 1 An example of a control system for an electric vehicle is shown;

[0058] Figure 2A and Figure 2B An example of a battery pack, including a group of battery cells, is shown in an electric vehicle;

[0059] Figures 3A to 3C Examples of factors that affect the internal resistance of a battery are shown;

[0060] Figure 4 An example of the current distribution in a battery during a driving cycle of an electric vehicle is shown;

[0061] Figure 5 An example of a battery health monitoring system is shown;

[0062] Figure 6 This demonstrates a method for identifying faulty battery packs and groups of faulty battery cells in a storage battery.

[0063] Figure 7 and Figure 8 Show Figure 6 The method is a part of constructing battery pack-level features from battery cell group-level features used to identify faulty battery packs;

[0064] Figure 9 Show Figure 6 The method is used to further identify faulty parts of the battery pack;

[0065] Figure 10 Show Figure 6 The method is part of the approach used to reduce the number of faulty battery cell groups that require further detailed inspection;

[0066] Figure 11 Show Figure 6 The method is used to further identify faulty battery cell groups in more detail;

[0067] Figure 12A and Figure 12B Examples of battery pack-level features are shown; and

[0068] Figure 13 An example of battery cell group-level characteristics is shown.

[0069] In the accompanying drawings, reference numerals may be used repeatedly to identify similar and / or identical elements. Detailed Implementation

[0070] This disclosure provides a system and method for detecting and predicting the health status of high-voltage battery packs used in electric vehicles. The system and method utilize health indicators of the battery, such as discharge and charge resistance, to detect and predict the health status of the battery pack and groups of battery cells within the battery pack. The system and method provide enhanced monitoring capabilities to monitor batteries (at the battery pack, module, and group of battery cells level) for detecting high internal resistance in groups of battery cells as a failure condition. The system and method provide early detection and predictive capabilities for predicting battery degradation.

[0071] This disclosure provides an automated system for monitoring and predicting battery pack failure. As explained in detail below, in this automated system, several features are derived from generalized statistical data on the internal resistance of battery cell groups within the battery pack. These derived features are used to robustly detect and isolate faulty battery packs. Specifically, the system monitors the health of the high-voltage battery pack to detect / predict failure conditions by using health indicators such as discharge and charge resistance in a tiered manner at the battery pack level and battery cell group level. The system uses a combination of these features to allow robust fault detection and identification in the presence of various noise and environmental factors. The system uses a set of features to perform severity assessments to provide early detection and prediction of failures in the battery pack. The system proactively monitors failure progression and sends alerts / notifications to warn the user and prevent vehicle stalling before failure occurs. The system provides progressive trends in battery health and provides early warnings to the user before failure. The system manages vehicle operation upon detecting a fault.

[0072] More specifically, the system and method for monitoring the health of a battery pack and for early fault identification employ two hierarchical procedures that utilize statistical characteristics calculated based on the charging and discharging resistance of individual battery cell groups within a high-voltage battery pack. In a first procedure, the system derives battery pack-level characteristics from the statistical characteristics associated with the battery cell groups to detect deteriorated battery packs. In a second procedure, the system uses battery cell group-level statistical characteristics to isolate failures to one or more individual battery cell groups. The system uses both battery pack-level and battery cell group-level characteristics for fault prediction. The system assesses and indicates the severity level of the fault and provides the user with an early warning about the failure. The system employs a severity index derived from the internal resistance of the battery cell groups for early fault detection and is used to learn different types of battery-related failure modes. The system and method can be implemented in a vehicle, in the cloud, or using a combination thereof. These and other features of this disclosure are described in detail below.

[0073] This disclosure is organized as follows. Initially, referenced... Figure 1 A block diagram illustrating and describing the control system of an electric vehicle. (Reference) Figure 2A and Figure 2B An example of a battery cell group for an electric vehicle's battery is shown and described. (Reference) Figures 3A to 3C Examples of factors affecting the internal resistance of a battery are shown and described. (Reference) Figure 4 Examples of current distribution in a battery during a driving cycle of an electric vehicle and examples of operating regions defined within said current distribution are shown and described. References Figure 5 An example of a battery health monitoring system is shown and described. (Reference) Figure 6 Showing and describing by Figure 5 A health monitoring system is used to identify faulty battery packs and groups of faulty battery cells within a holistic approach. Subsequently, reference... Figures 7 to 11 Further details are shown and described. Figure 6 The steps of the method. By Figure 6 The method uses battery pack-level and battery cell group-level features shown in Figure 12A , Figure 12B and Figure 13 middle.

[0074] Figure 1 An example of a control system 100 for an electric vehicle is shown. The control system 100 includes a controller 102, a battery 104, a battery management system (BMS) 106, an infotainment subsystem 108, and an autonomous driving subsystem (implementing SAE Levels 1-5) 112. The controller 102 communicates with the battery 104 and implements the following references. Figure 5 A health monitoring system is shown and described in detail. Controller 102 communicates with various subsystems of the vehicle. Battery 104 supplies power to the various subsystems of the electric vehicle. BMS 106 performs battery management operations, including monitoring battery 104 and supplying power from battery 104 to the various subsystems of the vehicle. The health monitoring system can also be implemented in BMS 106.

[0075] The infotainment subsystem 108 may include an audiovisual multimedia subsystem and a human-machine interface (HMI) that allows the occupants of the electric vehicle to interact with the control system 100. The infotainment subsystem 108 also provides alerts from a health monitoring system to the occupants of the electric vehicle via the HMI.

[0076] The control system 100 further includes multiple navigation sensors 114 that provide navigation data to the autonomous driving subsystem 112. For example, the navigation sensors 114 may include cameras, radar and lidar sensors, a global positioning system (GPS), and so on. Based on data received from the navigation sensors 114, the autonomous driving subsystem 112 controls the steering subsystem 116 and braking subsystem 118 of the electric vehicle. The autonomous driving subsystem 112 also controls and manages the operation of the electric vehicle based on data regarding the health status of the battery 104 received from a health monitoring system (e.g., from controller 102 or BMS 106). It should be noted that the autonomous driving subsystem 112 is shown as an example only; and the capabilities of the health monitoring system described in this disclosure are equally applicable to non-autonomous electric vehicles.

[0077] The control system 100 further includes a communication subsystem 120, which can communicate with one or more servers 122 in the cloud via a distributed communication network 124. For example, the distributed communication network 124 may include a cellular network, a satellite-based communication network, a Wi-Fi network, the Internet, etc. The communication subsystem 120 may include one or more transceivers for communicating with the distributed communication network 124. The controller 102 communicates with one or more servers 122 in the cloud via the communication subsystem 120. The controller 102 communicates data processed by a health monitoring system (described below) from the battery 104 to one or more servers 122 via the communication subsystem 120. The controller 102 generates an alarm based on the data processed by the health monitoring system from the battery 104 and provides the alarm to the occupants of the electric vehicle via the HMI of the infotainment subsystem 108. The controller 102 can also receive alarms from one or more servers 122 based on data processed by one or more servers 122 from the battery 104 and provide the alarm to the occupants of the electric vehicle via the HMI of the infotainment subsystem 108.

[0078] Figure 2A and Figure 2B An example of a battery 104 comprising one or more battery packs is shown. In the following, the terms battery and battery pack are used interchangeably. Generally, battery 104 may include multiple battery packs. Each battery pack may include multiple modules. Each module may include multiple groups of battery cells. Each group of battery cells may include multiple battery cells.

[0079] exist Figure 2AIn this configuration, battery 104 includes one or more battery packs, each battery pack comprising multiple groups of battery cells. For example, battery 104 includes battery cell group 1 150-1, battery cell group 2 150-2, ..., battery cell group 150-(N-1), and battery cell group N 150-N, where N is a positive integer (collectively referred to as battery cell group 150). Battery cell groups 150 are connected in series with each other. For each battery pack including battery cell group 150, the battery pack level current I through the battery cell group 150 is measured by measuring the current through terminals 151-1, 151-2 (collectively referred to as terminal 151) of the battery pack including battery cell group 150. See below for reference. Figure 5 Describe current measurement.

[0080] exist Figure 2B In this configuration, each battery cell group 150 includes multiple battery cells (e.g., three battery cells). For example, each battery cell group 150 includes battery cells 152-1, 152-2, and 152-3 (collectively referred to as battery cell 152). Although only three battery cells are shown as an example, each battery cell group 150 may include fewer or more than three battery cells 152. The battery cells 152 in each battery cell group 150 are connected in parallel, in series, or using a combination of series and parallel connections. The voltage across each individual battery cell group 150 is measured across terminals 153-1 and 153-2 (collectively referred to as terminals 153) of each battery cell group 150. See below for further details. Figure 5 Describe voltage measurement.

[0081] Therefore, for each battery pack comprising N battery cell groups 150, a battery pack-level current I (also referred to herein as battery current I) and N voltages across the N battery cell groups 150 are measured. Current I can be measured during the charge and discharge cycles of the battery 104. These current and voltage measurements allow the calculation of the internal resistance of each individual battery cell group 150 during the charge and discharge cycles of the battery 104. The internal resistance of the battery 104 can serve as a health indicator for indicating the health status of the battery 104. Specifically, various statistical data can be calculated from the internal resistance calculated for each battery cell group 150 during the charge and discharge cycles of the battery 104. For example, the statistical data may include the maximum, minimum, average, and other values ​​of the internal resistance of each battery cell group 150. The statistical data can be used to initially establish battery pack-level characteristics for determining whether the battery pack is faulty. Subsequently, if the battery pack is faulty, the battery cell group-level characteristics (i.e., the statistical data) are used to identify and isolate the faulty battery cell group 150 within the faulty battery pack, as referenced below. Figures 6 to 11 Detailed description.

[0082] The following text is for reference only. Figure 5 The health monitoring system, shown and described in detail, measures the internal resistance of the battery cell group 150 by taking into account various factors affecting the internal resistance of the battery 104. These factors include, for example, the temperature, state of charge (SOC), and current of the battery 104. Therefore, the internal resistance measurement and the statistical data calculated based on the internal resistance measurement results are robust (i.e., independent of the noise and operating conditions of the battery 104). Thus, the health monitoring system can robustly detect and isolate faulty battery packs and faulty battery cell groups 150 that contribute most to the performance degradation of the battery 104. To identify faulty battery packs and faulty battery cell groups 150, the health monitoring system can process battery pack-level and battery cell group-level characteristics in an onboard controller (e.g., controller 102), in the cloud (e.g., on one or more servers 122), or using a combination of both. The health monitoring system can provide health indicators of the battery 104 to the occupants and maintenance technicians of the electric vehicle in the form of severity levels and active alerts for predictive and diagnostic purposes, as described in detail below.

[0083] Figures 3A to 3C This illustrates how the internal resistance of battery 104 is affected by various factors such as the temperature, state of charge (SOC), and current of battery 104. For example, in Figure 3A In this case, if the current I and SOC of battery 104 are within a narrow range, the internal resistance R of battery 104 is higher at the lower temperature T of battery 104 and lower at the higher temperature T of battery 104. Figure 3B In this case, if the temperature T and current I of the battery 104 are within a narrow range, the internal resistance R of the battery 104 is higher when the state of charge (SOC) of the battery 104 is low, and lower when the SOC of the battery 104 is high. Figure 3C If the temperature T and SOC of the battery 104 are within a narrow range, the internal resistance R of the battery 104 is lower when the current I of the battery 104 is higher (assuming that the current I is the discharge current), and higher when the current I of the battery 104 is lower.

[0084] Therefore, the internal resistance of battery 104 varies depending on the temperature, state of charge (SOC), and current of battery 104. Consequently, due to the varying operating conditions of battery 104 across its entire current distribution, the internal resistance data of battery 104 (including measurements at various vehicle speeds) is not comparable when measured across the entire current distribution. Therefore, the health monitoring system divides the current distribution of battery 104 into narrow operating regions and measures the current of battery 104 and the voltage of battery cell group 150 in each operating region to eliminate the influence of operating conditions.

[0085] Figure 4 An example of the current distribution of battery 104 during a driving cycle is shown. For example, a driving cycle is a trip undertaken by the vehicle. For example, suppose that during the trip, the vehicle initially moves at a slower speed (e.g., on a ground street), then at a relatively constant speed (e.g., on a highway), and then again at varying lower and higher speeds (e.g., on a ground street). The current distribution of battery 104 during said trip varies according to the vehicle's speed. For example, the current may be lower at the vehicle's slower speed and higher at the vehicle's higher speed.

[0086] Because the current and operating conditions of battery 104 vary during the trip, the entire current distribution of battery 104 is not selected for current and voltage measurements. Instead, multiple operating regions 160-1, ..., 160-M (collectively referred to as operating regions 160) of the current distribution of battery 104 within the driving cycle are selected, where M is a positive integer. Each operating region 160 is a function of the current I, state of charge (SOC), and temperature T of battery 104. Each operating region 160 is selected for a chosen SOC and temperature T of battery 104, where the current I is relatively stable over time t (i.e., within a narrow range). The chosen SOC and temperature T of battery 104 can be calibrated parameters. For example, these parameters can be set during vehicle manufacturing and can be changed via updates provided to the vehicle during its lifespan. The internal resistance of the battery cell group 150 is measured in operating region 160, and statistical data (e.g., R0) are calculated based on the measured internal resistance. max R min R avg (and other statistical parameters). The statistical data (also referred to as generalized statistical data throughout this disclosure) is then analyzed using two stratified procedures to detect faulty battery packs and faulty battery cell groups 150 within the faulty battery packs, as described below. Figures 6 to 11 As stated above.

[0087] Figure 5An example of a health monitoring system implemented in controller 102 is shown. The health monitoring system includes a current measurement circuit 140, a multiplexer 142, a voltage measurement circuit 144, a processor 146, and a memory 148. The current measurement circuit measures the current I passing through N battery cell groups 150 during charge and discharge cycles of the battery 104. The multiplexer 142 is connected to the voltage measurement circuit 144 across each of the battery cell groups 150. The processor 146 controls the multiplexer 142. The voltage measurement circuit 144 measures the voltage across each of the battery cell groups 150. The processor 146 processes the current and voltage measurements, calculates the internal resistance of the battery cell groups 150, and calculates statistical data for the battery cell groups 150 based on the internal resistance measurements. For example, the statistical data includes R0 of the internal resistance of each battery cell group 150. max R min R avg Other values, such as standard deviation, variance, etc., are also considered. Statistical data is calculated for each trip of the vehicle and stored in memory 148. This statistical data serves as a battery cell group-level characteristic.

[0088] Processor 146 derives battery pack-level characteristics from generalized statistical data and analyzes these characteristics using a first battery pack-level program to detect faulty battery packs. Processor 146 then uses a second program to analyze battery cell group-level characteristics (i.e., statistical data) to isolate faulty battery cell groups 150 within the faulty battery packs, as described below. Figures 6 to 11 As stated above.

[0089] Figure 6 This illustrates an overall method 200 for detecting faults in a battery pack of a battery 104 and for detecting faulty battery cell groups 150 in a faulty battery pack, according to the present disclosure. Figures 7 to 11 Some steps of method 200 are shown in further detail below. Method 200 is composed of... Figure 5 The health monitoring system is implemented. For example, the processor 146, together with other circuitry of the controller 102, can implement method 200.

[0090] Figure 6 This illustrates an overall method for detecting a faulty battery pack and a group of faulty battery cells 150 within the faulty battery pack. At 202, method 200 collects statistical data (R) calculated for the group of battery cells 150 of one or more battery packs of battery 104. max R min R avg(and other statistical values). Method 200 collects statistical data from Y trips made by the vehicle, where Y is a positive integer. For example, method 200 collects statistical data stored in memory 148 of controller 102. Additionally, the method receives the average current I, average SOC, and average temperature of battery 104. For example, method 200 receives these measurements for each battery pack, each module within each battery pack, and / or each group of battery cells 150. For each trip made by the vehicle, these statistical data and measurements are stored in memory 148 of controller 102.

[0091] At point 204, method 200 calculates battery cell group-level features and battery pack-level features based on the battery cell group-level features for each trip. Method 200 calculates battery cell group-level features and battery pack-level features from statistical data, as referenced below. Figure 7 and Figure 8 Detailed explanation.

[0092] At point 206, method 200 evaluates the battery pack-level characteristics of each battery pack. At point 208, method 200 performs a first procedure to evaluate the battery pack-level characteristics and determine if the battery pack is faulty. See below for further details. Figure 9 The first procedure is shown and described in detail. At 210, method 200 determines whether the battery pack is faulty. If method 200 determines that the battery pack is not faulty, method 200 returns to 202.

[0093] If the battery pack fails, at point 212, method 200 uses a battery cell group-level feature to isolate one or more faulty battery cell groups 150 within the faulty battery pack. Method 200 uses the following reference... Figure 10 The method shown and described in detail isolates one or more faulty battery cell groups 150 from a faulty battery pack. At 214, method 200 performs a second procedure to identify which battery cell groups 150 contribute most to the fault in the faulty battery pack. See below for reference. Figure 11 The second procedure is shown and described in detail.

[0094] At 216, method 200 determines the severity level of the fault in the group of battery cells 150 identified as contributing most to the fault in the faulty battery pack. At 218, method 200 informs the user of the fault (e.g., which battery pack is faulty and which groups of battery cells in the faulty battery pack are faulty) and the severity level of the fault as described below, so that maintenance can be scheduled to diagnose and correct the fault.

[0095] Figure 7 and Figure 8The battery pack-level characteristics are shown as formulated by the battery cell group-level characteristics (i.e., generalized statistics) of the battery cell group 150. Figure 7 Step 204 of method 200 is shown in further detail. Figure 7 In step 252, method 200 calculates summary statistics for each trip (i.e., battery cell group-level characteristics of all battery cell groups 150). These statistics can be pre-calculated and stored in the memory 148 of the controller 104. As described above, these statistics include R of all battery cell groups 150. max R min R avg And other values. Therefore, step 252 is shown only for completeness.

[0096] At point 254, method 200 measures / calculates the average current I, average SOC, and average temperature T of battery 104. These measurements can also be pre-calculated and stored in the memory 148 of controller 104. At point 256, method 200 adjusts (e.g., compensates) the battery cell group statistics R based on the average current I, average SOC, and average temperature T of battery 104. max R min R avg Other values.

[0097] At point 258, method 200 makes additional adjustments to the battery cell group statistics based on other factors that may affect the statistics. These other factors may include, but are not limited to, the location of the battery cell group within the battery pack, the ambient temperature, and the difference between the ambient temperature and the average temperature T of the battery 104.

[0098] These adjustments improve battery cell group statistics and compensate for noise and environmental factors. This compensation makes both the battery cell group-level characteristics and the battery pack-level characteristics derived from them robust. Due to this compensation, the determination of battery pack health based on battery pack-level characteristics and the determination of battery cell group health based on battery cell group-level characteristics are also robust.

[0099] At 260, method 200 is as follows: battery pack-level features are formulated based on battery cell group-level features. Figure 8 An example of the internal resistance distribution across a group 150 of battery cells in a battery pack is shown. Figure 8 In this example, the internal resistance R of battery cell group 150 is plotted for each battery cell group ID. The internal resistance distribution across battery cell group 150 in the battery pack serves as an indicator of the battery pack's health. The battery pack-level characteristics are derived from battery cell group statistics and subsequently used with reference... Figure 9The first-level decision-making process shown and described analyzes the data to determine if there is a fault in the battery pack.

[0100] exist Figure 8 In this context, if the battery pack is healthy (i.e., if all battery cell groups 150 in the battery pack are operating normally and do not exhibit abnormal increases in internal resistance), then the internal resistance of all battery cell groups 150 will be within a narrow range. For example, the point shown at 170 represents the average internal resistance of battery cell groups 150 when the battery pack is healthy.

[0101] However, if the internal resistance of one or more battery cell groups 150 in the battery pack deviates from the average internal resistance of normal battery cell groups (shown at 172), this deviation can serve as an indicator of an abnormal condition and determine whether the battery is faulty. After determining that the battery pack is faulty using the first-level procedure, a second-level procedure is used to analyze the battery cell group-level characteristics to identify which battery cell groups 150 contribute to the fault in the battery pack. Subsequently, the severity level of the fault (e.g., low, medium, high) is determined. An example of a battery cell group contributing to the fault in the battery pack is shown at 174, whose internal resistance deviates the most from the internal resistance of other battery cell groups 150.

[0102] For example, suppose the battery cell group statistics (i.e., battery cell group-level characteristics) of each of the battery cell groups 150 in the battery pack include R max R min and R avg The following description is not limited to R. max R min and R avg Additionally, other statistical parameters such as standard deviation and variance can also be included in the battery cell group statistics. Therefore, in addition to those described below, many more or different battery pack-level characteristics can be derived. Furthermore, battery pack-level characteristics do not need to be derived from the battery cell group-level characteristics of each individual battery cell group 150. Instead, battery pack-level characteristics can be derived based on the battery cell group-level characteristics of each module in the battery pack, where each module comprises multiple battery cell groups 150.

[0103] For example, for a battery pack, the battery cell group-level feature R of the battery cell group 150 in the battery pack is used. max R min and R avg The first battery pack level feature (feature 1) can be max[R avg, i ] - min[R avg, i The second battery pack level feature (feature 2) can be max[R]. max, i ] - min[R max,iThe third battery pack level feature (feature 3) can be max[R]. max,i ] - min[R min,i The fourth battery pack level feature (feature 4) can be max[R]. max,i ] - avg[R max,i The fifth battery pack level feature (feature 5) can be max[R]. avg，i ] - avg[R max,i The sixth battery pack level feature (feature 6) can be max[R]. avg，i The seventh battery pack level feature (feature 7) can be max[R]. avg，i ] - max[R min,i The eighth battery pack level feature (feature 8) can be max[R]. max,i ] - max[R min,i ] etc., where i is the battery cell group ID (CGID). Some of these battery pack-level characteristics are typically shown in Figure 8 There are 180 such features. These battery pack-level characteristics can be used as is, or they can be standardized (e.g., based on the battery pack's current I, SOC, or temperature) to determine if the battery pack is faulty using a first-level procedure as follows.

[0104] Examples of battery pack-level characteristics are shown in Figure 12A and Figure 12B For illustrative purposes, in the example shown, all characteristics are shown as failed (i.e., exceeding the corresponding threshold) for at least some battery cell groups 150. In this example, the Y-axis represents the number of battery packs or vehicles, denoted as P; and the X-axis represents the internal resistance distribution of the battery cell groups 150, denoted as ΔR. At 182, the number of battery cell groups 150 whose battery pack-level characteristics are less than a predetermined threshold 180 (indicating that these battery cell groups 150 are healthy) is shown; and at 184, the number of battery cell groups 150 whose battery pack-level characteristics are greater than the predetermined threshold 180 (indicating that these battery cell groups 150 are faulty) is shown. For convenience, for... Figure 12A and Figure 12B All battery pack level features shown use the same reference numerals.

[0105] Figure 12A Shown from left to right and from top to bottom:

[0106]

[0107] Figure 12B Shown from left to right and from top to bottom:

[0108]

[0109] Figure 9 This illustrates the first-level procedure used to determine if there is a fault in the battery pack. Figure 9 Step 208 of method 200 is shown in further detail. If any one or more of the battery pack-level characteristics deviates from their respective thresholds, method 200 determines that the battery pack is faulty. At 302, method 200 determines whether one or more of the battery pack-level characteristics deviates from their normal values. For example, the normal values ​​of each battery pack-level characteristic can be empirically calibrated using known healthy battery packs. Similarly, threshold levels for battery pack-level characteristics can be calibrated to identify faults in the battery pack. If no battery pack-level characteristic deviates from its corresponding normal value (i.e., does not exceed its corresponding threshold), method 200 determines that the battery pack is not faulty, and the method returns to... Figure 6 Step 202 is shown in the diagram. If one or more battery pack-level characteristics deviate from their respective normal values ​​(i.e., exceed their respective thresholds), method 200 determines that the battery pack is faulty.

[0110] Alternatively, if one or more battery pack-level characteristics deviate from their respective normal values ​​(i.e., exceed their respective thresholds), method 200 may perform the following additional steps (304 or 306) before determining that the battery pack is faulty. At 304, method 200 determines whether one or more battery pack-level characteristics deviate from their respective normal values ​​only for X out of Y trips, where X is a positive integer less than Y. If one or more battery pack-level characteristics do not deviate from their respective normal values ​​for X out of Y trips, method 200 determines that the battery pack is not faulty (e.g., a fault indication might be abnormal behavior), and method 200 returns to... Figure 6 Step 202 is shown. If one or more battery pack-level characteristics deviate from their corresponding normal values ​​for X of the Y trips, then at 308, method 200 determines that the battery pack is faulty.

[0111] Instead of step 304, at 306, method 200 determines whether one or more battery pack-level characteristics are gradually deviating from their respective normal values ​​(i.e., whether the deviation increases over a continuous journey). If one or more battery pack-level characteristics are not gradually deviating from their respective normal values, method 200 determines that the battery pack is not faulty (e.g., a fault indication might be abnormal behavior), and the method returns to... Figure 6 Step 202 is shown. If one or more battery pack-level characteristics gradually deviate from their corresponding normal values, then at 308, method 200 determines that the battery pack is faulty. Steps 304 or 306 can be performed to confirm the fault determination in step 302.

[0112] If battery 104 comprises more than one battery pack, method 200 can identify which of the battery packs is faulty by repeating the above procedure for each battery pack. Once method 200 determines that a battery pack is faulty, method 200 performs a second-level procedure described below for each faulty battery pack to determine which of the battery cell groups 150 contributed most to the fault in the battery pack.

[0113] Figure 10 This paper illustrates a method for reducing the number of faulty battery cell groups 150 analyzed by a second-level procedure before determining which of the faulty battery cell groups 150 contributes the most to the failure in the battery pack. Figure 10 Step 212 of method 200 is shown in further detail. A second-level procedure can be used to analyze a reduced number of faulty battery cell groups 150 to determine which of the battery cell groups 150 contributes most to the failure in the battery pack. Alternatively, the second-level procedure can be performed on all battery cell groups 150 in the faulty battery pack.

[0114] At position 352, method 200 selects the battery pack-level characteristic that indicates a fault in the battery pack. For illustrative purposes only, it is assumed that the battery pack-level characteristic indicating a fault in the battery pack is characteristic 1: max[R] avg, i ] – min[R avg, i ]≥Th1. The method described below can be used to indicate any battery pack-level characteristic of a faulty battery pack. Method 200 can determine, in one of the two ways shown at 354 and 356, which battery cell group 150 in the faulty battery pack contributes the most to the fault in the battery pack.

[0115] Before describing steps 354 and 356, Figure 13 The diagram shows three battery cell group-level features R of battery cell group 150. avg R max and R min Examples are provided. In these examples, the Y-axis represents the number of battery packs or vehicles, denoted as P; and the X-axis represents the internal resistance distribution of the battery cell group 150, denoted as ΔR. The battery cell group-level characteristic R is shown at 192. avg R max and R min The number of battery cell groups 150 that are less than a predetermined threshold 190 (indicating that these battery cell groups 150 are healthy); and the battery cell group-level characteristic R is shown at 194. avg R max and R minThe number of battery cell groups 150 that exceed a predetermined threshold 190 (indicating that these battery cell groups 150 are faulty). For convenience, in Figure 13 The three figures shown use the same reference numerals for all battery cell group-level features.

[0116] exist Figure 10 In the middle, at position 354, method 200 selects the battery cell group-level feature R. avg Battery cell groups 150 that exceed a predetermined threshold. Alternatively, method 200 can select all three battery cell group-level features R. avg R max and R min All battery cell groups 150 are greater than the corresponding predetermined threshold. As an alternative to step 354, at 356, method 200 can select the closest value to max[R]. avg，i ] & min[R avg，i Battery cell group 150. This can be configured for any other battery cell group-level feature (e.g., R). max or R min A similar method is used. The selection method reduces the number of faulty battery cell groups 150 for which a second-level procedure is implemented.

[0117] Figure 11 This illustrates the implementation of a second-level procedure to isolate the battery cell group 150 that contributes the most to the failure in the battery pack. Figure 11 Step 214 of method 200 is shown in further detail. At 362, for each selected group of battery cells 150 (as referenced above) Figure 10 The selection method 200 selects one or more battery cell group-level features. Selecting fewer than all battery cell group-level features may be more lenient. That is, selecting all battery cell group-level features for a selected battery cell group 150 may result in the elimination of features as described above. Figure 10 The described method selects more battery cell groups 150. Alternatively, selecting fewer than all battery cell group-level features for a selected battery cell group 150 may result in the elimination of features as described in the above reference. Figure 10 The method described selects fewer battery cell groups, 150.

[0118] At 364, method 200 determines whether any (or all) selected battery cell group-level characteristics of the selected battery cell group 150 deviate from their respective normal values ​​by more than a predetermined amount (e.g., exceeding a corresponding threshold). The normal values ​​and thresholds are empirically calibrated. If none of the selected battery cell group-level characteristics of the selected battery cell group 150 deviates from their respective normal values ​​by more than a predetermined amount, method 200 returns to... Figure 6Step 202 is shown. If one or more of the selected battery cell group-level characteristics deviate from their respective normal values ​​(i.e., exceed the respective thresholds), method 200 determines that the selected battery cell group 150 is faulty.

[0119] Alternatively, if one or more of the selected battery cell group-level characteristics deviate from their respective normal values, method 200 may perform the following additional steps (366 or 368) before determining that the selected battery cell group 150 is faulty. At 366, method 200 determines whether one or more of the selected battery cell group-level characteristics deviate from their respective normal values ​​only for X of Y trips, where X is a positive integer less than Y. If one or more of the selected battery cell group-level characteristics do not deviate from their respective normal values ​​for X of Y trips, then method 200 determines that the selected battery cell group 150 is not faulty (e.g., a fault indication might be abnormal behavior), and the method returns to... Figure 6 Step 202 is shown. If one or more of the selected battery cell group-level characteristics deviate from their respective normal values ​​for Y strokes, then at 370, method 200 determines that the selected battery cell group 150 is faulty.

[0120] Instead of step 366, at 368, method 200 determines whether one or more of the selected battery cell group-level characteristics are gradually deviating from their respective normal values ​​(i.e., whether the deviation increases over continuous travel). If one or more of the selected battery cell group-level characteristics are not gradually deviating from their respective normal values, method 200 determines that the selected battery cell group 150 is not faulty, and the method returns to... Figure 6 Step 202 is shown. If one or more of the selected battery cell group-level characteristics gradually deviate from their respective normal values, then at 370, method 200 determines that the selected battery cell group 150 is faulty. Steps 366 or 368 can be performed to confirm the fault determination in step 364.

[0121] When a battery pack is determined to be faulty and one or more of the battery cells in group 150 are determined to be faulty, method 200 indicates the severity level of the fault by calculating a severity index. For example, the severity index can be calculated using a sigmoid function. A sigmoid function has a characteristic "S"-shaped curve or S-curve. An sigmoid function is a bounded, differentiable, and real-valued function defined for all real input values ​​and has a non-negative derivative at every point. Generally, sigmoid functions are monotonic and have a bell-shaped first derivative. Conversely, the integral of any continuous and non-negative bell-shaped function (which has only one local maximum and no local minimum unless it degenerates) is sigmoid. Additionally, method 200 can also use the sigmoid function to generate a weighted severity index called the historical severity index. The severity index can vary between 0 (low or no severity level) and 1 (high severity level). Therefore, severity levels (e.g., low, medium, high) can be indicated on the infotainment subsystem 108 along with fault information (e.g., which battery packs and which battery cell groups 150 are faulty).

[0122] The systems and methods disclosed herein improve battery technology. Specifically, the systems and methods passively identify faulty battery packs and cell groups within the battery while the vehicle is being driven, without affecting driving. Further, the systems and methods actively identify faulty battery packs and cell groups within the battery; that is, before a fault occurs and the vehicle stalls, thus impounding the occupants. The systems and methods provide early fault indication and predictive capabilities to predict battery performance degradation while managing vehicle operation. The systems and methods monitor progressive trends in battery health and provide early warnings (active alerts) to the user before battery failure.

[0123] The foregoing description is illustrative in nature and is not intended to limit this disclosure, its application, or use. The broad teachings of this disclosure can be implemented in many forms. Therefore, while this disclosure includes specific examples, its true scope should not be so limited, as other modifications will become apparent from a study of the accompanying drawings, the specification, and the following claims.

[0124] It should be understood that one or more steps within the method may be performed in different orders (or simultaneously) without altering the principles of this disclosure. Furthermore, while each embodiment has been described above as having certain features, any one or more of those features described with respect to any embodiment of this disclosure may be implemented in and / or combined with features of any other embodiment, even if such combination is not explicitly described. In other words, the described embodiments are not mutually exclusive, and the arrangement of one or more embodiments with respect to each other remains within the scope of this disclosure.

[0125] Spatial and functional relationships between components (e.g., between modules, between circuit elements, between semiconductor layers, etc.) are described using various terms, including “connected,” “joined,” “linked,” “adjacent,” “next to,” “on top of,” “above,” “below,” and “set.” Unless explicitly described as “direct,” when a relationship between first and second components is described in the foregoing disclosure, the relationship can be a direct relationship in which no other intermediate components exist between the first and second components, or an indirect relationship (spatial or functional) in which one or more intermediate components exist between the first and second components. As used herein, at least one of the phrases A, B, and C should be interpreted as representing the logic (A or B or C) using the non-exclusive logic “OR,” and should not be interpreted as representing “at least one A, at least one B, and at least one C.”

[0126] In a diagram, as indicated by the arrows, the direction of the arrows typically shows the flow of information of interest to the diagram, such as data or instructions. For example, when components A and B exchange various types of information, but the information passed from component A to component B is relevant to the diagram, the arrow might point from component A to component B. This unidirectional arrow does not mean that no other information is passed from component B to component A. Furthermore, for information sent from component A to component B, component B may send a request for or confirmation of receipt of that information to component A.

[0127] In this application, the terms "module" or "controller" are used in accordance with the following definitions and may be replaced by the term "circuit". The term "module" may refer to, belong to, or include: application-specific integrated circuits (ASICs); digital, analog, or mixed-signal analog / digital discrete circuits; digital, analog, or mixed-signal analog / digital integrated circuits; combinational logic circuits; field-programmable gate arrays (FPGAs); processor circuitry (shared, dedicated, or grouped) that executes code; memory circuitry (shared, dedicated, or grouped) that stores code executed by the processor circuitry; other suitable hardware components that provide the described functionality; or combinations of some or all of the above, such as in a system-on-a-chip.

[0128] A 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 disclosed herein may be distributed across multiple modules connected via the interface circuit. For example, multiple modules may allow for load balancing. In another example, a server (also known as a remote or cloud) module may perform certain functions on behalf of a client module.

[0129] The term "code" as used above can include software, firmware, and / or microcode, and can 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 conjunction with additional processor circuitry, executes some or all of the code from one or more modules. References to multiple processor circuits include multiple processor circuits on a discrete chip, multiple processor circuits on a single chip, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or combinations thereof. The term "shared memory circuit" includes a single memory circuit that stores some or all of the code from multiple modules. The term "group memory circuit" includes a memory circuit that, in conjunction with additional memory, stores some or all of the code from one or more modules.

[0130] The term memory circuit is a subset of the term computer-readable medium. As used herein, the term computer-readable medium does not include transient electrical or electromagnetic signals propagating through a medium such as on a carrier wave; therefore, the term computer-readable medium can 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 disk drives), and optical storage media (such as CDs, DVDs, or Blu-ray discs).

[0131] The apparatus and methods described in this application can be implemented, in part or in whole, by a special-purpose computer, which is created by configuring a general-purpose computer to perform one or more specific functions contained in a computer program. The function blocks, flowchart components, and other elements described above serve as software specifications that can be translated into computer programs through the daily work of a skilled technician or programmer.

[0132] A computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. A computer program may also include or depend on stored data. A computer program may include a basic input / output system (BIOS) for interacting with the hardware of a special-purpose computer, device drivers for interacting with specific devices of the special-purpose computer, one or more operating systems, user applications, background services, background applications, etc.

[0133] Computer programs 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 from source code by a compiler; (iv) source code executed by an interpreter; and (v) source code compiled and executed by a just-in-time (JIT) compiler, etc. As an example only, source code may be written using the syntax of languages ​​including C, C++, C#, Objective C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, JavaScript®, HTML5 (Hypertext Markup Language Version 5), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, MATLAB, SIMULINK, and Python®.

Claims

1. A system for monitoring a vehicle's battery, the system comprising: processor; as well as A memory for storing instructions, which, when executed by the processor, configure the processor to: The battery receives a first feature, which includes statistical data on the internal resistance of multiple battery cell groups in the battery pack of the battery, wherein the first feature includes at least the maximum, minimum and average internal resistance values ​​of each of the battery cell groups. The second characteristic of the battery pack is calculated using a combination of the maximum, minimum, and average values ​​of the first characteristic, wherein the first characteristic is adjusted based on one or more parameters of the battery before the second characteristic is calculated based on the first characteristic; The battery pack is determined to be faulty in response to one or more of the second features (i) during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle gradually deviating from the corresponding normal value to the corresponding threshold value, wherein a driving cycle is a trip taken by the vehicle. In response to a fault in the battery pack, it is determined whether one or more of the battery cells in the group are faulty based on one or more of the first features. Perform a severity assessment to achieve at least one of the detection or prediction of battery pack failure; Actively monitor the progress of faults and send notifications before a fault occurs to prevent vehicle failure; and The vehicle is controlled in response to a detected fault.

2. The system according to claim 1, wherein, The instructions further configure the processor to identify, based on one or more of the first features indicating a faulty battery pack and the faulty battery cell group, the one or more of which contribute the most to the faulty battery pack.

3. The system according to claim 1, wherein, The instructions further configure the processor to determine the severity level of a fault in response to a fault in one or more of the battery pack and the group of battery cells.

4. The system according to claim 3, wherein, The instructions further configure the processor to provide indications about the fault in one or more of the battery pack and the group of battery cells, as well as the severity level of the fault.

5. The system according to claim 1, wherein, The internal resistance of the battery cell group includes at least one of the charging and discharging resistances of the battery cell group.

6. The system according to claim 1, wherein, The instructions further configure the processor to determine that one or more of the battery cell groups are faulty in response to one or more of the first features (i) gradually deviating from a corresponding normal value to a corresponding threshold during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle.

7. The system according to claim 1, wherein, The instructions further configure the processor to normalize the second feature based on one or more parameters of the battery before determining whether the battery pack is faulty based on one or more of the second features.

8. A method for monitoring a vehicle's battery, the method comprising: The first feature is received, which includes statistical data on the internal resistance of multiple battery cell groups in the battery pack of the battery, wherein the data includes at least the maximum, minimum and average internal resistance values ​​of each of the battery cell groups. The second characteristic of the battery pack is calculated using a combination of the maximum, minimum, and average values ​​of the first characteristic; Before calculating the second feature based on the first feature, the first feature is adjusted based on one or more parameters of the battery; The battery pack is determined to be faulty in response to one or more of the second characteristics (i) during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle gradually deviating from the corresponding normal value to the corresponding threshold. In response to a fault in the battery pack, it is determined whether one or more of the battery cells in the group are faulty based on one or more of the first features. Perform a severity assessment to achieve at least one of the detection or prediction of battery pack failure; Actively monitor the progress of faults and send notifications before a fault occurs to prevent vehicle failure; and The vehicle is controlled in response to a detected fault.

9. The method of claim 8, further comprising identifying, based on one or more of the first features of the second feature indicating that the battery pack is faulty and the faulty battery cell group, one or more of the first features indicating that the battery pack is faulty, one or more of ... first features indicating that the battery cell group is faulty, one or more of the first features indicating that the battery cell group is faulty, one or more of the first features indicating that the battery cell group is faulty, one or more of the first features indicating that the battery cell group is faulty, one or more of the first features indicating that the battery cell group is faulty, one or more of the first features indicating that the battery cell group is faulty, one or more of the first features indicating that the battery cell group 10. The method of claim 8, further comprising determining a fault severity level in response to a fault in one or more of the battery pack and the group of battery cells.

11. The method of claim 10, further comprising providing an indication of a fault in one or more of the battery pack and the group of battery cells and the severity level of the fault.

12. The method of claim 8, further comprising determining the internal resistance of the battery cell group, the internal resistance including at least one of the charging and discharging resistances of the battery cell group.

13. The method of claim 8, further comprising determining that one or more of the battery cell groups is faulty in response to one or more of the first features (i) gradually deviating from a corresponding normal value to a corresponding threshold during one or more driving cycles of the vehicle or (ii) during multiple driving cycles of the vehicle.

14. The method of claim 8, further comprising standardizing the second feature based on one or more parameters of the battery before determining whether the battery pack is faulty based on one or more of the second features.

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