Resistance estimation of high voltage battery packs during vehicle charging operation

Abstract

A current measurement circuit measures the charging current of a battery comprising multiple battery cell groups when the battery is charged using one of a plurality of charging systems. A voltage measurement circuit measures the voltage of the battery cell groups. A controller defines multiple operating regions in the battery's charging current profile during a vehicle charging cycle, filters the charging current and voltage measured in the operating regions, calculates the internal resistance of the battery cell groups in the operating regions based on the filtered current and voltage, generates at least one of a statistical value and a distance metric based on the internal resistance of the battery cell groups, and determines whether one or more battery cell groups are faulty, independent of the plurality of charging systems used to charge the battery.

Description

[0001] Cross-reference to related applications

[0002] U.S. Patent Application No. 17 / 516,223 (Attorney's File No. P100533-PRI-NP-US01), filed November 1, 2021, entitled "Resistance Estimation of High-Voltage Battery Packs During Vehicle Driving Operation," and U.S. Patent Application No. 17 / 516,279 (Attorney's File No. P100534-PRI-NP-US01), entitled "Health Monitoring Method for Early Failure Detection of High-Voltage Battery Packs Used in Electric Vehicles," are relevant to this application. The entire disclosure of the above-cited applications is incorporated herein by reference.

[0003] The information provided in this section is intended to provide a general overview of the context of this disclosure. The work of the currently nominated inventors to the extent described in this section, and any aspects of the specification at the time of filing that may not conform to prior art, are not expressly or impliedly acknowledged as prior art against this disclosure. Technical Field

[0004] This disclosure relates generally to electric vehicles, and more specifically to the estimation of the resistance of high-voltage battery packs during vehicle charging operations. Background Technology

[0005] The use of electric vehicles is surging. Electric vehicles are powered by batteries. Battery performance tends to degrade over time. Batteries can also develop problems during use. For example, one or more battery cells in a battery pack may develop problems and / or degrade 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. If one or more battery cells in a battery pack develop problems, the internal resistance also changes. Changes in battery internal resistance can indicate the degradation of battery performance over time and can be used to detect problems that may arise during battery use. Summary of the Invention

[0006] A system for monitoring a vehicle battery includes a current measurement circuit, a voltage measurement circuit, and a controller in communication with the current and voltage measurement circuits. The current measurement circuit is configured to measure the charging current of a battery comprising multiple groups of battery cells connected to each other when the battery is charged using one of a plurality of charging systems. The voltage measurement circuit is configured to measure the voltage of the battery cell groups when the battery is charged using one of the plurality of charging systems. The controller is configured to define multiple operating regions in the battery's charging current profile during a charging cycle of the vehicle when the battery is charged using one of the plurality of charging systems. The controller is configured to filter the charging current and voltage measured in the operating regions and calculate the internal resistance of the battery cell groups in the operating regions based on the filtered current and voltage. The controller is configured to generate at least one of a statistical value and a distance metric based on the internal resistance of the battery cell groups. The distance metric is generated by dividing the difference between the internal resistance and the average internal resistance by the standard deviation of the internal resistance. The controller is configured to determine whether one or more battery cell groups in the battery cell groups are faulty, independent of the plurality of charging systems used to charge the battery, based on at least one of the statistical value and the distance metric.

[0007] In another feature, the controller is configured to determine whether one or more battery cell groups in a battery cell group are faulty based on at least one of the following: (i) the highest value of the difference between the maximum and minimum values ​​of one or more statistics across the battery cell group and (ii) the highest value of the distance metric across the battery cell group.

[0008] In another feature, the controller is configured to determine whether one or more battery cell groups in a battery cell group are faulty based on at least one of the statistical values ​​calculated for each of the multiple charging systems charging the battery and a distance metric.

[0009] In another feature, the controller is configured to define an operating region based on the battery's charging current, temperature, and state of charge when the charging current is stable within an operating region for a predetermined time period.

[0010] Among other features, the controller is configured to define an operating area based on a predetermined range of battery charging current, temperature, and state of charge; and to determine whether one or more battery cell groups in a battery cell group are faulty, regardless of changes in battery temperature and state of charge.

[0011] Among other features, the controller is configured to define multiple frequency bands within an operating region based on a predetermined range of charging current and multiple predetermined ranges of battery temperature and state of charge. The controller is configured to calculate the internal resistance of battery cell groups within the frequency bands based on filtered current and voltage. The controller is configured to generate a single statistical value for the frequency band based on the internal resistance of the battery cell groups within the frequency band. The controller is configured to determine the difference between the maximum and minimum values ​​of one or more of the single statistical values. The controller is configured to select internal resistance data for battery cell groups from the frequency bands corresponding to the highest value of the difference between the maximum and minimum values. The controller is configured to diagnose the health status of the battery based on the selected internal resistance data.

[0012] Among other features, the controller is configured to select internal resistance data for a battery cell group from an operating region of the highest value among the differences between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group. The controller is configured to store information including at least one of the selected internal resistance data and the highest value of a distance metric across the battery cell group for diagnosing one or more battery cell groups. The controller is configured to send the stored information to a server for predicting and determining trends in battery health. The controller is configured to provide messages about the battery's health based on the stored information.

[0013] Among other features, the controller is configured to, for each of a plurality of charging systems used to charge the battery, select internal resistance data of a battery cell group from an operating region of the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group. The controller is configured to store at least one of the selected internal resistance data and the highest value of the distance metric. The controller is configured to compare at least one of the selected internal resistance data and the highest value of the distance metric with corresponding, calibrated thresholds for the plurality of charging systems. The controller is configured to determine whether one or more battery cell groups have failed across the plurality of charging systems.

[0014] Among other features, the system also includes a server configured to receive at least one of the highest values ​​of selected internal resistance data and distance metrics from multiple vehicles, analyze the distribution of at least one of the highest values ​​of selected internal resistance data and distance metrics, and calibrate thresholds based on the analysis.

[0015] Among other features, the server is also configured to monitor distribution trends, identify one or more vehicles in which one or more battery cell groups continue to fail, and send notifications to the identified vehicles.

[0016] Among other features, a method for monitoring a vehicle battery includes measuring the charging current of a battery comprising a group of battery cells interconnected with each other while charging the battery using one of a plurality of charging systems. The method includes measuring the voltage of the battery cell groups while charging the battery using one of the plurality of charging systems. The method includes defining multiple operating regions in the battery's charging current profile during a charging cycle of the vehicle while charging the battery using one of the plurality of charging systems. The method includes filtering the charging current and voltage measured in the operating regions. The method includes calculating the internal resistance of the battery cell groups in the operating regions based on the filtered current and voltage. The method includes generating at least one of a statistical value and a distance metric based on the internal resistance of the battery cell groups. The distance metric is generated by dividing the difference between the internal resistance and the average internal resistance by the standard deviation of the internal resistance. The method includes determining whether one or more battery cell groups in the battery cell group are faulty, independent of the plurality of charging systems used to charge the battery, based on at least one of the statistical value and the distance metric.

[0017] In another feature, the method includes determining whether one or more battery cell groups in a battery cell group are faulty based on at least one of the following: (i) the highest value of the difference between the maximum and minimum values ​​of one or more statistics across the battery cell group and (ii) the highest value of the distance metric across the battery cell group.

[0018] In another feature, the method also includes determining whether one or more battery cell groups in a battery cell group are faulty based on at least one of a statistical value calculated for each of the multiple charging systems charging the battery and a distance metric.

[0019] In another feature, the method also includes defining an operating region based on the battery's charging current, temperature, and state of charge when the charging current is stable in an operating region for a predetermined time period within the operating region.

[0020] Among other features, the method also includes defining an operating area based on predetermined ranges of battery charging current, temperature, and state of charge; and determining whether one or more battery cell groups are faulty, regardless of changes in battery temperature and state of charge.

[0021] Among other features, the method includes defining multiple frequency bands within an operating region based on a predetermined range of charging current and multiple predetermined ranges of battery temperature and state of charge. The method also includes calculating the internal resistance of battery cell groups within the frequency bands based on filtered current and voltage. The method further includes generating a single statistical value for the frequency band based on the internal resistance of the battery cell groups within the frequency band. The method also includes determining the difference between the maximum and minimum values ​​of one or more individual statistical values. The method further includes selecting internal resistance data for battery cell groups from one frequency band corresponding to the highest value of the difference between the maximum and minimum values. The method also includes diagnosing the health status of the battery based on the selected internal resistance data.

[0022] Among other features, the method further includes selecting internal resistance data for a battery cell group from an operating region of the highest value among the differences between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group. The method also includes storing information including at least one of the selected internal resistance data across the battery cell group and the highest value of a distance metric for diagnosing one or more battery cell groups. The method further includes sending the stored information to a server for predicting and determining trends in battery health. The method also includes providing messages about the battery health based on the stored information.

[0023] Among other features, the method includes, for each of a plurality of charging systems used to charge the battery, selecting internal resistance data of a battery cell group from an operating region of the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group. The method also includes storing at least one of the selected internal resistance data and the highest value of a distance metric. The method further includes comparing at least one of the selected internal resistance data and the highest value of the distance metric with calibrated corresponding thresholds for the plurality of charging systems. The method also includes determining whether one or more battery cell groups have failed across the plurality of charging systems.

[0024] Among other features, the method also includes receiving at least one of the highest values ​​of selected internal resistance data and distance metrics from multiple vehicles by the server, analyzing the distribution of at least one of the highest values ​​of selected internal resistance data and distance metrics, and calibrating a threshold based on the analysis.

[0025] Among other features, the method also includes monitoring distribution trends on the server, identifying one or more vehicles in which one or more battery cell groups continue to fail, and sending notifications to the identified vehicles.

[0026] This disclosure includes the following solutions.

[0027] Option 1. A system for monitoring vehicle batteries, the system comprising:

[0028] A current measurement circuit is configured to measure the charging current of the battery when the battery is charged using one of a plurality of charging systems, the battery comprising a plurality of battery cell groups connected to each other.

[0029] A voltage measurement circuit configured to measure the voltage of the battery cell group when the battery is charged using one of the plurality of charging systems; and

[0030] The controller communicates with the current and voltage measurement circuitry and is configured to:

[0031] When charging the battery using one of the plurality of charging systems, a plurality of operating regions are defined in the charging current curve of the battery during the charging cycle of the vehicle.

[0032] The charging current and voltage measured in the operating area are filtered;

[0033] The internal resistance of the battery cell group in the operating region is calculated based on the filtered current and voltage.

[0034] At least one of a statistical value and a distance metric is generated based on the internal resistance of the battery cell group, wherein the distance metric is generated by dividing the difference between the internal resistance and the average internal resistance by the standard deviation of the internal resistance; and

[0035] The determination of whether one or more battery cell groups in the battery cell group are faulty is based on at least one of the statistical values ​​and the distance metric, regardless of the plurality of charging systems used to charge the battery.

[0036] Option 2. The system according to Option 1, wherein the controller is configured to determine whether one or more battery cell groups in the battery cell group are faulty based on at least one of the following: (i) the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group and (ii) the highest value of the distance metric across the battery cell group.

[0037] Option 3. The system according to Option 1, wherein the controller is configured to determine whether one or more battery cell groups in the battery cell group are faulty based on at least one of the statistical values ​​calculated for each of the plurality of charging systems charging the battery and the distance metric.

[0038] Option 4. The system according to Option 1, wherein the controller is configured to define an operating region based on the battery's charging current, temperature, and state of charge when the charging current is stable in an operating region for a predetermined time period within the operating region.

[0039] Option 5. The system according to Option 1, wherein the controller is configured to:

[0040] The operating area is defined based on a predetermined range of the battery's charging current, temperature, and state of charge; and

[0041] Determine if one or more battery cell groups in the battery cell group are faulty, regardless of changes in the battery's temperature and state of charge.

[0042] Option 6. The system according to Option 1, wherein the controller is configured to:

[0043] Multiple frequency bands within one operating region are defined based on a predetermined range of charging current and multiple predetermined ranges of battery temperature and charging state.

[0044] The internal resistance of the battery cell group in the frequency band is calculated based on the filtered current and voltage;

[0045] A single statistical value for the frequency band is generated based on the internal resistance of the battery cell group in the frequency band;

[0046] Determine the difference between the maximum and minimum values ​​of one or more individual statistical values;

[0047] The internal resistance data of the battery cell group is selected from one of the frequency bands corresponding to the highest value of the difference between the maximum and minimum values; and

[0048] The health status of the battery is diagnosed based on the selected internal resistance data.

[0049] Option 7. The system according to Option 1, wherein the controller is configured to:

[0050] The internal resistance data of the battery cell group is selected from one of the operating regions corresponding to the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group;

[0051] The system stores information including at least one of selected internal resistance data across the battery cell groups and the highest value of the distance metric, for diagnosing one or more battery cell groups within the battery cell groups.

[0052] The stored information is sent to the server to predict and determine trends in the health status of the battery; and

[0053] The system provides messages about the health status of the battery based on the stored information.

[0054] Option 8. The system according to Option 1, wherein the controller is configured for each of the plurality of charging systems used to charge the battery:

[0055] The internal resistance data of the battery cell group is selected from one of the operating regions corresponding to the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group;

[0056] Store at least one of the selected internal resistance data and the highest value of the distance metric;

[0057] At least one of the selected internal resistance data and the highest value of the distance metric is compared with a calibrated corresponding threshold for the plurality of charging systems; and

[0058] Determine whether one or more battery cell groups in the battery cell group have failed across the multiple charging systems.

[0059] Option 9. The system according to Option 8 further includes a server, the server being configured to:

[0060] Receive at least one of the selected internal resistance data and the highest value of the distance metric from multiple vehicles;

[0061] Analyze the distribution of at least one of the selected internal resistance data and the highest value of the distance metric; and calibrate the threshold based on the analysis.

[0062] Option 10. The system according to Option 9, wherein the server is further configured to:

[0063] Monitor the trend of the distribution;

[0064] Identify one or more vehicles in which one or more battery cell groups continue to fail; and

[0065] Send a notification to the identified vehicle.

[0066] Option 11. A method for monitoring a vehicle battery, the method comprising:

[0067] The charging current of the battery is measured when the battery is charged using one of a plurality of charging systems, the battery comprising a plurality of battery cell groups connected to each other;

[0068] The voltage of the battery cell group is measured when the battery is charged using one of the plurality of charging systems;

[0069] When charging the battery using one of the plurality of charging systems, a plurality of operating regions are defined in the charging current curve of the battery during the charging cycle of the vehicle.

[0070] The charging current and voltage measured in the operating area are filtered;

[0071] The internal resistance of the battery cell group in the operating region is calculated based on the filtered current and voltage.

[0072] At least one of a statistical value and a distance metric is generated based on the internal resistance of the battery cell group, wherein the distance metric is generated by dividing the difference between the internal resistance and the average internal resistance by the standard deviation of the internal resistance; and

[0073] The determination of whether one or more battery cell groups in the battery cell group are faulty is based on at least one of the statistical values ​​and the distance metric, regardless of the plurality of charging systems used to charge the battery.

[0074] Option 12. The method according to Option 11 further includes determining whether one or more battery cell groups in the battery cell group are faulty based on at least one of the following: (i) the highest value of the difference between the maximum and minimum values ​​of one or more statistics across the battery cell group and (ii) the highest value of the distance metric across the battery cell group.

[0075] Option 13. The method according to Option 11, further comprising determining whether one or more battery cell groups in the battery cell group are faulty based on at least one of the statistical values ​​calculated for each of the plurality of charging systems charging the battery and the distance metric.

[0076] Option 14. The method according to Option 11 further includes defining an operating region based on the battery's charging current, temperature, and state of charge when the charging current is stable in an operating region of the operating region for a predetermined time period.

[0077] Option 15. The method according to Option 11 further includes:

[0078] The operating area is defined based on a predetermined range of the battery's charging current, temperature, and state of charge; and

[0079] Determine if one or more battery cell groups in the battery cell group are faulty, regardless of changes in the battery's temperature and state of charge.

[0080] Option 16. The method according to Option 11 further includes:

[0081] Multiple frequency bands within one operating region are defined based on a predetermined range of charging current and multiple predetermined ranges of battery temperature and charging state.

[0082] The internal resistance of the battery cell group in the frequency band is calculated based on the filtered current and voltage;

[0083] A single statistical value for the frequency band is generated based on the internal resistance of the battery cell group in the frequency band;

[0084] Determine the difference between the maximum and minimum values ​​of one or more of the individual statistical values;

[0085] The internal resistance data of the battery cell group is selected from one of the frequency bands corresponding to the highest value of the difference between the maximum and minimum values; and

[0086] The health status of the battery is diagnosed based on the selected internal resistance data.

[0087] Option 17. The method according to Option 11 further includes:

[0088] The internal resistance data of the battery cell group is selected from one of the operating regions corresponding to the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group;

[0089] The system stores information including at least one of selected internal resistance data across the battery cell groups and the highest value of the distance metric, for diagnosing the one or more of the battery cell groups.

[0090] The stored information is sent to the server to predict and determine trends in the health status of the battery; and

[0091] The system provides messages about the health status of the battery based on the stored information.

[0092] Option 18. The method according to Option 11, further comprising, for each of the plurality of charging systems used to charge the battery:

[0093] The internal resistance data of the battery cell group is selected from one of the operating regions corresponding to the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group;

[0094] Store at least one of the selected internal resistance data and the highest value of the distance metric;

[0095] At least one of the selected internal resistance data and the highest value of the distance metric is compared with a calibrated corresponding threshold for the plurality of charging systems; and

[0096] Determine whether one or more battery cell groups in the battery cell group have failed across the multiple charging systems.

[0097] Option 19. The method according to Option 18, further comprising, at the server:

[0098] Receive at least one of the selected internal resistance data and the highest value of the distance metric from multiple vehicles;

[0099] Analyze the distribution of at least one of the selected internal resistance data and the highest value of the distance metric; and calibrate the threshold based on the analysis.

[0100] Option 20. The method according to Option 19, further comprising, at the server:

[0101] Monitor the trend of the distribution;

[0102] Identify one or more vehicles in which one or more battery cell groups continue to fail; and

[0103] Send a notification to the identified vehicle.

[0104] Other areas of application of this disclosure will become apparent from the detailed description, claims, and drawings. The detailed description and specific examples are for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0105] This disclosure will be more fully understood through detailed description and accompanying drawings, in which:

[0106] Figure 1 An example of a control system for an electric vehicle is shown;

[0107] Figure 2A and 2B An example of a battery for an electric vehicle, including a group of battery cells, is shown;

[0108] Figures 3A-3D Examples of factors affecting battery internal resistance are shown;

[0109] Figure 4 An example of a battery current curve during a charging cycle in an electric vehicle is shown.

[0110] Figure 5 An example of a health monitoring system for batteries is shown;

[0111] Figure 6A and 6B This demonstrates methods for monitoring and assessing battery health.

[0112] Figure 7-11 The method in Figure 6 is shown in further detail; and

[0113] Figures 12A-12E A diagram illustrating the internal resistance distribution of a group of battery cells in a battery is shown.

[0114] In the accompanying drawings, reference numerals may be reused to identify similar and / or identical elements. Detailed Implementation

[0115] This disclosure provides a system and method for estimating the internal resistance of a battery in an electric vehicle and for monitoring battery performance at the individual battery cell level to detect faults caused by high internal resistance. The method is performed while charging the battery of the electric vehicle using any of a variety of charging methods. These charging methods include L1 charging using a 120VAC power supply, L2 charging using a 240VAC power supply, and DC fast charging (DCFC). The charging current profile of the battery varies depending on the charging method used. Therefore, the internal resistance of the battery exhibits different behavior in different charging methods. This disclosure provides a robust internal resistance estimator implemented in the vehicle's controller, taking into account different noise factors. The estimator estimates the internal resistance of the battery regardless of the charging method used to charge the battery. The estimator is designed to optimize the controller's memory. The estimator provides a health indicator that allows for the detection of anomalies in battery cells at an early stage and the identification of abnormal battery cells within the battery.

[0116] Specifically, the estimator provides health indicators, which are monitored to characterize the battery's internal resistance, thereby quantifying battery performance degradation. The estimator provides the ability to detect anomalous behavior at the individual battery cell group level and isolate faults to specific battery cell groups. The estimator is implemented in the vehicle's onboard controller to estimate the internal resistance of individual battery cell groups, which are referred to as health indicators. Various group-level and battery cell group-level characteristics are calculated based on the estimated internal resistance of individual battery groups. These characteristics can be used to diagnose faulty battery cell groups within the battery. Furthermore, the characteristics can be transmitted to servers in the cloud for monitoring trends and performing predictions and repair / diagnostics.

[0117] This disclosure provides a general system for estimating battery charging resistance during L1, L2, and DCFC charging scenarios to monitor battery performance at the individual battery cell group level and detect / isolate faulty battery cell groups. The system provides a solution for designing a robust resistance estimator that accounts for different noise factors and optimizes the in-vehicle implementation process across different charging curves. The designed health indicator provides the ability to detect and identify one or more abnormal battery cells in a high-voltage battery at an early stage.

[0118] As explained in detail below, the system automatically detects and selects operating conditions based on the constant current charging phases of different charging curves. The system monitors a metric designed to characterize the battery's internal resistance and quantifies battery performance degradation based on this metric. The system can detect abnormal behavior at the level of each individual battery cell group and can isolate faults to specific battery cell groups / modules. The system resides in the vehicle's onboard controller to estimate the internal resistance as a health indicator for individual battery cell groups. The calculated characteristics can be transmitted to the cloud for trend monitoring and for diagnostic and predictive purposes.

[0119] More specifically, the system for estimating the internal resistance of individual battery cell groups and monitoring the overall health of the battery pack utilizes multiple narrow operating regions of the battery charging current curve, regardless of the charging method used to charge the battery. These narrow operating regions are used to estimate the battery's internal resistance and minimize the impact of battery conditions (e.g., temperature, state of charge (SOC), and current) on the internal resistance estimate. The system estimates the battery's internal resistance in each operating region and tracks statistical characteristics such as the minimum, maximum, average, and other values ​​of the internal resistance for each individual operating region. The system uses low-pass filters to filter the charging current and the battery cell voltages to minimize the effects of high-frequency measurement noise and system dynamic variations. The system uses a stability criterion (described below) for the filtered current as a threshold condition to calculate the internal resistance of each battery cell group.

[0120] The system performs internal resistance estimation based on a DC-equivalent circuit model for each battery cell group, expressed as R = [V - OCV] / I, where V and I are the filtered battery cell group voltage and filtered battery pack current, respectively; and OCV is the open-circuit voltage of the battery pack. By using the group-level resistance difference as a feature, the system selects optimal internal resistance data to track the internal resistance of battery cell groups across multiple operating regions. The system provides the ability to calculate battery internal resistance in multiple narrow operating regions and store only the optimal data in the controller's memory, referring to the maximum resistance distribution across different battery cell groups within the battery pack. Storing only the optimal data optimizes memory requirements and usage in the controller.

[0121] The system uses optimal data selected based on resistance statistics to detect anomalous behavior at the level of each individual battery cell group and isolates faults to specific battery cell groups / modules within the battery pack. The system also calculates distance metrics to detect outlier battery cells (i.e., battery cells with abnormal internal resistance relative to the rest of the battery pack), explained in detail below. The system monitors the progression of battery cell faults and sends proactive alerts / notifications to warn customers and prevent vehicle stalling before a fault occurs. The system manages vehicle operation upon detecting a fault. The calculated characteristics can be transmitted to servers in the cloud for monitoring trends in battery health and for performing predictions and aiding in battery repair / diagnosis. Final health indicator data (historical statistics) observed from each vehicle provides diagnostic data for repair technicians to assess battery health. The historical data distribution of the vehicle fleet can be used to learn the charging resistance behavior of different charging methods, which can be used to design estimators to track internal resistance degradation. The system resides in the vehicle's onboard controller to estimate the internal resistance (i.e., health indicators) of individual battery cell groups. The system provides a passive approach to internal resistance estimation that does not affect the driving of the electric vehicle. These and other features of this disclosure are described in detail below.

[0122] This organization is publicly disclosed as follows. First, refer to... Figure 1 A block diagram illustrating and describing the control system of an electric vehicle. (Reference) Figure 2A and 2B An example of a group of battery cells in an electric vehicle is shown and described. (Reference) Figures 3A-3D Examples of factors affecting battery internal resistance are shown and described. Reference 4 shows and describes examples of battery charging current profiles and examples of the operating regions defined within the charging current profiles. Figure 5 An example of a battery health monitoring system is shown and described. (Reference) Figure 6A and 6B The diagram illustrates and depicts the monitoring and assessment of battery health status by [the method described]. Figure 5 The overall approach used in the health monitoring system. Subsequently, refer to... Figure 7-11 The steps of the method in Figure 6 are shown and described in further detail. (Refer to...) Figures 12A-12E The internal resistance distribution of the battery cell group is shown and described.

[0123] 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 battery 104 can be charged using an L1 charging system 130-1, an L2 charging system 130-2, and a DCFC charging system 130-3 (collectively referred to as charging system 130). 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 may also be implemented in BMS 106.

[0124] 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.

[0125] The control system 100 also 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 the 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).

[0126] The control system 100 also 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 transmits data from the battery 104 processed by a health monitoring system (described below) to one or more servers 122 via the communication subsystem 120. The controller 102 generates an alarm based on the data from the battery 104 processed by the health monitoring system and provides the alarm to the occupants via the HMI of the infotainment subsystem 108. The controller 102 may also receive alarms from one or more servers 122 based on data from the battery 104 processed by one or more servers 122 and provide the alarm to the occupants via the HMI of the infotainment subsystem 108.

[0127] Figure 2A and 2BAn 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. Typically, 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.

[0128] exist Figure 2A In this 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 groups 150, the group-level current I through the battery cell group 150 of the battery pack is measured by measuring the current passing through terminals 151-1, 151-2 (collectively referred to as terminals 151) of the battery pack including battery cell group 150. Current measurement is described below. Figure 5 Describe it.

[0129] 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 only, 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 to each other 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. Voltage measurement is described below. Figure 5 Describe it.

[0130] Therefore, for each battery pack comprising N battery cell groups 150, a group-level current I (also referred to as battery current I throughout this disclosure) and N voltages across the N battery cell groups 150 are measured. These current and voltage measurements allow the calculation of the internal resistance of each individual battery cell group 150. The internal resistance of the battery 104 can be used as a health indicator to indicate the health status of the battery 104.

[0131] The following text is for reference only. Figure 5The health monitoring system described in detail generates health indicators by considering various factors affecting the internal resistance of battery 104. These factors include, but are not limited to, the temperature, state of charge (SOC), and current of battery 104. The health monitoring system can identify and isolate one or more battery cell groups 150 that are faulty and contribute most significantly to the performance degradation of battery 104. To identify and isolate one or more faulty battery cell groups 150, the health monitoring system can process group-level current and voltage of individual battery cell groups 150 in an onboard controller (e.g., controller 102), in the cloud (e.g., one or more servers 122), or using a combination of both. The health monitoring system can provide health indicators of battery 104 to the occupants of the electric vehicle and maintenance technicians for predictive and diagnostic purposes in the form of proactive alerts, as described in detail below.

[0132] Figures 3A-3D This illustrates how the internal resistance of battery 104 is affected by various factors, such as the temperature, state of charge (SOC), and charging current of battery 104, under different charging methods. For example, in... Figure 3A In this embodiment, during the charging of battery 104, the behavior of the internal resistance R of the battery cell group of battery 104 can vary with the state of charge (SOC). For example, as battery 104 is charged, the SOC of battery 104 increases. The internal resistance of the battery cell group can remain within a relatively narrow range 132 until the SOC reaches a predetermined level shown in 134. Thereafter, as battery 104 continues to charge, the internal resistance of most healthy (i.e., normally operating) battery cell groups can still remain within the relatively narrow range 132. However, the internal resistance of one or more unhealthy or defective (i.e., abnormal) battery cell groups can deviate from the relatively narrow range 132 and can exhibit an increased internal resistance as shown in 136. If the average internal resistance of all battery cell groups in the battery cell group is considered over the entire charging cycle of battery 104, this deviation may not be noticeable. Therefore, the system of this disclosure uses a method that defines a narrow operating region in the charging current profile of battery 104 to detect abnormal battery cell groups.

[0133] also, Figure 3B-3D This illustrates that battery 104 can exhibit different charging current profiles depending on the type of charging method used. For example, Figure 3B-3DThe charging current curves of battery 104 are shown when it is charged using L1, L2, and DCFC charging methods, respectively. In each charging method, the charging current I and charging time t are different and are denoted as I1, I2, I3; and t1, t2, t3, respectively. In each charging method, the peak charging current is different and reaches at different times. Specifically, the peak charging current (shown in 137) is the highest and occurs earliest in DCFC charging; the peak charging current in L2 charging (shown in 138) is smaller and occurs later than in DCFC charging, and the peak charging current in L1 charging (shown in 139) is even smaller and occurs later than in both DCFC and L2 charging.

[0134] Due to the differences in charging current curves among different charging methods, it is typically necessary to define multiple operating regions for each charging method. However, defining multiple operating regions based on each charging method is not computationally optimal. Instead, the system of this disclosure automatically selects and defines narrower operating regions, regardless of the different charging methods used, and provides a universal method applicable to all charging methods and battery operating conditions. As explained in detail below, the system selects optimal internal resistance data to monitor the health of the high-voltage battery pack and uses the optimal internal resistance data to detect and isolate one or more faulty battery cell groups for predictive purposes. Specifically, the health monitoring system of this disclosure, described below, divides the charging current curve of battery 104 into narrow operating regions and measures the charging current of battery 104 and the voltage of battery cell group 150 in each operating region to eliminate the influence of battery operating conditions as described below.

[0135] Figure 4 An example of the charging current profile of battery 104 during a charging cycle is shown. For example, a charging current profile for DCFC charging is shown, but any other charging method can be used alternatively. Because the charging current and operating conditions of battery 104 vary during charging, the entire charging current profile of battery 104 is not selected for current and voltage measurements. Instead, multiple operating regions 160-1, ..., 160-M of the charging current profile of battery 104 within a charging cycle are selected, where M is a positive integer (collectively referred to as operating region 160). The procedure for defining operating region 160 is described below. Figure 6A , 6B And 7 will be described in more detail.

[0136] In short, each operating region 160 is a function of the charging current I, state of charge (SOC), and temperature T of the battery 104. The selected SOC and temperature T for the battery 104 are detailed below. Figure 8A and 8BTo explain in detail, each operating region 160 is selected where the charging current is relatively stable over time t (i.e., within a narrow range). The selected SOC and temperature T of the battery 104 can be calibrated parameters. For example, these parameters can be set during the manufacture of the battery 104 and can be changed via updates provided to the vehicle during its lifespan.

[0137] Figure 5 An 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 temperature sensing circuit 145, a processor 146, and a memory 148. The current measurement circuit 140 measures the charging current I through N battery cell groups 150. The multiplexer 142 connects to the voltage measurement circuit 144 across each battery cell group 150. The processor 146 controls the multiplexer 142. The voltage measurement circuit 144 measures the voltage across each battery cell group 150. The temperature sensing circuit 145 senses the temperature of the battery 104. The processor 146 processes the current and voltage measurements and stores only the selected processed data in the memory 148, as described below with reference to Figures 6-11. Due to the selective storage of processed data, the size (i.e., quantity) and usage of the memory 148 are optimized.

[0138] Figure 6A and 6B A method 200 for generating a health indicator based on the internal resistance of a group of battery cells 150 in a plurality of operating regions 160, according to this disclosure, is shown. The health indicator may include a statistical measure, a distance measure, or both. That is, the system may use a statistical measure, a distance measure, or a combination of both. Figure 7-11 Some steps of method 200 are shown in more detail. Method 200 is... Figure 5 The health monitoring system executes this method. For example, the processor 146, together with other circuitry of the controller 200, can execute the method 200.

[0139] exist Figure 6A and 6B In step 190, method 200 determines whether battery 104 is being charged. If battery 104 is being charged, then in step 192, method 200 determines whether the battery operating conditions are stable. Specifically, method 200 determines whether the following conditions are met for at least a predetermined time period: SOC > x%, and charging current I > a threshold (to avoid low current), where x and the threshold are calibrable parameters. Furthermore, method 200 determines whether the charging current I is stable by determining whether I(t) - I(t-1) < a predetermined threshold at time t, said predetermined threshold being a calibrable parameter as well. For example, in... Figure 4In the charging current curve shown, the charging current is stable from 160-1 to 160-M.

[0140] In method 202, method 200 defines the operating region 160 in the charging current profile of battery 104 as a function of the charging current I, state of charge (SOC), and temperature T of battery 104. (See reference) Figure 7 The defined operating region 160 is described in more detail. At 204, for each operating region 160, method 200 measures the charging current I through the battery pack (i.e., through N groups of battery cells 150), the voltage across each group of battery cells 150, and the average temperature of the battery 104, as referenced above. Figure 5 The explanation.

[0141] In step 206, method 200 uses a low-pass filter to filter the measured current I passing through the battery pack and the measured voltage across each battery cell group 150. Low-pass filtering is performed to minimize high-frequency noise in the current and voltage measurements and to reduce the effects of diffusion and charge transfer phenomena occurring in the battery 104. For example, diffusion and charge transfer phenomena can be approximated as corresponding resistance-capacitance (RC) pairs. Low-pass filtering reduces the influence of these capacitances at lower frequencies, thereby allowing for accurate measurement of the internal resistance of the battery cell group 150. Furthermore, when the current I is stable, method 200 uses a stability criterion to estimate the internal resistance. [Stability criterion reference] Figure 8A and 8B To describe in more detail.

[0142] At 208, for each group of battery cells, method 200 estimates the internal resistance of the battery cell group 150 based on the filtered current I and filtered voltage of the battery cell group 150 measured in each operating region 160. Subsequently, method 200 may determine a statistical metric based on the internal resistance estimated in steps 210, 212, and 214; or method 200 may calculate a distance metric in step 216; or method 200 may determine both the statistical metric and the distance metric in parallel.

[0143] In step 210, the method calculates estimated internal resistance statistics (e.g., minimum, maximum, average, and other values ​​of internal resistance) for each group of battery cells 150 within each operating region 160. While minimum, maximum, and average values ​​are used only by way of example throughout this disclosure, statistics may also include other statistical data, including but not limited to standard deviation, variance, etc. Statistics are also referred to as summary statistics. References for internal resistance estimation and statistical calculations are available. Figure 8A To describe in more detail.

[0144] In step 212, method 200 selects only the optimal data, which captures or indicates the maximum deviation in internal resistance across cell groups 150. Selecting the optimal data ensures that data is derived only from one or more cell groups 150 that contribute the most to the increase in the internal resistance of battery 204, and ignores any outliers that contribute only a small amount to the increase in the internal resistance of battery 204. A standard reference is selected. Figure 9 To describe in more detail.

[0145] In step 214, method 200 stores only the selected data in the memory 148 of controller 102 for diagnostic monitoring (e.g., by maintenance technicians and / or to send messages about battery health status to infotainment subsystem 108). Method 200 may also upload the selected data to one or more servers 122 in the cloud for predicting and trending the health status of battery 104. Storing only the selected data optimizes the size and usage of memory 148.

[0146] In 216, method 200 calculates the distance metric D for each group of 150 battery cells. i To detect abnormal battery cell groups (i.e., battery cell groups where the internal resistance of a cell differs from that of cells in most other battery cell groups). The distance metric calculation references... Figure 8B To describe in more detail.

[0147] At 218, method 200 detects faulty battery cell groups (i.e., battery cell groups with abnormally high internal resistance) based on statistical data generated at 214, distance metrics generated at 216, or both. Method 200 may utilize either statistical data or distance metrics to detect faulty battery cell groups. Alternatively, method 200 may utilize both statistical data and distance metrics to detect faulty battery cell groups.

[0148] When both statistical data and distance metrics are used, in step 220, method 200 combines the results of detections based on both statistical data and distance metrics to identify which battery cell groups have failed. The detection reference for failed battery packs performed in steps 218 and 220... Figure 10 To be described in more detail. Method 200 (e.g., via infotainment subsystem 108) generates a notification indicating a faulty group of battery cells, and method 200 ends.

[0149] Figure 7 The procedure for defining the operating region 160 in the charging current profile of battery 104 as a function of the current I, SOC, and temperature T of battery 104 is shown in further detail (i.e., step 202 of method 200 shown in FIG. 6). Figure 7In the following description, step 202 of method 200 is referred to simply as method 202. In step 202, method 202 determines whether the charging current I is stable within a narrow range of time t. Method 202 waits until the charging current I stabilizes within the selected narrow range. The stability criteria used to determine whether the current I is stable are referred to below. Figure 8A and 8B Describe it.

[0150] If the charging current I is stable within a narrow range selected over time t, then in step 254, method 202 obtains the state of charge (SOC) of battery 104 over time t as SOC(t) and the average temperature of battery 104 over time t as T(t). In step 256, method 202 selects a calibrated SOC range (e.g., SOC(t) - X%, where X is a positive integer). In step 258, method 202 selects B temperature ranges near T(t) (e.g., T(t) ± Y, where Y is an integer), where B is a positive integer.

[0151] In 260, method 202 defines B frequency bands (e.g., B = 3) in each operating region 160 as follows: First frequency band, band 1: a narrow current range, SOC(t) - X%, and a first temperature range selected for current I (e.g., by selecting a first value of Y); Second frequency band, band 2: a narrow current range selected for current I, which is the same as band 1, SOC(t) - X%, which is also the same as band 1, and a second temperature range (e.g., by selecting a second value of Y); Third frequency band, band 3: a narrow current range selected for current I, which is the same as band 1, SOC(t) - X%, which is also the same as band 1, and a third temperature range (e.g., by selecting a third value of Y).

[0152] Note that the State of Charge (SOC) and temperature can vary during charging cycles. While all three frequency bands can have different temperature ranges, two of the three frequency bands may have overlapping temperature ranges. Furthermore, instead of keeping the SOC constant across the three frequency bands and selecting different temperature ranges, the temperature can be kept constant across the three frequency bands, and different SOC ranges can be selected for at least two of the three frequency bands. Additionally, although B=3 is used in this disclosure only for simplicity of illustration, methods similar to those described above can be extended to any number of frequency bands. Furthermore, the number of frequency bands can vary within different operating regions 160. Frequency bands can also be referred to as sub-regions of operating region 160.

[0153] At 262, method 202 determines whether the operating conditions of battery 104 (i.e., SOC and temperature T) have changed. If the operating conditions have not changed, then at 264, method 202 defines the selected narrow current range as operating region 160 in the charging current profile of battery 104.

[0154] However, the SOC of battery 104 can change beyond SOC(t) - X%. Alternatively or additionally, the average temperature T of battery 104 can also change beyond T(t) ± Y. If the operating conditions of battery 104 (i.e., SOC and / or average temperature T) have changed, then method 202 reconstructs the new B-band as follows.

[0155] Before reconstructing a new frequency band, method 200 determines whether the existing frequency band has sufficient samples. If the existing frequency band has sufficient samples, then method 200 stores a copy of the data and the frequency band with the largest DeltaR (see below). Figure 9 (To be explained).

[0156] At 266, to reconstruct the new frequency band, method 202 selects a new time t, a new SOC(t), and a new T(t) for the narrow range of the selected charging current I. At 268, method 202 selects B new temperature ranges near the new T(t) (e.g., T(t) ± Z, where Z is an integer), and method 202 repeats steps 260 and 262 until an operating region can be defined. Subsequently, method 202 returns to estimate the internal resistance of the battery cell group 150 in the defined operating region 160 (i.e., within the selected narrow range of current I) and calculates the summary statistics and / or distance metrics for the battery cell group 150 in the defined operating region 160, as described above with reference to Figure 6.

[0157] Figure 8A Steps 206-210 of method 200 shown in Figure 6 are illustrated in further detail. The following procedure is performed on the battery cell group 150 in each operating region 160. After filtering the charging current I in the operating region 160 and the voltage measured across the battery cell group 150, method 200 applies stability criteria and estimates the internal resistance of the battery cell group 150 as follows.

[0158] In step 302, method 200 determines whether the filtered current I is within a predetermined range (e.g., as mentioned above). Figure 7 (The narrow range described). If the filtered current I is not within the predetermined range, then method 200 waits. If the filtered current I is within the predetermined range, then at 304, method 200 determines whether the SOC of battery 104 is within the predetermined range. If the SOC is not within the predetermined range, then method 200 waits.

[0159] If both the filtered current I and the state of charge (SOC) have their respective predetermined ranges, then at 306, method 200 determines whether the filtered current I is stable. For example, if the first derivative of the filtered current falls within a first predetermined range with respect to a predetermined time period, then method 200 determines that the filtered current I is stable. In some embodiments, if the second derivative of the filtered current also falls within a second predetermined range with respect to a predetermined time period, then method 200 determines that the filtered current I is stable.

[0160] If the filtered current I is stable, method 200 waits. If the filtered current I is stable and the SOC is within a predetermined range, then at 308, method 200 calculates the internal resistance of the battery cell group 150 in the operating region 160 as follows.

[0161] For i=1 to N (N=the number of battery cell groups 150), method 200 uses the following equation to calculate the internal resistance: R i (t)= [V f (t) i - V OCV (t)] / I f (t), where t is the time exponent; R i V is the internal resistance of the i-th battery cell group 150. f and I f These are the filtered voltage of the i-th battery cell group 150 and the filtered current I, V of battery 104, respectively. OCV The open-circuit voltage is obtained from the pre-calibrated SOC-to-OCV curve of battery 104.

[0162] In step 310, method 200 calculates summary statistics for each battery cell group 150 within the operating region 160. For example, for i = 1 to N (N = the number of battery cell groups 150), the summary statistics include Max R i Min R i Avg. R i During the charging cycle, a summary statistic of all battery cell groups 150 in each operating region 160 is calculated.

[0163] Figure 8B Steps 206, 208, and 216 of method 200 shown in Figure 6 are illustrated in further detail. The following procedure is performed for each group of battery cells 150 in each operating region 160. After filtering the charging current I in operating region 160 and the voltage measured across battery cell groups 150, method 200 performs steps 302-308, as referenced above. Figure 8A As described above. Therefore, for the sake of brevity, the descriptions of steps 302-308 will not be repeated.

[0164] In 312, for i = 1 to N (N = the number of battery cell groups 150), method 200 uses the following equation to calculate the distance metric D. i :D i [t] = {R i [t] - mean(R[t])} / σ(R[t]), where R[t] = {R i [t], R2[t], ..., R p [t]} and D(t) = {D i [t], D2[t], ..., D p [t]}; where p = N (the number of battery cell groups in the battery pack), and p == m ≤ N (the number of battery cell groups in the module); where mean and σ represent the statistical mean and standard deviation, respectively. Therefore, the distance metric D can be evaluated across battery packs or across battery cell groups in modules. Specifically, the distance metric D for each battery cell group can be evaluated based on the distance across groups (i.e., using mean and σ across all battery cell groups in the group). Alternatively, the distance metric D for each battery cell group can be evaluated based on the distance across modules (i.e., using mean and σ within a specific module).

[0165] In 314, method 200 determines what is called max(D) i The maximum value of the distance metric [t] is greater than or equal to a predetermined threshold. If max(D i If [t]) is less than a predetermined threshold, then at 316, method 200 determines that the battery cell group is normal (i.e., the battery cells in the battery cell group have internal resistance within the normal range). If max(D) i If [t]) is greater than or equal to a predetermined threshold, then in step 318, method 200 determines that the battery cell group is abnormal or an outlier (i.e., the battery cells in the battery cell group have internal resistance outside the normal range). This is determined based on the assumption that in a faulty battery pack, only a few battery cell groups may exhibit faulty or abnormal behavior, while the majority of battery cell groups are healthy. Therefore, the internal resistance of the battery cells across the battery pack is most likely to be the same except for a few faulty battery cell groups (i.e., within a narrow range), and these faulty battery cell groups are detected as outliers as described above, where the distance metric D acts as an indicator to detect faulty battery cell groups.

[0166] Figure 9The process for selecting the best data from the aggregate statistics is shown in further detail (i.e., steps 212 and 214 of method 200 shown in Figure 6). During a charging cycle, battery 104 may operate in different operating regions 160 at different times. When battery 104 is in operating region 160, the charging current I and the voltage across the battery cell group 150 are measured in operating region 160, and B statistical groups are calculated for that operating region 160. Therefore, for M operating regions 160, each operating region 160 has B frequency bands, and the total number of statistics for one charging cycle will be M*B. At the end of the charging cycle, only the best data is selected from these statistics and stored in the memory 148 of controller 102.

[0167] If battery 104 is healthy (i.e., operates normally without any battery cell group 150 exhibiting abnormal internal resistance), all battery cell groups 150 will have an internal resistance within a narrow range. If the internal resistance of one of the battery cell groups 150 deviates from this narrow range, then this deviation results in a resistance distribution of all statistics collected across the entire charging cycle. The resistance distribution can be used as a metric to determine which data are optimal, and can be further evaluated to isolate abnormal (i.e., faulty) battery cell groups 150 within battery 104. The optimal data is selected from these statistics as follows.

[0168] In 352, during the charging cycle, method 200 targets M operating regions 160 (e.g., ...). Figure 7 Internal resistance data of battery cell groups 150 are collected in each operating region (as shown in the definition). In method 200, statistics calculated for all battery cell groups 150 in each frequency band of each of the M operating regions 160 are collected (as referenced above). Figure 8A (As described).

[0169] In 356, from these M*B statistics, for each frequency band, method 200 calculates the difference between the maximum and minimum average internal resistances of all N battery cell groups 150. The difference between the maximum and minimum average internal resistances of battery cell groups 150 is called DeltaR. Method 200 calculates DeltaR using the following equation: for i = 1 to N, DeltaR = max i (R avg，i )-min i (R avg，i Method 200 calculates DeltaR for each B-band in each operating region 160 within the charging cycle of battery 104.

[0170] Specifically, in each frequency band, method 200 calculates DeltaR as the difference between the maximum and minimum average internal resistances of all battery cell groups 150. Based on R for all battery cell groups 150...avg Method 200 uses the maximum and minimum R values ​​of all battery cell groups 150. avg Calculate deltaR. This calculation is performed for all operating regions 160. Therefore, for B = 3, method 200 calculates DeltaR1, DeltaR2, and DeltaR3 for each operating region 160.

[0171] At 358, method 200 selects the optimal frequency band with the maximum DeltaR from the M*B frequency bands. The frequency band with the maximum DeltaR is the frequency band with the largest difference in R across the battery cell group 150. At 360, method 200 determines whether the charging cycle is complete. If the charging cycle is not complete, then method 200 returns to 352. If the charging cycle is complete, then at 362, method 200 saves the internal resistance data from the selected frequency band in the memory 148 of the controller 102 at the end of the charging cycle.

[0172] Method 200 determines whether to overwrite a previously saved frequency band as follows. If the DeltaR of the frequency band is similar to the DeltaR of the previously saved frequency band, then Method 200 saves (i.e., overwrites) the frequency band data with more samples. Alternatively, Method 200 selects a frequency band with a larger DeltaR than the previously saved one. That is, Method 200 only overwrites the data of the old frequency band and saves the data of the new frequency band if the DeltaR of the new frequency band is greater than the DeltaR of the old frequency band. Basically, if abs(DeltaR) old DeltaR new If the deltaR difference of the new frequency band is very close to the already saved best frequency band, then method 200 selects the frequency band with a larger number of samples to save. If delta_R abs(New - Old) > the threshold, then method 200 saves the frequency band with a larger DeltaR.

[0173] The data stored in memory 148 can be used for diagnostics by service technicians and / or to provide alerts to infotainment subsystem 108. Method 200 can also provide the infotainment subsystem 108 with messages regarding the health status of battery 104 based on the data stored in memory 148. Method 200 can also send data from selected frequency bands to one or more servers 122 in the cloud for predicting and trending the health status of battery 104.

[0174] Selecting only the best data (i.e., data from the selected frequency band) is helpful because anomalies in internal resistance data due to faulty battery cell groups 150 may not be apparent in all operating regions 160. Typically, only a few battery cell groups 150 may exhibit abnormally high internal resistance and contribute more to the internal resistance deviation of battery 104 than other battery cell groups 150. Therefore, in order to isolate only the battery cell groups(s) 150(s) that contribute most to the internal resistance deviation of battery 104, the above references... Figure 9 The procedures shown and described are used to select optimal internal resistance data from battery cell group 150.

[0175] Figure 10 The procedure for detecting a faulty battery pack in battery 104 is shown in further detail (i.e., steps 218 and 220 of method 200 shown in Figure 6), based on optimal data selected from summary statistics or distance metrics, or by using a combination of summary statistics and distance metrics. At 380, method 200 acquires stored statistical data and / or distance metrics for multiple charging cycles, including data from different types of charging methods used. At 382, ​​method 200 compares the statistical data and / or distance metrics with corresponding thresholds calibrated for each type of charging method. At 384, method 200 generates a comparison result (i.e., an indication of a faulty battery pack). For example, method 200 compares data from similar charging currents and similar charging methods to detect anomalous behavior in groups of battery cells.

[0176] At 386, method 200 analyzes the results to determine whether some battery cell groups consistently fail in all charging methods or in some charging methods used to charge battery 104. At 388, method 200 identifies the faulty battery cell groups and the charging methods in which the battery cell groups fail. This part of method 200 can be executed in the vehicle's onboard controller 102 or in the cloud, and can output relevant messages via the vehicle's infotainment subsystem 108.

[0177] Figure 11 Method 400 is illustrated, which learns from the behavior of a vehicle platoon to improve method 200 and the calibration of the thresholds used in method 200. The vehicles in the platoon use the same battery 104 and the same method 200 is used in the vehicle's onboard controller 102. Furthermore, the vehicles can charge the battery using any charging method (L1, L2, and / or DCFC). Method 400 can use the best data selected from aggregated statistics and / or distance metrics stored in the vehicle's onboard controller 102. Method 400 is executed in the cloud.

[0178] At 402, method 400 acquires the best data selected from aggregated statistics and / or distance metrics (hereinafter referred to as "data") stored in the vehicle's onboard controller 102 within multiple charging cycles performed using different charging methods. At 404, method 400 analyzes the distribution of the data across the fleet (e.g., to detect trends in the data). For example, the distribution may indicate that some battery cell groups are consistently failing in all charging methods, which may indicate that method 200 is functioning normally as designed (i.e., no changes to method 200 are required). Alternatively, the distribution may indicate that some battery cell groups are failing when using a particular charging method but not in other charging methods, which may indicate that method 200 may require fine-tuning (e.g., the logic and / or thresholds used by method 200 for one of the charging methods may need adjustment / recalibration). Additionally, it is possible that only some vehicles experience more failures than others, which may indicate that method 200 is functioning normally, but there may be hardware problems in some vehicles; and so on.

[0179] At 406, based on distribution analysis, method 400 adjusts logic and / or calibrates thresholds used by method 200 to identify faulty battery cell groups in one or more charging methods. The adjustment and / or calibration can be downloaded to the vehicle. At 408, method 400 continues to acquire data from the vehicle and monitor trends in the data distribution. At 410, method 400 identifies vehicles where the fault trend persists (i.e., where battery cell groups continue to be identified as defective). Method 400 sends notifications to the identified vehicles, allowing their users to schedule repairs, use one or more specific charging methods where fewer battery cell groups have failed, or avoid using one or more specific charging methods where more battery cell groups have failed.

[0180] Figures 12A-12E An example of the internal resistance distribution of a group of battery cells is shown. Figure 12A The graph shows the SOC versus time during the charging cycle of battery 104. Figure 12B The graph shows the internal resistance R versus SOC during the charging cycle of battery 104. Internal resistance data when battery cell group 150 is healthy is shown at 450. Internal resistance data when battery cell group 150 begins to degrade is shown at 452. Internal resistance data for faulty battery groups (i.e., those with the highest internal resistance distribution) is shown at 454 and is stored in controller 102.

[0181] Figure 12CA graph showing DeltaR versus SOC during the charging cycle of battery 104 is presented, where DeltaR is the difference or distribution between the maximum and minimum average values ​​of the internal resistance R of all battery cell groups 150. Data when battery cell group 150 is healthy is shown at 460. Data when battery cell group 150 begins to degrade is shown at 462. Data for faulty battery cell groups (i.e., maximum DeltaR) is shown at 464 and stored in controller 102.

[0182] Figure 12D The graph shows the internal resistance R versus time during the charging cycle of battery 104. Data for the healthy battery cell group is shown at 470. Data for the faulty battery cell group is shown at 472. The distance between the data for the healthy and faulty battery cell groups is shown at 474. Figure 12E This shows the maximum [D] during the charging cycle of battery 104. i [Graph showing data relative to time. Data for healthy battery cell groups is shown at 480. Data for faulty battery cell groups is shown at 482. The distance between the data for healthy and faulty battery cell groups is shown at 484.]

[0183] The systems and methods disclosed herein improve battery technology. Specifically, the systems and methods provide a robust internal resistance estimator because estimations using different operating regions take into account battery noise and varying operating conditions, and reliably detect battery faults. The estimator optimizes controller memory by storing only optimal health indicator data in controller memory. The systems and methods passively identify faults in battery packs and individual battery cell groups during vehicle charging, regardless of the charging method used. Additionally, the systems and methods actively identify faulty battery packs and battery cell groups; that is, before a fault occurs and the vehicle stalls, leaving occupants stranded. The systems and methods provide early fault indication and prediction capabilities for predicting battery performance degradation when managing vehicle operation. The systems and methods monitor trends in battery health and provide early warnings (active alerts) to the user before battery failure.

[0184] The foregoing description is illustrative in nature and is not intended to limit this disclosure, its application, or its 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 upon examination of the drawings, description, and appended claims.

[0185] It should be understood that one or more steps within the method can be performed in different orders (or simultaneously) without altering the principles of this disclosure. Furthermore, although each embodiment is 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 each other remains within the scope of this disclosure.

[0186] Spatial and functional relationships between components (e.g., between modules, circuit elements, semiconductor layers, etc.) are described using various terms, including “connected,” “joined,” “linked,” “adjacent,” “next to,” “on top,” “above,” “below,” and “placed.” Unless explicitly described as “direct,” when describing a relationship between first and second components 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 in which one or more intermediate components (spatial or functional) exist between the first and second components. As used herein, the phrases A, B, and C at least one should be interpreted as representing a logical (A or B or C) using a non-exclusive logical OR, and should not be interpreted as representing “at least one of A, at least one of B, and at least one of C.”

[0187] In a diagram, the direction of the arrows typically indicates the flow of information (e.g., data or instructions) that is relevant to the explanation. For example, when components A and B exchange various types of information, but the information transmitted from component A to component B is relevant to the explanation, the arrow might point from component A to component B. This unidirectional arrow does not imply that no other information is transmitted 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 the information to component A.

[0188] In this application, the term "module" or "controller" is replaced by the term "circuit". The term "module" may refer to, be part of, 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 aforementioned functionality; or combinations of some or all of the above, such as in a system-on-a-chip.

[0189] A module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that connect 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 among multiple modules connected via the interface circuits. For example, multiple modules may allow for load balancing. In another example, a server (also known as a remote or cloud) module may perform some functionality on behalf of a client module.

[0190] As used above, the term "code" 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" covers a single processor circuit that executes some or all of the code from multiple modules. The term "group processor circuit" covers a processor circuit that, in conjunction with additional processor circuitry, executes some or all of the code from one or more modules. References to multiprocessor circuitry cover multiple processor circuits on a discrete die, multiple processor circuits on a single die, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or combinations thereof. The term "shared memory circuit" covers a single memory circuit that stores some or all of the code from multiple modules. The term "group memory circuit" covers a memory circuit that, in conjunction with additional memory, stores some or all of the code from one or more modules.

[0191] The term memory circuit is a subset of the term computer-readable medium. As used herein, the term computer-readable medium does not cover 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 (e.g., 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).

[0192] The apparatus and methods described in this application can be implemented, in part or in whole, by a special-purpose computer created by configuring a general-purpose computer to perform one or more specific functions implemented in a computer program. The aforementioned function blocks, flowchart components, and other elements serve as software specifications that can be translated into a computer program through the daily work of a skilled technician or programmer.

[0193] 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.

[0194] 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 syntax from 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 Web 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 battery, the system comprising: A current measurement circuit is configured to measure the charging current of the battery when the battery is charged using one of a plurality of charging systems, the battery comprising a plurality of battery cell groups connected to each other. A voltage measurement circuit is configured to measure the voltage of the battery cell group when the battery is charged using one of the plurality of charging systems; as well as The controller communicates with the current and voltage measurement circuitry and is configured to: When charging the battery using one of the plurality of charging systems, a plurality of operating regions are defined in the charging current curve of the battery during the charging cycle of the vehicle. The charging current and voltage measured in the operating area are filtered; The internal resistance of the battery cell group in the operating region is calculated based on the filtered current and voltage. At least one of a statistical value and a distance metric is generated based on the internal resistance of the battery cell group, wherein the distance metric is generated by dividing the difference between the internal resistance and the average internal resistance by the standard deviation of the internal resistance; and The determination of whether one or more battery cell groups in the battery cell group are faulty is based on at least one of the statistical values ​​and the distance metric, regardless of the plurality of charging systems used to charge the battery. The controller is configured to determine whether one or more battery cell groups in the battery cell group are faulty based on at least one of the following: i) the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group, and ii) the highest value of the distance metric across the battery cell group.

2. The system of claim 1, wherein, The controller is configured to determine whether one or more battery cell groups in the battery cell group are faulty based on at least one of the statistical values ​​calculated for each of the plurality of charging systems that charge the battery and the distance metric.

3. The system of claim 1, wherein, The controller is configured to define an operating region based on the battery's charging current, temperature, and state of charge when the charging current is stable within an operating region over a predetermined time period.

4. The system of claim 1, wherein, The controller is configured to: The operating area is defined based on a predetermined range of the battery's charging current, temperature, and state of charge; and Determine if one or more battery cell groups in the battery cell group are faulty, regardless of changes in the battery's temperature and state of charge.

5. The system of claim 1, wherein, The controller is configured to: Multiple frequency bands within one operating region are defined based on a predetermined range of charging current and multiple predetermined ranges of battery temperature and charging state. The internal resistance of the battery cell group in the frequency band is calculated based on the filtered current and voltage; A single statistical value for the frequency band is generated based on the internal resistance of the battery cell group in the frequency band; Determine the difference between the maximum and minimum values ​​of one or more individual statistical values; The internal resistance data of the battery cell group is selected from one of the frequency bands corresponding to the highest value of the difference between the maximum and minimum values; as well as The health status of the battery is diagnosed based on the selected internal resistance data.

6. The system according to claim 1, wherein, The controller is configured to: The internal resistance data of the battery cell group is selected from one of the operating regions corresponding to the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group; The system stores information including at least one of selected internal resistance data across the battery cell groups and the highest value of the distance metric, for diagnosing one or more battery cell groups within the battery cell groups. The stored information is sent to the server to predict and determine trends in the health status of the battery; as well as The system provides messages about the health status of the battery based on the stored information.

7. The system according to claim 1, wherein, The controller is configured to work for each of the plurality of charging systems used to charge the battery: The internal resistance data of the battery cell group is selected from one of the operating regions corresponding to the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group; Store at least one of the selected internal resistance data and the highest value of the distance metric; Compare at least one of the selected internal resistance data and the highest value of the distance metric with a calibrated corresponding threshold for the plurality of charging systems; as well as Determine whether one or more battery cell groups in the battery cell group have failed across the multiple charging systems.

8. The system of claim 7, further comprising a server, the server being configured to: Receive at least one of the selected internal resistance data and the highest value of the distance metric from multiple vehicles; Analyze the distribution of at least one of the selected internal resistance data and the highest value of the distance metric; and calibrate the threshold based on the analysis.

9. The system of claim 8, wherein, The server is also configured to: Monitor the trend of the distribution; Identify one or more vehicles in which one or more battery cell groups continue to fail; and Send a notification to the identified vehicle.

10. A method for monitoring a vehicle battery, the method comprising: The charging current of the battery is measured when the battery is charged using one of a plurality of charging systems, the battery comprising a plurality of battery cell groups connected to each other; The voltage of the battery cell group is measured when the battery is charged using one of the plurality of charging systems; When charging the battery using one of the plurality of charging systems, a plurality of operating regions are defined in the charging current curve of the battery during the charging cycle of the vehicle. The charging current and voltage measured in the operating area are filtered; The internal resistance of the battery cell group in the operating region is calculated based on the filtered current and voltage. At least one of a statistical value and a distance metric is generated based on the internal resistance of the battery cell group, wherein the distance metric is generated by dividing the difference between the internal resistance and the average internal resistance by the standard deviation of the internal resistance; and The determination of whether one or more battery cell groups in the battery cell group are faulty is based on at least one of the statistical values ​​and the distance metric, regardless of the plurality of charging systems used to charge the battery. The method further includes determining whether one or more battery cell groups in the battery cell group are faulty based on at least one of the following: i) the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group, and ii) the highest value of the distance metric across the battery cell group.

11. The method of claim 10, further comprising determining whether one or more battery cell groups in the battery cell group are faulty based on at least one of the statistical values ​​calculated for each of the plurality of charging systems charging the battery and the distance metric.

12. The method of claim 10, further comprising defining an operating region based on the battery's charging current, temperature, and state of charge when the charging current is stable in an operating region for a predetermined time period.

13. The method of claim 10, further comprising: The operating area is defined based on a predetermined range of the battery's charging current, temperature, and state of charge. as well as Determine if one or more battery cell groups in the battery cell group are faulty, regardless of changes in the battery's temperature and state of charge.

14. The method of claim 10, further comprising: Multiple frequency bands within one operating region are defined based on a predetermined range of charging current and multiple predetermined ranges of battery temperature and charging state. The internal resistance of the battery cell group in the frequency band is calculated based on the filtered current and voltage; A single statistical value for the frequency band is generated based on the internal resistance of the battery cell group in the frequency band; Determine the difference between the maximum and minimum values ​​of one or more of the individual statistical values; The internal resistance data of the battery cell group is selected from one of the frequency bands corresponding to the highest value of the difference between the maximum and minimum values; as well as The health status of the battery is diagnosed based on the selected internal resistance data.

15. The method of claim 10, further comprising: The internal resistance data of the battery cell group is selected from one of the operating regions corresponding to the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group; The system stores information including at least one of selected internal resistance data across the battery cell groups and the highest value of the distance metric, for diagnosing the one or more of the battery cell groups. The stored information is sent to the server to predict and determine trends in the health status of the battery; as well as The system provides messages about the health status of the battery based on the stored information.

16. The method of claim 10, further comprising, for each of the plurality of charging systems used to charge the battery: The internal resistance data of the battery cell group is selected from one of the operating regions corresponding to the highest value of the difference between the maximum and minimum values ​​of one or more statistical values ​​across the battery cell group; Store at least one of the selected internal resistance data and the highest value of the distance metric; Compare at least one of the selected internal resistance data and the highest value of the distance metric with a calibrated corresponding threshold for the plurality of charging systems; as well as Determine whether one or more battery cell groups in the battery cell group have failed across the multiple charging systems.

17. The method of claim 16, further comprising, at the server: Receive at least one of the selected internal resistance data and the highest value of the distance metric from multiple vehicles; Analyze the distribution of at least one of the selected internal resistance data and the highest value of the distance metric; and calibrate the threshold based on the analysis.

18. The method of claim 17, further comprising, at the server: Monitor the trend of the distribution; Identify one or more vehicles in which one or more battery cell groups continue to fail; and Send a notification to the identified vehicle.

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