Real-time battery fault detection and health state monitoring

Abstract

To provide a method for monitoring internal resistance of a battery cell.SOLUTION: A method for monitoring internal resistance of a battery cell includes the steps of: initializing a running estimation value of internal resistance using a stored value in response to detecting transition of a battery cell from an idle state to an active state; measuring a potential, a current, and a temperature of the battery cell while the battery cell is in the active state; iteratively updating the running estimation value of the internal resistance based on the measured potential, current, and temperature; calculating a change in the internal resistance based on the stored value and a final value of the running estimation value in response to detecting the transition of the battery cell from the active state to the idle state; and updating the stored value using the final value of the running estimation value.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to U.S. Provisional Application No. 62 / 988,853, filed on Mar. 12, 2020, and U.S. Patent Application No. 17 / 196,848, filed on Mar. 9, 2021, the disclosures of which are incorporated herein by reference.

[0002] The present disclosure generally relates to battery monitoring, and more particularly to systems and methods for real - time monitoring of battery health and performance.

Background Art

[0003] A battery is an electrochemical device that can convert stored chemical energy into electrical energy. Numerous examples of battery technologies are known in this technical field, including lithium - ion batteries, nickel - metal hydride batteries, lead - acid batteries, nickel - cadmium batteries, alkaline batteries, etc. Batteries are made in many sizes and with various operating characteristics (e.g., voltage (or potential), maximum current, charge capacity, etc.). To accommodate high voltages and large charge capacities, battery packs can be made by electrically connecting multiple battery cells in series and / or in parallel. Depending on the technology, there are also batteries that can be charged by connecting to a charging current source.

[0004] Batteries (especially lithium - ion batteries) are used in a wide variety of applications, such as as a portable power source to drive the motors of vehicles such as automobiles, airplanes, and ships. In some cases, a battery or a battery pack may be the sole power source of a vehicle. A vehicle powered only by battery power may suddenly lose its driving force if the battery fails. Depending on the state of the vehicle when the battery fails, the consequences can range from inconvenient to tragic. Therefore, it may be desirable to monitor battery performance, detect conditions that indicate problems, and enable repair or replacement before the battery fails.

Summary of the Invention

[0005] This specification describes examples (or embodiments) of a battery monitoring system and method that can provide real-time automatic monitoring of the health and various aspects of the operation of a battery. In various embodiments, different aspects of the health and operation of the battery can be monitored. For example, the battery monitoring system can use an equivalent cell circuit model to predict in real time a range (or "envelope") that defines the expected behavior (e.g., the potential or voltage of the cell) of the battery cell under actual operating conditions (e.g., a specific load current or a charging current at a specific temperature). By comparing the predicted value with the actual behavior of the cell (e.g., the measured potential of the cell), it can be determined whether there is a potential problem, which is referred to herein as a "model failure" state. As another example, the battery monitoring system can maintain estimated values of the health state parameters of the battery, such as the charging capacity and the internal resistance, and these estimated values are updated in real time while the battery is discharging and / or charging, and abnormal variations in the health state parameters can suggest a "suspected parameter" failure. The battery monitoring system can notify the detected failure in real time, enabling prompt corrective action.

[0006] According to some embodiments, a method for monitoring the charging capacity of a battery cell can include determining an initial charge state of the battery cell while the battery cell is in an idle state, then monitoring the total amount of charge that moves from or to the battery cell while the battery cell is in an active state (e.g., a discharging state where charge moves from the battery cell to a load, or a charging state where charge moves from an external power source to the battery cell), determining a final charge state of the battery cell after the battery cell returns to the idle state, calculating an unfiltered charging capacity value using the initial charge state, the final charge state, and the total amount of charge that has moved, and updating an estimated charging capacity value using the unfiltered charging capacity value.

[0007] In some embodiments, it is possible to calculate the magnitude of the change in the charge capacity estimate with respect to the previous estimate, and when the magnitude of the change in the charge capacity exceeds a threshold value, a cell capacity failure notification can be generated.

[0008] In some embodiments, the step of determining the initial state of charge can include the step of measuring the initial cell potential and the initial cell temperature of the battery cell while the battery cell is in the initial idle state, and the step of calculating the state of charge of the battery cell based on the equivalent cell circuit model using the initial cell potential and the initial cell temperature.

[0009] In some embodiments, the step of determining the final state of charge can include the step of measuring the final cell potential and the final cell temperature of the battery cell when the battery cell returns to the idle state, and the step of calculating the state of charge of the battery cell based on the equivalent cell circuit model using the final cell potential and the final cell temperature.

[0010] In some embodiments, the step of monitoring the total amount of charge transferred while the battery cell is in the active state includes the step of measuring the current passing through the battery cell at regular time intervals, and the step of adding the product of the measured current and the time step defined by the regular time intervals to the cumulative value of the charge transferred.

[0011] In some embodiments, the step of updating the charge capacity estimate can include the step of applying an infinite impulse response filter to the unfiltered charge capacity value and the previously stored charge capacity estimate.

[0012] According to some embodiments, a method for monitoring the internal resistance of a battery cell includes initializing a running estimate of the internal resistance using a stored value in response to detecting a transition of the battery cell from an idle state to an active state (which can be, for example, a charging state or a discharging state); measuring the potential, current, and temperature of the battery cell while the battery cell is in the active state; iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature; calculating a change in the internal resistance based on the stored value and the final value of the running estimate in response to detecting a transition of the battery cell from the active state to the idle state; and updating the stored value using the final value of the running estimate.

[0013] In some embodiments, a cell resistance fault notification can be generated if the change in the internal resistance exceeds a threshold.

[0014] In some embodiments, the step of iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature can include, for each iterative update, determining whether the measured potential, current, and temperature are within a predetermined valid range. If the measured potential, current, and temperature are within the predetermined valid range, a raw estimate of the internal resistance can be calculated based on an equivalent cell circuit model, and the running estimate can be updated using the raw estimate and the running estimate from the previous time step. In some embodiments, the raw estimate is compared to a range of reasonable values and used to update the running estimate only if the raw estimate is within the range of values that appear reasonable. If one or more of the measured potential, current, or temperature are not within the predetermined valid range, the method can include waiting for the next time step without updating the running estimate.

[0015] In some embodiments, the step of updating the running estimate can include applying an infinite impulse response filter to the raw estimate and the previous running estimate.

[0016] According to some embodiments, a method for monitoring the state of a battery cell includes determining a current value of one or more healthy state parameters of the battery cell (e.g., internal resistance of the battery cell, charge capacity of the battery cell), measuring an actual potential of the battery cell, measuring values of a plurality of operating state parameters of the battery cell (e.g., current passing through the battery cell, temperature of the battery cell), calculating an optimistic potential based on a cell state model (e.g., equivalent cell circuit model), measured values of a first subset of the operating state parameters, and optimistic values corresponding to a healthier state than the current value of the one or more healthy state parameters, calculating a pessimistic potential based on the cell state model, measured values of the first subset of the operating state parameters, and pessimistic values corresponding to a less healthy state than the current value of the one or more healthy state parameters, determining whether the actual potential of the battery cell is substantially within an envelope defined by the optimistic potential and the pessimistic potential, and generating a model failure notification if the actual potential is not substantially within the envelope. In various embodiments, the method can be performed in real time while the battery cell is powering a load and / or while the battery cell is charging. The model failure notification can be generated in real time while the battery is actively being used (powering a load and / or charging). In some embodiments, a predicted potential of the battery cell can also be calculated based on the cell state model, measured values of the first subset of the operating state parameters, and current values of the one or more healthy state parameters.

[0017] In some embodiments, estimated values of the charge capacity and / or internal resistance of the battery cell can be determined while the battery is actively being used according to the techniques described herein, and these estimated values can be used in the state monitoring method.

[0018] Any one or all of the above or other methods, or any combination thereof, including while the battery is powering a load and / or while the battery is charging, can be implemented in a real-time battery monitoring system (using hardware, software / firmware, or any combination thereof) that operates regardless of whether the battery is actively in use. In some embodiments, the battery monitoring system can generate a warning or perform other actions based on the results of any of the above or other methods.

[0019] The following detailed description, together with the accompanying drawings, provides a better understanding of the nature and advantages of the claimed invention.

Brief Description of the Drawings

[0020]

Figure 1

Figure 2

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Figure 4

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Figure 8

Modes for Carrying Out the Invention

[0021] The following description of exemplary embodiments of the present invention is presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the claimed invention to the precise forms described, and those skilled in the art will understand that many modifications and variations are possible. The embodiments are chosen and described in order to best explain the principles of the invention and its practical applications, thereby enabling others skilled in the art to best utilize the invention in various embodiments suitable for particular intended uses and with various modifications.

[0022] System Overview FIG. 1 shows a high-level block diagram of an operating environment 100 for battery monitoring according to some embodiments. The environment 100 can be, for example, an electric vehicle such as an aircraft, a ship, a railway vehicle, an automobile, a truck, an off-road vehicle, etc. The environment 100 includes a battery 102 that supplies power to an electrical load 104. The battery 102 can be any type of battery including, for example, a lithium-ion battery, a lead battery, a nickel-metal hydride battery, etc. The battery 102 can be implemented as a single battery cell or as a battery pack including a plurality of battery cells connected to each other in series and / or in parallel as needed. (As used herein, the term "battery cell" or "cell" can be understood to include a stand-alone battery or, in the case of a battery pack, one of several independent replaceable battery units within the battery pack.) The electrical load 104 can include, for example, a vehicle motor (or engine), or any other battery-powered mechanism or device. The electrical load 104 can draw various amounts of power from the battery 102 at different times. For example, a vehicle motor may consume more power when the vehicle is accelerating than when the vehicle is stationary or moving at a constant speed.

[0023] The control system 106 communicates with the load 104. For example, the control system 106 can send commands to the load 104 to, for example, increase or decrease the motor speed or to enable, disable, or change the operating state of any power-consuming component. The control system 106 can also receive feedback from the load 104 indicating its operating state, possible abnormal states that may occur, and the like. The control system 106 can also include human interface components such as a display screen, indicator lights, speakers, human-operable control devices (e.g., keyboard, mouse, touch screen or touch pad, joystick, control wheel, foot pedal, etc.). The control system 106 can be local to the load 104 (e.g., the load 104 is within a vehicle that includes a motor) or located remotely from the load 104 and communicate via a suitable connection that includes a short-range or long-range network connection. In some embodiments, the control system 106 can include both local and remote elements. For example, the environment 100 can be an autonomous or remotely piloted vehicle that is monitored and guided from a location outside of the operating location.

[0024] In some embodiments, battery 102 is a rechargeable battery that can be charged by connecting battery 102 to a charging power source such as charger 110. Charger 110 can include any system or device that can supply the power (or charge) stored by battery 102 from an external supply source (e.g., a standard wall outlet or any other power source external to battery 102), and numerous examples are known in the art. Charger 110 can also include a control circuit that controls the operation of charger 110, including when and how much power to supply. In some embodiments, charger 110 is shown using a dashed line because battery 102 can be coupled to charger 110 at certain times and disconnected from charger 110 at other times. Depending on the embodiment, battery 102 may or may not be able to supply power to load 104 while receiving power from charger 110. In some embodiments, control system 106 can coordinate the operation of charger 110 with the load-power supply operation of battery 102.

[0025] The battery monitoring system 108 can be coupled to the battery 102, the control system 106, and the charger 110. The battery 102 can be equipped with sensors for measuring various state parameters of the battery or individual cells, such as potential, current (which can flow in either direction depending on whether the battery is being charged or discharged), and operating temperature. The battery monitoring system 108 can receive the state parameters measured from the battery 102 in real time (e.g., while the load 104 is drawing power from the battery 102). Based on the state parameters, the battery monitoring system 108 can perform various calculations to determine the values of the healthy state parameters (e.g., charge capacity and / or internal resistance) of the battery or individual cells, and / or to detect whether the actual battery behavior matches the expected behavior. Examples of calculations that can be implemented in the battery monitoring system 108 are described below. Based on the calculation results, the battery monitoring system 108 can provide battery state information to the control system 106 and / or the charger 110. The battery state information can include, for example, the measured state parameters reported from the battery 102, the values of the healthy state parameters calculated by the battery monitoring system 108, information comparing the actual battery behavior to a model of the expected battery behavior, a "fault" notification indicating that some aspects of the battery performance deviate from the prediction, and / or any combination of any other information available in the battery monitoring system 108. The control system 106 and / or the charger 110 can use this information to generate warnings for the operator of the environment 100, change the operation of the load 104 based on the battery state information, change the operation of the charger 110 (e.g., the rate at which charging power is supplied to the battery 102) based on the battery state information, maintain the battery history information of the battery 102, and / or perform other response actions that can be programmed into the control system 106.

[0026] In one specific example, the operating environment 100 corresponds to a battery-powered aircraft that can fly under local or remote control. The load 104 can include the motors of the aircraft, and the battery 102 can be used as the power source for the motors. In such an environment, the reliability of the battery 102 is important to enable the aircraft to complete a flight safely. To supply the required amount of power with high reliability, the battery 102 can include a high-voltage battery bank composed of a plurality of battery packs (which can provide redundancy) arranged in parallel, and each battery pack incorporates a plurality of cells arranged in series and / or in parallel. The battery monitoring system 108 can provide real-time information regarding the state of each battery pack, for example, for each cell or for a group of cells, and enables problems to be detected and addressed before a battery failure occurs. For example, when the battery monitoring system 108 generates a fault notification, it can notify a service technician, and the battery 102 can be repaired or replaced before the next flight. In this example, the battery 102 can be coupled to a charger 110 for recharging during flight, and the battery monitoring system 108 can continue to provide information regarding the state of each battery pack during charging.

[0027] It should be understood that the operating environment 100 is exemplary and not limiting. Any type of battery (including any number and arrangement of battery cells) driving any type of load can be monitored using the systems and methods of the type described herein.

[0028] FIG. 2 shows a simplified schematic diagram of a battery bank 200 according to some embodiments. The battery bank 200 incorporates a battery monitoring system as an embedded system within the battery bank. The battery bank 200 can be used as the battery 102 in an operating environment such as the above-described operating environment 100. In this example, the battery bank 200 includes three high-voltage (HV) battery packs 202a-202c connected in parallel between a terminal 204 (which can be connected to a load) and a ground point 206. The battery packs 202a-202c are rechargeable, and each battery pack 202a-202c is provided with a charging terminal 208 for connecting to a charger.

[0029] Each of the battery packs 202a to 202c includes two HV batteries 210a to 210b connected in parallel. Each of the HV batteries 210a to 210b includes individual battery cells 212, which can be, for example, lithium-ion batteries. For example, each cell can have an operating voltage of 3 to 5V, an internal resistance of 1 to 50 mΩ, and an operating current range of 0 to 200 mA, although these parameters can be changed. The number of cells 212 can be very large. In this example, the battery cells 212 within each HV battery 210a to 210b are connected in series to form a string having a large number of cells 212 (for example, 144 cells per string), and three series strings are connected in parallel with each other within each HV battery 210a to 210b. The battery bank 200 can provide both a high operating voltage (for example, an overall system voltage in the range of about 400 to 800V) and a high level of redundancy so that the battery bank 200 can continue to supply power even if some of the cells 212 fail.

[0030] Each HV battery 210 also includes a battery management system (BMS) board 220, which can be a component of the battery monitoring system 108 of FIG. 1. Each BMS board 220 can be a printed circuit board having a circuit configured to monitor the state of one or more cells within the HV battery 210. In some embodiments, the BMS board 220 can monitor all of the cells of each HV battery 210. For example, each HV battery 210 can include 12 BMS boards 220, and each BMS board can monitor 36 cells. Examples of components and operations that can be implemented on the BMS board 220 will be described below.

[0031] It should be understood that the battery bank 200 is exemplary and not limiting. The battery system can include any number of batteries, and each battery can include any number of cells. The battery monitoring described herein can be performed at the level of individual cells, or groups of cells can be monitored as units as needed.

[0032] FIG. 3 shows an example of a battery monitoring system 300 according to some embodiments. The battery monitoring system 300 can be used, for example, to implement the battery monitoring system 108 of FIG. 1. In some embodiments, each BMS board 220 of the battery bank 200 of FIG. 2 can include one or more instances of the components of the battery monitoring system 300.

[0033] The battery monitoring system 300 includes a processor 302, a memory 304, a battery interface 306, and a control system interface 308. The processor 302 can be a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other circuitry that implements the logical operations described herein. In some embodiments, the battery monitoring system 300 can be an embedded system, and as described below, the calculations can be performed in a way that can be executed in real time using a small low-power processor. The memory 304 can include semiconductor-based memory (e.g., DRAM, SRAM), flash memory, magnetic storage devices, optical storage devices, or other computer-readable storage media. The memory 304 can store information regarding each battery cell monitored by the battery monitoring system 300. For example, the memory 304 can store cell configuration parameters 310 and cell state parameters 312. The cell configuration parameters 310 can include parameters that are static or change gradually over the life of a particular cell, and the cell state parameters 312 can include parameters that change dynamically as the battery is used. The memory 304 can also store a cell state model (e.g., an equivalent cell circuit model) that can be used to repeatedly predict the state parameters of the cell. For example, the cell state model can predict the charge state and polarization state (or voltage) of the cell at a given time step based on the current consumption, temperature, internal resistance, charge capacity, and charge state at the previous time step. In some embodiments, the same cell state model is applied to all cells, but the predictions for different cells can be different due to differences between the cells with respect to the configuration parameter values and / or state parameter values. As another example, the cell state model can be used to estimate cell configuration parameters that can change over time, such as charge capacity and internal resistance. Specific examples are shown below.

[0034] The battery interface 306 can include hardware and / or software components that enable the processor 302 to obtain status information from sensors of cells (or batteries) connected to the battery interface 306. The status information can include, for example, measurements of cell current, potential, and operating temperature. In some embodiments, the battery interface 306 provides status information at regular time intervals. Optionally, this interval can be varied according to the current operating mode. For example, the status information can be provided once or twice per second while the battery is actively in use, and three or four times per hour when the battery is in an idle state. In some embodiments, the battery interface 306 can send a request for status information to the battery at a desired interval. Alternatively, the battery cell may have sensors that continuously provide data (e.g., as an analog signal), and the battery interface 306 can sample and digitize the analog signal at appropriate times. The processor 302 can use the sensor data thus obtained to calculate various status parameter values, for example, as described below.

[0035] The control system interface 308 can include hardware and / or software components that enable the processor 302 to provide battery status information to a control system (e.g., the control system 106 of FIG. 1). In various embodiments, the battery status information can include data received from battery cells, status parameters calculated from the data, configuration parameters calculated from the data, and / or fault notifications or other warnings, examples of which are described below.

[0036] The battery monitoring system 300 is exemplary, and it should be understood that variations and modifications are possible. In some embodiments, the battery monitoring system 300 can include a separate instance of its components for each cell being monitored, or can monitor multiple cells using a single processor having an associated memory device(s). In the examples described herein, battery monitoring is performed on cells, but battery monitoring at higher levels (e.g., groups of serially connected cells, or an entire battery pack or battery bank) is not excluded. The battery monitoring system 300 can implement any number and combination of monitoring operations, including but not limited to any one or more of the examples described below. In some embodiments, the battery monitoring system can also accommodate other battery management operations such as calibration, self-testing, etc.

[0037] During operation, a battery monitoring system such as the battery monitoring system 300 can monitor the battery state and evaluate the battery state in real time, considering the natural aging of the battery (which can gradually degrade performance) and other considerations that affect battery performance, to determine whether the battery (or a particular cell) is operating as expected. Next, an example of the battery monitoring process will be described.

[0038] Model Fault Detection One type of battery monitoring process can be based on predicting and observing the behavior of battery cells during normal use. Battery cells are generally inherently electrochemical, and the response of a cell to specific conditions, such as potential as a function of current or current at a given operating potential, can be quantitatively modeled using an equivalent cell circuit model or the like. Thus, some embodiments of the battery monitoring system can use a prediction function derived from a quantitative model of the monitored cell (referred to herein as a "cell state model") to predict the cell potential at any given time during battery operation. The actual (measured) cell potential can be compared to the prediction, and if the actual cell potential does not match the prediction, a "model fault" notification can be generated.

[0039] Figure 4 is a flowchart of a process 400 for modeling cell behavior and detecting model faults according to some embodiments. The process 400 can be implemented, for example, in the battery monitoring system 300 or other battery monitoring systems described above. The process 400 can operate in real time to monitor the cell state and determine whether the cell state matches the prediction of the cell state model. In some embodiments, the process 400 maintains an estimated (or predicted) cell state for each cell that is dynamically updated at regular time intervals based on measurement data received from the battery. The cell state can be defined and monitored using the variables defined in Table 1, where the subscript k indicates the time step.

Table 1

[0040] In some embodiments, using the notation of Table 1, the cell state model update function is defined as follows. [μ k , V pred、k =modelUpdate(μ k-1 , I k , C0, R i , T k , dt) (1) The modelUpdate() function calculates the SOC and predicted potential of the cell for time step k based on the previous cell state, measured current and temperature, estimated values of the charge capacity C0 and internal resistance R i , and the time step. The modelUpdate() function can be based on a conventional cell behavior model that uses an equivalent cell circuit model. Examples of such models are known in the art, and an appropriate model can be selected based on the specific type of cell.

[0041] The time step dt may be selected based on the current operating state of the cell. For example, the cell can have a finite state model that includes the following states. These states are "discharging" (current is being drawn from the battery), "charging" (current is being applied to recharge the battery), "relaxed" (the current exiting (or entering) the battery is below a threshold indicating inactivity), and "idle" (entering from the relaxed state when the current remains below the inactivity threshold and the voltage remains stable for a long time, e.g., at least 15 minutes). In some embodiments, process 400 can update the battery state at a higher speed (e.g., dt~1 second) when in the discharging, charging, or relaxed state, and at a lower speed (e.g., dt~900 seconds) when in the idle state. Other state models can also be used.

[0042] In some embodiments, the equivalent cell circuit model depends on the state of health parameters such as the charging capacity C0 and the internal resistance R i These parameters are expected to degrade over time as the cell ages, with the maximum charging capacity gradually decreasing and the internal resistance gradually increasing. In some embodiments, the battery monitoring system uses a passive real-time process based on cell sensor data to estimate C0 and R i and an example of such a process will be described below. In other embodiments, C0 and R i are measured from time to time (e.g., by performing a test procedure when the battery is expected to be in the idle state for a long time) and can be treated as constants between measurements.

[0043] Process 400 can start from initialization at block 402. Initialization can occur, for example, when the power of the battery management system is turned on or reset, or when the battery transitions from the idle state to any other state. Initialization can include setting initial values for various parameters of the cell state model based on the assumption that the cell is not in use when initialization occurs. For example, V pcan be initialized to 0. C0 and R i can be initialized based on the last measured or estimated value. SOC0 can be initialized to the last estimated SOC, or if the battery is not polarized during initialization, SOC0 can use the getSOC(V, T) function that calculates the cell state of charge as a function of the cell potential and temperature based on an equivalent cell circuit model to calculate from the measured values of V actual、0 and T0. In some embodiments, to facilitate real-time calculations, getSOC() can be calculated in advance for a discrete set of potential values and temperature values and stored in a lookup table. Other parameters and flags can also be initialized. For example, Q discharge、0 can be initialized to 0.

[0044] In block 404, for each iteration of process 400 (at time step k), new values of the cell state parameters are determined. In some embodiments, determining the new state parameters can include measuring the drawn current (I k ), the actual potential of the entire cell (V actual、k ), and the cell temperature (T k ), and applying Equation (1) to determine SOC k and the predicted potential V p , V pred、k .

[0045] In some embodiments, the step of determining the new state parameters can also include the step of estimating the cell energy (U k ) and the cell discharge Q discharge、k . For example, the following equations can be used.

Equation

[0046] In block 406, the state parameters determined in block 404 are used to calculate a set of "optimistic" cell parameters and a set of "pessimistic" cell parameters, specifically, the optimistic cell potential (V opt ) and the pessimistic cell potential (V pess ). The optimistic cell parameters represent the state parameters of a virtual cell in which the charge capacity has increased and the internal resistance has decreased (compared to the current estimated values of C0 and R i ), and the same charge or discharge event as the modeled cell occurs. Conversely, the pessimistic cell parameters represent the state parameters of a virtual cell in which the charge capacity has decreased and the internal resistance has increased (again, compared to the current estimated values of C0 and R i ), and the same charge or discharge event as the modeled cell occurs. In some embodiments, the optimistic and pessimistic cell parameters are not defined as a fixed tolerance around the state parameters determined in block 404, but instead they are calculated dynamically using a cell state model.

[0047] As an example, in some embodiments, the optimistic cell potential V C0、opt is defined based on the assumption that the maximum charge capacity C0 of the cell is underestimated by an amount K i , the internal resistance R Ri、opt of the cell is overestimated by an amount K η、opt , and the overvoltage of the cell is turned off by a scaling factor K opt . Based on this assumption, the following calculation is used to calculate V optcan be determined. C 0、opt = C0 + K C0、opt (4) C init、opt = C 0、opt SOC0(5) C opt、k = C init、opt - Q discharge、k (6)

Number

[0048] Similarly, in some embodiments, based on the assumption that the maximum charge capacity C0 of the cell is overestimated by an amount of K C0、pess , the internal resistance R i of the cell is underestimated by an amount of K Ri、pess , and the overvoltage of the cell is turned off by a scaling factor of K η、pess , the pessimistic cell potential V pessis defined. Based on this assumption, the following calculations can be used to determine V pess can be determined. [Number] C 0、pess = C0 + K C0、pess (12) C init、pess = C 0、pess SOC0(13) C pess、k = C init、pess - Q discharge、k (14) [Number] η ec、pess = K η、pess (V c + V e )(16) R i、pess、k =(R i + K Ri、pess )exp(- E Ro (T k - T ref ))(17) V pess、k = getOCV(SOC pess、k , T k ) - CF * (η ec、pess - I k R i、pess、k )(18) Similar to the optimistic cell, the K coefficient of the pessimistic cell can be optionally selected based on empirical observations of cell parameters (and their variability) and / or based on specific changes indicating electrical faults. In some embodiments of the battery bank of the type described above with reference to FIG. 2, K C0、pess ~ - 0.1 Ah (e.g., 0.2 Ah), K Ri、pess ~ 0.0003 Ω (e.g., 0.0001 Ω) and K η、pess~0.7 (for example, 0.75). In equations (11) and (18), CF is a scaling factor applied to the total overvoltage of the cell when the cell is charging, and explains the increase in the variability of the cell potential during charging. (As will become apparent, applying the scaling factor only to the pessimistic potential estimate or only to the optimistic potential estimate has the effect of widening the envelope of acceptable potentials when the cell is charging.)

[0049] The labels "optimistic" and "pessimistic" are not intended to, and need not, mean that V opt、k > V pess、k . Note that for example, according to equations (4) to (10) and equations (11) to (18), when the cell is discharging, V opt、k > V pess、k , but when the cell is charging, the result is that V pess、k > V opt、k . In some embodiments, a padding potential V pad is used to finely adjust V opt、k and V pess、k to ensure that the envelope has at least a minimum width. For example, when V opt、k > V pess、k , the following equations can be used for fine adjustment. V opt、k = V opt、k + V pad (19) V pess、k = V pess、k - V pad (20) Similarly, when V pess、k ≥V opt、k , the following equations can be used for fine adjustment. V opt、k = V opt、k - V pad (21) V pess、k = V pess、k + V pad (22) V pad can be selected as needed. For example, V pad = 0.01V.

[0050] In block 408, using the optimistic parameter and the pessimistic parameter, an envelope of acceptable battery performance, such as an acceptable potential range, is defined. For example, if V opt、k >V pess、k the case, the envelope can be defined as V opt、k ≧V k ≧V pess、k (23) and if V V pess、k ≧V opt、k the case, the envelope can be defined as V pess、k ≧V k ≧V opt、k (24) where V k is the cell potential at time step k.

[0051] In block 410, the actual potential V actual、k across the cell is measured. In some embodiments, the measurement can be performed as part of the step of determining the state parameters of the cell in block 404.

[0052] In block 412, it can be determined whether the measured potential V actual、k is within the range of acceptable battery performance, for example, using either equation (23) or equation (24) as required. In some embodiments, additional conditions may be applied. For example, the equivalent cell circuit model used to define the envelope may be unreliable when the cell SOC k is below a threshold (e.g., 0.1), or when the current I k exceeds a threshold (e.g., 70 A for a certain type of cell). Under conditions where the cell state model is unreliable, the envelope defined by the cell state model can be ignored (e.g., the measured V actual、k can always be treated as being within the envelope).

[0053] Measured potential V outside the envelope curve actual、k may indicate a cell problem and may result in generating a model fault notification. In some embodiments, any instance of the measured potential V actual、k outside the envelope curve may result in generating a model fault notification. In other embodiments, transient variations outside the envelope curve are ignored as non-substantive variations, and a fault counter can be used to determine when variations outside the envelope curve are considered to be substantially outside the envelope curve. Thus, in block 414, when the measured potential is not within the envelope curve, the value of the fault counter can be incremented, and in block 416, when the measured potential is within the envelope curve, the fault counter can be reset. In block 418, it is determined whether the fault counter has exceeded a threshold, and if so, a model fault notification is generated in block 420. In some embodiments, the step of generating a model fault notification in a battery management system such as the battery management system 108 of FIG. 1 can include the step of sending a notification to the control system 106 (and / or other system components as needed). In various embodiments, the model fault notification can include other actions such as turning on a fault indicator light mounted on the battery, sending a message to a maintenance service to request battery maintenance at the next opportunity, etc.

[0054] The counter threshold for generating a model fault notification can be a constant defined based on a trade-off between sensitivity (the ability to detect problems) and specificity (avoiding generating a fault notification when there is no actual problem). In one example, the determination of whether the actual cell potential is within the envelope curve is performed at a rate of 1 Hz, and the threshold of the fault counter is set to 30. As a result, if the actual potential remains outside the envelope curve for more than 30 seconds, a model fault event is generated. Other thresholds can also be selected.

[0055] To further illustrate the operation of process 400, FIG. 5 shows an exemplary plot of cell potential as a function of time for a battery cell used to power an aircraft, according to some embodiments. In this example, the aircraft is in an idle state until shortly before time 1000 seconds when the aircraft ascends to cruise altitude and begins cruising. Shortly before time 3000 seconds, the aircraft descends. The solid line 502 corresponds to the measured cell potential V actual、k The line 504 corresponds to the predicted potential V pred、k predicted using a cell state model applied to the actual cell parameters. (In this example, the line 504 closely tracks the measured potential.)

[0056] The line 506 corresponds to the predicted potential V opt、k predicted using optimistic cell parameters as described above (using the same cell state model as line 504), and the line 508 corresponds to the predicted potential V pess、k predicted using pessimistic cell parameters as described above (using the same cell state model as line 504). As can be seen from the figure, the width of the envelope defined by lines 506 and 508 varies as a function of time. The width is affected by the current cell behavior (e.g., how much current is being drawn) and the effect of hysteresis. In this example, since the actual potential (line 502) remains within the envelope defined by lines 506 and 508 or within the measured duration, no model failure event is generated.

[0057] Process 400 is exemplary, and it will be understood that variations and modifications are possible. To the extent logic permits, the operations described sequentially may be performed in parallel or the operations may be performed in a different order. It is also possible to perform other operations not specifically described or, if necessary, omit the operations specifically described. The cell state model and other parameter values (e.g., parameters for determining the optimistic potential and the pessimistic potential) can be optimized for a particular implementation based on the type and characteristics of the battery cell and the desired specific sensitivity and specificity. The monitoring process can be performed in parallel or sequentially for any number of cells. Monitoring using Process 400 or a similar process can be performed while the battery is in any state. Alternatively, if necessary, the monitoring can be performed only while in a particular state (e.g., only during a discharge event or only during a charging event).

[0058] Estimation of the healthy state of the cell As described above, model fault detection relates to the detection of abnormal behavior of an actively used battery or cell. Apart from abnormalities, the overall performance of a battery or battery cell (especially a rechargeable battery or cell) can be expected to gradually deteriorate over time due to electrochemistry and thermodynamics until the battery or battery cell reaches a point where it is no longer usable. Thus, in addition to or instead of detecting abnormal behavior, some embodiments of a battery monitoring system can also monitor the "healthy state" of the individual cells of a battery or multi-cell battery. In the examples herein, the healthy state is characterized by the maximum charge capacity C0 of the cell and the internal resistance R i of the cell. As the cell deteriorates, the maximum charge capacity decreases while the internal resistance tends to increase. In other embodiments, other parameters may be associated with the healthy state in addition to or instead of C0 and R i .

[0059] In some systems, C0 and R iMonitoring can be part of an active test process that is performed while the battery is connected to a charger or load in an idle state. Examples of such active test processes are known in the art. However, an active test process typically requires that the battery be in an idle state for an extended period (which can be several hours) and remain connected to a charger or load. In the case of batteries operating at a high duty cycle, an alternative process may be preferred.

[0060] Accordingly, some embodiments of a battery monitoring system can use a "passive" process to monitor the cell's C0 and R i . The passive process relies on real-time monitoring of voltage, current, and temperature (the same state parameters measured during the model fault detection process above), and C0 and R i can be quantitatively estimated from the measured parameters. In some embodiments, estimating C0 and R i can also include using a filtering function (e.g., a moving average) to smooth the variation of the estimated values. This process can be performed during battery use without affecting the battery's performance, so it is called "passive". Here, an example of a passive process for estimating C0 and R i will be described.

[0061] In the example described herein, C0 is defined as the maximum charge amount that the cell can discharge at a reference current (e.g., 1 A) and a reference temperature (e.g., T ref = 25 °C). In some embodiments, if a discharge event meets certain criteria regarding the reliability and stability of the C0 estimated value, the raw charge capacity value C 0、raw can be calculated at the end of the discharge event. For example, the raw C 0、raw of a discharge event can be defined as follows.

Equation

[0062] FIG. 6 is a flowchart of a process 600 for estimating C0 of a cell based on a discharge event according to some embodiments. Process 600 can be implemented, for example, in battery monitoring system 300 or other battery monitoring systems described above. Process 600 can perform C0 estimation while the battery is in an idle state. Since process 600 does not include battery activities other than normal operation (i.e., power supply to and / or charging of a load), it is an example of a passive monitoring process.

[0063] Process 600 can be started while the battery is in an idle state. At block 602, process 600 can determine the initial SOC (SOC init ) of the cell. In some embodiments, the initial SOC can be calculated as follows. SOC init =getSOC(V h , T h ) (26) Here, V h is the measured cell potential at initialization, T h is the measured temperature at initialization, and getSOC() is the same function as the function described above with reference to FIG. 4.

[0064] In some embodiments, SOC init is established only when the cell potential V h and the temperature T h are within a specific range. This range can be selected based on the ranges of potential and temperature for which the getSOC() function is a reliable model of cell behavior. If V h or T h is outside the appropriate range, SOC init is not established.

[0065] SOC init In addition to establishing the init , the processing in block 602 can also include initializing other parameters used for C0 monitoring. For example, the cumulative value of the charge discharged from the cell (Q defined above) can be initialized to 0, and the runtime parameter t related to C0 monitoring can also be reset to 0. discharge、k ) run、C0 )

[0066] In block 604, the battery enters a discharge state, and process 600 can monitor the discharge event. (Even when process 400 is implemented, this monitoring can be performed as part of the determination of the state parameters in block 404.) For example, the total amount of discharged charge Q is updated according to the above formula (3) at each time step (e.g., every 1 second), and the runtime parameter t can be incremented at each time step. In block 606, the discharge event ends, and the battery enters the idle state again (at which point, the update of Q and the runtime parameter t can be stopped). discharge、k run、C0 discharge、k run、C0

[0067] final ) SOC final = getSOC(V l , T l ) (27) Here, V l is the cell potential measured at the end of the discharge event, and T l is the temperature measured at the end of the discharge event. Since the measured values of V and / or T after discharge generally differ from the measured values of V and T before discharge, the results of formulas (26) and (27) generally differ from each other. l l h h

[0068] ​​​​​​​​In block 610, process 600 can determine whether all the measured values are within a range where the SOC final is considered reliable. For example, in some embodiments, the SOC final is established only when the cell potential V l and the temperature T l are within a specific range. This range can be selected based on the ranges of potential and temperature for which the getSOC() function is a reliable model of cell behavior. If V l or T l is outside the appropriate range, the SOC final is not established, and instead, the calculation can be reset in block 612, and process 600 can return to block 602 to start over.

[0069] Other requirements regarding the reliability of the SOC final can also apply. For example, in some embodiments, if the runtime parameter t run、C0 exceeds an upper limit, the result may become unreliable (e.g., due to Q discharge、k integrating the offset error of the current sensor), and process 600 can stop at block 612 and return to block 602 to start over.

[0070] As another example, block 610 can require that the change in SOC, defined as follows, ΔSOC = |SOC init - SOC final | (28) exceeds a minimum threshold of reliability. The threshold can be selected such that a discharge event consumes a significant percentage of the cell's charge capacity. For example, the threshold can be 0.4 or 0.5, etc. Otherwise, the calculation can be reset in block 612, and process 600 can return to block 602 to start over.

[0071] In block 614, process 600 filters the unfiltered C0 value (C0、raw ) can be calculated, where Q discharge is the final value of Q from the discharge event in block 604, and ΔSOC is given by Equation (25). In block 616, process 600 can calculate a filtered C0 value using an infinite impulse response filter that approximates a moving average. discharge、k C 0、filt = γC 0、raw +(1 - γ)C 0、prev (29) where C 0、prev is the stored estimated value of C0 (e.g., from the previous iteration of process 600), and γ is a filter decay constant that can be selected based on the desired sensitivity to the updated value. In one example, γ = 0.05, but other values can be selected. Other techniques can be used to combine the newly calculated C 0、raw with the previous estimate of C0, including a moving average, a weighted moving average (where a greater weight is given to the newer estimate), a recursive moving average, etc. In some embodiments, a statistical analysis of the distribution of the most recent C0 estimates for the cell can be performed to determine, for example, how far the most recent C 0、raw value deviates from the expected distribution and to smooth out random measurement noise. In some embodiments, prior to the first iteration of process 600 for a new cell, the charge capacity measured during testing of the cell, a nominal value (e.g., based on the cell's design specifications), or another value as needed can be used to initialize C 0、prev .

[0072] In block 618, process 600, for example, by comparing C 0、filt to a threshold, C 0、filtcan be determined to be improbably large. This threshold can be set to correspond to (or exceed) the maximum charge capacity that the cell can be expected to have. In some embodiments, the maximum charge capacity can be determined based on the cell's design specifications, allowing for some degree of better-than-design performance. In other embodiments, the maximum charge capacity of a particular cell can be determined by actively measuring the charge capacity of the cell during pre-installation testing, assuming that the charge capacity of the cell does not increase with use. C 0、filt If it exceeds the threshold, at block 620, C 0、filt is discarded, process 600 can be reset, and return to block 602. In some embodiments, improbably large C 0、filt is assumed to be a numerical artifact and simply ignored. In other embodiments, process 600 can generate a C0 error notification. In still other embodiments, process 600 tracks whether improbably large C 0、filt occurs repeatedly, and if so, can generate a C0 error notification.

[0073] In some embodiments, the C0 estimate C 0、filt can be used to trigger a cell capacity failure notification. For example, C0 is expected to change gradually over time, and an unexpected rapid change may indicate a problem. Thus, at block 622, process 600 can use, for example, the following to ΔC0 = |C 0、prev - C 0、filt | (30) The change in C0 can be calculated, and if ΔC0 exceeds a threshold value, the result is treated as suspect. For example, in block 624, process 600 can generate a "cell capacity failure" notification. In some embodiments, the threshold value(s) can be defined, and different failure notifications can be generated based on which threshold value is exceeded. For example, if ΔC0 exceeds a first threshold value (e.g., 0.4 Ah), a "suspect cell capacity" failure notification can be generated, and if ΔC0 exceeds a second larger threshold value (e.g., 0.8 Ah), a "highly suspect cell capacity" failure notification can be generated. When a cell capacity failure notification is generated in block 624, process 600 can reset, discard the result of the C0 calculation, and return to block 602.

[0074] In block 626, the stored C 0、prev value can be updated, for example, by replacing the stored value with the C 0、filt calculated in block 616. The updated C 0、prev value can be reported to the control system 106 or other system components. In some embodiments, the control system 106 can incorporate the estimated charge capacity into the battery state report (e.g., for review by a service technician).

[0075] Process 600 can be repeated for each discharge event, assuming that the battery enters an idle state between discharge events. In some embodiments, each time the battery enters an idle state, the battery management system can determine whether a valid SOC high is currently stored. If not, block 602 can be executed, and if so, block 608 and subsequent blocks can be executed. In some embodiments, process 600 or a similar process can also be used to estimate C0 based on measurements during a charge event. (This may not be desirable, for example, in a battery system design where measurements of current or other related parameters are less reliable during a charge event than during a discharge event.)

[0076] Process 600 is exemplary, and it will be understood that variations and modifications are possible. As far as logic permits, the operations described sequentially can be executed in parallel, or the operations can be executed in a different order. It is also possible to execute other operations not specifically described, or to omit the operations specifically described if necessary. For example, in the example described, a single value representing the current estimated value and the previous estimated value is used, but in other embodiments, the previous estimated values from multiple iterations of Process 600 can be stored, and a statistical analysis of the set of estimated values can be performed. (Such statistical analysis can improve accuracy and reduce variation, but also increases the amount of memory required to store the previous estimated values.) The cell state model and other parameter values in a particular embodiment can be selected to obtain optimal results based on the type and characteristics of the battery cell, and the specific sensitivity and specificity desired. The C0 estimation process can be executed in parallel or sequentially for any number of cells. In some embodiments, a low C0 fault notification can be generated if the estimated C0 (after filtering) is below a lower limit, which may be an indication that the cell is due for replacement.

[0077] In some embodiments, the internal resistance R of the cell i can be estimated in addition to, or instead of, C0. The internal resistance R i can be defined as the resistance (ohm) component of the impedance of the cell at standard SOC, current, and temperature, and without cell polarization. The resistance component of the impedance can be understood as the instantaneous change in potential with respect to current, but the instantaneous measurement of the change may not be practical for an operating cell. Also, since the internal resistance generally depends on SOC, temperature, and discharge current, a reliable estimated value may not be obtained simply by measuring ΔV / ΔI over a short period of time. Therefore, some embodiments introduce a compensation factor to improve the estimated value of R i

[0078] ​FIG. 7 is a flowchart of a process 700 for estimating R of a cell based on a discharge event, according to some embodiments. The process 700 can be implemented, for example, in the battery monitoring system 300 or other battery monitoring systems described above. The process 700 can be iteratively (running) calculated while the battery is operating actively (e.g., discharging or charging), and the running calculation can be used to update the estimated value when the battery enters the idle state. The process 700 is another example of a passive monitoring process that does not include battery activities other than normal operation. i The process 700 can start at block 702 when the battery transitions from the idle state to the active state (e.g., charging or discharging state). In response to the transition, at block 704, the process 700 can initialize a running estimate of the internal resistance (R i ). For example, the running estimate can be initialized to a value determined from a previous execution of the process 700. In some embodiments, during the first iteration of the process 700 for a new cell, R i can be initialized based on the internal resistance measured during cell testing, a nominal value (e.g., based on the cell design specification), or another value as needed.

[0079] At block 706, as long as the battery remains in the active state, R i、run is updated iteratively. (If the process 400 is also implemented, this update can be performed as part of the determination of the state parameters at block 404.) FIG. 8 shows a flowchart of a process 800 for iteratively updating R i according to some embodiments. The process 800 can be used, for example, to implement block 704 of the process 700, and the process 800 can be executed at regular time intervals (time index k) while the battery is in the active state. i、run

[0080] At block 706, as long as the battery remains in the active state, R i、run is updated iteratively. (If the process 400 is also implemented, this update can be performed as part of the determination of the state parameters at block 404.) FIG. 8 shows a flowchart of a process 800 for iteratively updating R i、run according to some embodiments. The process 800 can be used, for example, to implement block 704 of the process 700, and the process 800 can be executed at regular time intervals (time index k) while the battery is in the active state.

[0081] In block 802, process 800 can measure the current (I k )), potential (V k ), and temperature (T k ) of the cell. In block 804, process 800 can check whether the reliability conditions regarding current, potential, and temperature are satisfied. For example, the following reliability conditions can be applied. |I k - I k-1 | > ΔI min (31) |V k - V k-1 | > ΔV min (32) T max ≥ mean(T k , T k-1 ) ≥ T min (33) Equations (31) and (32) require that the changes in potential and current from one time step to the next are large enough to be measured. Equation (33) requires that the temperature is within a range where the resistance behavior model used to update R i、run is considered reliable. The limit parameters ΔI min , ΔV min , T max , and T min can be selected as needed for a particular system. In an example using the embodiment of the battery bank 200 of FIG. 2, ΔI min = 2 A, ΔV min = 0.010 V, T max = 50 °C, and T min = 20 °C. Other conditions, such as upper limits on the changes in current and / or potential and limits on the change in temperature, can also be applied.

[0082] In block 806, if at least one of the reliability conditions of block 804 is not satisfied, process 800 can wait for the next time step and retry in block 808. In block 806, if all reliability conditions are satisfied, in block 810, process 800 can calculate the raw estimate R i、raw for time step k. In some embodiments, the following calculation can be used. [Number] Here, SOC k can be determined using the above function getSOC(V k , T k ) with reference to equation (26), and IRComp() is a function that returns an internal resistance compensation coefficient depending on SOC, current, and temperature. For example, IRComp() can be defined as follows. IRComp(SOC, I, T) = calcRI(SOC ref , I ref , T ref ) - calcRI(SOC, I, T) (35) The function calcRI() can be defined as follows, [Number] Here, A() and B() are functions that can be empirically defined by testing a number of cells of a given design specification under controlled conditions. In some embodiments, empirical analysis can be used to add values of A() and B() corresponding to various combinations of SOC and I to a lookup table. In equation (35), SOC ref , I ref , and T ref are constant reference values of SOC, current, and temperature, which can be preselected in relation to the definitions of the functions A() and B() in equation (36). For example, SOC ref = 1, I ref = 22 A, T refA reference value of = 25°C can be selected.

[0083] In block 812, process 800 can determine whether R calculated in block 810 i、raw is within a range that is considered reasonable. In one example, the reasonable range is defined as 0.005 Ω to 0.03 Ω, but other ranges can be used depending on the cell design. If R i、raw is not within the range that is considered reasonable, in block 814, process 800 can discard or ignore the calculated R i、raw value and wait for the next time step to retry. In other embodiments, process 800 can generate an R i error notification. In still other embodiments, process 800 can track whether an unreasonably large R i、raw occurs repeatedly, and if so, can generate an R i error notification

[0084] R i、raw If R is within the range that is considered reasonable, in block 816, process 800 can update R using an infinite impulse response filter that approximates a moving average. i、run R i、run = γR i、raw +(1 - γ)R i、run (37) Here, γ is a filter decay constant that can be selected based on the desired sensitivity to the updated value. In one example, γ = 0.01, but other values can also be selected. In embodiments implementing both process 600 and process 800, the decay constant for R in equation (37) i can have the same value as the decay constant for C0 estimation in equation (29), but it is not necessary. Other techniques for combining the newly calculated R, including a moving average, a weighted moving average (where a larger weight is given to the newer estimate), a recursive moving average, etc., i、raw with the previous estimate of R i can be used. In some embodiments, the most recent R for the cell​i Perform a statistical analysis of the distribution of the estimated value of, for example, the most recent R i、raw value to determine how far it deviates from the expected distribution and can be used to smooth the random measurement noise.

[0085] In some embodiments, process 800 (corresponding to block 704 of process 700) can be repeatedly executed as long as the battery remains in the active state. Referring again to FIG. 7, at block 708, the battery transitions to the idle state. After the battery enters the idle state, the final value of R (from process 800) i、run can be used as the new R i estimated value. In some embodiments, the R i estimated value can be used to trigger a fault notification. For example, R i is expected to change gradually over time, and an unexpected sharp change may indicate a problem. Thus, at block 710, process 700 can calculate the change in R, for example, using the following. i The change in R can be calculated. ΔR i =|R i、run -R i | (38) where R i is the R value from block 702. If ΔR i exceeds a threshold value, the result is treated as suspect. For example, at block 714, process 700 can generate a "cell resistance fault" notification. In some embodiments, one or more threshold values can be defined, and different fault notifications can be distinguished based on which threshold is exceeded. For example, if ΔR i exceeds a first threshold value (e.g., 0.002 Ω), a "cell resistance is suspect" fault notification can be generated, and if ΔR i exceeds a second larger threshold value (e.g., 0.01 Ω), a "cell resistance is very suspect" fault notification can be generated. When a cell resistance fault notification is generated at block 714, process 700 results in the resulting R i i、run ​The value can be discarded and the process can return to block 702 and wait for the transition to the next active state.

[0086] At block 716, for example, the stored R i value can be updated by replacing it with the R i、run value. The updated R i value can be reported to the control system 106 or other system components. In some embodiments, the control system 106 can incorporate the estimated internal resistance into the battery state report (e.g., for review by a service technician). i i、run

[0087] Process 700 can calculate a new R i、run each time the battery enters an active state (e.g., using process 800), and update the R i when the battery is in an idle state (if the conditions are met), and repeat this. In some embodiments, process 700 or a similar process can be used to estimate the R i based on either a charge event or a discharge event, or alternatively, process 700 can be selectively invoked only in relation to a discharge event (or only in relation to a charge event) as needed.

[0088] Processes 700 and 800 are exemplary and it will be understood that variations and modifications are possible. To the extent logic permits, the operations described sequentially may be performed in parallel or the operations may be performed in a different order. It is also possible to perform other operations not specifically described or, if necessary, to omit the operations specifically described. For example, in the example described, a single value representing the current estimate and the previous estimate is used, but in other embodiments, the previous estimates from multiple iterations of Process 800 can be stored and a statistical analysis of the set of estimates can be performed. (Such statistical analysis can increase accuracy and reduce variation, but also increases the amount of memory required to store the previous estimates.) The cell state model and other parameter values in a particular embodiment can be selected to obtain optimal results based on the type and characteristics of the battery cell and the particular sensitivity and specificity desired. R i The estimation process can be performed in parallel or sequentially for any number of cells. In some embodiments, the estimated R i If it rises above the upper limit, a high R i fault notification can be generated, which may indicate that the cell is due for replacement.

[0089] Additional embodiments Although the present invention has been described with reference to specific embodiments, those skilled in the art who access the present disclosure will understand that variations and modifications are possible. The types of battery monitoring systems and processes described herein can be used to monitor any number of batteries or any number of battery cells, and the systems and processes can be adapted to cells implemented using various battery technologies. The notifications generated during battery monitoring are not limited to the above examples, and the use of the notifications is not limited to the above use cases. Any combination of the monitoring processes can be implemented in a specific system that includes any one or more of the above processes. The functions described as being based on a cell state model (e.g., an equivalent cell circuit model) can be implemented by providing a lookup table that is key to the input of the function with an appropriate accuracy based on the resolution of the measurement of the cell state parameters (e.g., the resolution of the potential, current, temperature sensors). In some embodiments, the determination of the monitoring parameters can be conditional on the input to the function being within the range covered by the lookup table.

[0090] The types of computational operations described herein can be implemented in computer systems of generally conventional designs such as desktop computers, laptop computers, tablet computers, mobile devices (e.g., smartphones), etc. Such systems can include one or more processors that execute program code (e.g., general-purpose microprocessors that can be used as a central processing unit (CPU), and / or dedicated processors such as a graphics processing unit (GPU) that can provide enhanced parallel processing capabilities), memory and other storage devices that store program code and data, user input devices (e.g., pointing devices such as a keyboard, mouse or touchpad, microphone), user output devices (e.g., display devices, speakers, printers), composite input / output devices (e.g., touch screen displays), signal input / output ports, network communication interfaces (e.g., wired network interfaces such as an Ethernet interface and / or wireless network communication interfaces such as Wi-Fi), and the like. Computer programs incorporating various features of the claimed invention may be encoded and stored on various computer-readable storage media, and suitable media include magnetic disks or tapes, optical storage media such as compact discs (CDs) or digital versatile discs (DVDs), flash memory, and other non-transitory media. (It should be understood that "storage" of data is different from the transmission of data using transitory media such as carrier waves.) The computer-readable medium encoding the program code may be packaged with a compatible computer system or other electronic device, or the program code may be provided separately from the electronic device (e.g., via an Internet download or as a separately packaged computer-readable storage medium).

[0091] It should be understood that all numerical values used herein are for illustrative purposes and may be changed. Also, although ranges may be set forth to give a sense of scale, numerical values outside the disclosed ranges are not excluded.

[0092] It should also be understood that all the figures in this specification are schematic. Unless otherwise specified, the drawings do not imply a particular physical arrangement of the elements shown therein, nor do they mean that all the elements shown are necessary. Those skilled in the art accessing this disclosure will understand that they can modify or omit the elements shown or otherwise described in this disclosure and can add other elements not shown or described.

[0093] The above description is illustrative and not restrictive. Many variations of the present invention will be apparent to those skilled in the art upon consideration of this disclosure. Accordingly, the scope of patent protection should not be determined by reference to the above description, but instead should be determined by reference to the following claims, along with their full scope or equivalents.

Claims

1. A method for monitoring the internal resistance of a battery cell, comprising: initializing a running estimate of the internal resistance using a stored value in response to detecting a transition of the battery cell from an idle state to an active state; while the battery cell is in the active state measuring the potential, current, and temperature of the battery cell; iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature; in response to detecting a transition of the battery cell from the active state to the idle state, calculating a change in the internal resistance based on the stored value and a final value of the running estimate; updating the stored value using the final value of the running estimate; and a method comprising the steps of.

2. generating a cell resistance fault notification when the change in the internal resistance exceeds a threshold value; The method according to claim 1, further comprising the step of.

3. The step of iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature comprises: determining, for each iterative update, whether the measured potential, current, and temperature are within a predetermined valid range; if the measured potential, current, and temperature are within the predetermined valid range, calculating a raw estimate of the internal resistance based on an equivalent cell circuit model; updating the running estimate using the raw estimate and the running estimate of the previous time step; if one or more of the measured potential, current, or temperature are not within the predetermined valid range, waiting for the next time step without updating the running estimate; The method according to claim 1, comprising the steps of.

4. The step of updating the running estimate comprises applying an infinite impulse response filter to the raw estimate and the previous running estimate; The method according to claim 3, comprising the steps of.

5. The step of iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature comprises: determining, for each iterative update, whether the measured potential, current, and temperature are within a predetermined valid range; if the measured potential, current, and temperature are within the predetermined valid range, calculating a raw estimate of the internal resistance based on an equivalent cell circuit model; When the estimated value of the raw value is outside the range of values considered reasonable, the step of discarding the estimated value of the raw value; When the estimated value of the raw value is within the range of values considered reasonable, the step of updating the running estimate using the estimated value of the raw value and the running estimate of the previous time step; When one or more of the measured potential, current, or temperature are not within the predetermined valid range, the step of waiting for the next time step without updating the running estimate The method according to claim 1, comprising:

6. The step of updating the running estimate includes applying an infinite impulse response filter to the estimated value of the raw value and the previous running estimate The method according to claim 5, comprising:

7. The method according to claim 1, wherein the active state is a charging state.

8. The method according to claim 1, wherein the active state is a discharging state.

9. A battery monitoring system, comprising: A battery interface that receives sensor data from a battery sensor of a battery cell; A control system interface for providing output data to a control system; A memory; A processor coupled to the memory, the battery interface, and the control system, In response to detecting a transition of the battery cell from an idle state to an active state, initializing the running estimate of the internal resistance using a stored value, While the battery cell is in the active state Measuring the potential, current, and temperature of the battery cell, Iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature, In response to detecting a transition of the battery cell from the active state to the idle state, Calculating a change in internal resistance based on the stored value and the final value of the running estimate, Updating the stored value using the final value of the running estimate A processor configured to: A battery monitoring system comprising:

10. The processor is Further configured to generate a cell resistance fault notification when the change in the internal resistance exceeds a threshold. The battery monitoring system according to claim 9.

11. The processor is The step of iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature is ​ For each iterative update, determining whether the measured potential, current, and temperature are within a predetermined valid range; If the measured potential, current, and temperature are within the predetermined valid range, Calculating a raw estimated value of the internal resistance based on an equivalent cell circuit model; If the raw estimated value is outside the range of values considered reasonable, discarding the raw estimated value; If the raw estimated value is within the range of values considered reasonable, updating the running estimated value using the raw estimated value and the running estimated value of the previous time step; If one or more of the measured potential, current, or temperature are not within the predetermined valid range, waiting for the next time step without updating the running estimated value Including, The battery monitoring system according to claim 9, further configured as such.

12. The processor is The step of updating the running estimated value includes applying an infinite impulse response filter to the raw estimated value and the previous running estimated value The battery monitoring system according to claim 11, further configured as such.

13. The battery monitoring system according to claim 9, wherein the active state is a charging state.

14. The battery monitoring system according to claim 9, wherein the active state is a discharging state.

15. When executed by a processor in a battery monitoring system coupled to a battery cell, the processor In response to detecting a transition of the battery cell from an idle state to an active state, initializing a running estimated value of the internal resistance using a stored value; While the battery cell is in the active state Measuring the potential, current, and temperature of the battery cell; Iteratively updating the running estimated value of the internal resistance based on the measured potential, current, and temperature; In response to detecting a transition of the battery cell from the active state to the idle state, Calculating a change in internal resistance based on the stored value and the final value of the running estimated value; Updating the stored value using the final value of the running estimated value A computer-readable storage medium storing therein program instructions for executing a method including the above.

16. The method is Generating a cell resistance fault notification when the change in internal resistance exceeds a threshold The computer-readable storage medium according to claim 15, further comprising

17. The step of repeatedly updating the running estimate of the internal resistance based on the measured potential, current, and temperature includes For each iterative update, determining whether the measured potential, current, and temperature are within a predetermined valid range; When the measured potential, current, and temperature are within the predetermined valid range, Calculating a raw estimate of the internal resistance based on an equivalent cell circuit model; Updating the running estimate using the raw estimate and the running estimate of the previous time step; When one or more of the measured potential, current, or temperature are not within the predetermined valid range, waiting for the next time step without updating the running estimate The computer-readable storage medium according to claim 15, comprising

18. The computer-readable storage medium according to claim 17, wherein the step of updating the running estimate includes applying an infinite impulse response filter to the raw estimate and the previous running estimate.

19. The computer-readable storage medium according to claim 15, wherein the active state is a charging state.

20. The computer-readable storage medium according to claim 15, wherein the active state is a discharging state.

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