Real-time battery failure detection and health status 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 of related applications This application claims priority to U.S. Provisional Application No. 62 / 988,853 filed on 12 March 2020 and U.S. Patent Application No. 17 / 196,848 filed on 9 March 2021, the disclosures of which are incorporated herein by reference.

[0002] This disclosure relates in general to battery monitoring, and more particularly to systems and methods for monitoring the health and performance of batteries in real time. [Background technology]

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

[0004] Batteries (especially lithium-ion batteries) are used in a wide variety of applications, including as portable power sources to drive motors in vehicles such as automobiles, aircraft, and ships. In some cases, the battery or battery pack may be the vehicle's sole power source. Vehicles powered solely by batteries may suddenly lose their power if the battery fails. Depending on the state of the vehicle when the battery fails, the consequences can range from inconvenient to disastrous. Therefore, it is desirable to monitor battery performance, detect conditions that suggest problems, and ensure that batteries can be repaired or replaced before they fail. [Overview of the project]

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

[0006] According to some embodiments, a method for monitoring the charge capacity of a battery cell may include the steps of: determining the initial charge state of the battery cell while it is idle; then monitoring the total amount of charge moving to or from the battery cell while it is active (e.g., a discharge state where charge moves from the battery cell to a load, or a charge state where charge moves from an external power source to the battery cell); determining the final charge state of the battery cell after it has returned to the idle state; calculating an unfiltered charge capacity value using the initial charge state, the final charge state, and the total amount of charge moved; and updating a charge capacity estimate using the unfiltered charge capacity value.

[0007] In some embodiments, the magnitude of the change in the charge capacity estimate relative to the previous estimate can be calculated, and if the magnitude of the change in charge capacity exceeds a threshold, a cell capacity failure notification can be generated.

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

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

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

[0011] In some embodiments, the step of updating the charge capacity estimate may include 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 may include the steps of: initializing a running estimate of the internal resistance using a stored value in response to the detection of a transition of the battery cell from an idle state to an active state (which may 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 an 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 a stored value and a final running estimate in response to the detection of a transition of the battery cell from an active state to an idle state; and updating the stored value using the final running estimate.

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

[0014] In some embodiments, the step of iteratively updating the running estimate of internal resistance based on measured potential, current, and temperature may include, with each iterative update, a step of 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 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 the running estimate is updated only if the raw estimate falls within a range of values ​​deemed reasonable. If one or more of the measured potential, current, or temperature are not within the predetermined valid range, the method may include a step of waiting for the next time step without updating the running estimate.

[0015] In some embodiments, the step of updating the running estimate may 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 may include the steps of: determining the current value of one or more health state parameters of the battery cell (e.g., the internal resistance of the battery cell, the charge capacity of the battery cell); measuring the actual potential of the battery cell; measuring the values ​​of several operating state parameters of the battery cell (e.g., the current flowing through the battery cell, the temperature of the battery cell); calculating an optimistic potential based on a cell state model (e.g., an equivalent cell circuit model), measurements of a first subset of the operating state parameters, and optimistic values ​​for one or more health state parameters corresponding to health states better than the current value; calculating a pessimistic potential based on a cell state model, measurements of a first subset of the operating state parameters, and pessimistic values ​​for one or more health state parameters corresponding to health states worse than the current value; determining whether the actual potential of the battery cell is substantially within an envelope defined by the optimistic and pessimistic potentials; and generating a model failure notification if the actual potential is substantially not within the envelope. In various embodiments, the method can be performed in real time while the battery cell is supplying power to a load and / or while the battery cell is charging. Model failure notifications can be generated in real time while the battery is actively being used (supplying power to a load and / or charging). In some embodiments, the predicted potential of the battery cell can also be calculated based on a cell state model, measurements of a first subset of operating state parameters, and current values ​​of one or more health state parameters.

[0017] In some embodiments, estimates of the battery cell's charge capacity and / or internal resistance can be determined while the battery is actively in use according to the techniques described herein, and these estimates can be used in a condition monitoring method.

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

[0019] The following detailed description, along with the attached drawings, will provide a better understanding of the nature and merits of the claimed invention. [Brief explanation of the drawing]

[0020] [Figure 1] This is a high-level block diagram of an operating environment for battery monitoring according to several embodiments. [Figure 2] This is a simplified schematic diagram of a battery bank according to several embodiments. [Figure 3] An example of a battery monitoring system according to several embodiments is shown. [Figure 4] This is a flowchart of a process for modeling cell behavior and detecting model failures, based on several embodiments. [Figure 5] Exemplary plots of the cell potential as a function of time for battery cells used to power an aircraft, according to several embodiments, are shown. [Figure 6] This is a flowchart of a process for estimating the charge capacity of a cell based on discharge events, according to several embodiments. [Figure 7] This is a flowchart of a process for estimating the internal resistance of a cell based on discharge events, according to several embodiments. [Figure 8] This is a flowchart of a process for iteratively updating the running estimate of internal resistance according to several embodiments. [Modes for carrying out the invention]

[0021] The following description of exemplary embodiments of the present invention is presented for illustrative and explanatory purposes only. It is not intended to be exhaustive or to limit the claimed invention to the exact forms described, and those skilled in the art will understand that many modifications and variations are possible. The embodiments are selected and described to best illustrate the principles and practical applications of the present invention, thereby enabling those skilled in the art to best utilize the invention in various embodiments and with various modifications suitable for a particular intended use.

[0022] System Overview Figure 1 shows a high-level block diagram of an operating environment 100 for battery monitoring according to several embodiments. Environment 100 can be, for example, an electric vehicle such as an aircraft, ship, train, automobile, truck, or off-road vehicle. Environment 100 includes a battery 102 that supplies power to an electrical load 104. Battery 102 can be any type of battery, including lithium-ion batteries, lead-acid batteries, nickel-metal hydride batteries, etc. Battery 102 can be implemented as a single battery cell or as a battery pack including multiple battery cells connected to each other in series and / or parallel as needed. (As used herein, the terms “battery cell” or “cell” can be understood as including an independent battery or, in the case of a battery pack, one of several independent, replaceable battery units within a battery pack.) The electrical load 104 can include, for example, the motor (or engine) of a vehicle, or any other battery-powered mechanism or device. The electrical load 104 can draw varying amounts of power from battery 102 at different times. For example, a vehicle's 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, for example, to increase or decrease 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, any abnormal conditions that may occur, etc. The control system 106 may also include human interface components such as a display screen, indicator lights, speakers, and human-operable control devices (e.g., a keyboard, mouse, touchscreen or touchpad, joystick, control wheel, foot pedal, etc.). The control system 106 may be local to the load 104 (e.g., the load 104 is located in a vehicle containing a motor) or located remotely from the load 104 and can communicate via appropriate connections, including short-range or long-range network connections. In some embodiments, the control system 106 may include both local and remote elements. For example, the environment 100 may be an autonomous or remotely operated vehicle that is monitored and guided from a location other than where it operates.

[0024] In some embodiments, the battery 102 is a rechargeable battery that can be charged by connecting it to a charging power source such as a charger 110. The charger 110 may include any system or device that can supply power (or charge) stored by the battery 102 from an external power source (e.g., a standard wall outlet or any other power source outside the battery 102), and numerous examples are known in the art. The charger 110 may also include a control circuit that controls the operation of the charger 110, including when and how much power to supply. In some embodiments, the battery 102 can be coupled to the charger 110 at certain times and disconnected from the charger 110 at other times, so the charger 110 is shown using a dashed line. In some embodiments, the battery 102 may or may not be able to power a load 104 while receiving power from the charger 110. In some embodiments, a control system 106 may coordinate the operation of the charger 110 with the load-power supply operation of the 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 may be equipped with sensors to measure 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 (for example, 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 health state parameters of the battery or individual cells (e.g., charge capacity and / or internal resistance), and / or to detect whether the battery performance matches 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 status information may include, for example, measured status parameters reported from battery 102, values ​​of health status parameters calculated by the battery monitoring system 108, information comparing actual battery behavior with a model of expected battery behavior, “failure” notifications indicating that some aspect of battery performance has deviated from expectations, and / or any combination of other information available in the battery monitoring system 108. The control system 106 and / or charger 110 may use this information to generate warnings to the operator in environment 100, modify the operation of load 104 based on the battery status information, modify the operation of charger 110 (e.g., the rate at which charging power is supplied to battery 102) based on the battery status information, maintain battery history information for 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 may include the aircraft's motor, and the battery 102 may serve as the power source for the motor. In such an environment, the reliability of the battery 102 is crucial to enabling the aircraft to safely complete its flight. To supply the required amount of power with high reliability, the battery 102 may include a high-voltage battery bank composed of multiple battery packs arranged in parallel (which can provide redundancy), each battery pack incorporating multiple cells arranged in series and / or parallel. The battery monitoring system 108 can provide real-time information on the status of each battery pack, for example, per cell or for groups of cells, enabling detection and intervention of problems before battery failure occurs. For example, if the battery monitoring system 108 generates a failure notification, a service technician can be notified so that 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 between flights, and the battery monitoring system 108 can continue to provide information on the status of each battery pack during charging.

[0027] It should be understood that operating environment 100 is illustrative 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 types of systems and methods described herein.

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

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

[0030] Each HV battery 210 also includes a Battery Monitoring System (BMS) board 220, which can be a component of the Battery Monitoring System 108 shown in Figure 1. Each BMS board 220 may be a printed circuit board having circuits configured to monitor the status of one or more cells within the HV battery 210. In some embodiments, the BMS board 220 can monitor all cells in each HV battery 210. For example, each HV battery 210 may include 12 BMS boards 220, and each BMS board can monitor 36 cells. The following describes examples of components that can be mounted on the BMS board 220 and their operation.

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

[0032] Figure 3 shows an example of a battery monitoring system 300 according to several embodiments. The battery monitoring system 300 can be used, for example, to implement the battery monitoring system 108 in Figure 1. In some embodiments, each BMS board 220 of the battery bank 200 in Figure 2 may 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 may be a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other circuitry that implements the logic operations described herein. In some embodiments, the battery monitoring system 300 may be an embedded system, and the calculations may be performed in a manner that can be performed in real time using a small, low-power processor, as described below. The memory 304 may include semiconductor-based memory (e.g., DRAM, SRAM), flash memory, magnetic memory, optical memory, or other computer-readable storage media. The memory 304 may store information about each battery cell monitored by the battery monitoring system 300. For example, the memory 304 may store cell configuration parameters 310 and cell state parameters 312. The cell configuration parameters 310 may include parameters that are static or gradually change over the lifetime of a particular cell, and the cell state parameters 312 may include parameters that change dynamically as the battery is used. Memory 304 can also store cell state models (e.g., equivalent cell circuit models) that can be used to iteratively predict the state parameters of a cell. For example, a cell state model can predict the charge state and polarization state (or voltage) of a 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 predictions for different cells may differ due to differences between cells regarding configuration parameter values ​​and / or state parameter values. As another example, a cell state model can be used to estimate cell configuration parameters that may change over time, such as charge capacity and internal resistance. Specific examples are given below.

[0034] The battery interface 306 may include hardware and / or software components that enable the processor 302 to acquire state information from sensors of cells (or batteries) connected to the battery interface 306. State information may include, for example, measurements of the cell's current, potential, and operating temperature. In some embodiments, the battery interface 306 provides state information at regular time intervals. If necessary, this interval can be varied depending on the current operating mode. For example, state information may 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 idle. In some embodiments, the battery interface 306 can send requests for state information to the battery at desired intervals. Alternatively, the battery cell may have sensors that continuously provide data (e.g., as analog signals), and the battery interface 306 can sample and digitize these analog signals at appropriate times. The processor 302 can use the sensor data thus obtained to calculate various state parameter values, for example, as described below.

[0035] The control system interface 308 may include hardware and / or software components that enable the processor 302 to provide battery status information to the control system (e.g., the control system 106 in Figure 1). In various embodiments, the battery status information may include data received from the 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] It should be understood that the battery monitoring system 300 is illustrative and subject to modification and alteration. In some embodiments, the battery monitoring system 300 may include a separate instance of its components for each cell being monitored, or it may monitor multiple cells using a single processor having associated memory devices(s). In the examples described herein, battery monitoring is performed on a cell-by-cell basis, but higher-level battery monitoring (e.g., a group of series-connected cells, or an entire battery pack or battery bank) is not excluded. The battery monitoring system 300 may implement any number and combinations of monitoring operations, including but not limited to one or more of the examples described below. In some embodiments, the battery monitoring system may also accommodate other battery management operations, such as calibration and self-testing.

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

[0038] Model failure 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 electrochemical in nature, and the cell's response to current under specific conditions, such as potential as a function of current or current at a given operating potential, can be quantitatively modeled using equivalent cell circuit models, etc. Therefore, some embodiments of a battery monitoring system can use a prediction function derived from a quantitative model of the monitored cell (referred to herein as the “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 failure” notification can be generated.

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

[0040] In some embodiments, using the notation in 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 retrieves the previous cell state, measured current and temperature, charging capacity C0 and internal resistance R. i Based on the estimated values ​​and time step, the SOC and predicted potential of the cell for time step k are calculated. The modelUpdate() function can be based on conventional cell behavior models using equivalent cell circuit models. Examples of such models are known in the art, and an appropriate model can be selected based on a particular 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), "relaxing" (the current exiting (or entering) the battery is below a threshold indicating inactivity), and "idle" (entering from the relaxing 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 relaxing 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 charge capacity C0 and the internal resistance R i These parameters are expected to degrade over time as the cell ages, with the maximum charge capacity gradually decreasing and the internal resistance gradually increasing. In some embodiments, the battery monitoring system can use a passive real-time process based on cell sensor data to estimate C0 and R i as will be described by way of example of such a process 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 in 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 pC0 and R can be initialized to 0. i It 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 is initialized using the getSOC(V, T) function, which calculates the cell charge state as a function of cell potential and temperature based on an equivalent cell circuit model, V actual、0 And it can be calculated from the measured values ​​of T0. In some embodiments, to facilitate real-time calculations, getSOC() can be pre-calculated for a discrete set of potential and temperature values ​​and stored in a lookup table. Other parameters and flags can also be initialized. For example, Q discharge、0 It can be initialized to 0.

[0044] In block 404, a new value for the cell state parameter is determined for each iteration of process 400 (in time step k). In some embodiments, determining the new state parameter is the current being drawn (I k ), the actual potential of the entire cell (V actual、k ), and cell temperature (T k ) is measured and equation (1) is applied to SOC k and predicted potential V p , V pred、k This could include making a decision.

[0045] In some embodiments, the step of determining the new state parameter also involves the cell energy (U k ) and cell discharge Q discharge、k This may include a step to estimate the value. For example, the following formula can be used:

number

[0046] In block 406, the state parameters determined in block 404 are a set of "optimistic" cell parameters and a set of "pessimistic" cell parameters, in particular the optimistic cell potential (V opt ) and pessimistic cell potential (V pess This is used to calculate (C0 and R). The optimistic cell parameters are (C0 and R i (Compared to the current estimate) This represents the state parameters of a hypothetical cell where the charging capacity has increased and the internal resistance has decreased, and the same charging or discharging events occur as in the modeled cell. Conversely, the pessimistic cell parameters are (again, the current C0 and R) i This represents the state parameters of a hypothetical cell with decreased charging capacity and increased internal resistance (compared to the estimated values), where the same charging or discharging events occur as in the modeled cell. In some embodiments, the optimistic and pessimistic cell parameters are not defined as fixed tolerances around the state parameters determined in block 404, but rather they are calculated dynamically using the cell state model.

[0047] As an example, in some embodiments, the maximum charge capacity C0 of the cell is K C0、opt The internal resistance R of the cell is underestimated in terms of quantity. i is K Ri、opt The overvoltage of the cell is overestimated in terms of quantity, and the scaling factor K η、opt Based on the assumption that it is turned off by, the optimistic cell potential V opt This is defined. Based on this assumption, V is calculated using the following calculation. optIt is possible to make a decision. 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, the maximum charge capacity C0 of the cell is K C0、pess The internal resistance R of the cell is overestimated by a certain amount. i is K Ri、pess The amount of the cell overvoltage is underestimated by K η、pess Based on the assumption that it is turned off by the scaling factor, 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 selected as needed 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 (e.g., 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 clear, 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, to ensure that the envelope has at least a minimum width, the padding potential V pad can be used to finely adjust V opt、k and V pess、k . 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[[ID=S9]] 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, optimistic and pessimistic parameters are used to define the envelope of acceptable battery performance, such as the acceptable potential range. For example, V opt、k >V pess、k In this case, the envelope is, V opt、k ≥V k ≥V pess、k (twenty three) It can be defined as follows: V pess、k ≥V opt、k In this case, the envelope is, V pess、k ≥V k ≥V opt、k (twenty four) It can be defined as follows, where V k This is the cell potential at time step k.

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

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

[0053] The measured potential V outside the envelope actual、k This may suggest a problem with the cell and could result in a model failure notification. In some embodiments, the measured potential V outside the envelope actual、k Any instance of this may result in the generation of a model fault notification. In other embodiments, transient fluctuations outside the envelope are ignored as insubstantial fluctuations, and a fault counter can be used to determine when fluctuations outside the envelope are considered substantially outside the envelope. Thus, in block 414, if the measured potential is not within the envelope, the value of the fault counter can be incremented, and in block 416, if the measured potential is within the envelope, the fault counter can be reset. In block 418, it is determined whether the fault counter has exceeded a threshold, and if it has, 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 in Figure 1, may 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 may include other actions, such as illuminating a fault indicator light on the battery, or sending a message to maintenance service to request battery servicing at the next opportunity.

[0054] The counter threshold for generating model failure notifications can be a constant defined based on a trade-off between sensitivity (the ability to detect problems) and specificity (avoiding the generation of failure notifications when there are no actual problems). For example, if the determination of whether the actual cell potential is within the envelope is performed at a speed of 1 Hz, and the failure counter threshold is set to 30, then a model failure event is generated if the actual potential remains outside the envelope for 30 seconds or more. Other thresholds can also be selected.

[0055] To further illustrate the operation of process 400, Figure 5 shows exemplary plots of the cell potential as a function of time of a battery cell used to power an aircraft, according to several embodiments. In this example, the aircraft is idle until a little before time 1000 seconds, when the aircraft has climbed to cruising altitude and begun cruising. A little before time 3000 seconds, the aircraft descends. The solid line 502 represents the measured cell potential V actual、k This corresponds to line 504, which shows the predicted potential V predicted using a cell state model applied to the actual cell parameters. pred、k This corresponds to [the measured potential]. (In this example, line 504 faithfully tracks the measured potential.)

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

[0057] Process 400 is illustrative and can be modified and altered. Where logic allows, the operations described sequentially can be performed in parallel, or the operations can be performed in a different order. Other operations not specifically described can be performed, and the specifically described operations can be omitted if necessary. The cell state model and other parameter values ​​(e.g., parameters for determining optimistic and pessimistic potentials) can be optimized for specific embodiments based on the type and characteristics of the battery cells 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, monitoring can be performed only while the battery is in a specific state (e.g., only during a discharge event, or only during a charge event).

[0058] Estimation of cell health status As described above, model failure detection relates to the detection of abnormal behavior of a battery or cell in active use. Apart from abnormalities, the overall performance of a battery or battery cell (especially a rechargeable battery or cell) can be expected to gradually degrade over time due to electrochemistry and thermodynamics, eventually reaching a point where the battery or battery cell becomes unusable. Therefore, in addition to, or instead of, detecting abnormal behavior, some embodiments of a battery monitoring system can also monitor the "health status" of individual cells in a battery or multi-cell battery. In the examples herein, the health status is defined as the cell's maximum charge capacity C0 and its internal resistance R i Characterized by the following: As the cell degrades, the maximum charging capacity tends to decrease, while the internal resistance tends to increase. In other embodiments, other parameters are C0 and R i In addition to or instead of this, it may be associated with a state of health.

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

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

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

number

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

[0063] Process 600 can be started while the battery is idle. In block 602, process 600 starts the initial SOC (SOC) of the cell. init ) can be determined. In some embodiments, the initial SOC can be calculated as follows: SOC init =getSOC(V h , T h ) (26) Here, V h This is the cell potential measured at initialization, and T h This is the temperature measured at the time of initialization, and getSOC() is the same function as described above, refer to Figure 4.

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

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

[0066] In block 604, the battery enters a discharge state, and process 600 can monitor the discharge event. (Even if process 400 is implemented, this monitoring can be done as part of determining the state parameters in block 404.) For example, the total amount of charge discharged Q discharge、k This is updated at each time step (e.g., every second) according to equation (3) above, and the runtime parameter t run、C0 This can be incremented at each time step. In block 606, the discharge event ends and the battery returns to an idle state (at that point, Q discharge、k and runtime parameter t run、C0 (You can stop the updates.)

[0067] In block 608, process 600 performs the final SOC (SOC) of the cell. final ) can be established. In some embodiments, the final SOC can be calculated as follows: SOC final =getSOC(V l , T l ) (27) Here, V l This is the cell potential measured at the end of the discharge event, T l This is the temperature measured at the end of the discharge event. V measured after discharge. l and / or T l Generally, the V measured before discharge is h and T h Since they are different, the results of equations (26) and (27) are generally different from each other.

[0068] In block 610, process 600 is SOC final It is possible to determine whether all measurements are within a range that can be considered reliable. For example, in some embodiments, SOC final The cell potential V l and temperature T l It is established only if it is within a specific range. This range can be selected based on the potential and temperature ranges necessary for the getSOC() function to be a reliable model of cell behavior. l or T l If it is outside the appropriate range, SOC final The process is not established, and instead, the calculation can be reset in block 612, allowing process 600 to return to block 602 and try again.

[0069] SOC final Other reliability requirements may also apply. For example, in some embodiments, the runtime parameter t run、C0 If it exceeds the upper limit, the result will be (for example, Q integrates the offset error of the current sensor) discharge、k (This may lead to a loss of trust), and process 600 can stop at block 612 and return to block 602 to try again.

[0070] As another example, block 610 defines a change in the SOC as follows: ΔSOC = |SOC init -SOC final (28) It can be required that a minimum reliability threshold be exceeded. The threshold can be chosen to require that the discharge event consumes a significant percentage of the cell's charge capacity, for example, the threshold can be 0.4 or 0.5. Otherwise, the calculation can be reset in block 612, and process 600 can return to block 602 and try again.

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

[0072] In block 618, process 600 is, for example, C 0、filt By comparing it with the threshold, C 0、filtThis allows us to determine whether the value is exceptionally large. This threshold can be set to correspond to (or exceed) the maximum charge capacity that the cell is 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 performance that is better than the design. In other embodiments, the maximum charge capacity of a particular cell can be determined by actively measuring the cell's charge capacity during pre-installation testing, assuming that the cell's charge capacity does not increase with use. 0、filt If the threshold is exceeded, in block 620, C 0、filt It is discarded, and process 600 can be reset and return to block 602. In some embodiments, an incredibly large C 0、filt It is assumed to be a numerical artifact and is simply ignored. In other embodiments, process 600 can generate a C0 error notification. In yet another embodiment, process 600 can generate an incredibly large C 0、filt It is possible to track whether this occurs repeatedly and, if so, generate a C0 error notification.

[0073] In some embodiments, the estimated value of C0 is C 0、filt This can be used to trigger cell capacity failure notifications. For example, C0 is expected to change gradually over time, and an unexpectedly rapid change may indicate a problem. Therefore, in block 622, process 600 uses, for example, the following: ΔC0 = |C 0、prev -C 0、filt (30) The change in C0 can be calculated, and if ΔC0 exceeds a threshold, the result is treated as suspicious. For example, in block 624, process 600 can generate a "cell capacity failure" notification. In some embodiments, thresholds can be defined, and different failure notifications can be generated based on which threshold is exceeded. For example, if ΔC0 exceeds a first threshold (e.g., 0.4Ah), a "cell capacity is suspicious" failure notification can be generated, and if ΔC0 exceeds a second, larger threshold (e.g., 0.8Ah), a "cell capacity is very suspicious" 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, stored C 0、prev The value is, for example, the stored value calculated in block 616 as C 0、filt It can be updated by replacing it with the updated C. 0、prev The 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 status report (for example, 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 checks the valid SOC high It can be determined whether C0 is currently stored. If not, block 602 can be executed; if it is, 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 charging event. (This may be undesirable in battery system designs where, for example, measurements of current or other relevant parameters are less reliable during a charging event than during a discharge event.)

[0076] Process 600 is illustrative and can be modified and altered. Where logic allows, the described operations can be performed sequentially or in parallel, or in a different order. Other operations not specifically described can be performed, or the specifically described operations can be omitted if necessary. For example, in the described example, a single value is used to represent the current estimate and the previous estimate, but in other embodiments, previous estimates from multiple iterations of process 600 can be stored, and a statistical analysis of the set of estimates can be performed. (Such statistical analysis can improve accuracy and reduce variability, but it also increases the amount of memory required to store 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 desired specific sensitivity and specificity. The C0 estimation process can be performed in parallel or sequentially for any number of cells. In some embodiments, a low C0 failure notification can be generated if the estimated C0 (after filtering) falls below a lower limit, which may indicate that the cell is due for replacement.

[0077] In some embodiments, the internal resistance R of the cell i The internal resistance R can be estimated in addition to or instead of C0. i R can be defined as the resistance (ohm) component of the cell's impedance at standard SOC, current, and temperature, and without cell polarization. While the resistance component of impedance can be understood as the instantaneous change in potential with respect to current, instantaneous measurement of the change may not be practical for a working cell. Furthermore, since internal resistance generally depends on SOC, temperature, and discharge current, simply measuring ΔV / ΔI over a short time may not yield a reliable estimate. Therefore, in some embodiments, R i A compensation factor is introduced to improve the estimated value.

[0078] Figure 7 shows the R of the cell based on the discharge event in several embodiments. i This is a flowchart of process 700 for estimating R. Process 700 can be implemented, for example, in battery monitoring system 300 or other battery monitoring systems mentioned above. Process 700 repeatedly (runs) R while the battery is actively operating (e.g., discharge state or charge state). i It can perform calculations, and when the battery enters an idle state, it uses running calculations to R i The estimated values ​​can be updated. Process 700 is another example of a passive monitoring process that does not involve battery activity other than normal operation.

[0079] Process 700 can be started in block 702 when the battery transitions from an idle state to an active state (e.g., a charging state or a discharging state). In response to the transition, in block 704, process 700 calculates a running estimate of the internal resistance (R i、run ) can be initialized. For example, the running estimate is determined from the previous run of process 700. i It can be initialized to a value. In some embodiments, during the first iteration of process 700 for a new cell, R i、run The internal resistance measured during cell testing, the nominal value (e.g., based on the cell's design specifications), or another value as needed can be used for initialization.

[0080] In block 706, as long as the battery remains active, R i、run This is updated iteratively. (If process 400 is also implemented, this update can be done as part of determining the state parameters in block 404.) Figure 8 shows R in several embodiments. i、run A flowchart of process 800 for iteratively updating is shown. Process 800 can be used, for example, to implement block 704 of process 700, and process 800 can be executed at regular time intervals (time index k) while the battery is in an active state.

[0081] In block 802, process 800 controls the cell current (I k ), potential (V k ), and temperature (T k ) can be measured. In block 804, process 800 can check whether the reliability conditions for current, potential and temperature are met. 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 R i、run It is required that the resistance behavior model used to update the model is within a range considered reliable. The limiting parameter ΔI min , ΔV min , T max , and T min This can be selected as needed for a specific system. In one example using the embodiment of battery bank 200 in Figure 2, ΔI min =2A, ΔV min = 0.010V, T max =50℃, and T min = 20°C. Other conditions, such as upper limits on the change 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 met, process 800 may wait for the next time step in block 808 and retry. If all reliability conditions are met in block 806, in block 810, process 800 obtains a raw estimate R for time step k. i、raw It is possible to calculate this. In some embodiments, the following calculation can be used.

number

number

[0083] In block 812, process 800 uses the R calculated in block 810. i、raw It is possible to determine whether it falls within a reasonable range. In one example, the reasonable range is defined as 0.005Ω to 0.03Ω, but other ranges can be used depending on the cell design. i、raw If it is not within a reasonable range, in block 814, process 800 calculates R i、raw The value can be discarded or ignored, and the next time step can be waited for and the process can be retried. In other embodiments, process 800 is R i Error notifications can be generated. In yet another embodiment, process 800 is incredibly R i、raw Track whether this occurs repeatedly, and if so, R i It is possible to generate error notifications.

[0084] R i、raw If it is within a reasonable range, in block 816, process 800 uses an infinite impulse response filter that approximates the moving average R i、run It can be updated. R i、run =γR i、raw +(1-γ)R i、run (37) Here, γ is a filter attenuation constant that can be selected based on the desired sensitivity to the update value. In one example, γ = 0.01, but other values ​​can also be selected. In embodiments that implement both process 600 and process 800, R in equation (37) i The decay constant of the estimation can, but does not have to be, the same value as the decay constant of the C0 estimation in equation (29). Newly calculated R includes moving averages, weighted moving averages (where newer estimates are given greater weight), recursive moving averages, etc. i、raw to R i Other techniques can be used to combine with previous estimates of R for the cell. In some embodiments, recent R for the celli Perform a statistical analysis of the distribution of the estimates, for example, the most recent R i、raw It is possible to determine how far the values ​​deviate from the expected distribution and to smooth out random measurement noise.

[0085] In some embodiments, process 800 (corresponding to block 704 of process 700) can be executed repeatedly as long as the battery remains active. Referring again to Figure 7, in block 708, the battery transitions to an idle state. After the battery enters an idle state, R (from process 800) i、run The final value of the new R i It can be used as an estimate. In some embodiments, R i Estimates can be used to trigger fault notifications. For example, R i It is expected that this will change gradually over time, and any unexpectedly rapid changes may indicate a problem. Therefore, in block 710, process 700 uses, for example, the following: i The change can be calculated. ΔR i =|R i、run -R i (38) Here, R i R from block 702 i It is the value of ΔR. i If the threshold is exceeded, the result is treated as questionable. For example, in block 714, process 700 can generate a "cell resistance failure" notification. In some embodiments, thresholds(s) can be defined to distinguish different failure notifications based on which threshold is exceeded. For example, ΔR i If it exceeds the first threshold (e.g., 0.002Ω), a fault notification "suspected cell resistance" can be generated, and ΔR i If it exceeds a second larger threshold (e.g., 0.01Ω), a "cell resistance highly suspicious" failure notification can be generated. When a cell resistance failure notification is generated in block 714, process 700 then generates the resulting R i、runYou can discard the value and return to block 702 to wait for the transition to the next active state.

[0086] In block 716, for example, stored R i Value R i、run By replacing it with the stored R i The value can be updated. i The 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 status report (for example, for review by a service technician).

[0087] Process 700 generates a new R each time the battery enters an active state (for example, using Process 800). i、run Calculate R when the battery is idle (if the conditions are met) i The process can be updated and repeated. In some embodiments, process 700 or a similar process is used to update R based on either a charging event or a discharging event. i This can be estimated, or, if necessary, process 700 may be selectively invoked only in relation to discharge events (or only in relation to charge events).

[0088] Processes 700 and 800 are illustrative and can be modified and altered. Where logic allows, the operations described sequentially can be performed in parallel, or the operations can be performed in a different order. Other operations not specifically described can be performed, and the operations specifically described can be omitted if necessary. For example, in the described examples, a single value is used to represent the current estimate and the previous estimate, but in other embodiments, 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 improve accuracy and reduce variability, but it also increases the amount of memory required to store 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 desired specific sensitivity and specificity. 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, high R i It is possible to generate a failure notification, which may indicate that the cell is due for replacement.

[0089] Additional Embodiments While the present invention has been described with reference to specific embodiments, those skilled in the art who access this disclosure will understand that modifications and alterations 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 examples above, and the use of notifications is not limited to the use cases described above. Any combination of monitoring processes can be implemented in a particular system that includes any one or more of the processes described above. Functions described as being based on a cell state model (e.g., an equivalent cell circuit model) can be implemented by providing a key lookup table as input to the function with appropriate precision based on the resolution of the measurement of the cell state parameters (e.g., the resolution of a potential, current, or temperature sensor). In some embodiments, the determination of monitoring parameters may 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, and mobile devices (e.g., smartphones). Such systems may include one or more processors that execute program code (e.g., a general-purpose microprocessor that can be used as a central processing unit (CPU), and / or a dedicated processor such as a graphics processor (GPU) that can provide enhanced parallel processing capabilities), memory and other storage devices for storing program code and data, user input devices (e.g., pointing devices such as a keyboard, mouse, or touchpad, microphone), user output devices (e.g., a display device, speaker, printer), composite input / output devices (e.g., a touchscreen display), signal input / output ports, and network communication interfaces (e.g., a wired network interface such as an Ethernet interface and / or a wireless network communication interface such as Wi-Fi). Computer programs incorporating various features of the claimed invention may be encoded and stored in various computer-readable storage media, suitable of which include magnetic disks or tapes, optical storage media such as compact discs (CDs) or DVDs (digital multipurpose discs), flash memory, and other non-temporary media. (It should be understood that "storing" data is different from the propagation of data using temporary media such as carrier waves.) The computer-readable medium on which the program code is encoded may be packaged together with a compatible computer system or other electronic device, or the program code may be provided separately from the electronic device (for example, via internet download or as separately packaged computer-readable storage medium).

[0091] Please understand that all figures used herein are for illustrative purposes only and are subject to change. While ranges may be defined to provide a sense of scale, figures outside these ranges are not excluded.

[0092] It should be understood that all figures in this specification are schematic. Unless otherwise specified, the drawings do not imply any particular physical arrangement of the elements shown therein, nor do they imply that all elements shown are necessary. A person skilled in the art who accesses this disclosure will understand that elements shown in the drawings or otherwise described in this disclosure may be modified or omitted, and other elements not shown or described may be added.

[0093] The above description is illustrative and not limiting. Many variations of the present invention will become apparent to those skilled in the art by examining this disclosure. Accordingly, the scope of patent protection should not be determined by reference to the above description, but rather by reference to the following claims, together with their entire scope or equivalents.

Claims

1. A method for monitoring the internal resistance of a battery cell, In response to the detection of the transition of the battery cell from an idle state to an active state, the steps include: initializing a running estimate of the internal resistance using a stored value; While the aforementioned battery cell is in an active state The steps include measuring the potential, current, and temperature of the aforementioned battery cell, The steps include iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature, In response to the detection of the transition of the battery cell from the active state to the idle state, A step of calculating the change in internal resistance based on the stored value and the final running estimate, The steps include updating the stored value using the final value of the running estimate, and Methods that include...

2. A step to generate a cell resistance failure notification when the change in the internal resistance exceeds a threshold. The method according to claim 1, further comprising:

3. The step of iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature is: Each iterative update includes the step of determining whether the measured potential, current, and temperature are within a predetermined effective range, If the measured potential, current, and temperature are within the predetermined effective range, The steps include: calculating a raw estimate of the internal resistance based on an equivalent cell circuit model; A step of updating the running estimate using the raw estimate and the running estimate from the previous time step, If one or more of the measured potential, current, or temperature are not within the predetermined effective range, the process involves waiting for the next time step without updating the running estimate. The method according to claim 1, including the method described in claim 1.

4. The step of updating the running estimate is to apply an infinite impulse response filter to the raw estimate and the previous running estimate. The method according to claim 3, including the method described in claim 3.

5. The step of iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature is: Each iterative update includes the step of determining whether the measured potential, current, and temperature are within a predetermined effective range, If the measured potential, current, and temperature are within the predetermined effective range, The steps include: calculating a raw estimate of the internal resistance based on an equivalent cell circuit model; If the raw estimate falls outside the range of values ​​considered reasonable, the raw estimate is discarded. If the raw estimate falls within the range of the values ​​deemed reasonable, the running estimate is updated using the raw estimate and the running estimate from the previous time step. If one or more of the measured potential, current, or temperature are not within the predetermined effective range, the process involves waiting for the next time step without updating the running estimate. The method according to claim 1, including the method described in claim 1.

6. The step of updating the running estimate is to apply an infinite impulse response filter to the raw estimate and the previous running estimate. The method according to claim 5, including the method described in claim 5.

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

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

9. A battery monitoring system, A battery interface that receives sensor data from the battery sensor of the battery cell, A control system interface for providing output data to the control system, Memory and A processor coupled to the memory, the battery interface, and the control system, In response to detecting the transition of the battery cell from an idle state to an active state, the running estimate of the internal resistance is initialized using the stored value, While the aforementioned battery cell is in an active state The potential, current, and temperature of the aforementioned battery cell are measured. The running estimate of the internal resistance is iteratively updated based on the measured potential, current, and temperature. In response to the detection of the transition of the battery cell from the active state to the idle state, Based on the stored value and the final running estimate, the change in internal resistance is calculated. The stored value is updated using the final value of the running estimate. A processor configured in such a way A battery monitoring system equipped with this feature.

10. The aforementioned processor, A cell resistance failure notification is generated when the change in the internal resistance exceeds a threshold. The battery monitoring system according to claim 9, further configured as described above.

11. The aforementioned processor, The step of iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature is: Each iterative update includes the step of determining whether the measured potential, current, and temperature are within a predetermined effective range, If the measured potential, current, and temperature are within the predetermined effective range, The steps include: calculating a raw estimate of the internal resistance based on an equivalent cell circuit model; If the raw estimate falls outside the range of values ​​considered reasonable, the raw estimate is discarded. If the raw estimate falls within the range of the values ​​deemed reasonable, the running estimate is updated using the raw estimate and the running estimate from the previous time step. If one or more of the measured potential, current, or temperature are not within the predetermined effective range, the process involves waiting for the next time step without updating the running estimate. to include, The battery monitoring system according to claim 9, further comprising the configuration described in claim 9.

12. The aforementioned processor, The step of updating the running estimate is to apply an infinite impulse response filter to the raw estimate and the previous running estimate. The battery monitoring system according to claim 11, further configured to include:

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 discharge state.

15. When executed by a processor in a battery monitoring system coupled to a battery cell, the processor: In response to the detection of the transition of the battery cell from an idle state to an active state, the steps include: initializing a running estimate of the internal resistance using a stored value; While the aforementioned battery cell is in an active state The steps include measuring the potential, current, and temperature of the aforementioned battery cell, The steps include iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature, In response to the detection of the transition of the battery cell from the active state to the idle state, A step of calculating the change in internal resistance based on the stored value and the final running estimate, The steps include updating the stored value using the final value of the running estimate, and A computer-readable storage medium that stores program instructions within which to perform a method including [a specific action].

16. The method described above is A step of generating a cell resistance failure notification when the change in the internal resistance exceeds a threshold. A computer-readable storage medium according to claim 15, further comprising:

17. The step of iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature is: Each iterative update includes the step of determining whether the measured potential, current, and temperature are within a predetermined effective range, If the measured potential, current, and temperature are within the predetermined effective range, The steps include: calculating a raw estimate of the internal resistance based on an equivalent cell circuit model; A step of updating the running estimate using the raw estimate and the running estimate from the previous time step, If one or more of the measured potential, current, or temperature are not within the predetermined effective range, the process involves waiting for the next time step without updating the running estimate. A computer-readable storage medium according to claim 15, including the following:

18. The computer-readable storage medium according to claim 17, wherein the step of updating the running estimate includes the step of 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 charged state.

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

Images