Cell and rack performance monitoring system and method

Abstract

The battery cell performance monitoring system includes an energy storage system including at least one inverter and battery nodes. Each battery node includes a plurality of battery racks, each of which includes a plurality of battery cells. The battery cell performance monitoring system also includes a processor, a memory, and programming in the memory. The programming causes the battery cell performance monitoring system to determine an extreme rack cell voltage value for each battery rack. Then, the programming causes the battery cell performance monitoring system to determine an average extreme rack cell voltage value based on the extreme rack cell voltage values for each battery rack. The programming also causes the battery cell performance monitoring system to compare the extreme rack cell voltage value for each battery rack with the average extreme rack cell voltage value. Additionally, the programming causes the battery cell performance monitoring system to identify one or more abnormal battery racks based on the comparison of the extreme rack cell voltage value for each battery rack with the average extreme rack cell voltage value.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001]

[0001] This application claims priority to U.S. patent application Ser. No. 17 / 810,983, filed July 6, 2022, entitled "Cell and Rack Performance Monitoring System and Method," the entire contents of which are incorporated herein by reference. [Technical Field]

[0002]

[0002] The present subject matter relates to an example of efficient detection of insufficiently charged or discharged battery cells in an energy storage system consisting of multiple battery cells divided into battery racks. [Background technology]

[0003]

[0003] Energy storage systems typically include multiple individual battery cells to provide electrical power, specifically, the battery cells working in concert to provide direct current (DC) power to a power inverter, which then provides this as alternating current (AC) to an end-use electrical device.

[0004]

[0004] When battery cells are networked and wired to function as a single DC power supply, in most situations the battery cells are charged and discharged as a group or functionally as a single battery. As a result, a collection of individual battery cells can all be charged from an external power source or all be discharged to an external power source. To avoid damage to the battery cells, the collection of battery cells stops charging once a single battery cell in the collection is fully charged, and the collection also stops discharging once a single battery cell in the collection is fully discharged.

[0005]

[0005] Therefore, the charge / discharge capacity of an entire collection of battery cells is limited by its worst-charging battery cell and its worst-discharging battery cell (which may be different battery cells). As a result, identifying poorly performing battery cells is paramount to performance. A single battery cell operating at 50% charge capacity may limit 10,000 other battery cells to operating only at the same 50% capacity. In some systems, poorly performing battery cells are checked only when the system is established, periodically during scheduled inspections, or when a user reports performance degradation. However, there is a strong interest in identifying poorly performing battery cells before their performance deteriorates significantly, and potentially isolating, repairing, or replacing them without affecting devices supplying or consuming electricity from the energy storage system.

[0006]

[0006] Auditing the performance of individual battery cells after each charge or discharge of a collection of battery cells provides superior performance information compared to one-time, periodic, or reactive battery cell performance audits. However, evaluating the performance of all battery cells in an energy storage system can be computationally expensive. Furthermore, if the battery cells have an extremely low total failure rate (e.g., a 1% chance of one battery cell failing every 1000 charge cycles), the value of any individual series of calculations is largely wasted (a 1% failure rate over 1000 cycles may result in the identification of an average of only one failure every 100,000 cycles). However, once a failure occurs, it will persist until corrected, and therefore, rapid identification of failures is paramount to maintaining optimal performance of the entire energy storage system. Summary of the Invention

[0007]

[0007] Therefore, there is room for further improvement in methods for identifying low-performing battery cells in energy storage systems, and energy storage systems incorporating such methods. The performance monitoring techniques disclosed herein can identify low-performing battery cells in energy storage systems within a single charge or discharge period of a charge cycle, and can further identify the classification of the type of fault the low-performing battery is exhibiting within a single charge cycle. The performance monitoring techniques can potentially reduce the computational overhead of battery cell audits by 99% or more by proactively evaluating battery cell performance data at the battery node, battery rack, battery module, battery sub-module, or other battery element level.

[0008]

[0008] In a first example, a battery cell performance monitoring system includes an energy storage system including at least one inverter and a plurality of battery nodes. Each of the battery nodes includes a plurality of battery racks, each of the battery racks including a respective plurality of battery cells. The battery cell performance monitoring system also includes a processor and a memory coupled to the processor. The memory includes performance monitoring programming that, when executed, configures the battery cell performance monitoring system to perform the following functions: First, determine an extreme rack cell voltage value for each battery rack; Second, determine an average extreme rack cell voltage value based on the extreme rack cell voltage values for each battery rack; Third, compare the extreme rack cell voltage value for each battery rack to the average extreme rack cell voltage value; and Fourth, identify one or more abnormal battery racks based on the comparison of the extreme rack cell voltage value for each battery rack to the average extreme rack cell voltage value.

[0009] In a second example, the method includes: first, determining an extreme element cell performance value for each battery element of a plurality of battery elements; second, determining an average extreme element cell performance value based on the extreme element cell performance value of each battery element; third, comparing the extreme element cell performance value for each battery element to the average extreme element cell performance value; and fourth, identifying one or more abnormal battery elements based on the comparison of the extreme element cell performance value for each battery element to the average extreme element cell performance value.

[0010] In a third example, a battery cell performance monitoring system includes an energy storage system including at least one inverter and a plurality of battery nodes. Each of the battery nodes includes a plurality of battery elements, each of the battery elements including a respective plurality of battery cells. The battery cell performance monitoring system also includes a processor and a memory coupled to the processor. The memory includes performance monitoring programming that, when executed, configures the battery cell performance monitoring system to perform the following functions: First, determine an extreme element cell performance value for each battery rack; Second, determine an average extreme element cell performance value based on the extreme element cell performance value of each battery element; Third, compare the extreme element cell performance value for each battery element to the average extreme element cell performance value; and Fourth, identify one or more abnormal battery elements based on the comparison of the extreme element cell performance value for each battery element to the average extreme element cell performance value.

[0011] Additional objects, advantages and novel features of the present subject matter will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by the production or operation of the present subject matter. The objects and advantages of the present subject matter may be realized and attained by means of the methodologies, instrumentalities and combinations particularly pointed out in the appended claims.

[0012]

[0012] The figures of the drawings illustrate, by way of example only and not by way of limitation, one or more implementations of the present concepts. In the figures, like reference numerals refer to the same or similar elements. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is an isometric view of an energy storage system including multiple battery nodes. [Figure 2] FIG. 1 is an isometric view of a battery node including multiple battery racks or elements of multiple battery cells. [Figure 3] FIG. 2 is a diagram of a performance monitoring subsystem of the battery cell performance monitoring system. [Figure 4] 1 is a diagram of multiple battery cells in a battery rack or element with subsets of cells in various classifications of abnormal conditions. [Figure 5] 10 is a graph of cell voltage versus time for an abnormal battery cell classified as a high resistance cell compared to a non-abnormal battery cell. [Figure 6] 10 is a graph of cell voltage versus time for an abnormal battery cell classified as a low capacity cell compared to a non-abnormal battery cell. [Figure 7] 1 is a graph of cell voltage versus time for two abnormal battery cells classified as high imbalance cells and low imbalance cells compared to non-abnormal battery cells. [Figure 8] 1 is a flowchart illustrating a cell and element performance monitoring protocol. DETAILED DESCRIPTION OF THE INVENTION

[0014]

[0021] In the following detailed description, numerous specific details are set forth by way of example to provide a thorough understanding of the relevant teachings. However, it will be apparent to those skilled in the art that the present teachings may be practiced without such details. In other instances, well-known methods, procedures, components, and / or circuits have been described at a relatively high-level, without detail, to avoid unnecessarily obscuring aspects of the present teachings.

[0015]

[0022] As used herein, the term "coupled" refers to any logical, physical, electrical, or optical connection, linkage, etc., in which signals or light generated or provided by one system element are provided to another coupled element. Unless otherwise stated, coupled elements or devices are not necessarily directly connected to each other, but may be separated by intervening components, elements, or communication media that may modify, manipulate, or convey light or signals.

[0016]

[0023] Unless otherwise specified, any and all measurements, values, orders, positions, dimensions, sizes, and other specifications set forth in this specification, including the appended claims, are approximate rather than precise. Such quantities are intended to have a reasonable range consistent with the functions to which they relate and the practices in the technical field to which they pertain. For example, unless expressly specified otherwise, parameter values, etc. may vary by ±10% from the stated amount. The terms "approximately" and "substantially" mean that the parameter values, etc. may vary by up to ±10% from the stated amount.

[0017]

[0024] The orientation of any completed device, such as a battery node, rack, module, or cell, associated components, and / or energy storage system incorporating the battery node, rack, module, or cell as shown in any of the figures, is provided by way of example only for purposes of illustration and discussion. During operation in a particular energy storage application, the battery node, rack, module, or cell may be oriented in any other orientation suitable for the particular application of the energy storage system, such as upright, sideways, or any other orientation. Also, as used herein, any directional terms, such as left, right, front, back, back, edge, top, bottom, upper, lower, top, bottom, and side, are used by way of example only and are not limiting with respect to the direction or orientation of any energy storage system or battery node, rack, module, or cell, or component of an energy storage system or battery node, rack, examples of which are shown in the accompanying figures and discussed below.

[0018]

[0025] 1 is an isometric view of a battery cell performance monitoring system 1, which primarily includes an energy storage system 100 and a performance monitoring subsystem 112. The energy storage system 100 includes multiple battery nodes 110A-N. The battery nodes 110A-N include batteries of any existing or future reusable battery technology, including lithium-ion batteries or flow batteries. The battery nodes 110A-N, collectively and individually, can be discharged by supplying DC electricity to an external load and can be charged by receiving DC electricity from an external source.

[0019]

[0026] To facilitate the supply and receipt of DC, the battery nodes 110A-N are connected to one or more power inverters 104. The power inverters 104 are configured to normalize the input and output of power to and from the battery nodes 110A-N. As the battery nodes 110A-N supply DC, the power inverters 104 either convert the DC to AC for use by the connected loads 106, normalize the DC from the battery nodes 110A-N to the connected loads 106, or simply pass the DC from the battery nodes 110A-N to the connected loads. Additionally, as the battery nodes require DC, the power inverters either convert the AC from the energy source 102 to DC, normalize the DC from the energy source 102 to the battery nodes 110A-N, or simply pass the DC from the energy source 102 to the battery nodes 110A-N.

[0020]

[0027] The power inverter is shown as having separate lines to the energy source 102 and the connected load 106. Separate lines may be advantageous in scenarios where the energy source 102 is unstable, such as a wind- or solar-powered energy source 102. In such scenarios, power from the energy source 102 is pushed through a unidirectional power inverter 104 to the battery nodes 110A-N, which then either charge or discharge and provide stable energy to the connected load 106 through another unidirectional power inverter 104. However, the connected load 106 and the energy source 102 may be connected to the power inverter 104 on the same line through a bidirectional power inverter 104. In scenarios where the energy source 102 is complex and connected to the connected load 106, such as a power grid with consuming devices, a single connection to an energy storage system may absorb energy generated by the energy source 102 in excess of the demand of the connected load 106 or may provide energy to the connected load 106 in excess of the capacity of the energy source 102.

[0021]

[0028] The power inverter 104 may also include power converters to facilitate normalization of input or output wattage or voltage to provide a matched output and protect the battery nodes 110A-N, the energy source 102, or the connected load 106 from damage.

[0022]

[0029] Energy source 102 may be any suitable system for generating electrical energy, such as a turbine or a photovoltaic cell. Connected load 106 may include a power grid or smaller local loads, such as a backup power system for a facility such as a hospital, manufacturing site, residence, or other suitable facility.

[0023]

[0030] The battery cell performance monitoring system 1 includes a performance monitoring subsystem 112 connected to the energy storage system 100 that monitors the battery nodes 110A-N for abnormal charge / discharge cycles. Typically, the battery nodes 110A-N of the energy storage system 100 connected to an inverter 104 or group of inverters 104 operate in coordination to either supply and discharge power to a connected load 106 or receive and charge power from an energy source 102. As a result, a fault in a single battery node 110A-N reduces the effectiveness of the entire energy storage system 100. The performance monitoring subsystem 112 is configured to receive power system data from the battery nodes 110A-N connected to the power inverter 104. As discussed in subsequent figures, this data is used to identify poorly performing battery nodes 110A-N so that action can be taken to restore performance of the energy storage system 100 despite the poorly performing battery node 110A.

[0024]

[0031] FIG. 2 is an isometric view of a battery node 110A including multiple battery racks 210A-F of multiple battery cells 212A-N. The battery node 110A houses the multiple battery racks 210A-F. The battery node 110A is both a physical collection of the battery racks 210A-F and a logical and electrical collection of the battery racks 210A-F. The battery node 110A physically houses the battery racks 210A-F, and the electrical performance of the battery racks 210A-F within the battery node 110A may be attributed to the battery node 110A itself. For example, if the battery rack 210A is capable of storing 102 kilowatt-hours of energy and the battery node 110A houses six battery racks 210A-F, the battery node 110A may be understood and described as storing 612 kilowatt-hours of energy. The battery node 110A may house more or fewer battery racks 210A than shown in the figure.

[0025]

[0032] A given battery rack 210A houses multiple battery cells 212A-N. Similar to the relationship between the battery node 110A and the housed battery racks 210A-F, the battery rack 210A is both a physical collection of battery cells 212A-N and a logical and electrical collection of battery cells 212A-N. As an example, if a battery cell 212A is capable of storing 6 kilowatt-hours of energy and the battery rack 210A houses 17 battery cells 212A-N, the battery node 210A may be understood and described as storing 102 kilowatt-hours of energy. The battery rack 210A may house more or fewer battery cells 212A than shown in the figures.

[0026]

[0033] Because the battery rack 210A is a logical and electrical collection of the battery cells 212A-N, the collection is not defined by the physical structure or order of the battery cells 212A-N. Thus, the battery rack 210A may alternatively be described as a battery module, a battery sub-module, or a battery array, where each of these terms (rack, module, sub-module, array) is a classification of the battery elements 210A, which are logical and electrical collections of the battery cells 212A-N, without explicit regard to the physical structure or order of the battery cells 212A-N. In some implementations, there is a gap-level enclosure between the battery cells 212A and the battery rack 210A or battery element 210A that may be identified as a battery module within the battery element 210A that houses prismatic, pouch-shaped, or cylindrical battery cells 212A-C. In this context, the distinction is largely meaningless, as the primary structural organization concern is logical and electrical structure or order, not physical.

[0027]

[0034] The battery node 110A represents a single physical installation whose maximum size may be limited by the mass or volume that a person, forklift, or vehicle can transport as a single atomic unit. The battery rack 210A or battery element 210A within the battery node 110A represents an organizational structure for organizing and stacking the battery cells 212A-N within the battery node 110A. The battery cell 212A is generally the largest manufacturing unit a battery manufacturer can produce, capable of charging and discharging electricity at a chemical level. In some examples, the battery cells 212A-C may be grouped into battery modules within battery elements 210A-F, which represent the smallest unit a particular worker can remove or replace in the energy storage system 100. In these examples, the individual battery cells 212A are too small or delicate to perform specialized maintenance in the field; instead, the entire battery module of battery cells 212A-C is repaired or replaced together. As outlined below, the performance monitoring subsystem 112 may be able to detect anomalies at the battery cell 212A level, but in an energy storage system 100 that includes battery modules within battery elements 210A-F, an operator would need to replace the entire battery module containing the abnormal cell 212A even if the remaining battery cells 212B-C within the battery module are not performing satisfactorily.

[0028]

[0035] 3 is a diagram of the performance monitoring subsystem 112. The performance monitoring subsystem 112 collects performance data for each battery rack or element 210A, along with performance data for all of the battery cells 212A-R in the energy storage system 100. In other implementations, performance data may be collected at the battery node 110A-B level, along with performance data for the entire energy storage system 100. In this example, the performance monitoring subsystem 112 is connected to the battery racks or elements 210A-B and battery cells 212A-D, O-R via a wired connection, although the performance monitoring subsystem 112 may also be connected to the battery racks or elements 210A-B and battery cells 212A-D, O-R via a wireless connection.

[0029]

[0036] The performance monitoring subsystem 112 includes a processor 330. The processor 330 functions to perform various operations, e.g., according to instructions or programming executable by the processor 330. For example, such operations may include operations related to communication with various energy storage system 100 elements, such as the battery nodes 110A-N, the power inverter 104, and the energy source 102, or across the distributed performance monitoring subsystem 112, to implement the performance monitoring protocol 800. While the processor 330 may be configured using hardwired logic, a typical processor is a general-purpose processing circuit configured by executing programming. The processor 330 includes elements constructed and arranged to perform one or more processing functions, typically various data processing functions. While discrete logic components may also be used, the present example utilizes components forming a programmable CPU. The processor 330 may include, for example, one or more integrated circuit (IC) chips incorporating electronic elements for performing the functions of a CPU. Processor 330 may be based on any known or available microprocessor architecture, such as, for example, a reduced instruction set computing (RISC) using the ARM architecture, as commonly used today in mobile devices and other portable electronic devices. Of course, other processor circuits may be used to form the CPU or processor hardware. For convenience, the illustrated example of processor 330 includes only one microprocessor, but multi-processor architectures may also be used. A digital signal processor (DSP) or field programmable gate array (FPGA) may be suitable alternatives for processor 330, but may consume more power with added complexity.

[0030]

[0037] Memory 335 is coupled to processor 330. Memory 335 is for storing data and programming. In this example, memory 335 may include flash memory (non-volatile or persistent storage) and / or random access memory (RAM) (volatile storage). RAM serves as short-term storage of instructions and data being processed by processor 330, e.g., working data processing memory. Flash memory typically provides longer-term storage.

[0031]

[0038] Of course, other storage devices or configurations may be used in addition to or in place of those shown in the present example. Such other storage devices may be implemented using any type of storage medium having computer- or processor-readable instructions or programming stored thereon, and may include, for example, any or all of the tangible memory of a computer, processor, etc., or associated modules.

[0032]

[0039] The performance monitoring subsystem 112 may also include a network interface 332 coupled to the processor 330. The network interface 332 is configured to report performance data from the battery racks 210A-B and the battery cells 212A-D, O-R to a network server. Additionally, the network interface 332 may collect performance data from the battery racks or elements 210A-B if the battery racks 210A-B are equipped with a network interface, and from the battery cells 212A-D, O-R if the battery cells 212A-D, O-R are equipped with a network interface.

[0033]

[0040] The performance monitoring subsystem 112 may be implemented in a distributed manner, with the processor 330 being divided into two or more processors with two or more memory devices 335. The processors 330 may operate in parallel or may be specialized to perform specific tasks. The memory 335 devices may store a complete copy of all performance data or may be specialized to store specific data relevant to a particular processor 330. In one example, the performance monitoring subsystem 112 is divided into local and remote groups. The local processor 330, local memory 335, and local network interface 332 may collect performance data from the battery racks or elements 210A-B and battery cells 212A-D, O-R, while the remote processor 330, remote memory 335, and remote network interface 332 may receive the collected data, analyze it, and make decisions.

[0034]

[0041] Performing an analysis on the battery racks or elements 210A-B and the battery cells 212A-D, O-R to detect poorly performing or abnormal battery cells 212A can be computationally expensive, and a full investigation may be performed at the end of every charging and discharging period of every charging cycle. In particular, if the likelihood that any battery cell 212A-D, O-R is abnormal in any given charging cycle is very low, the results of the calculations are typically of little use if they do not identify the abnormal battery cell 212A. Therefore, the analysis process searches for abnormal battery cells 212A in at least two phases: first, at the level of the battery rack or element 210A (whether the battery rack 210A contains the abnormal battery cell 212A); and second, at the level of the battery cells 212A-D within the battery rack or element 210A from the first phase (which battery cell 212A among the battery cells 212A-D is abnormal in this battery rack 210A).

[0035]

[0042] To facilitate this process, memory 335 includes a number of objects. In particular, performance monitoring programming 337 is programming that implements performance monitoring protocol 800.

[0036]

[0043] The extreme rack cell voltage value 339A is the most extreme voltage value of any battery cell 212A-D in a given battery rack 210A. The “extreme” voltage value may be a high voltage value or a low voltage value. Generally, the extreme voltage value is a high voltage value at the end of charge (EOC) and a low voltage value at the end of discharge (EOD). Each functional battery rack 210A-G in the energy storage system 100 should have an extreme rack cell voltage value 339A-G. The extreme rack cell voltage value 339A may be more loosely identified as an extreme element cell performance value 339A, which may encompass performance values other than voltage, such as current or temperature, i.e., values that describe or result in the performance of any battery cell 212A-D in a given battery element 210A. For example, the “extreme” performance value may be a high temperature value or a low current value.

[0037]

[0044] The average extreme rack cell voltage value 341 is the average value of each extreme rack cell voltage value 339A-G. Each extreme rack cell voltage value 339A-G used in the average extreme rack cell voltage value 341 is collected from the same EOC or EOD to perform a one-to-one performance comparison. The average extreme rack cell voltage value 341 may be more loosely identified as the average extreme element cell performance value 341, which is the average value of each extreme element cell performance value 339A-G.

[0038]

[0045] Once the average extreme rack cell voltage value 341 is calculated, each extreme rack cell voltage value 339A-G is individually compared to the average extreme rack cell voltage value 341. If one extreme rack cell voltage value 339A substantially deviates, either by an excessively high voltage or an excessively low voltage, the battery rack 210A associated with that extreme rack cell voltage value 339A is identified as an abnormal battery rack with an abnormal battery rack identifier 343. A "substantial deviation," "excessively high voltage," or "excessively low voltage" may be as little as a one percent deviation from the average. Alternatively, each extreme element cell performance value 339A-G is individually compared to the average extreme element cell performance value 341, and an extreme element cell performance value 339A that substantially deviates from the average extreme element cell performance value 341 identifies an abnormal battery element with an abnormal battery element identifier 343.

[0039]

[0046] When the abnormal battery rack identifier 343 identifies an abnormal battery rack 210A or the abnormal battery element identifier 343 identifies an abnormal battery element 210A, the performance monitoring subsystem 112 locates the battery cell 212A that caused the battery rack or element 210A to be identified as abnormal. Cell voltage values 345A-D of each battery cell 212A-D in the abnormal rack or element 210A are recorded in memory 335, or alternatively, cell performance values 345A-D of each battery cell 212A-D in the abnormal rack or element 210A are recorded in memory. Based on the cell voltage values 345A-D, an average rack cell voltage value 347A is calculated, which is the average of the cell voltage values 345A-D. Alternatively, based on the cell performance values 345A-D, an average element cell performance value 347A is calculated, which is the average of the cell performance values 345A-D.

[0040]

[0047] Once the cell voltage values 345A-D or cell performance values 345A-D are collected from a given EOD or EOC of the charge cycle and the average rack cell voltage value 347A or average element cell performance value 347A is calculated, any battery cell 212 with a substantial deviation is identified as an abnormal battery cell 212A and an abnormal battery cell identifier 349A is stored in memory 335. The charge cycle may be initiated at any point in the charge cycle, or the charge cycle may begin by discharging the energy storage system 100 or a subcomponent, reaching EOD, charging the energy storage system 100, and reaching EOC. Alternatively, the charge cycle may begin by charging the energy storage system 100, reaching EOC, discharging the energy storage system 100, and reaching EOD. Notably, when the energy storage system 100 is initialized, the state of charge of any battery cell 212 may be unknown, and in some cases, the battery cell 212 may be partially charged. An operator may choose to run the energy storage system 100 towards EOD or towards EOC in order to synchronize all of the battery cells 212 at the same EOC and EOD points.

[0041]

[0048] Multiple abnormal battery cells 212A-B may be identified, multiple abnormal battery cell identifiers 349A-B may be stored, and similarly, multiple abnormal battery racks 210A-B may be identified at once when inspecting an abnormal battery rack 210A. Generally, barring external interference such as physical damage to the energy storage system 100, only one battery cell 212A in one battery rack 210A will be abnormal at a time. When a single abnormal battery cell 212A is identified, personnel often take immediate action to repair or replace the abnormal battery cell 212A. In some implementations, the abnormal battery cell 212A or abnormal battery rack or element 210A may be taken out of operation or disconnected from the rest of the battery rack or element 210A, the battery node 110A, or the remaining set of battery nodes 110B-N. Doing so may allow the energy storage system 100 to fully charge and discharge at the expense of loss of capacity in disconnected battery cells 212A, battery racks or elements 210A, or battery nodes 110A.

[0042]

[0049] After an abnormal battery cell 212A is identified, an operator may be interested in the particular type of abnormal behavior that the battery cell 212A is exhibiting. Four such types of abnormal behavior may include high resistance, low capacity, high-side imbalance, and low-side imbalance. Battery cells with high resistance or low capacity are typically replaced, while unbalanced (high or low) battery cells may be repaired or rebalanced. To characterize an abnormal battery cell 212A into one of these categories, multiple data points must be collected.

[0043]

[0050] In a given charge cycle, the voltage of the battery cell 212A at EOC is stored as EOC cell voltage 351, and the voltage of all cells across the battery rack 210A at EOC is stored as EOC average rack voltage value 361. The voltage of the battery cell 212A at EOD is stored as EOD cell voltage 355, and the voltage of the entire battery rack 210A at EOD is stored as EOD average rack voltage value 365. The voltage of the battery cell 212A during mid-discharge (MOD) is stored as MOD cell voltage 353, and the voltage of the entire battery rack 210A at MOD is stored as EOD average rack voltage value 363.

[0044]

[0051] Identification of high resistance, low capacity, high-side imbalance, and low-side imbalance of battery cells 212 can be most effectively performed using voltages (i.e., EOC cell voltage 351, MOD cell voltage 353, EOD cell voltage 355, EOC average rack voltage value 361, MOD average rack voltage value 363, EOD average rack voltage value 365), although other similar performance values, such as current or temperature, may also be used. As a result, the performance monitoring system may store the EOC cell performance value 351, MOD cell performance value 353, EOD cell performance value 355, EOC average element performance value 361, MOD average element performance value 363, and EOD average element performance value 365 for a given charge cycle.

[0052]

[0045]

[0053] Although the figure shows storing a single copy of cell voltages or performance values 351, 353, 355 and rack voltages or element performance values 361, 363, 365, in some embodiments, all battery cells 212A-R and all battery racks or elements 210A-G have copies of these cell voltages or performance values 351, 353, 355 and rack voltages or element performance values 361, 363, 365 stored in memory 335. If all values are stored, a single charge cycle may be used to identify the characteristics of the abnormal rack 210A, the abnormal cell 212A, and the abnormal cell 212A.

[0046]

[0054] 4 is a diagram of multiple battery cells 212A-L with subsets of battery cells 212A, D, G, J in various classifications of abnormal conditions. Each battery cell 212A-L is configured to generally target three voltages: 3.6 volts at EOC, 3.2 volts at MOD, and 2.8 volts at EOD.

[0047]

[0055] To characterize the problem type of the abnormal battery cells 212A, D, G, J, cell voltage differences at different points in the cycle are compared. Voltage differences are calculated at EOC, EOD, and mid-discharge (MOD). The voltage differences are calculated by comparing the EOC cell voltage 351 to the average EOC cell voltage 361 of all cells in rack 361, the MOD cell voltage 353 to the average MOD cell voltage of all cells in rack 363, and the EOD cell voltage 355 to the average EOD cell voltage of all cells in rack 365. The abnormal battery cells 212A, D, G, J are classified into four types: high resistance, low capacity, high imbalance, or low imbalance.

[0048]

[0056] A high-resistance battery cell 212A has a greater resistance to the flow of electrons or ions. According to Ohm's law (V = IR), a higher resistance means a greater voltage drop or rise (depending on whether the battery cell 212A is discharging or charging). A high-resistance battery cell 212A reaches its cutoff voltage earlier due to a shift in the voltage curve, limiting the full utilization of the capacity of the battery cell 212A and preventing the other battery cells 212B-L from fully cycling. Whenever a load is applied to the high-resistance battery cell 212A, the voltage deviates significantly from the average cell voltage, i.e., there are larger voltage differences at EOC, EOD, and MOD.

[0049]

[0057] Battery cell 212A may be recognized as exhibiting high resistance, with its EOC value being greater than the other two EOC values in battery rack 210H, its MOD value being lower than the other two MOD values in battery rack 210H, and its EOD value being lower than the other two EOD values in battery rack 210H.

[0050]

[0058] The low-capacity battery cell 212D has fewer ions available to participate in charge transfer. Low capacity can be caused by several degradation mechanisms, including active material loss, SEI formation, or lithium plating, or by manufacturing defects. The low-capacity battery cell 212D inherently has a lower capacity than the other battery cells 212E-F in the battery rack 210I. This battery cell 212D reaches the voltage cutoff limit sooner than the other battery cells 212E-F, thereby restricting the other battery cells 212E-F from fully cycling. The low-capacity battery cell 212D is detected by a large voltage difference at EOC and EOD and a low voltage difference at MOD.

[0051]

[0059] Battery cell 212D may be recognized as exhibiting low capacity, with its EOC value being greater than the other two EOC values in battery rack 210I, its MOD value being close to the other two MOD values in battery rack 210I, and its EOD value being lower than the other two EOD values in battery rack 210I.

[0052]

[0060] The imbalanced battery cell 212G,J is a battery cell 212G,J that is more charged or discharged than the other battery cells 212H-I, 212K-L in the battery racks 210J-K. The highly unbalanced battery cell 212G can be observed as having a higher voltage than the other battery cells 212H-I in the battery rack 210J throughout the cycle, even during the rest phase. The low unbalanced battery cell 212J can be observed as having a lower voltage than the other battery cells 212K-L in the battery rack 210K throughout the cycle, even during the rest phase.

[0053]

[0061] The highly unbalanced battery cell 212G is more charged than the other battery cells 212H-I, has a higher voltage, and reaches the upper voltage limit of the battery cell 212G sooner than the other battery cells 212H-I, thereby limiting the total charge capacity of the battery rack 210J. Conversely, the more discharged unbalanced battery cell 212J has a lower voltage, and reaches the lower voltage limit sooner, limiting the total discharge capacity of the battery rack 210K.

[0054]

[0062] The battery cell 212G may be recognized as exhibiting high imbalance, with the EOC value of the battery cell 212G being greater than the EOC values of the other two in the battery rack 210J and the EOD value being close to the EOD values of the other two in the battery rack 210J. Note that the MOD value is generally not used to determine the high imbalance battery cell 212G.

[0055]

[0063] Battery cell 212J may be recognized as exhibiting low imbalance, with the EOC value of battery cell 212G being close to the EOC values of the other two in battery rack 210K and the EOD value being lower than the EOD values of the other two in battery rack 210K. Note that the MOD value is generally not used to determine low imbalance battery cell 212J.

[0056]

[0064] 5 is a graph of the cell voltage over time for the abnormal battery cell 212A classified as a high resistance cell compared to the non-abnormal battery cells 212B to C. As described in FIG. 4, it can be seen that, over time, the voltage peak at EOC is higher for the abnormal battery cell 212A, the voltage at MOD is lower for the abnormal battery cell 212A, and the voltage valley at EOD is lower for the abnormal battery cell 212A.

[0057]

[0065] 6 is a graph of the cell voltage over time for the abnormal battery cell 212D classified as a low-capacity cell compared to the non-abnormal battery cells 212E-F. As described in FIG. 4, it can be seen that, over time, the voltage peak at EOC is higher for the abnormal battery cell 212D, the voltage at MOD matches the average for the abnormal battery cell 212D, and the voltage valley at EOD is lower for the abnormal battery cell 212D.

[0058]

[0066] 7 is a graph of the cell voltages over time for two abnormal battery cells 212G and 212J classified as a high imbalance cell 212G and a low imbalance cell 212J, compared to non-abnormal battery cells 212H-I and 212K-L. As described in FIG. 4, it can be seen that, over time, the voltage peaks at EOC are higher for the abnormal battery cell 212G, and the voltage valleys at EOD are closer to the average for the abnormal battery cell 212G. In addition, the voltage peaks at EOC are closer to the average for the abnormal battery cell 212J, and the voltage valleys at EOD are lower for the abnormal battery cell 212J.

[0059]

[0067] 1-7 illustrate a battery cell performance monitoring system 1 including an energy storage system 100 including at least one inverter 104 and a plurality of battery nodes 110A-N. Each of the battery nodes 110A includes a plurality of battery racks 210A-F. Each of the battery racks 210A includes a respective plurality of battery cells 212A-N. The battery cell performance monitoring system 1 also includes a processor 330 and a memory 335 coupled to the processor 330. The memory includes performance monitoring programming 337 that, when executed, configures the battery cell performance monitoring system 1 to perform the following functions: First, determine extreme rack cell voltage values 339A-G of each of the battery racks 210A-G; and Second, determine an average extreme rack cell voltage value 341 based on the extreme rack cell voltage values 339A-G of each of the battery racks 210A-G. Third, the extreme rack cell voltage values 339A-G for each battery rack 210A-G are compared with the average extreme rack cell voltage value 341. Fourth, based on the comparison of the extreme rack cell voltage values 339A-G for each battery rack 210A-G with the average extreme rack cell voltage value 341, one or more abnormal battery racks 210A are identified.

[0060]

[0068] The determination of the extreme rack cell voltage value 339A may be based on the charging portion or the discharging portion of a cycle, or a combination thereof. The cycle includes charging the battery cell 212A from the energy source 102 and discharging the battery cell 212A to power the connected load 106. The determination of the extreme rack cell voltage value 399A may occur at the end of the charging portion of the cycle, or the determination of the extreme rack cell voltage value occurs at the end of the discharging portion of the cycle.

[0061]

[0069] The extreme rack cell voltage value 339A may represent an excessively high voltage compared to the other battery racks 210B-G in the energy storage system 100, where the excessively high voltage may be only one percent higher than the voltage of the other battery racks 210B-G in the energy storage system 100. The extreme rack cell voltage value 339A may represent an excessively low voltage compared to the other battery racks 210B-G in the energy storage system 100, where the excessively low voltage may be only one percent lower than the voltage of the other battery racks 210B-G in the energy storage system 100.

[0062]

[0070] Execution of the performance monitoring programming 337 may further configure the battery cell performance monitoring system 1 to achieve the following functions: First, determine an average rack cell voltage value 347A for each battery rack 210A. Second, compare the cell voltage values 345A-D for each battery cell 212A-D of the plurality of battery cells 212A-D in each battery rack 210A with the average rack cell voltage value 347A. Third, identify one or more abnormal battery cells 212A based on the comparison of the cell voltage values 345A-D for each battery cell 212A-D with the average rack cell voltage value 347A.

[0063]

[0071] Any averaging method may be used in calculating the average extreme rack cell voltage value 341 or the average rack cell voltage value 347A, including mean, median, mode, and leave-one-out averaging (e.g., using battery cells 212B-D to calculate the average against which battery cell 212A is compared, and using battery cells 212A, C-D, etc. to calculate the average against which battery cell 212B is compared). Further determinations may be made to exclude or weight different values in the averaging. The average may also be an expected performance value based on manufacturer specifications for battery cells 212A-D. The expected performance value may be modified by a degradation function that, for example, expects a 10% loss of functionality per year of use of battery cells 212A-D or a 1% loss of functionality per 1000 charging cycles.

[0064]

[0072] Execution of the performance monitoring programming 337 may further configure the battery cell performance monitoring system 1 to achieve the following functions: First, determine an end-of-charge (EOC) cell voltage 351, an end-of-discharge (EOD) cell voltage 355, and a mid-discharge (MOD) cell voltage 353 of a first abnormal cell 212A of the one or more abnormal battery cells 212A; Second, determine an EOC average rack cell voltage value 361, an EOD average rack cell voltage value 365, and a MOD average rack cell voltage value of each battery rack 363; Third, characterize the first abnormal cell 212A as a high-resistance cell in response to the EOC cell voltage 351 substantially deviating from the EOC average rack cell voltage value 361, the EOD cell voltage 355 substantially deviating from the EOD average rack cell voltage value 365, and the MOD cell voltage 353 substantially deviating from the MOD average rack cell voltage value 363.

[0065]

[0073] Fourth, characterize the first abnormal cell 212D as a low capacity cell in response to the EOC cell voltage 351 substantially deviating from the EOC average rack cell voltage value 361, the EOD cell voltage 355 substantially deviating from the EOD average rack cell voltage value 365, and the MOD cell voltage 253 being substantially similar to the MOD average rack cell voltage value 363.

[0066]

[0074] Fifth, characterize the first abnormal cell 212G as an unbalanced cell in response to the EOC cell voltage 351 substantially deviating from the EOC average rack cell voltage value 361 and the EOD cell voltage 355 being substantially similar to the EOD average rack cell voltage value 365.

[0067]

[0075] 1-7 also show a battery cell performance monitoring system 1 including an energy storage system 100 including at least one inverter 104 and a plurality of battery nodes 110A-N. Each of the battery nodes 110A includes a plurality of battery elements 210A-F. Each of the battery elements 210A includes a respective plurality of battery cells 212A-N. The battery cell performance monitoring system 1 also includes a processor 330 and a memory 335 coupled to the processor 330. The memory includes performance monitoring programming 337 that, when executed, configures the battery cell performance monitoring system 1 to perform the following functions: First, determine extreme element cell performance values 339A-G of each of the battery elements 210A-G; and Second, determine an average extreme element cell performance value 341 based on the extreme element cell performance values 339A-G of each of the battery elements 210A-G. Third, the extreme element cell performance values 339A-G for each battery element 210A-G are compared with the average extreme element cell performance value 341. Fourth, based on the comparison of the extreme element cell performance values 339A-G for each battery element 210A-G with the average extreme element cell performance value 341, one or more abnormal battery elements 210A are identified.

[0068]

[0076] The determination of the extreme element cell performance value 339A may be based on the charge portion or the discharge portion of a cycle, or a combination thereof. The cycle includes charging the battery cell 212A from the energy source 102 and discharging the battery cell 212A to power the connected load 106. The determination of the extreme element cell performance value 399A may occur at the end of the charge portion of the cycle, or the determination of the extreme element cell performance value occurs at the end of the discharge portion of the cycle.

[0069]

[0077] The extreme element cell performance value 339A may represent an excessively high voltage, current flow, temperature, or other property compared to the other battery elements 210B-G in the energy storage system 100, where an excessively high voltage may be as little as one percent higher than the voltages of the other battery elements 210B-G in the energy storage system 100. The extreme element cell voltage value 339A may represent an excessively low voltage, current flow, temperature, or other property compared to the other battery elements 210B-G in the energy storage system 100, where an excessively low voltage may be as little as one percent lower than the voltages of the other battery elements 210B-G in the energy storage system 100.

[0070]

[0078] Execution of the performance monitoring programming 337 may further configure the battery cell performance monitoring system 1 to achieve the following functions: First, determine an average element cell performance value 347A for each battery element 210A. Second, compare the cell performance values 345A-D for each battery cell 212A-D of the plurality of battery cells 212A-D of each battery element 210A with the average element cell performance value 347A. Third, identify one or more abnormal battery cells 212A based on the comparison of the cell performance values 345A-D of each battery cell 212A-D with the average element cell performance value 347A.

[0071]

[0079] Any averaging method may be used in calculating the average extreme element cell performance value 341 or the average element cell performance value 347A, including calculation of the mean, median, mode, and "leave one out" averaging (e.g., using battery cells 212B-D to calculate the average against which battery cell 212A is compared, and using battery cells 212A, C-D, etc. to calculate the average against which battery cell 212B is compared). Further determinations may be made to exclude or weight different values in the averaging. The average may also be an expected performance value based on manufacturer specifications for battery cells 212A-D. The expected performance value may be modified by a degradation function that, for example, expects a 10% loss of functionality per year of use of battery cells 212A-D or a 1% loss of functionality per 1000 charging cycles.

[0072]

[0080] Execution of the performance monitoring programming 337 may further configure the battery cell performance monitoring system 1 to achieve the following functions: First, determine an end-of-charge (EOC) cell performance value 351, an end-of-discharge (EOD) cell performance value 355, and a mid-discharge (MOD) cell performance value 353 for a first abnormal cell 212A of the one or more abnormal battery cells 212A; Second, determine an EOC average element cell performance value 361, an EOD average element cell performance value 365, and a MOD average element cell performance value for each battery element 363; Third, characterize the first abnormal cell 212A as a high resistance cell in response to the EOC cell performance value 351 substantially deviating from the EOC average element cell performance value 361, the EOD cell performance value 355 substantially deviating from the EOD average element cell performance value 365, and the MOD cell performance value 353 substantially deviating from the MOD average element cell performance value 363.

[0073]

[0081] Fourth, characterize the first abnormal cell 212D as a low capacity cell in response to the EOC cell performance value 351 substantially deviating from the EOC average element cell performance value 361, the EOD cell performance value 355 substantially deviating from the EOD average element cell performance value 365, and the MOD cell performance value 253 being substantially similar to the MOD average element cell performance value 363.

[0074]

[0082] Fifth, characterize the first abnormal cell 212G as an unbalanced cell in response to the EOC cell performance value 351 substantially deviating from the EOC average element cell performance value 361 and the EOD cell performance value 355 being substantially similar to the EOD average element cell performance value 365.

[0075]

[0083] 8 is a flow chart illustrating the cell and element performance monitoring protocol 800. The battery cell performance monitoring system 1 implements the performance monitoring protocol 800 to determine battery elements or racks 210 and battery cells 212 that have abnormal performance.

[0076]

[0084] The energy storage system 100 first performs a complete charge / discharge cycle. The test data is stored in the performance monitoring subsystem 112. In some examples, the test data is uploaded via Ethernet to a central data acquisition system (DAS) that implements some of the functionality of the performance monitoring subsystem 112 and the performance monitoring protocol 800. An analysis script pulls the data for analysis, and two analysis processes are performed: calculating capacity and energy, and identifying faulty cells.

[0077]

[0085] By integrating current with respect to time, capacity in ampere-hours is calculated. By integrating power (voltage x current) with respect to time, energy in watt-hours is calculated. In this analysis method, the performance monitoring protocol 800 distinguishes between charge and discharge steps and calculates the charge and discharge capacities and energies separately. The capacities and energies of both the battery node 110A and the battery elements or racks 210A are calculated using this method. However, the capacity of the battery elements is calculated only for battery nodes 110A identified as having a calculated low capacity. To perform the analysis, battery node / element data is first pulled into an analysis script in the performance monitoring subsystem 112. The start and stop times of charge and discharge are identified, and then the current is integrated to obtain capacity and the power is integrated to obtain energy. The analysis is performed in parallel to analyze multiple battery nodes / elements simultaneously and reduce computation time.

[0078]

[0086] The capacity data of the battery nodes 110A-N and battery elements 210A-N is then used to identify the abnormal battery cell 212A. The abnormal battery cell 212A limits the capacity of the battery node 110A and the battery element 210A as a whole because charging or discharging will terminate if a single battery cell 212A reaches an upper or lower voltage limit for the battery node 110A, battery element 210A, or energy storage system 100. However, because there may be hundreds of battery cells 212A-N in a single battery node 110A and multiple nodes in the energy storage system 100, analyzing all the battery cell data is computationally expensive. Instead, the battery element or rack 210A containing the abnormal cell is first identified, and then the battery cell data for only those problematic battery elements 210A is examined in detail.

[0079]

[0087] The element 210A containing the abnormal cells is identified using the battery element's maximum and minimum cell voltage data, also referred to as the extreme element cell performance value 339A. The element maximum / minimum cell voltage data points (or extreme element cell performance values 339A) record the voltage of the battery cell 212A with the highest or lowest voltage among all the battery cells 212A-N in the battery element 210A. In the battery element 210C containing non-abnormal cells, the battery cells with the highest and lowest voltages will constantly vary among the multiple battery cells. This is because all the battery cells in the battery element 210C have nearly the same voltage. In the element 210A containing the abnormal cells, a single cell 212A may consistently have the highest and / or lowest voltage, indicating that the battery cell 212A has different performance from the other battery cells 212B-N.

[0080]

[0088] An abnormal battery cell 212A is most apparent at end-of-charge (EOC) and / or end-of-discharge (EOD). This is because a low-capacity, high-resistance, or unbalanced battery cell reaches the cutoff voltage before the other healthy battery cells 212B-N. EOC may be defined as 10 minutes before charge to 10 minutes after charge, and EOD may be defined as 10 minutes before discharge to 10 minutes after discharge. The maximum and minimum cell voltages (or extreme rack cell voltage values 339A) of each battery rack 210A-N at EOC and EOD are examined. Due to the nature of the voltage curve, abnormalities are also more apparent at EOC / EOD. A particular battery typically has a long plateau mid-discharge where the voltage changes very little despite the battery capacity potentially changing significantly.

[0081]

[0089] To determine whether the cell voltages of one battery rack 210A are "significantly different" from those of the other battery racks 210B-N, the maximum and minimum cell voltages of each battery rack 210A-N are compared to the average maximum and minimum cell voltages of all racks at the node. The difference between the maximum cell voltage of each battery rack 210A-F at EOC and the average rack maximum cell voltage at EOC, and the difference between the minimum cell voltage of each battery rack 210A-F at EOD and the average rack minimum cell voltage at EOD, are calculated. If the difference at EOC is greater than 80 millivolts (mV) or the difference at EOD is greater than 50 mV, battery rack 210A is flagged as containing an abnormal cell 212A. Note that these values are subjective. Lower or higher thresholds may be used depending on the user's definition of "significantly different" cell voltages.

[0082]

[0090] Using only the maximum / minimum cell voltages would result in identifying at most one or two abnormal cells 212A in a single battery rack (depending on whether the same or different cells are abnormal at EOC vs. EOD). Using individual cell voltage data, all abnormal cells 212A in the battery rack 210A can be identified. In this process, the voltage of each battery cell 212A-N is compared to the average cell voltage 347A for the entire battery rack 210A. The difference between the cell voltage and the average cell voltage is calculated at EOC and EOD, when this difference is most significant. If the difference at EOC is greater than 80 mV or the difference at EOD is greater than 50 mV, the battery cell 212A is flagged as abnormal.

[0083]

[0091] To characterize the problem type of the abnormal cell 212A, the cell voltage differences at different points in the cycle are compared. The voltage differences are calculated at EOC, EOD, and mid-discharge (MOD). The abnormal battery cells 212A are classified into four types: high resistance, low capacity, high imbalance, or low imbalance.

[0084]

[0092] Although the above description discloses an analysis based on performance values related to voltage, similar analyses may be performed using performance values based on other properties such as current flow or temperature.

[0085]

[0093] Therefore, to identify an abnormal battery rack 210A, in step 805, the performance monitoring protocol 800 determines the extreme element cell performance value 339A of each battery element 210A of the multiple battery elements 210A-N. In step 810, the performance monitoring protocol 800 determines an average extreme element cell performance value 341 based on the extreme element cell performance values 339A-G of each battery element 210A-G. In step 815, the performance monitoring protocol 800 compares the extreme element cell performance values 339A-G for each battery element 210A-G with the average extreme element cell performance value 341. In step 820, the performance monitoring protocol 800 identifies one or more abnormal battery racks 210A based on the comparison of the extreme element cell performance values 339A-G for each battery element 210A-G with the average extreme element cell performance value 341.

[0086]

[0094] Next, to identify abnormal battery cells 212A, in step 825, the performance monitoring protocol 800 determines the average element cell performance value 347A for each battery rack 210A. In step 830, the performance monitoring protocol 800 compares the cell performance value 345A for each battery cell 212A of the multiple battery cells 212A-D in each battery element 210A with the average element cell performance value 347A. In step 835, the performance monitoring protocol 800 identifies one or more abnormal battery cells 212A based on the comparison of the cell performance values 345A-D of each battery cell 212A-D with the average element cell performance value 347A.

[0087]

[0095] To characterize the abnormal battery cell 212A, in step 840, the performance monitoring protocol 800 determines the end-of-charge (EOC) cell performance value or voltage 351, the end-of-discharge (EOD) cell performance value or voltage 355, and the mid-discharge (MOD) cell performance value or voltage 353 of a first abnormal cell 212A of the one or more abnormal battery cells 212A. In step 845, the performance monitoring protocol 800 determines the EOC average element cell performance value 361, the EOD average element cell performance value 365, and the MOD average element cell performance value 363 of each battery element 210A. Finally, to characterize the abnormal battery cell 212A, the performance monitoring protocol 800 proceeds through steps 850, 855, and 860 to attempt to characterize the first abnormal cell 212A as either a high-resistance cell, a low-capacity cell, or an unbalanced cell.

[0088]

[0096] Alternatively, steps similar to steps 805, 810, 815, and 820 may be performed first before step 805, but in these alternative steps, abnormal battery nodes are identified based on extreme node cell performance values. Specifically, these steps include: first, determining an extreme node element performance value for each battery node of the plurality of battery nodes; second, determining an average extreme node element performance value based on the extreme node element performance values of each battery node; third, comparing the extreme node element performance value for each battery node with the average extreme node element performance value; and fourth, identifying one or more abnormal battery nodes. Once one or more abnormal battery nodes are identified, step 805 proceeds, but only among the battery elements of the abnormal battery nodes.

[0089]

[0097] The scope of protection is limited only by the claims that follow. That scope is intended, and should be interpreted, as broad as possible consistent with the ordinary meaning of the claim language used when interpreted in light of this specification and subsequent prosecution history, to encompass all structural and functional equivalents. However, no claim is intended, and should not be interpreted, to encompass subject matter that does not satisfy the requirements of 35 U.S.C. §§ 101, 102, or 103. Any unintended inclusion of such subject matter is hereby disclaimed.

[0090]

[0098] Except as immediately stated above, nothing described or shown is intended or should be construed as resulting in the offering to the public of any element, step, feature, object, benefit, advantage, or equivalent, whether or not claimed.

[0091]

[0099] It will be understood that terms and expressions used herein have the ordinary meanings ascribed to such terms and expressions with respect to their corresponding fields of inquiry and study, unless a specific meaning is otherwise stated herein. Relational terms, such as first and second, may be used merely to distinguish one entity or operation from another, without necessarily requiring or implying any actual relationship or order between such entities or operations. Terms such as "comprises," "comprising," "includes," "including," or any other variation thereof, are intended to cover non-exhaustive inclusions; thus, a process, method, article, or apparatus that comprises or includes a list of elements or steps may not only include those elements or steps, but may also include other elements or steps not expressly listed or inherent in such process, method, article, or apparatus. An element preceded by "a" or "an" does not, unless further constraints exist, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0092]

[0100] Additionally, in the foregoing Detailed Description, it will be seen that various features are grouped together in various instances for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed instances require more features than are expressly recited in each claim. Rather, as the appended claims reflect, protected subject matter consists in less than all features of any single disclosed instance. Accordingly, the appended claims are hereby incorporated into the Detailed Description, with each claim standing on its own as separately claimed subject matter.

[0093]

[0101] While the foregoing describes what is considered to be the best mode and / or other examples, it is understood that various modifications may be made therein and that the subject matter disclosed herein may be embodied in a variety of forms and examples and may be applied in numerous applications, only some of which are described herein. It is intended by the appended claims to claim any and all modifications and variations that fall within the true scope of the present concept. [Explanation of symbols]

[0094] 1... Battery cell performance monitoring system, 100... Energy storage system, 102... Energy source, 104... Power inverter, 106... Connected load, 110A-N... Battery node, 112... Performance monitoring subsystem, 210A-K... Battery element (e.g., battery rack), 212A-R... Battery cell (e.g., battery module), 330... Processor, 332... Network interface, 335... Memory, 337... Performance monitoring programming, 339A-G... Extreme element cell performance value (e.g., extreme rack cell voltage value), 341... Average extreme element cell performance value (e.g., average extreme rack cell voltage value), 343... Abnormal battery element identifier (e.g., abnormal battery rack identifier), 345A-D... Cell performance value, 347A... Average element cell performance value, 349A-B... Abnormal battery cell identifier Other symbols include: 351...end of charge (EOC) cell performance value (e.g., EOC cell voltage), 353...mid-discharge (MOD) cell performance value (e.g., MOD cell voltage), 355...end of discharge (EOD) cell performance value (e.g., EOD cell voltage), 361...EOC element performance value (e.g., EOC rack voltage), 363...MOD element performance value (e.g., MOD rack voltage), 365...EOD element performance value (e.g., EOD rack voltage), 501...high resistance peak, 502...high resistance plateau, 503...high resistance valley, 601...low capacity peak, 602...low capacity plateau, 603...low capacity valley, 701...high imbalance peak, 702...high imbalance plateau, 703...high imbalance valley, 751...low imbalance peak, 752...low imbalance plateau, 753...low imbalance valley, and 800...performance monitoring protocol.

Claims

1. A battery cell performance monitoring system, comprising: An energy storage system including at least one inverter and a plurality of battery nodes, each of the battery nodes includes a plurality of battery racks; each of the battery racks includes a respective plurality of battery cells; an energy storage system; a processor; a memory coupled to the processor; Equipped with the memory comprises performance monitoring programming, the performance monitoring programming, when executed, Determine the extreme rack cell voltage values for each battery rack; determining an average extreme rack cell voltage value based on the extreme rack cell voltage values of each battery rack; comparing the extreme rack cell voltage value for each battery rack with the average extreme rack cell voltage value; Identifying one or more abnormal battery racks based on the comparison between the extreme rack cell voltage value for each battery rack and the average extreme rack cell voltage value. configuring the battery cell performance monitoring system to achieve functionality including a function; Battery cell performance monitoring system.

2. 10. The battery cell performance monitoring system of claim 1, wherein the determination of the extreme rack cell voltage value is based on a charge portion or a discharge portion of a cycle, or a combination thereof.

3. 3. The battery cell performance monitoring system of claim 2, wherein said determination of said extreme rack cell voltage value occurs at the end of said charging portion.

4. 4. The battery cell performance monitoring system of claim 3, wherein the extreme rack cell voltage value represents an excessively high voltage compared to other battery racks in the energy storage system.

5. 3. The battery cell performance monitoring system of claim 2, wherein said determination of said extreme rack cell voltage value occurs at the end of said discharge portion.

6. 6. The battery cell performance monitoring system of claim 5, wherein the extreme rack cell voltage value represents an excessively low voltage compared to other battery racks in the energy storage system.

7. Execution of the performance monitoring programming includes: determining the average rack cell voltage value for each battery rack; comparing a cell voltage value for each of the plurality of battery cells in each battery rack with the average rack cell voltage value; Identifying one or more abnormal battery cells based on the comparison of the cell voltage value of each battery cell with the average rack cell voltage value. The battery cell performance monitoring system of claim 1 , further configured to implement functionality including:

8. Execution of the performance monitoring programming includes: determining an end-of-charge (EOC) cell voltage, an end-of-discharge (EOD) cell voltage, and a mid-discharge (MOD) cell voltage of a first abnormal cell among the one or more abnormal battery cells; determining an EOC average rack cell voltage value, an EOD average rack cell voltage value, and a MOD average rack cell voltage value for each battery rack; i) the EOC cell voltage deviates substantially from the EOC average rack cell voltage value; ii) the EOD cell voltages deviate substantially from the EOD average rack cell voltage values; iii) the MOD cell voltages deviate substantially from the MOD average rack cell voltage value; In response, characterizing the first abnormal cell as a high resistance cell. The battery cell performance monitoring system of claim 7 , further configured to implement functionality including:

9. Execution of the performance monitoring programming includes: determining an end-of-charge (EOC) cell voltage, an end-of-discharge (EOD) cell voltage, and a mid-discharge (MOD) cell voltage of a first abnormal cell among the one or more abnormal battery cells; determining an EOC average rack cell voltage value, an EOD average rack cell voltage value, and a MOD average rack cell voltage value for each battery rack; i) the EOC cell voltage deviates substantially from the EOC average rack cell voltage value; ii) the EOD cell voltages deviate substantially from the EOD average rack cell voltage values; iii) the MOD cell voltage is substantially similar to the MOD average rack cell voltage value; In response, characterizing the first abnormal cell as a low capacity cell. The battery cell performance monitoring system of claim 7 , further configured to implement functionality including:

10. Execution of the performance monitoring programming includes: determining an end-of-charge (EOC) cell voltage and an end-of-discharge (EOD) cell voltage of a first abnormal cell of the one or more abnormal battery cells; determining an EOC rack voltage and an EOD rack voltage for each battery rack; i) the EOC cell voltage deviates substantially from the EOC rack voltage and the EOD cell voltage is substantially similar to the EOD rack voltage, or The EOC cell voltage is substantially similar to the EOC rack voltage, and the EOD cell voltage deviates from the EOD rack voltage. In response to this, characterizing the first abnormal cell as an unbalanced cell. The battery cell performance monitoring system of claim 7 , further configured to implement functionality including:

11. determining an extreme element cell performance value for each battery element of the plurality of battery elements; determining an average extreme element cell performance value based on the extreme element cell performance values of each battery element; comparing the extreme element cell performance value for each battery element with the average extreme element cell performance value; identifying one or more abnormal battery elements based on the comparison of the extreme element cell performance value and the average extreme element cell performance value for each battery element; A method comprising:

12. 12. The method of claim 11, wherein the determination of the extreme element cell performance value is based on a charge portion or a discharge portion of a cycle, or a combination thereof.

13. The method of claim 12 , wherein the determination of the extreme element cell performance value occurs at the end of the charging portion.

14. 14. The method of claim 13, wherein the extreme element cell performance value represents a disproportionately high voltage compared to other battery racks in the energy storage system.

15. The method of claim 12 , wherein the determination of the extreme element cell performance value occurs at the end of the discharge portion.

16. determining an average elemental cell performance value for each battery element; comparing a cell performance value for each of the plurality of battery cells of the respective battery element with the average element cell performance value; identifying one or more abnormal battery cells based on the comparison of the cell performance value of each battery cell with the average element cell performance value; The method of claim 11 further comprising:

17. determining an end-of-charge (EOC) cell performance value, an end-of-discharge (EOD) cell performance value, and a mid-discharge (MOD) cell performance value of a first abnormal cell among the one or more abnormal battery cells; determining an EOC average element cell performance value, an EOD average element cell performance value, and an MOD average element cell performance value for each of the battery elements; iv) the EOC cell performance value deviates substantially from the EOC average element cell performance value; v) the EOD cell performance value deviates substantially from the EOD average element cell performance value; vi) the MOD cell performance value deviates substantially from the MOD average element cell performance value. responsively characterizing the first abnormal cell as a high resistance cell; 17. The method of claim 16, further comprising:

18. determining an end-of-charge (EOC) cell performance value, an end-of-discharge (EOD) cell performance value, and a mid-discharge (MOD) cell performance value of a first abnormal cell among the one or more abnormal battery cells; determining an EOC average element cell performance value, an EOD average element cell performance value, and an MOD average element cell performance value for each of the battery elements; iv) the EOC cell performance value deviates substantially from the EOC average element cell performance value; v) the EOD cell performance value deviates substantially from the EOD average element cell performance value; vi) the MOD cell performance value is substantially similar to the MOD average element cell performance value responsively characterizing the first abnormal cell as a low capacity cell; 17. The method of claim 16, further comprising:

19. determining an end-of-charge (EOC) cell performance value and an end-of-discharge (EOD) cell performance value of a first abnormal cell of the one or more abnormal battery cells; determining an EOC element performance value and an EOD element performance value for each of the battery elements; ii) the EOC cell performance value deviates substantially from the EOC element performance value and the EOD cell performance value is substantially similar to the EOD element performance value; or iii) the EOC cell performance value is substantially similar to the EOC element performance value, and the EOD cell performance value deviates from the EOD element performance value; responsively characterizing the first abnormal cell as an unbalanced cell; 20. The method of claim 17, further comprising:

20. A battery cell performance monitoring system, comprising: An energy storage system including at least one inverter and a plurality of battery nodes, each of the battery nodes includes a plurality of battery elements; each of the battery elements includes a respective plurality of battery cells; an energy storage system; a processor; a memory coupled to the processor; Equipped with the memory comprises performance monitoring programming, the performance monitoring programming, when executed, determining an extreme element cell performance value for each battery element; determining an average extreme element cell performance value based on the extreme element cell performance values of each battery element; comparing the extreme element cell performance value for each battery element with the average extreme element cell performance value; Identifying one or more abnormal battery elements based on the comparison of the extreme element cell voltage value and the average extreme element cell performance value for each battery element. configuring the battery cell performance monitoring system to achieve functionality including a function; Battery cell performance monitoring system.

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