System and method for adaptive allocation and dc balancing in battery energy storage systems

Abstract

An energy storage system includes a plurality of energy storage nodes. The energy storage nodes include a battery storage element and a control subsystem to receive battery data from the battery storage element. The energy storage system also includes a power conversion system (PCS) and a control system coupled to the energy storage nodes and configured to receive or store a required power flow. The control subsystem, the control system, or both are configured to determine, from the battery data, a state of charge with respect to one or more of the energy storage nodes. The control system is configured to dispatch the required power flow across the plurality of energy storage nodes based on a DC link balancing algorithm activated in response to the state of charge exceeding a high SOC threshold or falling below a low SOC threshold.

Description

Cross-references to related applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 541,533, filed September 29, 2023, entitled “Systems and Methods of Adaptive Distribution and DC Balancing in Battery Energy Storage Systems,” the entire disclosure of which is incorporated herein by reference. Technical Field

[0002] This topic relates to an energy storage system comprising multiple energy storage nodes. This topic also covers the use of adaptive dispatch based on state of charge (SOC), power command, and DC link (DC bus) balancing to control the dispatch of energy storage nodes. Background Technology

[0003] Energy storage systems, such as battery energy storage systems (BESS), can be deployed in a distributed manner to meet safety and economic considerations. An energy storage system typically includes associated components, such as numerous energy storage nodes, each comprising one or more casings housing multiple batteries, and a power conversion system. Typically, an energy storage system includes a control system that monitors the energy storage nodes.

[0004] Current existing control systems rely on State of Charge (SOC) to allocate energy storage nodes. Typically, SOC is determined by a supplier-provided Battery Management System (BMS). For example, SOC can be determined by coulomb counts, and the BMS can provide a figure between 0 and 100% regarding whether the battery is full or empty. Unfortunately, existing control systems may fail to allocate power for charging or discharging energy storage nodes under edge-case conditions due to inaccurate assessments based solely on SOC. Therefore, existing energy storage systems may fail to charge energy storage nodes when there is remaining power storage capacity and may fail to discharge energy storage nodes when stored power is available.

[0005] A control system based on SOC, power command and DC link (DC bus) balance is needed to adaptively distribute energy storage nodes to optimize the efficiency and performance of BESS. Summary of the Invention

[0006] In a first example, an energy storage system 101 includes a plurality of energy storage nodes 105A to 105N. The plurality of energy storage nodes 105A to 105N include: a battery storage element 106; and a control subsystem 110 for receiving battery data 111A to 111N from the battery storage element 106. The energy storage system 101 also includes: a power conversion system (PCS) 104; and a control system 115 coupled to the plurality of energy storage nodes 105A to 105N and configured to receive or store a desired power flow 112. The control subsystem 110, the control system 115, or both are configured to determine a state of charge (SOC) 116A for one or more of the energy storage nodes 105A to 105N from the battery data 111A to 111N. The control system 115 is configured to distribute the required power flow 112 across multiple energy storage nodes 105A to 105N based on a DC link balancing algorithm 190 activated in response to a state of charge 116A exceeding a high SOC threshold 181 or falling below a low SOC threshold 182.

[0007] In the second example, a non-transitory computer-readable medium 313, 353 includes adaptive dispatch programming 330A to 330B. Execution of the adaptive dispatch programming 330A to 330B by one or more processors 312, 352 configures one or more controllers 110, 115, 170 to 173 to receive or store a desired power flow 112 for use in an electrical application 103. Execution of the adaptive dispatch programming 330A to 330B by one or more processors 312, 352 configures one or more controllers 110, 115, 170 to 173 to determine a state of charge (SOC) 116A with respect to one or more of the energy storage nodes 105A to 105N from battery data 111A to 111N. The execution of adaptive dispatch programming 330A to 330B by one or more processors 312, 352 configures one or more controllers 110, 115, 170 to 173 to dispatch the required power flow 112 across multiple energy storage nodes 105A to 105N based on a DC link balancing algorithm 190 activated in response to a state of charge 116A exceeding a high SOC threshold 181 or falling below a low SOC threshold 182. For example, a kW command is provided to the power inverter 205 of PCS 104, and inverter 205 requests current on the DC bus 225 of the first energy storage node 105A.

[0008] In a third example, a method 600 includes receiving or storing the required power flow 112 for use in an electrical application 103. The method also includes determining a state of charge (SOC) 116A for one or more of the energy storage nodes 105A to 105N from battery data 111A to 111N. The method further includes distributing the required power flow 112 across multiple energy storage nodes 105A to 105N based on a DC link balancing algorithm 190 activated in response to the SOC 116A exceeding a high SOC threshold 181 or falling below a low SOC threshold 182.

[0009] Additional objects, advantages, and novel features of the examples 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 text and the accompanying drawings, or may be learned by the generation or operation of the examples. The objects and advantages of this subject matter can be achieved and attained by means of the methods, means, and combinations particularly pointed out in the appended claims. Attached Figure Description

[0010] The accompanying drawings depict one or more implementations by way of example only, and not by way of limitation. In the figures, similar reference numerals refer to the same or similar elements.

[0011] Figure 1A It describes a system that includes energy storage systems, energy systems, and electrical applications.

[0012] Figure 1B Depicting Figure 1A The example architecture of the control system includes a battery array, an array controller, and a core controller.

[0013] Figure 1C Depicting Figures 1A to 1B The example architecture of the control system includes an array controller, a core controller, a node controller, and a shell controller.

[0014] Figure 2A This illustrates coupling to electrical applications. Figures 1A to 1C The first energy storage node among multiple energy storage nodes in an energy storage system.

[0015] Figure 2B The first energy storage node is shown, comprising multiple battery cubes and multiple power conversion systems coupled to a DC link (DC bus).

[0016] Figure 3A yes Figure 1A The diagram is a high-level functional block diagram of an energy storage system, which depicts the components of the control system and control subsystem for adaptive allocation of energy storage nodes.

[0017] Figure 3B yes Figures 1B to 1C Another high-level functional block diagram of the energy storage system depicts the components of a control system with various controllers for adaptive dispatch of energy storage nodes.

[0018] Figure 4A It is implemented by a control system, a control subsystem, and multiple energy storage nodes for... Figure 1A An adaptive dispatch protocol for energy storage systems.

[0019] Figure 4B It is implemented by various controllers of the control system and multiple energy storage nodes for... Figures 1B to 1C An adaptive allocation scheme for energy storage systems.

[0020] Figure 5 It is a cross-sectional view of the first energy storage node among multiple energy storage nodes and shows details of multiple battery storage elements.

[0021] Figure 6 This is a flowchart of a method for adaptive allocation that can be implemented in energy storage systems.

[0022] Figure 7 It is a graph of two curves representing the DC link voltage and state of charge, showing the two A2 regions (e.g., areas) activated by the DC link balancing algorithm through adaptive dispatch programming.

[0023] Figure 8 This is a block diagram of SOC-DC link balancing control with adaptive dispatch programming, illustrating the DC link balancing algorithm.

[0024] Figure 9 This is a flowchart of the DC link balancing control algorithm for adaptive dispatch programming.

[0025] Figure 10 This is a line graph illustrating an example relationship between a predefined threshold voltage and the DC link voltage between energy storage nodes that could lead to further activation of the DC link balancing algorithm.

[0026] Parts list Detailed Implementation

[0027] In the following detailed description, numerous specific details are illustrated by way of example to provide a thorough understanding of the teachings. However, it will be apparent to those skilled in the art that these teachings can be practiced without such details. In other instances, well-known methods, processes, components, and / or circuit systems have been described at a relatively high level without detail to avoid unnecessarily obscuring aspects of these teachings.

[0028] Unless otherwise instructed, any implementation scheme may be combined with any other implementation scheme. In particular, Figures 1A to 10 All related texts can be combined with each other.

[0029] As used herein, the term "coupled" refers to any logical, physical, electrical, or optical connection, link, etc., through which electrical power, signal, or light generated or supplied by one system element is imparted to another coupled element. Unless otherwise described, coupled elements or devices are not necessarily directly connected to each other and can be separated by intermediate components, elements, or communication media that can modify, manipulate, or transmit electrical power, signal, or light.

[0030] For illustrative and discussion purposes, the orientations of the system 100, energy storage system 101, energy storage nodes 105A to 105N, associated components, and / or any complete assembly, incorporating battery storage elements 106A to 106N (such as batteries, as shown in any of the accompanying drawings), associated components, and / or any complete assembly, are given by way of example only. In operation for a particular energy storage application, the energy storage nodes 105A to 105N may be oriented in any other orientation suitable for the particular application of the energy storage system 101, such as upright, sideways, or any other orientation. Furthermore, for the purposes of this document, any directional terms such as left, right, front, rear, back, end, upper, lower, upper part, lower part, top, bottom, and side are used by way of example only and do not limit the following orientations or directions: any energy storage system 101 or energy storage nodes 105A to 105N; or components of the energy storage system 101 or energy storage nodes 105A to 105N constructed as otherwise described herein.

[0031] Unless otherwise indicated, any coupled electrical components may be connected in series or in parallel. In the case of energy storage nodes 105A to 105N or battery storage elements 106A to 106N, components may be connected in series, in parallel, or in a combination thereof, depending on the state of the switch or submodule.

[0032] Now, let’s refer in detail to the examples shown in the accompanying drawings, and discuss them below.

[0033] Figure 1AA system 100 is described, comprising an energy storage system 101, an energy system 102, and an electrical application 103. Figure 1B Depicting Figure 1A The example architecture of the control system 115 includes a battery array 150, an array controller 170, and core controllers 171A to 171N.

[0034] refer to Figure 1A and Figure 1B Both, for example, the energy storage system 101 may be a battery energy storage system (BESS). The energy storage system 101 is coupled to the energy system 102 and the electrical application 103. The energy storage system 101 may include one or more power conversion systems (PCS) 104A to 104N, multiple energy storage nodes 105A to 105N, an optional transformer 108, and a control system 115. Components of the energy storage system 101 may be located in a physical space 120, either outdoors or indoors, for example, inside a building, container, or other structure.

[0035] Energy storage system 101 includes a battery array 150 comprising a plurality of battery cores 151A to 151N, the plurality of battery cores including a first group of battery cores 151A to 151C and a second group of battery cores 151D to 151F. Each of the battery cores 151A to 151N includes at least one power conversion system 104A to 104N. In an example, each battery core 151A to 151N (at the battery core level) may have one PCS 104 and one transformer 108.

[0036] As described in further detail below, the energy storage system 101 may include a control system 115, which includes one or more controllers 170 to 174, such as an array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, enclosure controllers 173A to 173N, and a market distribution unit controller 174. The control system 115 is configured to control the battery cores 151A to 151N to distribute the required power flow 112.

[0037] Power conversion systems 104A to 104N are coupled to multiple energy storage nodes 105A to 105N. Power conversion systems 104A to 104N are coupled to energy system 102 and electrical application 103 to provide the required power flow 112 to electrical application 103 by discharging the multiple energy storage nodes 105A to 105N, or to charge the multiple energy storage nodes 105A to 105N by providing the required power flow 112 from energy system 102. Power conversion systems 104A to 104N may be coupled to an optional transformer 108. The optional transformer 108 can step up or step down the required power flow 112 to and from electrical application 103, such as AC voltage.

[0038] Energy system 102 may include any suitable system for generating electrical energy from energy source 109. Energy system 102 may be a renewable energy system in which energy source 109 can be replenished. Such renewable energy source 109 may include solar, wind, geothermal, biomass, and hydropower. For example, renewable energy system 102 may be implemented as a photovoltaic (PV) module array. Photovoltaic (PV) modules may include crystalline silicon, amorphous silicon, copper indium gallium selenide (CIGS) thin films, cadmium telluride (CdTe) thin films, and concentrated photovoltaics that use lenses and curved mirrors to focus sunlight onto small but extremely efficient multi-junction solar cells. In another example, energy system 102 may include a wind turbine or a gas turbine. In some examples, energy system 102 may be a non-renewable energy system in which energy source 109 includes non-renewable energy sources such as fossil fuels.

[0039] Electrical application 103 may include a power grid (such as a power grid) for a facility (such as a hospital, manufacturing site, residential building, or other suitable facility) or a smaller local load (such as a backup power system). Electrical application 103 may deliver AC or DC power for grid-connected or off-grid applications, including commercial, industrial, or residential applications. Electrical application 103 may deliver power to buildings, electric vehicle charging stations, and other electrical loads that consume AC or DC power. Electrical application 103 may be a pre-meter system owned or operated by a utility company or a post-meter system that supplies power directly to buildings and homes.

[0040] Energy source 109 can be a renewable energy source, such as solar and wind power, which may be intermittent and less reliable compared to fossil fuels. To improve resilience, energy storage system 101 can store energy from energy system 102 when production from energy source 109 is high. Subsequently, energy storage system 101 can dispatch energy to electrical application 103 when demand is high or production from energy source 109 cannot keep up with demand. Furthermore, events may occur when the connected loads or operational demands of electrical application 103 are excessive or when grid instability exists, such as during extreme weather. By storing energy from energy source 109 and then dispatching energy during such events, energy storage system 101 can continue to dispatch the required power flow 112 of electrical application 103.

[0041] Energy storage nodes 105A to 105N include battery storage elements 106A to 106N. Battery storage elements 106A to 106N may be: (1) a single battery cell; (2) a group of cells comprising several battery cells arranged in parallel; (3) a battery submodule or module comprising several battery cells arranged in parallel and series; (4) a battery string comprising several battery modules connected in series; (5) a battery pack comprising several battery strings connected in parallel; (6) other known energy storage elements; and / or (7) combinations thereof. For example, battery storage elements 106A to 106N may comprise multiple batteries of any existing or future reusable battery technology, including but not limited to lithium-ion, flow batteries, or mechanical storage such as flywheel energy storage, compressed air energy storage, pumped storage, gravitational potential energy, or hydraulic accumulators.

[0042] The control system 115 can be implemented as an adaptive dispatch scheme 400 for adaptive dispatch control of a split bus power conversion system (PCS) with adaptive DC balancing characteristics (see [link]). Figure 4A(Refer to Figure B). The adaptive dispatch scheme 400 ensures that the state of charge 116A of energy storage nodes 105A to 105N is balanced at the desired speed and accuracy; and the adaptive balancing control ensures that the DC link 225 of the split bus PCS is balanced. SOC balancing control can be adaptive, which may mean that, based on SOC imbalance, power commands can be smoothly controlled to ensure that the SOC is balanced as quickly and smoothly as possible. In this design, adaptive dispatch is controlled proportionally to the SOC. The proportionally dispatched power is controlled based on the SOC imbalance level. Furthermore, a DC link balancing algorithm 190 is added to the adaptive dispatch scheme 400 to balance the DC bus 225. The DC link balancing algorithm 190 can be activated in two alternative scenarios. First, if a very high SOC threshold 181 or a very low SOC threshold 182 is reached. Second, if the DC link voltage 191 between energy storage nodes 105A to 105N begins to deviate from a predefined threshold voltage 192. Deviation from a predefined threshold voltage 192 may include identifying battery cells that are outliers in the low SOC threshold 182 and determining whether delta is greater than a standard delta. The high SOC threshold 181 and low SOC threshold 182 are the amplitude or intensity of the state of charge 116A and may trigger the activation of the DC link balancing algorithm 190. For example, the high SOC threshold 181 may be approximately 90%, approximately 95%, or approximately 98%; and the low SOC threshold 182 may be approximately 2%, approximately 5%, or approximately 10%.

[0043] The DC link balancing algorithm 190 operates in the same manner as SOC balancing. The DC link balancing algorithm can be adaptive and proportional to the DC links of the power inverter 205 modules in the PCS 104's split-bus architecture. Conventional SOC balancing assignment algorithms are either too strong or too weak in response to SOC imbalances. This can be a significant problem, as it may exacerbate DC link imbalances between the power inverter 205 modules of the PCS 104. Furthermore, due to various hardware and firmware issues, the DC links 225 of the power inverter 205 modules of the PCS 104 may become skewed. This can accelerate and amplify circulating current in the PCS 104, ultimately leading to failure.

[0044] Figure 1C Depicting Figures 1A to 1BThe example architecture of the control system 115 includes an array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N. In this example, each of the energy storage nodes 105A to 105N may be a collection of one or more battery cubes 230A to 230N, and each battery cube 230A to 230N includes an enclosure controller 173. Node controller 172 is the lowest controllable element for the battery core 151 of the energy storage nodes 105A to 105N and controls individual energy storage nodes 105. Core controller 171 is the next higher level that controls subgroups of energy storage nodes 105A to 105N, where each core represents a branch of a component of the energy storage system 101. Core controller 171 is a logic controller and may represent the transformer 108 located between the PCS 104 and the rest of the device. The core controller 171 is an aggregator of the different node controllers 172A to 172N and propagates commands from the array controller 170 to the node controllers 172A to 172N.

[0045] The array controller 170 is positioned above the core controllers 171A to 171N and controls the entire energy storage system 101. Software for the array controller level can be installed at the customer's installation site and can be executed there. The array controller 170 can be a locally distributed service operating in real-time in the field.

[0046] Market allocation unit controller 174 is a network-range controller located on top of array controller 170 and takes into account specific market requirements. Market allocation unit controller 174 sets allocation setpoints for active and reactive power to array controller 170, which in turn controls energy storage system 101.

[0047] The battery core 151 can have multiple node controllers 172A to 172N, depending on the number of energy storage nodes 105A to 105N and the bus architecture of the battery core 151. In the example, if PCS 104 is used as a single bus element, then for a single energy storage node 105A, there may be only one node controller 172 behind the core controller 171, and only one PCS 104 per energy storage node 105A. However, if in a split bus architecture, PCS 104 is used with multiple DC connections, where multiple energy storage nodes 105A to 105D (e.g., four) are connected to the bus, then there can be multiple energy storage nodes 105A to 105D on the bus, and only one PCS 104 for all of the multiple energy storage nodes 105A to 105D.

[0048] Figure 2AThis illustrates coupling to electrical application 103. Figures 1A to 1C The first energy storage node 105A is one of a plurality of energy storage nodes 105A to 105N. The first energy storage node 105A may include a single battery cubic meter 230A (as in...). Figure 2A In such cases) or multiple battery cubic 230A to 230D (as in Figure 2B (as in the case of...). Energy storage nodes 105A to 105N may include battery storage element 106, power conversion system 104 (or power conversion subsystem 107) and node controller 172 (or control subsystem 110) to receive battery data 111A to 111N from battery storage element 106, power conversion subsystem 107 (or power conversion system 104) or combinations thereof.

[0049] The power conversion system 104 or power conversion subsystem 107 may include a power inverter 205, a rectifier 210, a DC-DC converter 215, other power conversion elements, or combinations thereof. The power inverter 205 may be configured to convert a DC source (such as from battery storage elements 106A to 106N) to an AC waveform. The rectifier 210 may be configured to convert an AC source (such as from energy system 102 or electrical application 103) to DC for use with battery storage elements 106A to 106N. The DC-DC converter 215 may be configured to convert a DC source (such as from battery storage elements 106A to 106N) to different DC source characteristics.

[0050] If the energy source 109 is wind power, the power conversion system 104 can convert the generated AC power to DC power via rectifier 210 for storage in multiple energy storage nodes 105A to 105N. If the energy source 109 is solar power, the power conversion system 104 can convert the DC power to different voltage levels via DC-DC converter 215. The power inverter 205 can convert the required power flow 112 from energy storage system 101 from DC power to AC power during dispatch to electrical application 103. For example, the power inverter 205 can be configured to convert power on power bus 125 (e.g., AC bus, DC bus, or both) for use by electrical application 103. For example, the power inverter 205 converts DC power stored in energy storage nodes 105A to 105N to AC power for the electrical load consumption of electrical application 103.

[0051] The power conversion subsystem 107 includes hardware and software similar to the more centralized power conversion system 104. The power conversion subsystem 107 can be more locally distributed across each of the energy storage nodes 105A to 105N. Node controller 172 and control subsystem 110 can be configured for local computation, processing, and control of the battery storage elements 106A to 106N and the power conversion subsystem 107. Control system 115 and array controller 170 can be configured for more centralized computation, processing, and control of the entire energy storage system 101, energy system 102, electrical application 103, and power conversion system 104. The various controllers 170 to 173 of control system 115 (including array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N) can include single-board computers, application-specific integrated circuits (ASICs), microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), or combinations thereof.

[0052] Figure 2B A first energy storage node 105A is shown, comprising multiple battery cubes 230A to 230N coupled to a DC link (DC bus) 225 and multiple power conversion systems 104A to 104N. As shown in the figure, in this example, the first energy storage node 105A includes four battery cubes 230A to 230D and two power conversion systems 104A to 104B coupled to the DC link (DC bus) 225.

[0053] Figure 3A yes Figure 1A A high-level functional block diagram of the energy storage system 101, which depicts the components of the control system 115 and control subsystem 110 for adaptive dispatch of energy storage nodes 105A to 105N. Figure 3B yes Figures 1B to 1C Another high-level functional block diagram of the energy storage system depicts the components of a control system 115 with various controllers 170 to 173 for adaptive dispatch of energy storage nodes 105A to 105N.

[0054] refer to Figures 3A to 3B As shown in the figure, the multiple energy storage nodes 105A to 105N include: battery storage elements 106A to 106N; a power conversion subsystem 107; and a control subsystem 110. Figure 3A ) or node controller 172 ( Figure 3BThe control subsystem or node controller is configured to receive battery data 111A to 111N from battery storage elements 106A to 106N, PCS data 157A to 157N from power conversion subsystem 107, or a combination thereof. The control system 115 may be coupled to energy storage nodes 105A to 105N and PCS 104 and is configured to receive battery data 111A to 111N from battery storage elements 106, PCS data 157A to 157N from power conversion system 104 (or power conversion subsystem 107), or a combination thereof.

[0055] The following components can communicate via network 305 or one or more networks 305A to 305N: control subsystem 110; control system 115, which includes array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N; energy storage nodes 105A to 105N; electrical application 103; and other components of system 100. Networks 305A to 305N can be local area network 305A, wide area network 305B, or a combination thereof. For example, control system 115 can be coupled to energy storage nodes 105A to 105N and electrical application 103 via local area network 305A. Alternatively or additionally, control system 115 can be coupled to energy storage nodes 105A to 105N and electrical application 103 via wide area network 305B. Alternatively, the control system 115 may be coupled via a combination of networks 305A to 305N, such as via local area network 305A to components of energy storage system 101, including energy storage nodes 105A to 105N, and via wide area network 305B to electrical application 103.

[0056] Figure 3A The control system 115 and Figure 3BThe array controller 170 includes a network communication interface 311 configured for wired or wireless communication via a network 305. The control system 115 and the array controller 170 also include a memory 313 and a processor 312 coupled to the network communication interface 311 and the memory 313. As shown, the memory 313 of the control system 115 and the array controller 170 is configured to store adaptive dispatch programming 330A, a DC link balancing algorithm 190, battery data 111A to 111N, required power flow 112, battery states 116A to 116N, state of charge 116A, high SOC threshold 181, low SOC threshold 182, power commands 183A to 183B, stored power 184, power storage capacity 185, DC link voltage 191, and a predefined voltage threshold 192. The control system 115 and array controller 170 may also include sensors 315A to 315N coupled to the processor 312 to detect or monitor various system parameters, such as power, temperature, voltage, current, resistance, and / or impedance. For example, sensors 315A to 315N may be coupled to power bus 125 and DC link (DC bus) 225.

[0057] Control system 115 and array controller 170 are configured to receive or store the required power flow 112 of electrical application 103. The required power flow 112 may include active power (e.g., measured in kW or mW), reactive power (e.g., measured in kVAR), or overall system power discharge or charging requirements. The required power flow 112 may be based on a power command requested by a customer or independent system operator for electrical application 103 received via network 305, in which case the power command 183 is determined externally.

[0058] The power command for electrical application 103 can be based on parameters received from a customer or independent system operator request via network 305. For example, the parameters can be used to provide frequency regulation with dead time and response slope. Control system 115 can take the parameters and attempt to determine the power command, for example, based on satisfying a customer or independent system operator request for electrical application 103.

[0059] The control system 115 can adopt the required power flow 112 needed by the electrical application 103, for example, as requested by a customer or software application, and determine the optimal way to distribute the required power flow 112 across all energy storage nodes 105A to 105N. This optimization can be performed in several ways, for example, using conventional operational optimization techniques or machine learning-based techniques. The control system 115 may include one or more processors, controllers, or computing devices that can be configured to perform closed-loop management of the active and reactive power supplied to the electrical application 103.

[0060] Energy storage nodes 105A to 105N include Figure 3A The control subsystem 110 and Figure 3B The node controller 172, battery storage elements 106A to 106N, and power conversion subsystem 107 (or power conversion system 104) can reside on each individual energy storage node 105A to 105N. The control subsystem 110 and node controller 172 of the energy storage nodes 105A to 105N include a network communication interface 351 configured for wired or wireless communication via network 305. The control subsystem 110 and node controller 172 also include a memory 353 and a processor 352, the processor being coupled to the network communication interface 351 and the memory 353. As shown, the memory 353 of the control subsystem 110 and node controller 172 is configured to store adaptive dispatch programming 330B, battery data 111A to 111N, battery states 116A to 116N, and state of charge 116A.

[0061] The control subsystem 110 and node controller 172 also include environmental sensors 370A to 370N and battery sensors 375A to 375N coupled to the processor 352. The environmental sensors 370A to 370N can collect environmental condition data, for example, by measuring humidity and temperature inside the housing 500 of the energy storage nodes 105A to 105N (such as one or more battery cubicles 230A to 230N). The battery sensors 375A to 375N may include a voltage sensor 375A, a current sensor 375B, and a temperature sensor 375C to measure readings of battery data 111A to 111N (such as voltage 111A, current 111B, temperature 111C, or other physical phenomena occurring within the battery storage elements 106A to 106N). The memory 353 can store the environmental condition data collected by the environmental sensors 370A to 370N and the battery data 111A to 111N measured by the battery sensors 375A to 375N.

[0062] Control subsystem 110 or control system 115 is configured to determine at least one battery state 116A to 116N with respect to one or more of energy storage nodes 105A to 105N from battery data 111A to 111N. For example, battery states 116A to 116N may be determined or estimated by an algorithm from battery data 111A to 111N, or from readings of sensors 315A to 315N monitoring various system parameters on power bus 125, DC link (DC bus) 225, or combinations thereof. The state estimation algorithm may take measured readings of battery data 111A to 111N (including voltage 111A, current 111B, temperature 111C, or combinations thereof) as input parameters and estimate battery states 116A to 116N based on battery data 111A to 111N.

[0063] For example, the state of charge 116A is a state estimate derived from readings of voltage 111A and current 111B. The state of charge 116A may be derived from the control system 115. Alternatively or additionally, at least one battery management system (BMS) or node controller 172 may derive the state of charge 116A.

[0064] The control system 115 and array controller 170 can respectively manage power commands 183A to 183N to the control subsystem 110 and node controller 172 to charge or discharge multiple energy storage nodes 105A to 105N based on the required power flow 112. For example, the control system 115 and array controller 170 can send power commands 183A to 183N to multiple energy storage nodes 105A to 105N based on the overall required power flow 112. Alternatively or additionally, the control subsystem 110 and node controller 172 can issue power commands 183A to 183N directly at the multiple energy storage nodes 105A to 105N based on the required power flow 112.

[0065] Figure 4A It is implemented by control system 115, control subsystem 110 and multiple energy storage nodes 105A to 105N for... Figure 1A An adaptive energy allocation scheme 400 for an energy storage system 101. Figure 4A In the example, the adaptive dispatch scheme 400 is implemented in the adaptive dispatch programming 330A of the control system 115 and the adaptive dispatch programming 330B of the control subsystem 110.

[0066] Figure 4B It is implemented by various controllers 170 to 173 of the control system 115 and multiple energy storage nodes 105A to 105N for... Figures 1B to 1C An adaptive energy allocation scheme 400 for an energy storage system 101. Figure 4BIn the example, the adaptive dispatch scheme 400 is implemented in the adaptive dispatch programming 330A of the array controller 170 and the adaptive dispatch programming 330B of the node controller 172.

[0067] refer to Figures 4A to 4B Both, the adaptive dispatch programming 330A stored in memory 313, executed by processor 312 of control system 115 (e.g., array controller 170), configures control system 115 (e.g., array controller 170) to implement blocks 405, 410, 415, and 420 described below. The execution of adaptive dispatch programming 330B stored in memory 353 by processor 352 of control subsystem 110 (e.g., node controller 172) is used to implement block 405 described below. More generally, the execution of adaptive dispatch programming 330A to 330B by one or more processors 312, 352 can configure one or more controllers 110, 115, 170 to 173 to implement blocks 405, 410, 415, and 420 described below.

[0068] Beginning at block 405, the adaptive allocation scheme 400 includes determining a state of charge (SOC) 116A for one or more of the energy storage nodes 105A to 105N from battery data 111A to 111N. The SOC 116A of a first energy storage node 105A comprising multiple battery cubic units 230A to 230N can be determined. The control subsystem 110 may include at least one battery management system (BMS).

[0069] Determine the State of Charge (SOC) 116A for the entire energy storage nodes 105A to 105N (e.g., the first energy storage node 105A includes all seven battery cubic meters 230A to 230G of all battery storage elements 106A to 106N behind the first energy storage node 105A). SOC calculation can take into account the voltage on the DC bus 225 over time. SOC 116A is a value calculated by summing up all battery cubic meters 230A to 230G on the first energy storage node 105A based on how much current is flowing and how much energy can be released. SOC 116A is a parameter reading for the first energy storage node 105A over the entire DC bus 225. However, because the battery chemistry and energy storage system 101 are operating at high power, SOC 116A may be inaccurate in edge cases; SOC is not always accurate when the SOC exceeds the high SOC threshold 181 or the low SOC threshold 182.

[0070] The adaptive dispatch scheme 400 addresses the fact that the SOC 116A does not provide the same level of accuracy for edge case conditions as the battery management system (BMS) of the energy storage system 101. Figure 7(Region A2 in the text). The adaptive dispatch scheme 400 balances the DC bus 225 in instances of very low or very high SOC116A, rather than depending on inaccurate SOC calculations. Under mid-range conditions ( Figure 7 In region A1), the provided SOC 116A is typically accurate, and the DC link balancing algorithm 190 for 330A to 330B is programmed using conventional SOC balancing instead of adaptive dispatch. Two SOC thresholds 181 and 182 can be used to control the DC link balancing algorithm 190. There are two methods to make the command proportional based on the DC link. One method can be based on the energy reflection of the DC link voltage 191.

[0071] The SOC 116A provided by the battery management system can be, for example, based on coulomb counts, and can be a value from 0 to 100% regarding whether the first energy storage node 105A is full or empty. Typically, the SOC 116A is provided at the node level for all battery cubes 230A to 230N on the DC bus 225. Each battery rack of battery cube 230 has a BMS, and this information can be propagated to the system-level BMS to determine the SOC 116A for all battery cubes 230A to 230N, rather than for each individual battery cube 230 or each cell within a battery cube 230. Very low or very high SOC 116A readings are not necessarily accurate, and there may be more energy available in the first energy storage node 105A. When the SOC 116A of the first energy storage node 105A is empty, a conventional control system will stop sending power commands 183A for discharging if the first energy storage node 105A has a very low SOC 116A. However, in this case, adaptive dispatch programming 330A to 330B can discharge the first energy storage node 105A because even if SOC 116A indicates that the first energy storage node 105A is too depleted, the first energy storage node 105A can discharge a little more based on the DC link balancing algorithm 190 without damaging the battery.

[0072] Similarly, when the SOC 116A of the first energy storage device 105A is 100%, the conventional control system will stop sending power commands 183B for charging. However, the first energy storage node 105A may not actually be full and is less than 100%, such as 99%. In this case, the adaptive dispatch programming 330A to 330B of the control system 115 can charge the first energy storage node 105A a little more without damaging the battery.

[0073] Moving now to box 410, the adaptive allocation scheme 400 also includes a DC link balancing algorithm 190 activated in response to a state of charge 116A exceeding a high SOC threshold 181 or falling below a low SOC threshold 182 to allocate the required power flow 112 across multiple energy storage nodes 105A to 105N.

[0074] You can refer to Figure 7 Let's understand box 410. In region A1, SOC 116A is accurate and can be calculated using the SOC provided by the BMS vendor. However, in region A2, the calculation of SOC 116A may not be very accurate, and adaptive allocation programming 330A to 330B can be done better by using DC link balancing algorithm 190 to obtain as much energy as possible from energy storage system 101. DC link balancing algorithm 190 can be executed at the node controller 172 level. Core controller 171 can view all its energy storage nodes 105A to 105N and perform aggregation across energy storage nodes 105A to 105N, but the primary focus can be at the node level.

[0075] For example, if SOC 116A is between 93% and 100%, SOC 116A is ignored, and DC link balancing algorithm 190 is activated to determine how much energy can be added to the first energy storage node 105A during charging. Similarly, if SOC 116A is 10% or lower, SOC 116A is ignored, and DC link balancing algorithm 190 is activated to determine how much energy can be supplied from the first energy storage node 105A during discharging. DC link balancing algorithm 190 can examine the DC link voltage 191 on DC bus 225 and what the power commands 183A to 183B are (e.g., the requested current on DC bus 225). Based on the power commands 183A to 183B (e.g., the requested power) and the DC link voltage 191 on DC bus 225, DC link balancing algorithm 190 can, for example, determine, based on the discharge rate in power command 183A, that an additional three minutes of energy can be obtained from the first energy storage node 105A. For example, if a low power is requested in the power command 183A for discharging, the first energy storage node 105A may not actually be at that level when SOC 116A indicates empty. However, if the discharge requested in the power command 183A is at full power, there may be 0 minutes remaining because less energy would be available from energy storage node 105A if operating at full power. In other words, if the power command 183A is for low power, more energy can be obtained from the first energy storage node 105A. If the power command 183A is for high power, less energy is available in the first energy storage node 105A because a battery cell with a very low SOC may deplete rapidly. If a battery cell has a very low SOC, the entire SOC of the first energy storage node 105A may be zero.

[0076] Proceeding to block 415, block 410, which distributes the required power flow 112 across multiple energy storage nodes 105A to 105N, may include a DC link balancing algorithm 190 activated in response to a state of charge 116A falling below a low SOC threshold 182, and calculating that the first energy storage node 105A contains available stored power 184 to satisfy a power command 183A for discharging the first energy storage nodes 105A to 105N. The DC link balancing algorithm 190 may calculate the available stored power 184 in the first energy storage node based on the DC link voltage 191 on the DC bus 225 of the first energy storage node 105A and the requested amount in the power command 183A for discharging.

[0077] Now ending at box 420, box 410, which distributes the required power flow 112 across multiple energy storage nodes 105A to 105N, may include a power command 183B for charging, executed based on a DC link balancing algorithm 190 activated in response to a state of charge 116A exceeding a high SOC threshold 181 and calculating that the first energy storage node 105A contains sufficient power storage capacity 185 to satisfy a power command 183B for charging the first energy storage node 105A. The DC link balancing algorithm 190 may calculate that the first energy storage node 105A contains sufficient power storage capacity 185 based on the DC link voltage 191 on the DC bus 225 of the first energy storage node 105A and the power command 183B for charging.

[0078] exist Figure 4A In this context, the local control subsystem 110 can implement a subset or all of blocks 405, 410, 415, and 420 of the adaptive dispatch scheme 400 without the need for a central control system 115. For example, the required power flow 112 can be stored or received from the electrical application 103 via network 305 by one, a subset, or all of the control subsystems 110 of energy storage nodes 105A to 105N. The control subsystems 110 that receive the required power flow 112 from energy storage nodes 105A to 105N can then implement blocks 410, 415, and 420.

[0079] exist Figure 4B In this configuration, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N can implement a subset or all of blocks 405, 410, 415, and 420 of the adaptive dispatch scheme 400 without the need for a central array controller 170. For example, the required power flow 112 can be stored or received from electrical application 103 via network 305 by one, a subset, or all of the core controllers 171A to 171N, the node controllers 172A to 172N of the energy storage nodes 105A to 105N, and the enclosure controllers 173A to 173N. Adaptive dispatch programming 330A can be stored on and executed on the core controllers 171A to 171N and the enclosure controllers 173A to 173N.

[0080] Figure 5 This is a cross-sectional view of the first energy storage node 105A among a plurality of energy storage nodes 105A to 105N, and details of a plurality of battery storage elements 106A to 106N are shown. As shown, energy storage node 105A includes a housing 500, such as a physical casing, for storing the plurality of battery storage elements 106A to 106N. Battery storage elements 106A to 106N may be a collection of one or more batteries, such as a plurality of battery strings or battery packs logically, physically, and electrically organized.

[0081] exist Figure 5 In the examples, battery storage elements 106A to 106N may include battery racks (e.g., six shown) that hold corresponding stacks of battery modules (e.g., seventeen shown). Battery modules may include arrays of prismatic, pouch, or cylindrical battery cells packaged together to increase voltage, current, or both. In some examples, battery modules may include electric vehicle battery packs, such as collections of lithium-ion battery cells packaged together.

[0082] Each of the energy storage nodes 105A to 105N may include an assembly of one or more enclosures 500A to 500N, such as Figure 5 As shown, the one or more housings are housed together in this example as a plurality of battery storage elements 106A to 106N of battery cube 230. Of course, housing 500 can be formed in various other form factors. Each of battery cubes 230A to 230N may also include a corresponding housing controller 173A to 173N as part of control system 115, said corresponding housing controller being controlled by a corresponding node controller 172A to 172N.

[0083] Figure 6 This is a flowchart of a method 600 for adaptive allocation that can be implemented in an energy storage system 100. Figure 6 In the example, method 600 implements the adaptive dispatch scheme 400 of Figure 4. Beginning at step 601, method 600 includes receiving or storing the required power flow 112 for use in electrical application 103.

[0084] Continuing with step 605, method 600 further includes determining a state of charge (SOC) 116A for one or more of the energy storage nodes 105A to 105N from battery data 111A to 111N. The SOC 116A of a first energy storage node 105A comprising a plurality of battery cubic cells 230A to 230N can be determined, and the control subsystem 110 includes at least one battery management system (BMS).

[0085] Now proceeding to step 610, method 600 further includes a DC link balancing algorithm 190 activated in response to a state of charge 116A exceeding a high SOC threshold 181 or falling below a low SOC threshold 182 to distribute the required power flow 112 across multiple energy storage nodes 105A to 105N.

[0086] refer to Figure 7In the depicted A2 segment, the SOC 116A provided by the BMS will be ignored. Instead, the DC link voltage balancing algorithm 190 is activated by adaptive dispatch programming 330A to 330B. The DC link voltage balancing algorithm 190 is based on the reading of the DC link voltage 191 on the DC bus 225 and power commands 183A to 183B. Based on the DC link voltage balancing algorithm 190, even if the SOC 116A may indicate zero, the adaptive dispatch programming 330A to 330B can continue to dispatch the first energy storage node 105A. When the SOC 116A is zero, the conventional control system does not perform dispatch. Using the DC link balancing algorithm 190, energy will not be idled when the SOC 116A of the first energy storage node 105A is close to empty in the first A2 segment; and even if the SOC is 100% in the second A2 segment, the DC link balancing algorithm 190 may charge the first energy storage node 105A a little more.

[0087] Moving now to step 615 of method 600, step 610, which involves distributing the required power flow 112 across multiple energy storage nodes 105A to 105N, may include being activated based on a DC link balancing algorithm 190 in response to a state of charge 116A falling below a low SOC threshold 182, and calculating that the first energy storage node 105A contains available stored power 184 to satisfy a power command 183A for discharging the first energy storage nodes 105A to 105N. The DC link balancing algorithm 190 may calculate the available stored power 184 in the first energy storage node 105A based on the DC link voltage 191 on the DC bus 225 of the first energy storage node 105A and the requested amount in the power command 183A for discharging.

[0088] The process now concludes at step 620 of method 600. Step 610, which involves distributing the required power flow 112 across multiple energy storage nodes 105A to 105N, may include executing a power command 183B for charging based on a DC link balancing algorithm 190 activated in response to a state of charge 116A exceeding a high SOC threshold 181 and calculating that the first energy storage node 105A contains sufficient power storage capacity 185 to satisfy a power command 183B for charging the first energy storage node 105A. The DC link balancing algorithm 190 may calculate that the first energy storage node 105A contains sufficient power storage capacity 185 based on the DC link voltage 191 on the DC bus 225 of the first energy storage node 105A and the power command 183B for charging.

[0089] Figure 7This is a graph of two curves representing DC link voltage 191 and state of charge 116A, showing two regions (e.g., areas) where DC link balancing algorithm 190 is activated by adaptive dispatch programming 330A to 330B. DC link voltage 191 belongs to all batteries on the DC bus 225 used for energy storage node 105. In region A1, adaptive dispatch programming 330A to 330B does not activate DC link balancing algorithm 190. The dashed line represents open-circuit voltage 720, and the solid line represents under-load voltage 725.

[0090] Adaptive dispatch programming 330A to 330B is a balancing method that divides the SOC voltage curve 700 into two parts, namely A1 and A2. A1 is the region where the SOC estimate may not be voltage-dependent, as the SOC estimate is relatively independent of voltage. In this region A1, the SOC estimate is the best feedback for balancing the SOC and thus the DC link voltage 191 of the energy storage nodes 105A to 105N. The two A2 regions are areas where the SOC estimate may have relatively large errors. However, in region A2, the SOC 116A and the DC link voltage 191 are highly correlated; therefore, the DC link voltage 191 in region A2 is the best feedback for balancing the SOC and node voltages. Regions A1 and A2 are connected via two... and This is used to differentiate them. However, the SOC voltage curve 700 may vary based on the load and may differ depending on the battery. Therefore, and These are the estimated boundaries between zones A1 and A2. If zone A1 is the active zone for balancing, but the battery is in zone A2, additional controls are implemented to ensure DC link balancing.

[0091] Figure 8 This is a block diagram of the SOC-DC link balancing control 800 for adaptive dispatch programming 330A to 330B, illustrating the DC link balancing algorithm 190. In block 805, the upper-level control of the adaptive dispatch programming 330A to 330B sends a total power command to the SOC-DC link balancing control in block 810. and settings (such as) , and ). It is the high SOC of entering zone A2, and It is the low SOC entering zone A2. The threshold voltage 192 is the voltage difference between the DC links of energy storage nodes 110A and 110N; this voltage triggers the power conversion system 104A to 104N of energy nodes 105A to 105N to trip. Figure 8 As shown, the balance control block also receives the following sensing information from the nodes: the node's state of charge. 116A, DC link voltage of the node Power of 191 and nodes In box 810, the SOC_DC link energy storage node balance block generates control power commands. The control power command is received in block 815 by battery energy storage nodes 105A to 105N.

[0092] Figure 9 This is flowchart 900 of the DC link balancing control algorithm 190 for adaptive dispatch programming 330A to 330B. Figure 10 The threshold voltage is 192 ( V th )and ΔP 节点 Line graph 1000 showing the example relationship between them and Example relationships between them. The control system 115 (including array controller 170) can be configured to distribute the required power flow 112 across multiple energy storage nodes 105A to 105N based on a DC link balancing algorithm 190 that is further activated based on deviations of the DC link voltage 191 between energy storage nodes 105A to 105N from a predefined threshold voltage 192. For example, adaptive distribution programming 330A to 330B will look for outlier battery cells. When looking at the battery cell hierarchy in each of the battery cubes 230A to 230B, the individual battery cells are rarely perfectly matched to exactly the same voltage. As long as the delta between battery cells is greater than a standard delta (predefined threshold voltage 192), conventional SOC balancing 116 can be used. However, if an imbalance exists such that the difference between high and low cells is greater than the predefined voltage threshold 192, the DC link balancing algorithm 190 is activated.

[0093] refer to Figure 9 In step 905, the adaptive dispatch programming 330A to 330B DC link balancing control algorithm 190 first calls a function to update the threshold voltage. Because this voltage value may depend on other parameters. In the example used here, Depends on the power difference between nodes .

[0094] In step 910, the DC link balancing control algorithm 190 is based on the settings , and To determine which area is active. Greater than and less than are... Figure 7The A2 segment is shown. Values ​​less than A1 and greater than A1 are at the high SOC threshold 181 and low SOC threshold 182 in the A2 region. In those regions, adaptive dispatch programming 330A to 330B can use its own SOC value instead of the SOC 116A value provided by the BMS, as shown in step 91. When the SOC is greater than SOCH_A1, it is... Figure 7 The A2 circle at the top, and when SOC is less than SOCL_A1, is in Figure 7 The A2 circle at the bottom. These two A2 areas are where the SOC 116A is calculated using the DC link balancing algorithm 190, rather than reported by the BMS.

[0095] Move to step 915. V_delta Defined as the maximum voltage difference between nodes. .if If true, then the DC link voltage 191 can be feedback to the balance controller, and step 912 is executed. Alpha This takes into account the tolerances for sensing errors and response speed. If If it is not true, then and This will be used to determine the correct area and feedback of the DC link balance control algorithm 190 in step 917.

[0096] DC link balancing control algorithm 190 is an adaptive proportional controller, where the power of proportional control is controlled by error. To achieve adaptive behavior, where It is the selected feedback of energy storage nodes 105A to 105N. or The maximum difference between the SOC 116A and the DC link voltage 191 is defined as the error. The goal of the balance controller is to reduce this error to zero. The control law is defined as follows: in It is based on The amplitude is adaptive. For example, It can be a linear function as follows: linearly increasing function This will allow the speed of the balance controller to be controlled based on the magnitude of the error. This will ensure smooth balancing of the energy storage nodes 105A to 105N, as their SOC 116A or DC link voltage 191 varies due to various physical and computational reasons.

[0097] In the above example, energy system 102, energy application 103, power conversion system 104, energy storage nodes 105A to 105N, control subsystem 110, control system 115, array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N each include network communication interfaces 311 and 351 for wired or wireless communication via one or more networks 305A to 305N. Networks 305A to 305N interconnect the links of network communication interfaces 311 and 351 from / to the device to provide data communication between energy application 103, energy storage nodes 105A to 105N, control system 115, array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N. Networks 305A to 305N can support data communication between devices in the field via wired (e.g., cable or fiber optic) media, or via wireless (e.g., WiFi, Bluetooth™, ZigBee, LiFi, IrDA, etc.), or a combination of wired and wireless technologies.

[0098] This document describes the functionality of the adaptive dispatch scheme 400 (including adaptive dispatch programming 330A to 330B and DC link balancing algorithm 190) in one or more applications or firmware as previously described, for the energy system 102, electrical application 103, power conversion system 104, energy storage nodes 105A to 105N, control subsystem 110, control system 115, array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N. According to some implementations, “function,” “functions,” “application,” “instruction,” or “programming” is a program that performs the functions defined in the program. Various programming languages ​​can be used to create one or more applications that can be constructed in various ways, such as object-oriented programming languages ​​(e.g., Objective C, Java, or C++) or procedural programming languages ​​(e.g., C or assembly language).

[0099] In the above example, energy system 102, energy application 103, power conversion system 104, energy storage nodes 105A to 105N, control subsystem 110, control system 115, array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N may each include a processor. As used herein, processors 312, 352 are hardware circuits having elements constructed and arranged to perform one or more processing functions (typically various data processing functions). Although discrete logic components may be used, the example utilizes components forming a programmable central processing unit (CPU). For example, processors 312, 352 include one or more integrated circuit (IC) cores or portions thereof, which incorporate electronic elements performing the functions of the CPU. For example, processors 312, 352 may be based on any known or available microprocessor architecture, such as Reduced Instruction Set Computing (RISC) using the ARM architecture. Of course, other processor circuits may be used to form the CPU or processor hardware. The illustrated examples of processors 312 and 352 may include a microprocessor or multiprocessor architecture. A digital signal processor (DSP) or a field-programmable gate array (FPGA) may be a suitable replacement for processors 312 and 352, but will consume more power and increase complexity.

[0100] Processors 312 and 352 are used to execute programming or instructions to configure energy system 102, energy application 103, power conversion system 104, energy storage nodes 105A to 105N, control subsystem 110, control system 115, array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, enclosure controllers 173A to 173N, etc., to perform various operations. Such operations may include various general-purpose operations (e.g., clock functions, recording and logging operation status and / or fault information) and various system-specific operations (e.g., energy management functions). Although processors 312 and 352 can be configured using hardwired logic, a typical processor is configured by executing general-purpose processing circuitry, such as instructions and any associated setup data received from illustrated memories 313 and 353 or from other included storage media and / or from remote storage media.

[0101] In the above example, energy system 102, energy application 103, power conversion system 104, energy storage nodes 105A to 105N, control subsystem 110, control system 115, array controller 170, core controllers 171A to 171N, node controllers 172A to 172N, and enclosure controllers 173A to 173N each include memory. Memory 313, 353 may include flash memory (non-volatile or permanent storage), read-only memory (ROM), and random access memory (RAM) (volatile storage). RAM is used for short-term storage of instructions and data processed by processors 312, 352, for example, as working data processing memory. Flash memory typically provides long-term storage.

[0102] Of course, other storage devices or configurations may be added to or replace the storage devices or configurations in the examples. Such other storage devices may be implemented using any type of storage medium in which computer or processor-readable instructions or programs are stored, and may include any or all tangible memory or associated modules such as computers, processors, etc.

[0103] Therefore, machine-readable or computer-readable media can take many forms of tangible storage media. Non-volatile storage media include, for example, optical discs or disks, any storage device such as any computer, etc., such as those used to implement the client devices, media gateways, code converters, etc., shown in the figures. Volatile storage media include dynamic memory, such as the main memory of such computer platforms. Tangible transmission media include coaxial cables; copper wires and optical fibers, including wires that include buses within a computer system. Carrier transmission media can take the form of electrical or electromagnetic signals, or sound or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Therefore, common forms of computer-readable media include, for example: floppy disks, floppy disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punched cards, paper tapes, any other physical storage media with a perforated pattern, RAM, PROMs and EPROMs, FLASH-EPROMs, any other storage core chips or cartridges, carriers for transmitting data or instructions, cables or links for transmitting such carriers, or any other media from which a computer can read programming code and / or data. Many of these forms of computer-readable media may involve loading one or more sequences of one or more instructions into a processor for execution.

[0104] According to exemplary embodiments of this disclosure, one or more processors and control circuitry may include one or more of any known general-purpose processors or integrated circuits, such as a central processing unit (CPU), microprocessor, field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), digital signal processor (DSP), or other suitable programmable processing or computing means or circuitry, which are specifically programmed as needed to perform operations for achieving the results of the exemplary embodiments described herein. The processor may be configured to include and execute features of the exemplary embodiments of this disclosure, such as adaptive dispatch scheme 400, adaptive dispatch programming 330A to 330B, and DC link balancing algorithm 190. These features may be executed by program code encoded or recorded on the processor, or stored in a non-volatile memory device, such as read-only memory (ROM), erasable programmable read-only memory (EPROM), or other suitable memory device or circuitry as needed. Thus, such a computer program may represent a controller for a computing device.

[0105] In another exemplary embodiment, program code (such as adaptive dispatch scheme 400, adaptive dispatch programming 330A to 330B, and DC link balancing algorithm 190) may be provided in a computer program product having a non-transitory computer-readable medium, such as a magnetic storage medium (e.g., a hard disk, floppy disk, or magnetic tape), an optical medium (e.g., any type of compact disc (CD), or any type of digital video disc (DVD), or other compatible non-volatile memory device as needed), and downloaded to the processor for execution as needed when the non-transitory computer-readable medium is in communicative contact with the processor.

[0106] One or more processors 312, 352 may be included in a computing system configured with components such as memory, hard disk drives, input / output (I / O) interfaces, communication interfaces, displays, and any other suitable components as needed. The exemplary computing device may also include a communication interface. The communication interface may be configured to allow software and data to be transferred between the computing device and external devices. The exemplary communication interface may include a modem, a network interface (e.g., an Ethernet card), a communication port, a PCMCIA slot and card, or any other suitable network communication interface as needed. Software and data transferred via the communication interface may be in the form of signals, which may be electronic, electromagnetic, optical, or other signals, as will be apparent to those skilled in the art. Signals may propagate via a communication path, which may be configured to carry signals and may be implemented using wires, cables, optical fibers, telephone lines, cellular telephone links, radio frequency links, or any other suitable communication links as needed.

[0107] When this disclosure is implemented using programming or software including adaptive dispatch scheme 400 and optimized dispatch programming 330A to 330B, the programming or software may be stored in a computer program product or a non-transitory computer-readable medium and loaded into a computing device using a removable storage drive or communication interface. In exemplary embodiments, any computing device disclosed herein (such as control subsystem 110, control system 115, and controllers 170 to 173) may also include a display interface that outputs display signals to a display unit, such as an LCD screen, plasma screen, LED screen, DLP screen, CRT screen, or any other suitable graphical interface as required.

[0108] It will be understood that the terms and expressions used herein have the general meanings given to such terms and expressions in their respective fields of inquiry and research, unless otherwise specified herein. Relational terms such as "first" and "second" may only be used to distinguish one entity or action from another entity or action, and do not necessarily require or imply any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," "includes," "including," "has," "having," "containing," "contains," "with," "formed of," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes or comprises a list of elements or steps includes not only those elements or steps, but may also include other elements or steps not expressly listed or inherent to such process, method, article, or apparatus. Without further constraints, an element preceded by "an" or "a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element. Unless otherwise stated, the article “a” or “an” preceding an element means one or more of the elements.

[0109] Unless otherwise stated, any and all measurements, values, ratings, locations, amplitudes, sizes, angles, and other specifications set forth in this specification (including in the appended claims) are approximate, not precise. Such quantities are intended to have a reasonable range, consistent with the functions they relate to and with convention in the fields to which they belong. For example, unless expressly stated otherwise, parameter values, etc., may vary from the stated quantity by up to ±5% or up to ±10%. The terms “approximately” and “substantially” mean that parameter values, etc., may vary from the stated quantity by up to ±10%.

[0110] Furthermore, as can be seen in the foregoing detailed embodiments, various features are combined together in various examples for the purpose of simplification. This approach of the present disclosure should not be construed as reflecting an intention that the claimed examples require more features than are expressly recited in each claim. Rather, as reflected in the appended claims, the subject matter to be protected is less than all the features of any single disclosed example. Therefore, the appended claims are hereby incorporated into the detailed embodiments, wherein each claim itself is a separately claimed subject matter.

[0111] While what is considered the best mode and / or other examples has been described above, it should be understood that various modifications may be made therein, and the subject matter disclosed herein can be implemented in various forms and examples, and can be applied to numerous applications, only some of which have been described herein. The appended claims are intended to cover any and all modifications and variations that fall within the true scope of the inventive concept.

[0112] The scope of protection is limited only by the appended claims. This scope is intended and should be interpreted as a broad range consistent with the ordinary meaning of the language used in the claims, and covering all structural and functional equivalents, when understood in accordance with this specification and subsequent examination history. Nevertheless, no claim is intended to cover, nor should it be interpreted in this way, any subject matter that fails to meet the requirements of Sections 101, 102, or 103 of the Patent Act. Protection for any such subject matter unintentionally covered is hereby waived.

Claims

1. An energy storage system, comprising: Multiple energy storage nodes, wherein the multiple energy storage nodes include: Battery storage components, and A control subsystem for receiving battery data from the battery storage element; Power conversion system (PCS); and A control system coupled to the plurality of energy storage nodes and configured to receive or store the required power flow; in: The control subsystem, the control system, or both are configured to determine the state of charge (SOC) of one or more of the energy storage nodes from the battery data; and The control system is configured to distribute the required power flow across the multiple energy storage nodes based on a DC link balancing algorithm activated in response to the state of charge exceeding a high SOC threshold or falling below a low SOC threshold.

2. The energy storage system of claim 1, wherein the state of charge is determined for a first energy storage node comprising a plurality of battery cubes, and the control subsystem comprises at least one battery management system (BMS).

3. The energy storage system of claim 2, wherein the distribution of the required power flow across the plurality of energy storage nodes comprises executing the power command for discharging based on the DC link balancing algorithm being activated in response to the state of charge being below the low SOC threshold and calculating that the first energy storage node contains available stored power to satisfy a power command for discharging the first energy storage node.

4. The energy storage system of claim 3, wherein the DC link balancing algorithm calculates the available stored power contained in the first energy storage node based on the DC link voltage on the DC bus of the first energy storage node and the requested amount in the power command for discharging.

5. The energy storage system of claim 2, wherein the distribution of the required power flow across the plurality of energy storage nodes comprises executing the power command for charging based on the DC link balancing algorithm being activated in response to the state of charge exceeding the high SOC threshold and calculating that the first energy storage node contains sufficient power storage capacity to satisfy a power command for charging the first energy storage node.

6. The energy storage system of claim 5, wherein the DC link balancing algorithm calculates that the first energy storage node contains sufficient power storage capacity based on the DC link voltage on the DC bus of the first energy storage node and the power command for charging.

7. The energy storage system of claim 1, wherein the control system is configured to be further activated based on the DC link balancing algorithm based on a predefined threshold voltage deviation of the DC link voltage between the energy storage nodes to distribute the required power flow across the plurality of energy storage nodes.

8. A non-transitory computer-readable medium comprising adaptive dispatch programming, wherein execution of the adaptive dispatch programming by one or more processors configures one or more controllers to: Receive or store the required power flow for electrical applications; Determine the state of charge (SOC) of one or more of the energy storage nodes from the battery data; and The required power flow is distributed across the multiple energy storage nodes based on a DC link balancing algorithm that is activated in response to the state of charge exceeding a high SOC threshold or falling below a low SOC threshold.

9. The non-transitory computer-readable medium of claim 8, wherein the state of charge is determined for a first energy storage node comprising a plurality of battery cubes, and the control subsystem comprises at least one battery management system (BMS).

10. The non-transitory computer-readable medium of claim 9, wherein the distribution of the desired power flow across the plurality of energy storage nodes comprises executing the power command for discharging based on the DC link balancing algorithm being activated in response to the state of charge being below the low SOC threshold and calculating that the first energy storage node contains available stored power to satisfy a power command for discharging the first energy storage node.

11. The non-transitory computer-readable medium of claim 10, wherein the DC link balancing algorithm calculates the available stored power contained in the first energy storage node based on the DC link voltage on the DC bus of the first energy storage node and the requested amount in the power command for discharging.

12. The non-transitory computer-readable medium of claim 9, wherein the distribution of the required power flow across the plurality of energy storage nodes comprises executing the power command for charging based on the DC link balancing algorithm being activated in response to the state of charge exceeding the high SOC threshold and calculating that the first energy storage node contains sufficient power storage capacity to satisfy a power command for charging the first energy storage node.

13. The non-transitory computer-readable medium of claim 12, wherein the DC link balancing algorithm calculates that the first energy storage node contains sufficient power storage capacity based on the DC link voltage on the DC bus of the first energy storage node and the power command for charging.

14. The non-transitory computer-readable medium of claim 8, wherein the control system is configured to be further activated based on the DC link balancing algorithm based on a deviation of the DC link voltage between the energy storage nodes from a predefined threshold voltage to distribute the desired power flow across the plurality of energy storage nodes.

15. A method comprising: Receive or store the required power flow for electrical applications; The state of charge (SOC) of one or more of the energy storage nodes is determined from the battery data; as well as The required power flow is distributed across the multiple energy storage nodes based on a DC link balancing algorithm that is activated in response to the state of charge exceeding a high SOC threshold or falling below a low SOC threshold.

16. The method of claim 15, wherein the state of charge is determined for a first energy storage node comprising a plurality of battery cubes, and the control subsystem comprises at least one battery management system (BMS).

17. The method of claim 16, wherein distributing the desired power flow across the plurality of energy storage nodes comprises executing the power command for discharging based on the DC link balancing algorithm being activated in response to the state of charge being below the low SOC threshold and calculating that the first energy storage node contains available stored power to satisfy a power command for discharging the first energy storage node.

18. The method of claim 17, wherein the DC link balancing algorithm calculates the available stored power contained in the first energy storage node based on the DC link voltage on the DC bus of the first energy storage node and the requested amount in the power command for discharging.

19. The method of claim 16, wherein distributing the required power flow across the plurality of energy storage nodes comprises executing the power command for charging based on the DC link balancing algorithm being activated in response to the state of charge exceeding the high SOC threshold and calculating that the first energy storage node contains sufficient power storage capacity to satisfy a power command for charging the first energy storage node.

20. The method of claim 19, wherein the DC link balancing algorithm calculates that the first energy storage node contains sufficient power storage capacity based on the DC link voltage on the DC bus of the first energy storage node and the power command for charging.

Images