Systems and methods for optimizing life and efficiency of battery energy storage systems

Abstract

An energy storage system includes: a power conversion system (PCS); and a plurality of energy storage nodes. The energy storage node includes: a battery storage element; and a control subsystem for receiving battery data from the battery storage element. The energy storage system also includes a control system coupled to the energy storage node and configured to receive or store a desired power flow. The control system is configured to receive or store the desired power flow for electrical applications. The control system is configured to dispatch the required power flow across the plurality of energy storage nodes based on at least two of: (1) battery cost life of the battery storage element; (2) the PCS cost life of the PCS; and (3) operating efficiencies of the battery storage element and the PCS.

Description

Cross-references to related applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 541,154, filed September 28, 2023, entitled “Systems and Methods for Optimizing Lifetime and Efficiency of a Battery Energy Storage System,” 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 optimized allocation to control the allocation of energy storage nodes, based on battery cost lifetime of battery storage elements, PCS cost lifetime of power conversion systems (PCS), or operational efficiency of battery storage elements and PCS. 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 a casing housing many batteries, and a power conversion system. Typically, an energy storage system includes a control system to monitor the energy storage nodes.

[0004] Energy storage systems (BESS) typically consist of large, often expensive components designed to perform the tasks of generating, storing, or distributing energy as efficiently as possible. Downtime of these components can incur prohibitively high costs, and therefore, in addition to the costs of parts and labor in repair and replacement, there may be expedited fees for the maintenance and replacement of degraded components. Consequently, operators of energy storage systems seek to operate their systems as efficiently as possible, at least in part considering maintenance and replacement costs.

[0005] However, determining efficient use is a challenging task. Typically, operators are primarily focused on meeting energy demands within their contractual terms: providing a certain amount of kilowatts over a specific period or a certain amount of kilowatt-hours over a given time span. However, existing energy storage system controls do not take into account the operational efficiency of PCS (e.g., inverters) and batteries. This lack of consideration can lead to inefficiencies when power distribution under energy storage system control offers flexibility.

[0006] Once contractual obligations are fulfilled, determining efficiency within those limits becomes less clear. In some cases, operating components at low but constant demand is efficient, but some components (such as inverters) are most efficient at 75% to 80% capacity. However, while the inverter is operating at this maximally efficient capacity, connected energy storage nodes may be operating at inefficient capacity and charging or discharging outside their optimal range. Furthermore, energy efficiency is not necessarily related to the lifetime efficiency of a component. It may be better to operate some components (such as inverters) at lower operating efficiency and allow energy loss as heat, but in the event of extending the inverter's lifespan or maintenance intervals.

[0007] The end-of-life (EOL) of batteries in energy storage systems is significantly affected by power control strategies. Numerous studies have shown that EOL can be improved when optimized methods are used to control batteries. The overall operator profitability of energy storage systems depends on the EOL of their batteries. The EOL of inverters and costly maintenance are affected by the stress factor defined by the power conversion system. Therefore, excessive stress affects the EOL of inverters and may require costly parts replacement.

[0008] Meeting the conflicting demands of contractual requirements and maximizing operational and life-cycle efficiency across different categories of components is challenging. This is especially true when different components connected together have different maximum operational efficiency capacities, and when specific components have different capacities for maximizing operational or life-cycle efficiency.

[0009] Currently, power control is performed without considering battery degradation factors. Current methods of commanding PCS (e.g., inverters) also ignore stress factors on the inverter. Faced with these multivariate problems of current power control tools, some operators can only guess at efficient operating capacity and hope they won't pay extra for or lose excess energy as heat dissipation, and that their components won't be subjected to excessive stress requiring premature maintenance or replacement. Summary of the Invention

[0010] In a first example, an energy storage system 101 includes a power conversion system (PCS) 104 and 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, PCS data 157A to 157N from the power conversion system 104, or combinations thereof. The energy storage system 101 also includes 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 system 115 is configured to receive or store the desired power flow 112 for use in an electrical application 103. The control system 115 is configured to distribute the required power flow 112 across multiple energy storage nodes 105A to 105N based on at least two of the following: (a) the cost lifetime 131 of the battery storage element 106; (b) the PCS cost lifetime 132 of the PCS 104; and (c) the operating efficiency 133 of the battery storage element 106 and the PCS 104.

[0011] In the second example, a non-transitory computer-readable medium 313, 353 includes optimized dispatch programming 330A to 330B. Execution of the optimized 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 the required power flow 112 for use in electrical application 103. Execution of the optimized 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 at least two of the following: (a) the battery cost lifetime 131 of the battery storage element 106; (b) the power conversion system cost lifetime 132 of the power conversion system (PCS) 104; and (c) the operating efficiency 133 of the battery storage element 106 and the PCS 104.

[0012] In a third example, a method 600 includes receiving or storing a required power flow 112 for use in an electrical application 103. The method also includes distributing the required power flow 112 across multiple energy storage nodes 105A to 105N based on at least two of the following: (a) the battery cost lifetime 131 of the battery storage element 106; (b) the power conversion system cost lifetime 132 of the power conversion system (PCS) 104; and (c) the operating efficiency 133 of the battery storage element 106 and the PCS 104.

[0013] Additional objects, advantages, and novel features of the examples will be set forth in part in the description which follows, and in part will be 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

[0014] 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.

[0015] Figure 1A It describes a system that includes energy storage systems, energy systems, and electrical applications.

[0016] Figure 1B Depicting Figure 1A The example architecture of the control system includes a battery array, an array controller, and a core controller.

[0017] 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.

[0018] Figure 1D Depicting Figures 1A to 1C The power conversion system at the core of the battery.

[0019] 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.

[0020] 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).

[0021] Figure 3A yes Figure 1A The high-level functional block diagram of the energy storage system depicts the components of the optimized allocation control system and control subsystem for energy storage nodes.

[0022] 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 optimized allocation of energy storage nodes.

[0023] Figure 4A It is implemented by a control system, a control subsystem, and multiple energy storage nodes for... Figure 1AAn optimized dispatch protocol for energy storage systems.

[0024] Figure 4B It is implemented by various controllers of the control system and multiple energy storage nodes. Figures 1B to 1C An optimized allocation scheme for the energy storage system.

[0025] 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.

[0026] Figure 6 It is a flowchart of an optimized allocation method that can be implemented for energy storage systems.

[0027] Figure 7A This is the ideal operating profile for the battery.

[0028] Figure 7B This is the ideal operating profile for PCS.

[0029] Figure 8 This is a block diagram of an optimized dispatch scheme implemented in optimized dispatch programming.

[0030] Figure 9 This is another block diagram of the optimized dispatch scheme implemented in optimized dispatch programming.

[0031] Parts list Detailed Implementation

[0032] 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.

[0033] Unless otherwise instructed, any implementation scheme may be combined with any other implementation scheme. In particular, Figures 1A to 9 All related texts can be combined with each other.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] Now, let’s refer in detail to the examples shown in the accompanying drawings, and discuss them below.

[0038] Figure 1A A 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.

[0039] refer to Figure 1A and Figure 1BBoth, 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.

[0040] For example, 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 the example, each battery core 151A to 151N (at the battery core level) may have one PCS 104 and one transformer 108.

[0041] 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.

[0042] 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 may step up or step down the required power flow 112 to and from electrical application 103, such as AC voltage.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] Control system 115 implements optimized dispatch scheme 400 (see...) Figures 4A to 4B The optimized dispatch scheme can be implemented in optimized dispatch programming 330A to 330B (see...). Figures 3A to 3B Typically, energy storage system 101 allocates the required power flow 112 based solely on the state of charge 116A of energy storage nodes 105A to 105N. An optimized allocation scheme 400 allocates power to energy storage nodes 105A to 105N and PCS 104A to 104N based on a cost model to extend the lifespan of energy storage system 101 by minimizing the degradation of battery storage elements 106A to 106N and PCS 104A to 104N. The optimized allocation scheme 400 determines the cost of the next allocation of the original power command 183 to maximize lifespan. For example, if energy storage system 101 includes two energy storage nodes 105A to 105B, then the power command 183 can still be satisfied to maximize device lifespan, and allocation may be made to only one energy storage node 105A instead of both energy storage nodes 105A to 105B.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] Figure 1D Depicting Figures 1A to 1C The power conversion system 104 of the battery core 151. As shown, the power conversion system 104 may include a power conversion unit 152, which may include a power inverter 205, a rectifier 210, a DC-DC converter 215, or combinations thereof. The power conversion unit 152 may be an insulated gate bipolar transistor (IGBT) module, which is part of the PCS 104. The IGBT module may include an array of transistors (e.g., switching semiconductors), capacitors (e.g., filter capacitors), and any other power electronics to convert power. AC current may be present on one side of the power conversion unit 152, and DC current may be present on the other side. IGBT modules are standard, but various architectures can be used.

[0053] The power conversion system 104 also includes heating, ventilation, and air conditioning (HVAC) equipment 153 to maintain the temperature of the equipment (such as the power conversion unit 152) of the PCS 104 within operating limits. HVAC equipment 153 may include air conditioners, such as a fan 154 and a condenser 155, to cool the power conversion unit 152 (e.g., an IGBT module). HVAC equipment 153 may also include a heater 156.

[0054] The power conversion system 104 also includes a PCS controller 160 and environmental sensors 164A to 164N to protect the device of the PCS 104. As shown, the PCS controller 160 includes a network communication interface 161, a processor 162, and a memory 163. The PCS 104 also includes PCS sensors 168A to 168N to measure current amplitude 722 and DC link voltage 723. The environmental sensors 164A to 164N are coupled to the processor 163 and can collect environmental condition data 165A to 165N, for example, by measuring the temperature 165A, 721, and humidity 165B inside the housing of the PCS 104. The memory 163 can store PCS data 157A to 157N, including environmental condition data 165A to 165N, temperature 721 collected by the environmental sensors 164A to 164N, and current amplitude 722 and DC link voltage 723 collected by the PCS sensors 168A to 168N. PCS data 157A to 157N (including environmental condition data 165A to 165N, such as temperature 165A, 721; and current amplitude 722 and DC link voltage 723) are distributed in an optimized scheme 400 (see [link to scheme]). Figures 4A to 4B During this period, it is monitored and used to make decisions on when to run PCS 104.

[0055] Figure 2A This illustrates coupling to electrical application 103. Figures 1A to 1CThe 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, PCS data 157A to 157N from power conversion system 104 (or power conversion subsystem 107) or combinations thereof.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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 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. The first energy storage node 105A can be arranged such that, in a split bus architecture, battery cubes 230A to 230B are connected to the DC bus 225A along with PCS 104A. In a split bus architecture, battery cubes 230C to 230D can also be connected to the DC bus 225B along with PCS 104B. Battery sensors 375A to 375N can measure the DC link voltage 705 of battery cube 230B on the DC bus 225A. PCS sensors 168A to 168N can measure the DC link voltage 723 of PCS 104B on DC bus 225B.

[0060] Figure 3A yes Figure 1A A high-level functional block diagram of the energy storage system 101, which depicts the components of the optimized dispatch control system 115 and control subsystem 110 for 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 optimized allocation of energy storage nodes 105A to 105N.

[0061] 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 subsystem 107; and a control subsystem 110. Figure 3A ) or node controller 172 ( Figure 3B The 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 a combination thereof.

[0062] 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.

[0063] 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 in the figure, the memory 313 of the control system 115 and the array controller 170 is configured to store: optimized dispatch programming 330A; battery data 111A to 111N; required power flow 112; battery states 116A to 116N (including state of charge 116A); environmental condition data 365A to 365N from energy storage nodes 105A to 105N; PCS data 157A to 157N (including environmental condition data 165A to 165N from PCS 104A to 104N); power commands 183A to 183B; battery cost lifetime 131 (including ideal battery operating profile 700); PCS cost lifetime 132 (including ideal PCS operating profile 720); and operating efficiency 133. 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 and battery sensors 375A to 375N may be coupled to the power bus 125 and the DC link (DC bus) 225.

[0064] Control system 115 and array controller 170 are configured to receive or store the required power flow 112 or power capacity 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 183 for electrical application 103 received from electrical application 103 via network 305 from a customer or independent system operator; in this case, the power command 183 is determined externally. Power capacity may be apparent power (e.g., kVA or MVA), such as nameplate capacity measured in volt-amperes, which can be used in power electronics or electronic devices to define capacity in terms of overall power. Active power and reactive power together form apparent power, and manufacturers define the power capacity capability of power electronic devices based on apparent power.

[0065] The power command 183 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 183, for example, based on satisfying a customer or independent system operator request for electrical application 103.

[0066] 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.

[0067] 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 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 optimized dispatch programming 330B, battery data 111A to 111N, battery states 116A to 116N (including state of charge 116A), and environmental condition data 165A to 116N, 365A to 365N.

[0068] 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 365A to 365N, 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 environmental condition data 365A to 365N collected by environmental sensors 370A to 370N and battery data 111A to 111N measured by battery sensors 375A to 375N.

[0069] 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, readings from sensors 315A to 315N, or battery sensors 375A to 375N 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.

[0070] For example, the state of charge (SOC) 116A is a state estimate derived from readings of voltage 111A and current 111B. SOC 116A may be derived from control system 115. Alternatively or additionally, at least one battery management system (BMS) or node controller 172 may derive SOC 116A. The SOC 116A of a first energy storage node 105A comprising multiple battery cubic units 230A to 230N can be determined. Control subsystem 110 may include at least one battery management system (BMS). For example, the SOC 116A of the entire energy storage nodes 105A to 105N can be determined (e.g., the first energy storage node 105A comprises all seven battery cubic units 230A to 230G of all battery storage elements 106A to 106N behind the first energy storage node 105A). SOC calculation may take into account the voltage on DC bus 225 over time. SOC 116A is a value calculated by summing up all the battery cubic meters 230A to 230G at the first energy storage node 105A based on the amount of current passing through and the amount of energy that can be released. SOC 116A can be a parameter reading for the first energy storage node 105A across the entire DC bus 225.

[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.

[0072] 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.

[0073] Figure 4AIt is implemented by control system 115, control subsystem 110 and multiple energy storage nodes 105A to 105N for... Figure 1A An optimized energy storage system 101 with an optimized energy distribution scheme 400. Figure 4A In the example, the optimized dispatch scheme 400 is implemented in the optimized dispatch programming 330A of the control system 115 and the optimized dispatch programming 330B of the control subsystem 110.

[0074] Figure 4B It is implemented by various controllers 170 to 173 of the control system 115 and multiple energy storage nodes 105A to 105N. Figures 1B to 1C An optimized energy storage system 101 with an optimized energy distribution scheme 400. Figure 4B In the example, the optimized dispatch scheme 400 is implemented in the optimized dispatch programming 330A of the array controller 170 and the optimized dispatch programming 330B of the node controller 172.

[0075] refer to Figures 4A to 4B Both, optimized 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. Optimized dispatch programming 330B, stored in memory 353, executed by processor 352 of control subsystem 110 (e.g., node controller 172), can configure control subsystem 110 (e.g., node controller 172) to implement some or all of blocks 405, 410, 415, and 420 described below. More generally, optimized dispatch programming 330A to 330B, executed 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.

[0076] Beginning at box 405, the optimized distribution scheme 400 includes receiving or storing the required power flow 112 for use in electrical application 103. For example, energy storage system 101 may distribute power commands 183 from electrical application 103 to control system 115 (or array controller 170) every 50 to 100 milliseconds.

[0077] Turning now to box 410, the optimized allocation scheme 400 also includes allocating the required power flow 112 across multiple energy storage nodes 105A to 105N based on at least two of the following: (a) the battery cost lifetime 131 of battery storage element 106; (b) the PCS cost lifetime 132 of PCS 104; and (c) the operating efficiency 133 of battery storage element 106 and PCS 104. The operating efficiency 133 may be a function of efficiency curves used to maximize the efficiency of PCS 104 and battery storage element 106. Efficiency curves are typically data charts provided by manufacturers of battery storage elements 106A to 106N and PCS 104A to 104N. The optimized allocation scheme 400 allocates power based on degradation costs to extend the lifespan of the devices (such as battery storage elements 106A to 106N and PCS 104A to 104N) of the energy storage system 101.

[0078] In the example, some of the energy storage nodes 105A to 105D may include newer, more recently replaced battery storage elements 106A to 106D compared to energy storage nodes 105E to 105H which have older battery storage elements 106E to 106H. Based on battery cost life 131, the control system 101 may assign power to energy storage nodes 105A to 105D instead of energy storage nodes 105E to 105H. Alternatively, if energy storage nodes 105H to 105L are hotter than energy storage nodes 105A to 105G, the control system 115 may not assign power to energy storage nodes 105H to 105L until they are cooler. Typically, the power command 183 for the desired power flow 112 does not request full power; for example, the request may be for 10%, 25%, or 50% of the power stored in the energy storage system 101. If the control system 115 can satisfy the power command 183 to extend the lifespan using four energy storage nodes 105A to 105D instead of ten energy storage nodes 105A to 105J, then the control system 115 can generate optimized power commands 183A to 183N.

[0079] If energy storage nodes 105A to 105N with degradation variables 701 to 705 within the minimum and maximum ranges 707 and 708 of the ideal battery operating profile 700 are selected in the allocation of the required power flow 112; and PCS 104A to 104N with stress factors 721 to 723 within the minimum and maximum ranges 726 and 727 of the ideal PCS operating profile 720 are selected, then the lifetime of the entire energy storage system 101 can be extended over time. For example, less heat loss from battery storage elements 106A to 106N and PCS 104A to 104N can improve efficiency. Battery storage elements 106A to 106N are typically the most expensive components of the energy storage system 101, and therefore optimizing the lifetime of these components allows customers to capture more value. Additionally, battery storage elements 106A to 106N cannot operate efficiently when their state of charge 116A is either very empty or very full. If battery storage elements 106A to 106N are selected within the range of states of charge 703A to 703B of the ideal battery operating profile 700, high efficiency and low losses can be achieved in the energy storage system 101. The optimal model for efficiency is when the curves for battery storage elements 106A to 106N and PCS 104A to 104N are aligned, in which case a round-trip efficiency of 98% to 99% from energy storage nodes 105A to 105N and PCS 104A to 104N can be achieved.

[0080] Continue with box 415 and refer to Figure 7A The allocation based on battery cost lifetime 131 can be based on an ideal battery operating profile 700 derived from a research-based battery model or experimental data. The ideal battery operating profile 700 includes at least one of the minimum and maximum ranges of temperature 701A to 701B, current amplitude 702A to 702B, state of charge (SOC) 703A to 703B, rate of change of SOC 704A to 704B, and DC link voltage 705A to 705B. Therefore, as shown in box 415, the allocation based on battery cost lifetime 131 can be a cost function in response to the degradation variables 701 to 705 of the battery storage element 106 to optimize power command 183.

[0081] The optimized allocation scheme 400 allocates power to devices (including energy storage nodes 105A to 105N and PCS 104A to 104N) in the most efficient manner and varies with the efficiency of the devices. For example, if the energy storage system 101 includes four energy storage nodes 105A to 105N, instead of allocating a quarter of the power command 183 to each energy storage node, the optimized allocation could assign 50% to the first energy storage node 105A, 10% to the second energy storage node 105B, and 40% to the third energy storage node 105C. The optimized allocation scheme 400 monitors the battery storage elements 106A to 106N and PCS 104A to 104N of the energy storage nodes 105A to 105N and understands how to degrade the battery storage elements 106A to 106N and PCS 104A to 104N. For example, if battery storage elements 106A to 106N need to be replaced every ten years and PCS 104A to 104N need to be replaced every five years, the optimized distribution scheme 400 can mitigate degradation by using the device in a more efficient manner.

[0082] Now we end at box 420 and refer to... Figure 7B The PCS cost lifetime 132 of PCS 104 can be a function of one or more stress factors 721 to 723 affecting at least one of the filter capacitors and switching semiconductors (or power conversion units 152) of PCS 104. The PCS cost lifetime 132 can be based on an ideal PCS operating profile 720 derived from a research-based PCS model or experimental data. The ideal PCS operating profile 720 includes at least one of the minimum and maximum ranges of temperature 721A to 721B, current amplitude 722A to 722B, and DC link voltage 723A to 723B.

[0083] exist Figure 4A In this context, local control subsystem 110 or control system 115 can implement a subset or all of blocks 405, 410, 415, and 420 of the optimized distribution scheme 400 without a central control system 115. For example, the required power flow 112 can be stored or received from electrical application 103 via network 305 by control system 115 or one, a subset, or all of the control subsystems 110 of energy storage nodes 105A to 105N. The control subsystems 110 of energy storage nodes 105A to 105N that receive the required power flow 112 can then implement blocks 410, 415, and 420.

[0084] exist Figure 4BIn 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 optimized 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. The optimized dispatch programming 330A can be stored on and executed on the core controllers 171A to 171N and the enclosure controllers 173A to 173N.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] Figure 6 This is a flowchart of an optimized allocation method 600 that can be implemented for an energy storage system 100. Figure 6 In the example, method 600 implements the optimized distribution scheme 400 of Figure 4. Starting at step 605, method 600 includes receiving or storing the required power flow 112 for use in electrical application 103.

[0089] Continuing with step 610, method 600 further includes allocating the required power flow across multiple energy storage nodes 105A to 105N based on at least two of the following: (a) the battery cost lifetime of battery storage element 106; (b) the power conversion system cost lifetime of power conversion system (PCS) 104; and (c) the operating efficiency of battery storage element 106 and PCS 104.

[0090] Now proceed to step 615 and refer to... Figure 7A The allocation based on battery cost life 131 can be based on the ideal operating profile 700 of the battery derived from research-based battery models or experimental data.

[0091] Now ending at step 620, the PCS cost lifetime 132 of PCS 104 can be a function of one or more stress factors 721 to 723 that affect at least one of the filter capacitors and switching semiconductors of PCS 104.

[0092] The optimized allocation scheme 400, implemented in optimized allocation programming 330A to 330B, issues optimized power commands 183A to 183N to energy storage nodes 105A to 105N and PCS 104A to 104N. These optimized power commands minimize device degradation while still meeting power requirements, such as the required power flow 112. In most cases, the original power command 183 does not require full-power discharge or charging from the energy storage system 101, meaning that it is not necessary to allocate the full power stored in the energy storage system 101. For example, the original power command 183 may only require fifty percent of the stored power. The optimized power commands 183A to 183N generated by the optimized allocation scheme 400 may utilize only twenty-five percent of the devices, rather than all devices at 50%, as long as it minimizes device degradation and extends the lifespan of the energy storage system 101. If power command 183 requires full power, all devices need to meet the required power flow 112, and a more efficient allocation cannot be achieved.

[0093] Figure 7AThis refers to the ideal operating profile 700 for the battery. The ideal operating profile 700 can be derived from a research-based battery model or experimental data. The ideal operating profile 700 includes at least one of the minimum and maximum ranges of temperature 701A to 701B, current amplitude 702A to 702B, state of charge (SOC) 703A to 703B, rate of change of SOC 704A to 704B, and DC link voltage 705A to 705B.

[0094] Figure 7B This is the ideal operating profile 720 for the PCS. The ideal operating profile 720 can be derived from a research-based PCS model or experimental data. The ideal operating profile 720 includes one or more stress factors 721 to 723 that affect at least one of the filter capacitor and the switching semiconductor (or power conversion unit 152). For example, the ideal operating profile 720 includes at least one of the minimum and maximum ranges of temperature 721A to 721B, current amplitude 722A to 722B, and DC link voltage 723A to 723B.

[0095] Figure 8 This is a block diagram of the optimized dispatch scheme 400 implemented in the optimized dispatch programming 330A to 330B. The optimized dispatch programming 330A to 330B may include a BESS lifetime and efficiency optimization intelligent module (BLIM), which implements... Figure 8 The control command flow and upper-level functions are described herein. BLIM can be implemented in the optimized dispatch scheme 400 described herein. Starting from block 805, the upper-level control functions of the optimized dispatch programming 330A to 330B are initiated.

[0096] Moving to box 825, battery data 111A to 111N and battery states 111A to 111N are provided to battery degradation cost model box 850 from energy storage nodes 105A to 105N via sensors 315A to 315N and battery sensors 375A to 375N. Environmental condition data 365A to 365N are also provided to battery degradation cost model box 850 from energy storage nodes 105A to 105N via environmental sensors 370A to 370N. For example, ambient temperature (T... amb )365A、t d State of charge (SOC) c 116A, Voltage (V) c )111A and current (C)111B are provided from energy storage nodes 105A to 105N to the battery degradation cost model box 850.

[0097] Battery degradation cost model box 850 output degradation cost CB battBattery temperature T batt 701. Current amplitude I702. State of charge (SOC) at the end of the command SOC 结束 703. Rate of change of SOC ΔSOC 704 and DC link voltage V dc 705. Battery cost-lifetime 131 cost function ( CB batt This is implemented in the battery degradation cost model framework 850.

[0098] Proceeding to block 830, PCS data 157A to 157N are provided from power conversion systems 104A to 104N to PCS degradation cost model block 855 via PCS sensors 168A to 168N. Environmental condition data 165A to 165N are also provided from power conversion systems 104A to 104N to PCS degradation cost model block 855 via environmental sensors 164A to 164N. PCS cost lifetime 132 is implemented in PCS degradation cost model block 855. For example, ambient temperature (T... amb )165A, Power Commands 183A to 183B, t d and voltage (V) c )157A is provided from PCS 104A to 104N to PCS degradation cost model box 855.

[0099] In box 855, the PCS degradation cost model box outputs the degradation cost. CI Temperature of PCS inverter T inv 721. Current (I) 722. DC Link Voltage V dc 723, which is related to power commands 183A to 183B ( P cmd This is directly related. Operational efficiency 133 is implemented in the operational efficiency cost model frame 860. The operational efficiency cost model frame 860 outputs the efficiency cost. CE and auxiliary power P aux The auxiliary power can be the energy consumed by the energy storage system 101 (including heating, ventilation and air conditioning (HVAC) equipment) to maintain the batteries 106A to 106N and the power conversion systems 104A to 104N at a suitable temperature; and to keep the associated components powered and connected to the power bus 125 or the DC link (DC bus) 225.

[0100] Proceed to box 880 and execute BLIM command optimization (node ​​command optimizer). As shown in the figure, the node command optimizer is based on at least two of the three cost functions: (1) Battery cost-lifetime cost function ( CB batt (Battery degradation cost model box); (2) PCS cost lifetime 132 function ( CI (PCS degradation cost model box); and (3) operational efficiency 133 cost function ( CE (Operational efficiency cost model framework).

[0101] The node command optimizer box 880 resolves the optimized assignment and outputs optimized power commands 183A to 183N to energy storage nodes 105A to 105N and PCS 104A to 104N. In this example, the power command... P cmd The current states of the batteries 183A to 183B and those from energy storage nodes 105A to 105N (e.g., current 111B) are the input vectors to the battery degradation cost model box 850. Power commands 183A to 183B are also provided. P cmd The current state of the inverters from PCS 104A to 104N is the input vector for the PCS degradation cost model block 855. The operating efficiency cost model 860 is based on power command. P cmd 183A to 183B. The goal of the optimized dispatch programming 330A to 330B for BLIM is to take into account the state of each energy storage node 105A to 105N and PCS 104A to 104N, including the degradation variables 701 to 705 of battery storage elements 106A to 106N and the stress factors 721 to 723 of PCS 104A to 104N, as well as the operating efficiency 133 and cost, to optimize the power command 183A to 183N in order to maximize the lifetime of energy storage nodes 105A to 105N and to enable energy storage nodes 105A to 105N and PCS 104A to 104N to operate at their most efficient operating points. The real-time optimized tuner block 885 uses predicted degradation variables 701 to 705 of the battery storage elements 106A to 106N and stress factors 721 to 723 of the PCS 104A to 104N, along with auxiliary power, to compare with the actually sensed degradation variables 701 to 705 and stress factors 721 to 723. Blocks 880 and 885 are in... Figure 9 A more detailed description is provided below.

[0102] Battery degradation is influenced by various key parameters, including temperature, depth of cycle, cycle frequency, charge / discharge current magnitude, rate of change of state of charge (SOC), terminal voltage, and cycle duration. Battery degradation can be measured at any level of the energy storage system 101, whether uniformly or discretely, from the entire energy storage system 101 through the battery array 150, battery cores 151A to 151N, energy storage nodes 105A to 105N, battery cubes 230A to 230N, battery storage elements 106A to 106N, battery racks, battery modules, battery strings, down to individual battery cells and sub-cells within the battery modules. Implementing optimized control strategies for the batteries can lead to a significant reduction in degradation, and optimizing the performance of the energy storage system 101 and improving degradation can be achieved without impacting profitability. Based on experimental data and taking into account factors such as battery chemistry and operational services, it is estimated that even a 2% improvement in degradation can have a significant impact on profits.

[0103] In the first optimization function, the BLIM's battery cost-lifetime 131 optimization function (box 850) will improve the overall customer profitability of the energy storage system 101 by extending battery life. In this example, the ideal battery operating profile 700 is used as algorithm input. This ideal battery operating profile 700 can be derived from various sources, such as research-based battery models or experimental data. The ideal battery operating profile 700 represents the multidimensional space of degradation variables 701 to 705 used by the cost function to optimize the power command 183. Figure 7A The parameters of the ideal battery operating profile 700 are shown. It can be seen that the number of cycles is not included in the ideal battery operating profile 700 because the ideal battery operating profile 700 is a supplement to the existing control, in which the charging and discharging power commands 183A to 183B have already been determined in the upper-level control function 805.

[0104] In other examples, the algorithm input can be a single value or an array of distinct values. A single value can be an aggregation of multiple values, or it can be the output of a formula or algorithm itself. Input values ​​can be fixed or variable, and can be instantaneous measurements or leading or lagging indicators.

[0105] Returning to the current example, the parameters of the ideal operating profile 700 of the battery affect the battery's round-trip efficiency (RTE). Therefore, the optimization algorithm also improves the RTE efficiency. For example, the battery's equivalent series resistance (ESR) depends on the battery temperature, and thus the battery's efficiency will be affected by temperature. There are also inter-couplings among some of these variables (such as temperature 701 and current amplitude 702). Therefore, the optimized control method reduces the risk of thermal runaway when both high ambient temperatures and high current amplitudes are present.

[0106] In optimal control, state variablesx ( t ) are the battery degradation variables 701 to 705, and the power command 183 is the control input u( t Using this method, the optimized formula is as follows: in This is the degradation cost function to be minimized. (Gain vector) Apply a penalty to each of the degenerate states.

[0107] A state-space model represents the battery cost-lifetime model. Various battery cost-lifetime models, crucial for the accuracy of optimization of energy storage nodes from 10⁵A to 10⁵N, were developed. In this battery cost-lifetime model, a black-box model using artificial intelligence is considered. Using this method, the following cost function was developed for the active operating state modes of each energy storage node from 10⁵A to 10⁵N: Furthermore, for each battery cubic unit from 230A to 230N, the following model is considered for the idle operating state mode: Gain k 1 to k 8 It is a configurable penalty gain. t min This is the expected time to achieve full SOC. (The last item...) k 8 (t - t min ) This is used to incorporate the need for shorter recharge times to ensure that the highest available capacity is obtained as quickly as possible. In this example, cycling (e.g., charging and discharging energy storage nodes 105A to 105N) is not considered. Cycling control is delegated to the upper-level control function 805 to determine whether to perform cycling. These cost functions... CB 活动 and CB 空闲 The value increases as the system stress state variable deviates from the optimal profile. If the state variable is exactly at the optimum, the effect of this parameter will be zero; and if the state variable is negative, it indicates that the energy storage system 101 is more relaxed, allowing for a margin to withstand more stress. Depending on the implementation, if it is desired to consider only cost and ignore the additional stress margin, the negative term can be cleared to zero.

[0108] The second optimization function is the PCS cost lifetime 132, shown in block 855 as the PCS degradation cost model. The inverter lifetime is a function of stress factors 721 to 723, which primarily affect the filter capacitors and switching semiconductors, such as insulated-gate bipolar transistors (IGBTs), field-effect transistors (FETs), etc. Reducing the number and duration of stress factors 721 to 723 can delay the degradation of the capacitors and semiconductor switches. Figure 7B These parameters are shown for the ideal operating profile 720 of the PCS. The following is about... CI The formula is a model of the cost function of inverter lifetime.

[0109] Gain k 1 to k 4 It is a configurable penalty gain. This cost function... CI The stress factor increases with each deviation from the optimum in the range of 721 to 723. If the state variable is zero, it does not cause stress; and if the state variable is negative, it indicates that the energy storage system 101 can withstand higher stress. (This is related to the battery cost function.) CB 活动 and CB 空闲 Similarly, depending on the implementation, if the expectation is to consider only cost and ignore additional stress margins, then the negative term can be cleared to zero.

[0110] The third optimization function is the operating efficiency cost function 133, shown in box 860 as an operating efficiency cost model. PCS 104 (e.g., the inverter) and battery storage elements 106A to 106N have efficiency curves indicating the most efficient operating mode. PCS 104A and 106N typically operate most efficiently at 75% load. Using these efficiency curves, an efficiency function can be created for the entire energy storage system 101.

[0111] In which gain k 1 , k 2 and k 3 It is a configurable penalty gain. P inv This is the calculated individual power of each inverter 104. P batt It is the calculated individual power of each battery storage element, ranging from 10⁶A to 10⁶N, and P aux This refers to the received power command 183.P cmd Auxiliary power. Function , and These are cost functions associated with deviations from the efficient operating point. These functions can be derived from the datasheets and specifications of PCS 104 (e.g., inverters) and battery storage elements 106A to 106N. For example, It can be defined as follows: In the example above of the PCS cost lifetime 132 function for inverter efficiency, the inverter specification indicates the inverter at nominal power... P nom It is most efficient at 75% or higher of the nominal power. At a percentage below 75% of the nominal power, a linear cost function is defined, which increases as the power decreases, and the coefficient is... .

[0112] The optimized dispatch programming 330A to 330B utilizes time periods permitted by requirements (e.g., contract execution) to optimize upper-level control functions 805. These available times are referred to as “flexible times.” Flexible times are the operational periods during which the energy storage system 101 does not participate in critical services. During these intervals, the optimized dispatch programming 330A to 330B optimizes the operation of the energy storage system 101 by charging and discharging the batteries (e.g., battery storage elements 106A to 106N of energy storage nodes 105A to 105N) to minimize degradation and loss, thereby extending the lifespan of the energy storage system 101.

[0113] Figure 9 This is another block diagram of the optimized dispatch scheme 400 implemented in the optimized dispatch programming 330A to 330B. The optimized dispatch programming 330A to 330B may include a BLIM, which implements... Figure 9 The supervisory control process. Starting from box 905, the optimized dispatch programming 330A to 330B's supervisory control process determines whether BLIM is enabled. If BLIM is enabled by the operator or... Figure 8 If the upper-level control function 805 is disabled, then the process enters box 910 and bypasses BLIM 500. As shown in the figure, the optimized dispatch programming of box 910 for 330A to 330B bypasses BLIM and causes the original power command to be executed. P cmd 183 is output directly and unaffected, as Figure 8 BLIM output in box 880 . Figure 8The upper-level control function block 805 determines whether to enable / disable optimization features based on the defined flexible time of the energy storage system 101. Raw power command 183 P cmd and optimized power command 183A It can be done in the form of a vector of power commands 183A to 183N to each energy storage node 105A to 105N.

[0114] If the optimized dispatch programming BLIM for 330A to 330B is not disabled by the operator or upper-level control function block 805, then BLIM is enabled in block 915, and raw power command 183 is started. P cmd ) modified to optimized power commands 183A to 183N ( The process proceeds to box 920, where degradation variables 701 to 705, stress factors 721 to 723, and raw power command 183 (e.g., power requested by a requirement or to fulfill a contractual obligation) are collected or measured for each energy node 105A to 105N. In box 925, optimized dispatch programming 330A to 330B determines whether a power command 183 has been issued to energy storage system 101. If a power command 183 has been issued to energy storage system 101, optimized dispatch programming 330A to 330B proceeds to box 930, where an AI-optimized activity mode is run.

[0115] Entering box 930, the AI-optimized activity mode of the optimized dispatch programming 330A to 330B is calculated based on the degradation variable 705, stress factors 721 to 723, and original power command 183 collected in box 920, as well as the configurable penalty gain for each energy storage node 105A to 105N. CB 活动 Function (Battery Cost Lifetime 131) CI Function (PCS cost lifetime 132) and CE The function (operational efficiency 133). Once the optimized value is reached, it is based on... CB 活动 Function (Battery Cost Lifetime 131) CI Function (PCS cost lifetime 132) and CE The result of function (operation efficiency 133) is used to modify the original power command 183. P cmd ), to generate optimized power commands 183A to 183N ( The optimized power command is based on the original power command 183. CB 活动 (Battery cost life 131) CI (PCS cost life 132) and CE The function (operational efficiency 133) results are used to adjust the performance of energy storage system 101, energy storage nodes 105A to 105N, PCS 104A to 104N and their components.

[0116] If no power command 183 has been issued to energy storage system 101, the optimized dispatch programming 330A to 330B BLIM proceeds to block 935, where an AI-optimized idle mode is run. The AI-optimized idle mode calculates the above based on the degradation variables 701 to 705 and stress factors 721 to 723 collected in block 920, as well as the configurable penalty gain for each energy storage node 105A to 105N. CB 空闲 Function (Battery Cost Lifetime 131) CI Function (PCS cost lifetime 132) and CE The function (operational efficiency 133). Once the optimized value is reached, it is based on... CB 空闲 Function (Battery Cost Lifetime 131) CI Function (PCS cost lifetime 132) and CE The result of function (operation efficiency 133) is used to modify the original power command 183. P cmd ), to generate optimized power commands 183A to 183N ( The optimized power command is based on... CB 活动 (Battery cost life 131) CI (PCS cost life 132) and CE The function (operational efficiency 133) results are used to adjust the performance of energy storage system 101, energy storage nodes 105A to 105N, PCS 104A to 104N, and their components. The optimized power allocation programming 330A to 330B is node-specific, allowing for optimization of power commands 183 for each inverter of PCS 104A to 104N and each energy storage node 105A to 105N. The optimized power allocation programming 330A to 330B allocates the required power flow 112 based on the degradation variables 701 to 705 of each energy storage node 105A to 105N and the stress factors 721 to 723 of PCS 104A to 104N. For illustration, if the first energy storage node 105A has a higher temperature 701 due to an HVAC system failure, the optimized power allocation programming 330A to 330B seeks to satisfy the optimized power commands 183A to 183N. Conditions that do not cause excessive stress to the first energy storage node 105A.

[0117] After running the AI-optimized idle mode in block 935 or the AI-optimized active mode in block 930, the optimized dispatch programming 330A to 330B is performed to determine whether any state variables exceed the optimal profile. Therefore, the degradation variables 701 to 705 of the energy storage nodes 105A to 105N are compared with the ideal battery operating profile 700, which includes the minimum and maximum ranges of temperature 701A to 701B, current amplitude 702A to 702B, state of charge (SOC) 703A to 703B, rate of change of SOC 704A to 704B, and DC link voltage 705A to 705B. Additionally, the stress factors 721 to 723 of PCS 104A to 104N are compared with the ideal PCS operating profile 720, which includes the minimum and maximum ranges of temperature 721A to 721B, current amplitude 722A to 722B, and DC link voltage 723A to 723B.

[0118] If the state variables do not exceed the optimal profile during the comparison in box 940, then in box 950, the optimized dispatch programming 330A through 330B exits BLIM. However, if any state variable exceeds the optimal profile during the comparison in box 940, then box 945 is reached. In box 945, the optimized penalty gain is updated for the associated state (active or idle). After box 945, the optimized dispatch programming 330A through 330B returns to either the active mode box 930 or the idle mode box 935, depending on which was most recently executed.

[0119] The cost functions described above for battery cost life 131, PCS cost life 132, and operating efficiency 133 can be directly used as models for the degradation of battery storage elements 106A to 106N, PCS 104A to 104N, and operating efficiency 133. Each model can be an analytical model or an AI-based model. In another example, an intermediate step can be added to first process the degradation variables 701 to 705 of the ideal battery operating profile 700 and the stress factors 721 to 723 of the ideal PCS operating profile 720 in the defined model, and then use these variables to calculate the cost functions 131 to 133 described above. However, the first method is simplified, and its processing is improved. The cost functions 131 to 133 described above can be defined by different functions (such as higher-order terms).

[0120] In the above example, 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 each include network communication interfaces 161, 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 161, 311, and 351 from / to the device to provide data communication between electrical 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., Wi-Fi, Bluetooth™, ZigBee, LiFi, IrDA, etc.), or a combination of wired and wireless technologies.

[0121] Any of the functionalities (including optimized dispatch programming 330A to 330B) of the optimized dispatch scheme 400 described herein for 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 can be embodied in one or more applications or firmware as previously described. 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).

[0122] In the above example, 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 may each include a processor. As used herein, processors 162, 312, and 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 that form a programmable central processing unit (CPU). For example, processors 162, 312, and 352 include one or more integrated circuit (IC) chips or portions thereof, which are incorporating electronic components to perform the functions of the CPU. For example, processors 162, 312, and 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 circuitry can be used to form the CPU or processor hardware. The examples shown for processors 162, 312, and 352 can include a microprocessor or multiprocessor architecture. Digital signal processors (DSPs) or field-programmable gate arrays (FPGAs) can be suitable replacements for processors 162, 312, and 352, but they will consume more power and increase complexity.

[0123] Applicable processors 162, 312, and 352 execute programming or instructions to configure 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, 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 162, 312, and 352 can be configured using hardwired logic, a typical processor is a general-purpose processing circuit configured by executing programs, such as instructions and any associated setup data received from illustrated memories 163, 313, and 353 or other included storage media and / or from remote storage media.

[0124] In the above example, 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 each include memory. Memory 163, 313, and 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 162, 312, and 352, for example, as working data processing memory. Flash memory typically provides long-term storage.

[0125] Of course, other storage devices or configurations can be added to or replace the storage devices or configurations in the examples. Such other storage devices can be implemented using any type of storage medium in which computer or processor-readable instructions or programs are stored, and can include any or all tangible memory or associated modules such as computers, processors, etc.

[0126] 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., which can be used to implement client devices, media gateways, code converters, etc., as 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 memory 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.

[0127] 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 optimized dispatch scheme 400 and optimized dispatch programming 330A to 330B. 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 of a computing device.

[0128] In another exemplary embodiment, program code (such as optimized dispatch scheme 400 and optimized dispatch programming 330A to 330B) 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.

[0129] One or more processors 162, 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.

[0130] When this disclosure is implemented using programming or software including optimized 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.

[0131] 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.

[0132] 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%.

[0133] 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.

[0134] 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.

[0135] 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: Power conversion system (PCS); Multiple energy storage nodes, wherein the multiple energy storage nodes include: Battery storage components, and A control subsystem is configured to receive battery data from the battery storage element, PCS data from the power conversion system, or a combination thereof; and A control system coupled to the plurality of energy storage nodes and configured to receive or store the required power flow; The control system is configured as follows: Receive or store the required power flow for electrical applications; and The required power flow is distributed across the plurality of energy storage nodes based on at least two of the following: (a) the battery cost lifetime of the battery storage element; (b) the PCS cost lifetime of the PCS; and (c) the operating efficiency of the battery storage element and the PCS.

2. The energy storage system of claim 1, wherein the battery cost lifetime is based on an ideal battery operating profile derived from a research-based battery model or experimental data.

3. The energy storage system of claim 2, wherein the ideal operating profile of the battery includes at least one of the minimum and maximum ranges of temperature, current amplitude, state of charge (SOC), rate of change of SOC, and DC link voltage.

4. The energy storage system of claim 2, wherein the allocation based on the battery cost lifetime is a cost function in response to the degradation variable of the battery storage element to optimize power command.

5. The energy storage system of claim 1, wherein the PCS cost lifetime is a function of one or more stress factors affecting at least one of the filter capacitors and switching semiconductors of the PCS.

6. The energy storage system of claim 5, wherein the PCS cost lifetime is based on the ideal operating profile of the PCS derived from a research-based PCS model or experimental data.

7. The energy storage system of claim 6, wherein the ideal operating profile of the PCS includes at least one of a minimum range and a maximum range of temperature, current amplitude, and DC link voltage.

8. The energy storage system of claim 1, wherein the operating efficiency is based on a function for maximizing the efficiency curves of the PCS and the battery storage element.

9. A non-transitory computer-readable medium comprising optimized dispatch programming, wherein execution of the optimized dispatch programming by one or more processors configures one or more controllers to: Receive or store the required power flow for electrical applications; and The required power flow is distributed across multiple energy storage nodes based on at least two of the following: (a) the battery cost lifetime of the battery storage element; (b) the power conversion system cost lifetime of the power conversion system (PCS); and (c) the operating efficiency of the battery storage element and the PCS.

10. The non-transitory computer-readable medium of claim 9, wherein the battery cost lifetime is based on an ideal battery operating profile derived from a research-based battery model or experimental data.

11. The non-transitory computer-readable medium of claim 10, wherein the ideal operating profile of the battery includes at least one of temperature, current amplitude, state of charge (SOC), rate of change of SOC, and minimum and maximum ranges of DC link voltage.

12. The non-transitory computer-readable medium of claim 10, wherein the assignment based on the battery cost lifetime is a cost function in response to a degradation variable of the battery storage element to optimize power command.

13. The non-transitory computer-readable medium of claim 9, wherein the PCS cost lifetime is a function of one or more stress factors affecting at least one of the filter capacitors and switching semiconductors of the PCS.

14. The non-transitory computer-readable medium of claim 13, wherein the PCS cost lifetime is based on an ideal operating profile of the PCS derived from a research-based PCS model or experimental data.

15. The non-transitory computer-readable medium of claim 14, wherein the ideal operating profile of the PCS includes at least one of a minimum range and a maximum range of temperature, current amplitude, and DC link voltage.

16. The non-transitory computer-readable medium of claim 9, wherein the operating efficiency is based on a function for maximizing the efficiency curves of the PCS and the battery storage element.

17. A method comprising: Receive or store the required power flow for electrical applications; as well as The required power flow is distributed across the plurality of energy storage nodes based on at least two of the following: (a) the battery cost lifetime of the battery storage element; (b) the power conversion system cost lifetime of the power conversion system (PCS); and (c) the operating efficiency of the battery storage element and the PCS.

18. The method of claim 17, wherein the battery cost life is based on an ideal battery operating profile derived from a research-based battery model or experimental data.

19. The method of claim 18, wherein the ideal operating profile of the battery includes at least one of temperature, current amplitude, state of charge (SOC), rate of change of SOC, and minimum and maximum ranges of DC link voltage.

20. The method of claim 18, wherein the allocation based on the battery cost lifetime is a cost function in response to the degradation variable of the battery storage element to optimize power command.

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