LARGE SCALE ENERGY STORAGE WITH AI-BASED GRADING AND EFFICIENCY ASSESSMENT

A large-scale energy storage facility that includes autonomous energy storage towers for housing cells in a manufacturing stage. Electrical energy is selectively stored in the cells from an energy source or grid as reserve energy by providing. This is performed during an inventorying stage of a manufacturing process of the cells. The energy storage includes a charge cycle that is exploited in a quality assessment process of the cells. The energy is selectively retrieved for provision back to the grid or energy source in a discharge cycle which is also exploited for quality assessment of the cells. A highly energy efficient and cost-effective framework is thus obtained from the architecture.

TECHNICAL FIELD

The present disclosure relates generally to a large-scale energy storage facility, battery grading using AI-based techniques, and efficiency assessment and control of power systems using AI-based techniques, some or all of which may be used individually or in conjunction with one another.

BACKGROUND

Electric power is typically obtained from one or more primary power generation sources, such as gas-fired, coal-fired, nuclear, and/or hydroelectric power plants, and delivered to users via a distribution grid. The amount of electricity provided by these sources and the demand placed on them may change at any time. These sources may be controlled to satisfy consumer demands while also adhering to industry standards for power, such as nominal voltage and frequency levels.

Backup energy storage systems may be used to supplement power supplied by the primary power generation sources during peak load or high demand periods, disruptions such as power interruptions from primary power generation sources or transmission constraints during normal operations, a power generator going offline, or to augment intermittent or variable power sources, among other things.

DETAILED DESCRIPTION

The illustrative embodiments recognize that on hand, sustainable, low-cost energy storage systems may be critical in the operation of a power grid. On another hand, demand for electric vehicles (EV) and EV batteries is rapidly increasing, bringing along with it increased cell manufacturing and efficiency requirements. By combining the provision of low-cost battery energy storage systems for a grid with a capacity to manufacture high quality battery cells, in a common architecture, a highly scalable and efficient factory model may be obtained. The illustrative embodiments recognize that a charging and discharging of cells during a cell manufacturing process may usually be confined to just a required minimum number of cycles (e.g., once or twice) due to associated costs and time requirements which may make it impractical to demand more than a standard conventional cell quality. However, by utilizing a framework that combines grid energy storage operations, in which energy may be stored in cells in a charging process and retrieved at a later time in a discharging process, with cell manufacturing operations in which cells in a manufacturing stage may undergo a series of charge-discharge cycles to access cell quality, not only may it be practical to perform much more than just the minimum number of cycles for grading cells, it may be more efficient because the charge-discharge cycles may utilize energy from the grid as opposed to external energy that may otherwise be disposed of as heat. The ability to more accurately determine cell quality may be enhanced and a use of temperature management systems, ventilation systems and air-conditioners to dissipate heat in conventional solutions during discharge cycles may be substantially eliminated due to the saving and re-utilization of energy.

In one aspect, the common architecture may be configured as an energy storage tower which may be autonomous. The autonomous energy storage tower may include a plurality of cells configured as one or more cell arrays having a temporary no-weld architecture which may comprise a temporary cell array electronic circuit to communicate with the cells without permanent physical connection or weld to the cells. The autonomous energy storage tower may also comprise a power conversion module configured to electrically couple the autonomous energy storage tower to a grid and to an energy source, with the power conversion module being configured to, responsive to completion of a SEI (solid electrolyte interface) layer formation process of the cells, receive electrical energy from the energy source and to selectively supply said electrical energy to one or more of the plurality of cells in a charging mode. The power conversion module may also be further configured to, responsive to completion of the SEI layer formation process of the cells, selectively retrieve electrical energy from one or more of the plurality of cells to supply said electrical energy to the grid or the energy source in a discharging mode. By using the temporary no-weld architecture, the cells may be easily removed upon completion of corresponding cell quality assessment procedures (and thus completion of cell manufacturing) and packed into an EV battery pack. Herein, the term grid may be used to generally refer to all types of power grids including a conventional grid and a micro grid.

In another aspect, a large-scale energy storage facility may be disclosed. The large-scale energy storage facility may be a “Gigafactory” and may comprise at least one autonomous energy storage tower. In an embodiment, the large-scale energy storage facility may comprise between 5000-50,000 autonomous energy storage towers and may be configured to store 1-20 GWh of energy.

In another aspect, a method may be disclosed comprising providing the large-scale energy storage facility which may include a plurality of manufacturing stations and selectively storing energy from an energy source as reserve energy by providing to at least one cell of a plurality of cells disposed in one or more energy storage towers the energy from the energy source, said selectively storing step being performed during an inventorying stage of the manufacturing process of the at least one cell by providing the energy as energy for a charge cycle of the at least one cell. The method may further comprise a step of selectively retrieving the reserve energy for provision to a grid or to the energy source by discharging energy from the at least one cell during a discharge cycle, obtaining cell parameter information about the at least one cell during the charge cycle and/or discharge cycle, and grading a quality of the at least one cell during a cell quality assessment process of the inventorying stage based on the obtained cell parameter information.

In an embodiment, utilizing the cells to store and provide energy to the grid while assessing a quality metric of the cells simultaneously may provide significant advantages by way of cost savings. Further, upon determining that the grid no longer needs reserve energy or can't accept extra energy, at least for a period of time, assessing the quality metric of the cells may still be performed through transferring energy bi-directionally in a series of charge-discharge between two groups of cells rather than between cells and the grid/energy source. For example, a first group of selected cells (e.g., 4 million cells) may be discharged in a first operation responsive to which a second group of selected already charged cells (e.g., 4 million previously charged cells) may be used as a source of energy for charging the first group discharged cells. By transferring energy between the first and second group of cells in a plurality of charge-discharge cycles, a quality metric of the cells may be monitored and assessed to grade the cells into one or more performance bins.

In another aspect, an intelligent proposal of one or more battery cell quality enhancement operations may be disclosed. The intelligent proposal may comprise the steps of independently measuring, by at least one cell array controller, parameters of one or more cells of a plurality of cells disposed in a cell array of an energy storage tower; receiving the measured parameters as at least a part of a set of subject large-scale energy storage facility parameters, indicative of one or more characteristics of the large-scale energy storage facility, for use by an efficiency assessment module; generating input data using at least the set of subject large-scale energy storage facility parameters; extracting one or more features from the input data, the one or more features are representative of a characteristic of a request for completing a operational efficiency enhancement proposal operation, and proposing, using the efficiency assessment module, at least one operational efficiency enhancement proposal for the large-scale energy storage facility. The efficiency assessment module may operate as a machine learning engine and may perform several steps including assessing a quality metric of the cells. Thus, the intelligent proposal may maximize cost efficiency by not significantly degrading cells in charge-discharge cycles. Further, should the grid have a need to dispose of a significant amount of energy to the large-scale energy storage facility for storage, the machine learning model may predict such a need and propose beforehand one or more recommendations for operating or manufacturing the cells in the large-scale energy storage facility to accommodate the predicted need. For example, it may be proposed that 1 GWh of storage may be needed in 7 days, for example, due to a forecasted increase in sunlight and thus an increase in the ability of a renewable energy source to provide energy for storage, or due to a forecasted power outage based on an analysis of power outage historic data and/or weather patterns

The architecture and manner of operating cells of the architecture described herein is unavailable in the presently available methods in the technological field of endeavor pertaining to battery energy storage systems and electric vehicles. The term electric vehicle is used hereinafter to collectively refer to vehicles such as motor vehicles, railed vehicles, watercraft and aircraft that are configured to utilize rechargeable electric batteries as their main source of energy to power their drive systems for propulsion. The illustrative embodiments are described with respect to certain types of data, functions, algorithms, equations, model configurations, locations of embodiments, additional data, devices, data processing systems, environments, components, and applications only as examples. Any specific manifestations of these and other similar artifacts are not intended to be limiting to the disclosure. Any suitable manifestation of these and other similar artifacts can be selected within the scope of the illustrative embodiments.

Furthermore, the illustrative embodiments may be implemented with respect to any type of data, data source, or access to a data source over a data network. Any type of data storage device may provide the data to an embodiment of the disclosure, either locally at a data processing system or over a data network, within the scope of the disclosure. Where an embodiment is described using a client device, any type of data storage device suitable for use with the client device may provide the data to such embodiment, either locally at the client device or over a data network, within the scope of the illustrative embodiments.

With reference to the figures and in particular with reference toFIG.1, this figure is an example diagram of a power delivery environments in which illustrative embodiments may be implemented.FIG.1is only an example and is not intended to assert or imply any limitation with regard to the environments in which different embodiments may be implemented. A particular implementation may make many modifications to the depicted environments based on the following description.

FIG.1depicts a block diagram of a network of systems in which illustrative embodiments may be implemented. Power delivery environment100is a network of power delivery systems102and computers in which the illustrative embodiments may be implemented. Power delivery environment100comprises power grids128, energy sources such as a renewable energy sources130, power delivery system102and network/communication infrastructure104. Network/communication infrastructure104is the medium used to provide communications links between various devices, databases and computers connected together within power delivery environment100. Network/communication infrastructure104may include connections, such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processing systems connected to network/communication infrastructure104and are not intended to exclude other configurations or roles for these data processing systems. Server106and server108couple to network/communication infrastructure104along with storage unit110. Software applications may execute on any computer in power delivery environment100. Client112and dashboard114are also coupled to network/communication infrastructure104. Client112may be a remote computer with a display or may even be a mobile device configured with an application to send or receive information, such as to receive a charge condition of the power delivery system102. Dashboard114may be located inside the large-scale energy storage facility and may be configured to send or receive any of the information discussed herein. A data processing system, such as server106or server108, or clients (client112, dashboard114) may contain data and may have software applications or software tools executing thereon.

Only as an example, and without implying any limitation to such architecture,FIG.1depicts certain components that are usable in an example implementation of an embodiment. For example, servers and clients are only examples and do not to imply a limitation to a client-server architecture. As another example, an embodiment can be distributed across several data processing systems and a data network as shown, whereas another embodiment can be implemented on a single data processing system within the scope of the illustrative embodiments. Data processing systems (server106, server108, client112, dashboard114) also represent example nodes in a cluster, partitions, and other configurations suitable for implementing an embodiment.

Power delivery system102comprises autonomous energy storage towers comprising cell arrays that contain cells that are yet to complete their manufacturing phase. The cells may be charged and discharged for grading purposes and for grid energy storage purposes. As discussed hereinafter, the autonomous energy storage towers may have different forms and may be operated by a mobile drive unit that may be separate from or integrated into the autonomous energy storage tower. The autonomous energy storage tower may have a single cell chemistry or a plurality of different cell chemistries. In one example, cells of the autonomous energy storage tower may have a chemistry that enables prioritizing high energy density applications over high cycle life applications or may have a chemistry that enables prioritizing high cycle life applications over high energy density applications, and each cell array may include a corresponding cell array controller, with quality metrics of each cell of the plurality of cells being independently measurable by a sensor or by the cell array controller.

Prioritizing depletion of energy may be achieved by prioritizing depletion of energy of a cell before extracting energy from another cell. This may enable balancing the needs of power delivery and preservation of charge cycles of said another other cell due to its low cycle life, high energy density chemistry. Prioritizing depletion of cycle life of a cell before extracting cycles from another cell may comprise utilizing the cycle life of high cycle life, low energy density cells before using the cycle life available in low cycle life, high energy density cells. Thus, high energy density cells may best be suited for capacity firming applications wherein high capacity is needed for relatively long-term storage, whereas high cycle life cells may best be suited for high cycling applications such as quality assessment of cells where numerous cycles may be required.

The method may also include prioritizing storing of energy in said at least one other cell before storing energy in the one cell (to provide large storage capacity due to the higher energy density of the one other cell. For the capacity market-capacity firming).

As used herein, the “cycle life” of a battery refers to the number of times the battery may be depleted to 100% depth of discharge (DoD) while still holding at least 80% of its original charge. So, for example, a battery having a cycle life of 100 cycles would hold 80% of its original charge after being charged and completely depleted 100 times. Herein, some cells may be configured for future use, when manufacturing of the cells is concluded, in a high cycle, low energy density traction battery of an electric vehicle and thus a corresponding cell chemistry may be selected to provide a high cycle life of about 3000 cycles (for example, at least 2500 or 3000 cycles). In conventional battery chemistries, this cycle life typically provides a corresponding cell a low energy density of about 400 Wh/L and below. To accommodate a predetermined range requirement for non-traction applications, other cells may also be configured for future use in a range battery, wherein the cell chemistry may be selected to provide, for example, a high energy density of between 1000 and 1100 Wh/L, or above. This typically provides a corresponding low cycle life of about 200 cycles (for example, between 200 and 350 cycles) or less. Depending on the energy requirements of a cells after completion of their manufacturing stage, other chemistries may optionally be configured.

Client application120, dashboard application122, or any other application such as server application116implements an embodiment described herein. Any of the applications may use data from power delivery system102and other sources to compute power or energy requirements. The applications may also obtain data from storage unit110for predictive analytics. The applications can also execute in any of data processing systems (server106or server108, client112, dashboard114).

In the depicted example, server106may provide data, such as boot files, operating system images, and applications to client112, and dashboard114. Client112, and dashboard114may be clients to server106in this example. Client112and dashboard114or some combination thereof, may include their own data, boot files, operating system images, and applications. Power delivery environment100may include additional servers, clients, and other devices that are not shown.

Server108may include a search engine configured to search information, such as weather condition, grid consumption data, frequency modulation, total energy input into the large-scale energy storage facility, a required cell lifetime at the end of a cell manufacturing stage, a required capacity at the end of a cell manufacturing stage, a maximum cell degradation at the end of a cell manufacturing stage, and a cell chemistry, user feedback, measured cell data, etc., automatically or in response to a request from an operator for power delivery as described herein with respect to various embodiments.

In the depicted example, power delivery environment100may be the Internet. Network/communication infrastructure104may represent a collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP), Controller Area Network BUS (CAN bus) and/or other protocols to communicate with one another. At the heart of the Internet is a backbone of data communication links between major nodes or host computers, including thousands of commercial, governmental, educational, and other computer systems that route data and messages. Of course, power delivery environment100also may be implemented as a number of different types of networks, such as for example, an intranet, a local area network (LAN), or a wide area network (WAN).FIG.1is intended as an example, and not as an architectural limitation for the different illustrative embodiments.

Among other uses, power delivery environment100may be used for implementing a client-server environment in which the illustrative embodiments may be implemented. A client-server environment enables software applications and data to be distributed across a network such that an application functions by using the interactivity between a client data processing system and a server data processing system. Power delivery environment100may also employ a service-oriented architecture where interoperable software components distributed across a network may be packaged together as coherent business applications. Power delivery environment100may also take the form of a cloud and employ a cloud computing model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service.

With reference toFIG.2, this figure depicts a block diagram of a data processing system in which illustrative embodiments may be implemented. Data processing system200is an example of a computer, such client112, dashboard114, s server106, or server108, in FIG.1, or another type of device in which computer usable program code or instructions implementing the processes may be located for the illustrative embodiments.

Data processing system200is described as a computer only as an example, without being limited thereto. Implementations in the form of other devices, may modify data processing system200, such as by adding a touch interface, and even eliminate certain depicted components from data processing system200without departing from the general description of the operations and functions of data processing system200described herein.

In the depicted example, local area network (LAN) adapter212is coupled to South Bridge and input/output (I/O) controller hub (SB/ICH)204. Audio adapter216, keyboard and mouse adapter220, modem222, read only memory (ROM)224, universal serial bus (USB) and other ports232, and PCI/PCIe devices234are coupled to South Bridge and input/output (I/O) controller hub (SB/ICH)204through bus218. Hard disk drive (HDD) or solid-state drive (SSD)226aand CD-ROM230are coupled to South Bridge and input/output (I/O) controller hub (SB/ICH)204through bus228. PCI/PCIe devices234may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. Read only memory (ROM)224may be, for example, a flash binary input/output system (BIOS). Hard disk drive (HDD) or solid-state drive (SSD)226aand CD-ROM230may use, for example, an integrated drive electronics (IDE), serial advanced technology attachment (SATA) interface, or variants such as external-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236may be coupled to South Bridge and input/output (I/O) controller hub (SB/ICH)204through bus218.

Memories, such as main memory208, read only memory (ROM)224, or flash memory (not shown), are some examples of computer usable storage devices. Hard disk drive (HDD) or solid-state drive (SSD)226a, CD-ROM230, and other similarly usable devices are some examples of computer usable storage devices including a computer usable storage medium.

Instructions for the operating system, the object-oriented programming system, and applications or programs, such as application116and client application120are located on storage devices, such as in the form of codes226bon Hard disk drive (HDD) or solid-state drive (SSD)226a, and may be loaded into at least one of one or more memories, such as main memory208, for execution by processing unit206. The processes of the illustrative embodiments may be performed by processing unit206using computer implemented instructions, which may be located in a memory, such as, for example, main memory208, read only memory (ROM)224, or in one or more peripheral devices.

Furthermore, in one case, code226bmay be downloaded over network214afrom remote system214b, where similar code214cis stored on a storage device214din another case, code226bmay be downloaded over network214ato remote system214b, where downloaded code214cis stored on a storage device214d.

A communications unit may include one or more devices used to transmit and receive data, such as a modem or a network adapter. A memory may be, for example, main memory208or a cache, such as the cache found in North Bridge and memory controller hub (NB/MCH)202. A processing unit may include one or more processors or CPUs.

The depicted examples inFIG.1andFIG.2and above-described examples are not meant to imply architectural limitations. For example, data processing system200also may be a tablet computer, laptop computer, or telephone device in addition to taking the form of a mobile or wearable device.

Factory to Grid Process & Energy Storage Tower

With reference toFIG.3, a flowchart describing a factory to grid process300is shown. The factory to grid process300may a manufacturing stage328wherein cells are manufactured to be used in fabricating EV battery packs. The cells may be inventoried or binned into performance bins during an inventorying stage326based on a grade assigned each cell from measuring a parameter of the cell. The factory to grid process300ofFIG.3begins at step302, at which the cell batteries are fabricated. For example, raw materials such as lithium carbonate may be processed into a powder to obtain active material for the electrode of a cell. In step304, the active materials may be mixed and coated (step306) onto the electrode substrate. Once complete, the cell may be assembled in step308responsive to which the cell may be activated in a formation step (step310) wherein an initial charge/discharge operation may be performed on the battery cell. During this stage, special electrochemical solid electrolyte interphase (SEI) may be formed at the electrode, mainly on an anode. However, an SEI layer may also be deposited in an anode-free cell. The SEI layer may be sensitive to many different factors and may have major impacts on battery performance during its lifetime. Battery formation can take many days depending on the battery chemistry. Using a 0.1 C (C is the cell capacity) current during formation may be typical, taking up to 20 hours for a full charge and discharge cycle, making up 20% to 30% of a total conventional battery cost. Further, the SEI film formation may have a significant impact on the performance of electrode materials. On the one hand, parts of the lithium ions may be consumed during the development of the SEI layer, increasing the irreversible capacity of batteries while decreasing the charge and discharge efficiency of the electrode material. The SEI, on the other hand, may be insoluble in organic solvents and may exist in a stable state in organic electrolyte solutions. Furthermore, solvent molecules may not travel through it, effectively preventing ion co-embedding and preventing electrode material degradation and thereby improving cycling performance and service life of the cell. By using an architecture disclosed herein, feedback may be provided to more precisely control the formation process and enhance the quality, capacity and lifetime of the cell. For example, in an anode-free (“anodeless”) cell, a larger amount of lithium than the amount used in conventional cells having a graphite anode, may be deposited. This may however be influenced by environmental factors and other factors that may cause a deviation from expected film thickness. By using the architecture disclosed herein, a cell parameter or quality metric may be measured during a series of charge-discharge cycles in an inventorying stage326to inform on cell quality and to provide feedback on precisely changing formation parameters during formation to deposit the precise amount of lithium ions needed, taking into consideration said environmental factors and improving future cell quality. By performing the charge-discharge cycles during the inventorying stage326over a substantially larger period of time than conventionally performed (e.g., 30 days instead of conventional 3 or 4 days) which may hitherto be considered significantly cost prohibitive and thus highly impractical, cell parameters may be more accurately monitored while efficiently recycling energy to inform on cell quality. More generally, other types of batteries and fabrication processes may be used to manufacture cells and battery packs during this manufacturing phase, which are then provided to later stages. Suitable cell parameters and quality metrics may be used depending on the specific cell chemistry and arrangement to achieve the benefits disclosed herein.

The inventorying stage326may begin at step312, wherein a decision may be made on whether to connect the cell to the grid/microgrid/renewable energy source. Upon making a decision to store the cell, the cell may be stored (step314) for future connection to the grid. Responsive to deciding to connect the cell to the grid, the cell may be disposed (step316) in an autonomous energy storage tower, discussed herein, and operated in a series of selective charge-discharge cycles for (i) for quality assessment and/or (ii) serving as a reservoir for temporarily storing energy from the grid to be retrieved later (step318). Upon a determination that the cell has passed quality assessment (performed using at least data obtained from the series of charge-discharge cycles), the manufacturing stage of the cell may be complete, and the cell may be used to fabricate a battery pack for an electric vehicle or for other purposes such as consumer purposes. Upon determining that the cell failed the quality assessment, the cell may be recycled back into raw materials for processing. Thus, a highly energy efficient and scalable large-scale energy storage facility may be obtained.

InFIG.4, another factory to grid process400is disclosed. Unlike process300, process400may begin at step402wherein a cell that has already undergone a formation process may be imported for inventorying. A decision may be made (step404) on whether to connect the cell to the grid. The cell may be stored (step406) upon deciding that a connection is not needed, and the cell may be connected to the grid via the autonomous energy storage tower (in step408) upon deciding that a connection is needed. Similarly, to process300, the cell may undergo a series of selective charge-discharge cycles for quality assessment and/or to serve as an energy storage reservoir. Upon passing quality assessment, the cell may be used in a battery pack manufacturing process (step416) or recycled if the cell fails quality assessment (step414).

FIG.5shows an energy storage tower126comprising a grid connection unit502, an energy storage tower door506, a plurality of cells510, a temporary cell array compression unit512, a temporary cell array electronic circuit516, cell terminals518, a battery management system (BMS520), a first electrical connector522, a mobile drive unit524, a second electrical connector526, a cell array tray528, power conversion module504, and energy management system514, and a magnetic interface lock with an electrical connector from grid530.

As shown inFIG.5, the plurality cells may be configured as one or more cell arrays508having a temporary no-weld architecture (no permanent physical connections between the cells and external devices, unlike in a battery pack). The temporary no-weld architecture may comprise the temporary cell array electronic circuit516configured to connect to positive and negative cell terminals518albeit in a non-permanent way. For example, there may not be any welds or permanent connections between the cells and the temporary cell array electronic circuit516. Further, the cells may be compressed by the temporary cell array compression unit512without any welds or permanent connections between the cells510and the temporary cell array compression unit512. Compressing the cells510may enable reduction of cell degradation and maintain cell health. Generally, an energy storage tower124may comprise any architecture configured to harness excess energy from external sources for use in a quality assurance charge-discharge process of a plurality of cells. A suitable arrangement of cells, electrical connections and switches may be used to construct and energy storage tower124depending on energy storage requirements as well as cell capacity requirements.

The energy storage tower126may have one or more BMSs520. In an embodiment, each cell array508may have a corresponding BMS520whereas in another embodiment, a housing or energy storage tower door506may comprise power electronics including one or more BMSs520and electric connectors (second electrical connector526) configured to send or receive information or cell-level measurements from the cells510or cell arrays. In some cases, to discharge cells, cells510may be connected in series with each cell having a balancing device connected in parallel to discharge individual cells as discussed herein. Of course, this is not meant to be limiting as other implementations may be obtained by persons of ordinary skill in the art in view of the descriptions herein. The BMS may be a core component of the energy storage tower and may performs several critical functions. The primary job of the BMS may be to protect the cells from damage in a wide range of operating conditions. It may ensure that the cells operate within their prescribed operating windows for the state of charge, voltage, current, and temperature. This may be essential to prevent fires or explosions caused by thermal runaway and combustion. The BMS may be configured to constantly monitor critical information of the individual cells and enable charging and discharging of individual cells through balancing circuits. This may include recording vital electrical operating parameters as well as electrolyte levels, internal cell temperature, and ambient temperature. All of this information may be collected and used for maintenance and runtime estimates of the cell array asset.

The grid connection unit502may be configured to connect to the cells to the grid via a magnetic interface lock with an electrical connector from grid530. The magnetic interface lock may enable automatic and fast connection without a need for human presence. However, other non-magnetic implementations to couple the cells to the grid may be realized. A first electrical connector522may enable a non-permanent electrical and physical connection between the cells or cell array to other electronic circuits outside of the cell array. The energy storage tower may be made autonomous via a separate or integrated mobile drive unit524. In an embodiment, the mobile drive unit may be configured to receive movement instructions (for example, information from a machine learning engine) about a position to drive the energy storage tower to and execute movement instructions based on said received instructions. Further, in an embodiment, each cell array508may have a cell array tray528upon which the cells510may be disposed.

The energy storage tower may also have a power conversion module504configured to convert energy into the appropriate form to electrically couple the energy storage tower to the grid or an energy source. For example, the power conversion module may be located in the energy storage tower door506or housing or grid connection unit of the energy storage tower. Further, the energy storage tower may be directly coupled to a renewable energy source (e.g. solar system) via a DC to DC converter or MPPT (Maximum Power Point Tracking) charge controller system. An MPPT charge controller system may be a DC to DC converter that may optimize the match between a solar array (PV panels), and a battery bank or utility grid. These direct coupling systems may possess higher efficiency that invertors. Moreover, as conventional grid scale invertors, typically rated 100 KW at 480 VAC may yield approximately 208 amps, string inverters (devices used with solar arrays to convert the energy that is generated to usable electricity for a home, and which may force performance to be equal to that of a worst performing solar panel) rated 100 KW at 1000 VAC may be used to yield approximately 100 amps. This reduction in amperage may reduce heat in cabling and may reduce the required diameter of wires resulting in cost savings. Cable connectors may also be smaller for easier wiring and connection. Thus, not only may towers be coupled with 480 VAC invertors with 400 VDC input, they may also be coupled with other systems and architectures such as a 1000 VAC invertor with 1000 VDC input. Further, multiple towers (e.g., up to 50, or 100 towers) may be combined to be electrically coupled to one large central inverter/converter, rated up to, for example, 3000V. Central inverters for utility scale may go up to 4-5 MW, for example. Thus, 40-50× less inverters may be used compared to using 100 KW string inverters.

The power conversion module may be configured to enable power to flow from DC to AC or vice-versa (bi-directional AC-DC converter), which may effectively enable the energy storage tower to both charge and discharge cells. The power conversion module may also have a bi-directional DC-DC converter to allow the energy storage tower to receive energy directly from a renewable energy source such as a DC output of a solar photovoltaic energy source. The power conversion module may direct the flow of energy by commanding the cells' charge and discharge behavior. Thus, the power conversion module may be informed directly or indirectly (through, for example, the BMS) on the available capacity of the cells responsive to which it may stop charging when the cell is full. This may also be driven by metered information at the large-scale energy storage facility or through external signals about when to charge and discharge cells. Thus, the power conversion module may be configured to, (i) responsive to determining or receiving confirmation that a formation process is complete (due to, for example, the cell being connected into the tower), receive electrical energy from the grid or energy source and to selectively supply said electrical energy to one or more of the plurality of cells in a charging mode, and (ii) responsive to determining or receiving confirmation that a formation process is complete (due to, for example, the cell being connected into the tower), selectively retrieve electrical energy from one or more of the plurality of cells to supply said electrical energy to a grid or the energy source in a discharging mode. It may be noted that, in some embodiments, the energy storage tower may also serve as the same infrastructure in which formation (step310ofFIG.3) is performed.

The energy storage tower may further comprise an energy management system514configured to handle the controls and coordination of energy storage and charge-discharge cycle activity. The energy management system may communicate directly with the power conversion module and the BMS to provide high-level coordination of the various energy storage towers in the large-scale energy storage facility, often by referencing external data points. The energy management system may make decisions on when and how to charge, discharge, remove a cell from the inventorying stage, as well as other operational efficiency decisions. This may be driven by an economic value stream, such as a maximum allowed cell degradation, demand-charge management, time-of-use arbitrage etc. The energy management system514may optimize the performance of the energy storage tower by weighing long-term cycling and capacity degradation with the return on investment of the asset. In some embodiments, this may be based on predetermined computational algorithms. However, in other embodiments, the energy management system may utilize or include a machine learning engine to optimize multiple value streams concurrently, as discussed herein.

FIG.6, illustrates another embodiment of the energy storage tower wherein cells may be stacked onto one another in a vertical direction (Y-Direction). Thus, cell array trays528may not be necessary. Further, the temporary cell array electronic circuits516may have non-permanent cell connection devices such as electrical contact springs602or other electrical contact configured to connect cell terminals518to the electronic circuit of the temporary cell array electronic circuit516. By using the electrical contact springs602, pressure exerted on lower lying cells by upper cells may ensure a consistent and reliable electrical coupling between cells510and other electronics of the energy storage tower via the temporary cell array electronic circuit516. Of course, other implementations of the energy storage tower126may be envisioned in light of the descriptions herein.

In one aspect, the one or more cells of the cell array have not completed a manufacturing stage and are awaiting a quality assessment. Further, the energy source may be a renewable energy source such as a solar photovoltaic energy source, a wind energy source, or a biomass energy source. Other intermittent energy sources may also be used, especially when it may be beneficial to store the produced energy at a time when a need for said energy by consumers is minimal. Further, heat produced in some instances in the operation of cells may be obtained for use in a cogeneration or “combined heat and power (CHP)” process to increase efficiency.

In another aspect, each cell array may be configured to provide an energy output of between 10 kWh to 20 kWh, or between 100 kWh and 200 kWh, or between 50 kWh and 500 kWh. The energy storage tower may also be configured to provide a 400V output which may be converted to a 3-phase AC voltage of 480V via a DC-AC converter (DC of cells to AC of grid).

In another aspect, all cells of the plurality of cells may be configured to have a first defined conventional cell chemistry (such as a lithium-ion phosphate (LFP) chemistry). Alternatively, all cells of the plurality of cells may be configured to have a second defined chemistry different from the first chemistry. As another option, the energy storage tower may have two or more distinct cell chemistries in the same tower.

In yet another aspect, a large-scale energy storage facility124is disclosed that may comprise at least one energy storage tower (the at least one energy storage tower may comprise a mobile drive unit and may thus be an autonomous energy storage tower). For example, the large-scale energy storage facility124may also comprise between 10,000-20,000 autonomous energy storage towers, or between 5,000-50,000 autonomous energy storage towers. Moreover, the large-scale energy storage facility may be configured to have a capacity to store between 1-20 GWh of energy, or between 5-15 GWh of energy storage.

The large-scale energy storage facility may be operated by tower-by-tower cycling of cells of the autonomous energy storage towers to provide energy to the grid. It may also be configured to inventory of only cells having a high cycle life, low energy density chemistry or only cells having a low cycle life, high energy density chemistry or both.

FIG.7is an embodiment illustrating a system diagram of an energy storage tower126. The embodiment recognizes that it may be advantageous to be able to independently measure parameters of individual cells in a cell array508. This may enable charging or discharging each cell individually, as opposed to charging or discharging all cells in the cell array at the same time. One way of achieving this may be to connect the cells in a series arrangement. The illustrative embodiments recognize that most conventional cells are connected in parallel, precluding an ability to control input and output currents passing through the cells. The illustrative embodiments also recognize that when individual cells fail, it may be difficult to maintain an integrity and performance, as the death of the cell is accelerated due to a failure to detect and/or mitigate said failure in time. Moreover, in some configurations, an entire array of cells may be rendered unusable when one cell fails.

As shown inFIG.7, energy storage tower126may be configured to include low performing, high energy density chemistries (e.g., cell with first chemistry714). The energy storage tower may also have a low energy density, high cycle life chemistries (e.g., cell with second chemistry716different from the first chemistry). In one embodiment, one cell array or tower has an LFP chemistry, and another cell array or tower has an anode free chemistry. Thus, the energy storage tower126may be designed to have one or a plurality of cell arrays508that are configured with respective bi-directional DC-DC converters704to act as standalone cell arrays. By being able to independently control the cell arrays508, and independently measure the health or state of its individual cells510, a charging and discharge rate the cells510can be regulated.

The energy storage tower comprises one or more processors708included in or outside an on-board or external computer system such the BMS520, power conversion module504or energy management system514to monitor and manage the electrical power discharging and charging processes of cells. Each cell array508may also comprise one or more sensors702configured to measure parameters of each cell, a bi-directional DC-DC converter704configured to enable transfer of energy between cell arrays, a cell array controller706that may handle cell-level operations and, a balance device712that may be connected in parallel with each cell to enable discharging the cell if needed. By using a balance device712connected in parallel with each cell, a some discharging steps of the cell can be controlled, i.e., Turning on the balancing device, discharges the electric charge stored in the cell. Further, one or more sensors702(such as a voltage sensor) are used to measure a state of the individual cells and/or the cell array508. The rate at which a battery is discharged relative to its maximum capacity is its C-rate. For example, a 1 C rate means that the discharge current will discharge the entire battery in 1 hour. Typically, a vehicle needs 4 C peak, and 1 C average. By controlling the cells in cell arrays508individually with bi-directional DC-DC converters704, a rate of C/5, for example, or less can be achieved. This may prevent triggering failure events associated with high energy density chemistries due to excessive charging and discharging.

The energy storage tower may also comprise one or more switches710that may be configured to connect or disconnected a conducting path. Further, as used herein, a sensor may be a device that can be a system, an apparatus, software, hardware, a set of executable instructions, an interface, a software application, a transducer and/or various combinations of the aforementioned that include one or more sensors/detectors utilized to indicate, respond to, detect and/or measure a physical property and generate data concerning the physical property.

In an aspect, a first energy storage tower may provide energy to a second energy storage tower as shown inFIG.7to facilitate the quality assessment of the cell during the series of charge-discharge cycles. This may be due to, for example, an indication that the grid no longer needs energy for a period of time. Thus, the grid connection unit502or another connection may be configured to couple the first energy storage tower with the second energy storage tower. Due to an availability of a bi-directional DC-DC converter between the two towers, energy may be transferred bi-directionally, and the quality assessment may thus proceed uninterrupted.

Further, with respect to the BMS, may be configured to monitor the state of the cells510to prevent overcharging and discharging that may reduce the battery's life span, and capacity. For instance, the BMS may monitor the power voltage, and when the required voltage is reached, it stops the charging process. In case irregular patterns in the power flow are detected, BMSs shut down and send out an alarm. Moreover, the BMSs can be configured to relay the information about the cell condition to the energy and power management systems.

In an embodiment, each cell array508also has one or more controllers or an operatively coupled cell array controller706configured to measure the health or state of the cells. For example, a cell array controller706can be configured to measure the voltage, current, temperature, SOC (State of Charge), SOH (State of Health) for each cell of the corresponding cell array508. It may also have a DC-DC converter control to allow isolation and current to be managed and throttle their contribution, both absorbing and providing energy to a grid. In case a cell array, malfunctions, one of more of other cell arrays may act as a replacement, (e.g., temporary replacement) for the energy storage tower126by supplying power directly to the grid connection unit502. Of course, other ways of measuring the parameters of each cell independently, such as by providing each cell with a controller or measurement device or sensor may be obtained by in view of the descriptions herein.

FIG.8depicts another embodiment of the energy storage tower126comprising more than two chemistries. Herein, cells with a third chemistry802(e.g., Gr+SS (Graphite+Solid State) chemistry) may be provided in a third cell array and cell operations may be dependent on the corresponding chemistry.

Grading and Sorting Cells

FIG.9illustrates a process900of grading and sorting the cells510based on measured cell parameter information. By running cycles of charging, resting and discharging, over a period of time, a plurality of cell parameters such as capacity, open-circuit voltage, cut-off electricity consumption, internal resistance etc., may be measured for each cell and used in grading said cells. Furthermore, precise voltages and current may be needed for charging and discharging the cells. The process may begin at step902wherein a large-scale energy storage facility comprising a plurality of manufacturing stations may be provided. In step904, process900may selectively store energy from an energy source or grid as reserve energy by providing to at least one cell of a plurality of cells disposed in one or more energy storage towers of the facility the energy from the energy source, the selectively storing step being performed during an inventorying stage of the cell manufacturing process. This energy storage also serves as a charge cycle during which parameters of the cell may be measured (step908). In step906, process900may selectively retrieve the reserve energy for provision to a grid or to the energy source by discharging energy from the at least one cell during a discharge cycle. Parameters of the cell may also be measured during the discharge (step908). After completion of the charge-discharge cycles, which may for example be determined by a pre-defined number of cycles, the quality of the cell may be assessed, and the cell may be graded. In step910, process900grades the quality of the at least one cell during the cell quality assessment process of the inventorying stage326based on the obtained cell parameter information. Based on the grades, the cells may be sorted into performance bins wherein cells of the same or substantially the same parameters may be grouped categorized together. Further, based on the grades, feedback may be provided for optimizing the formation process. For example, a time period of a formation process of the manufacturing stage328may be reduced compared to conventional formation times based on information retrieved about a quality of the cells during the quality assessment process. Even further, the steps of the selectively storing or selectively retrieving energy may be based on information selected from the list consisting of information about a transmission, a congestion, a frequency modulation of the grid, a flow of power to the grid, an environmental weather condition, an energy input into the large-scale energy storage facility, a required cell lifetime, a required cell-end-of-manufacturing capacity, a maximum cell-end-of-manufacturing degradation, and a cell chemistry and the like. In an aspect herein, when the energy source is a renewable energy source, the energy may be stored at peak production times of the renewable energy source to prevent loss of said energy, and where the stored energy may be selectively provided back to the renewable energy source at low production times of the renewable energy source.

Some or all of the process900may be implemented by a machine learning system as disclosed herein. For example, a machine learning process may be used to perform step910using the cell parameter information as inputs to the battery grading determination. In such an implementation, the cell parameters measured at step908or otherwise available for each cell may be used by the machine learning system to determine a grade for a battery, where the machine learning system was previously trained on batteries of various known grades in conjunction with the same cell parameters for each battery used during the training process. As another example, a process equivalent to the process shown inFIG.11may be performed using features related to cell performance disclosed herein, to derive a battery grade.

Of course, the examples described are not intended to be limiting as other variations may be envisioned by persons of ordinary skill in light of the descriptions presented.

FIG.10discloses another process1000for moving an autonomous energy storage tower and providing an indication that a cell has completed a manufacturing stage. In step1002, a large-scale energy storage facility is provided. The large-scale energy storage facility comprises a plurality of autonomous energy storage towers each having a plurality of cells disposed on a plurality of vertically stacked trays. In step1004, process1000receives removal information about a cell of the plurality of cells that has met a removal criterion. The removal criterion may include, for example, an assessment of an age of the cell (e.g., 30 days of charge-discharge cycles after connection to the grid), a maximum number of charge and/or discharge cycles or a required grade of the cells. The removal criterion may also be obtained from a machine learning engine. In step1006, process1000cause automatically move the autonomous energy storage tower having the cell to a cell removal area in the large-scale energy storage facility responsive to receiving the removal information. In an example, a first in-first out model may be adopted wherein the removal may be dependent on the time said cell has spent in the inventorying stage. Thus, energy may be selectively stored in one or more new cells via corresponding charging cycles of the one or more new cells and said energy may be selectively retrieved from the one or more existing cells via corresponding discharge cycles of the one or more existing cells. Thus, the first cell to be connected to the grid may more likely be removed first to conclude the manufacturing of said first cell. In step1008, process1000removes the cell.

Intelligent Operational Efficiency Enhancement-Machine Learning Engine

The illustrative embodiments further recognize that conventional storage systems for the grid may be mostly reactive, incapable of predicting energy consumption needs and restricted to storing energy in a way that is heavily dependent on an available capacity. The illustrative embodiments recognize that while storage needs may be estimated to prepare for incoming energy, this may be largely error prone and may not account for external influencing factors such as environmental conditions. Further, little to no mitigation measures may be available to ensure cell safety or preserve available life and capacities. Moreover, cells used may be already manufactured cells and thus there may not be any requirement to ensure a that a threshold amount of cell degradation is not exceeded. The illustrative embodiments further recognize that the load following nature of conventional grid systems, which may have limited control over changing consumption requirements, means that the current input and output for cells may not be precisely controlled.

As far as managing the chemistries of individual modules of an energy storage system, presently, conventional modules may charge and discharge all individual modules together. The illustrative embodiments recognize that monitoring the chemistries of individual cells in a larger system and controlling them individually to ensure the efficiency and safety of the system as a whole may be critical. For example, by being unable to disable individual cells and cell arrays without the need to disable the larger system beneficial as the safety of the system may be enhanced and the available life cycles of individual cells may be prevented from being unduly shortened from overcharging and over discharging.

The illustrative embodiments thus recognize that presently available tools or solutions do not address the need to provide intelligent management of cells in a large-scale energy storage facility where cell manufacturing is combined with energy storage.

The illustrative embodiments used to describe the disclosure generally address and solve the above-described problems and other related problems by intelligent proposal of large-scale energy storage facility operations that may enhance energy delivery, cost, and battery efficiency of the facility as a whole. The illustrative embodiments may solve these problems in a preparatory or “forward-thinking” process that anticipates the power demands of grids, microgrids, renewable energy sources and/or other energy sources and operates to meet said demands while exploiting the storage and retrieval operations to perform quality assessment of cells in a series of corresponding charge-discharge cycles.

Concerning intelligent proposals, certain operations are described as occurring at a certain component or location in an embodiment. Such locality of operations is not intended to be limiting on the illustrative embodiments. Any operation described herein as occurring at or performed by a particular component, e.g., a predictive analysis of cell data and/or a natural language processing (NLP) analysis of contextual calendar or weather data, can be implemented in such a manner that one component-specific function causes an operation to occur or be performed at another component, e.g., at a local or remote machine learning (ML) or NLP engine respectively.

An embodiment monitors and manages cumulative energy of the large-scale energy storage facility. Another embodiment may monitor a variety of profile sources configured for consumers of the facility, for example, a renewable energy storage plant that may need to store energy during high production times, or an EV manufacturing plant that may need to acquire a plurality of manufactured cells that meet a defined grade, or a facility owner with a target overall cost efficiency. A profile source may be an electronic data source from which information usable to determine a profile characteristic of the consumer can be obtained. For example, a profile source may be consumer preference configuration on a computing device such as a required time of storage, capacity available for storage, a calendar application where the consumer's energy generation events are planned, feedback from the consumer or group of consumers and the like. A profile source can be a device, apparatus, software or a platform that may provide information from which an energy storage or delivery characteristic of the consumer may be derived. For example, a dashboard114may operate as a profile source within the scope of the illustrative embodiments. Moreover, a community such as a group of renewable energy sources130or other energy sources can be a profile source wherein a plurality of storage and delivery characteristics may be obtained to derive a preference, liking, sentiment, or usage of energy. Further, measured health metrics or parameters about individual cells or cell arrays of energy storage towers126may be input data and may be utilized to learn from and derive patterns for storing and retrieving energy in the large-scale energy storage facility.

A consumer's profile data, information and preference, are terms that are used herein interchangeably to indicate a constraint of one or more users that may affect energy storage and delivery. Data from an environment such as weather data, environmental impact assessment, or otherwise other environmental data may form part of an environment profile1128.

Furthermore information/data about the large-scale energy storage facility124and cells510(such as cell manufacturing duration, number of activated energy storage towers, cell current, temperature, voltage, impedance, state of health (SOH), state of charge (SOC), average energy consumption, and the like or otherwise subject large-scale energy storage facility parameters1120may form part of or be separate from the constraints and may be obtained for use as input to an intelligent efficiency assessment module1116for predictive analytics as described hereinafter. Thus, the profile source1124information and subject large-scale energy storage facility parameters1120may collectively form at least a part of the input data1102or constraints for the intelligent efficiency assessment module1116to predict cell and manufacturing operations to maximize storage capacity while minimizing cell degradation.

The input data1102as determined by an embodiment may be variable over time. For example, cells may have time varying parameters that may be measured and used as input. This may provide real time proposals for efficiently operating the large-scale energy storage facility124. Similarly, the grid or energy source may indicate a need for a pause in energy exchange. However, cell manufacturing may still be needed. This the efficiency assessment module1116may propose an option to exchange energy between two sets of energy storage towers or between two sets of cell arrays508, through bi-directional DC-DC converters connected therebetween.

Further, based on predictive analytics about a power or energy consumption during certain seasons of the year, the efficiency assessment module1116may propose increasing or decreasing a manufacturing capacity, a number of cells disposed in energy storage towers in the large-scale energy storage facility, or relative proportion of cells to be maintained at various phases of the systems and techniques disclosed herein, to meet the predicted demand increase or decrease.

Ultimately, the efficiency assessment module1116may control input and output power of the cells while concurrently ensuring that the safety, maximum life cycle and maximum capacity attributes of the individual cells are considered. For example, upon determining that high energy density cell A has a fault based on sensor information obtained about the independently measurable cell, the efficiency assessment module1116may deactivate cell A or the corresponding cell array of cell A and utilize high energy density cell B or the corresponding cell array of cell B to for energy delivery purposes, thus ensuring the safety of the energy storage tower pack and allowing the eventual restoration of deactivated cell A or cell array of A through a formation recharge. In another example, upon determining that high cycle life cell C has 3000 cycles remaining, the efficiency assessment module1116may prioritize cycling cell C for energy storage before utilizing cycles from a lower cycle cell. User feedback indicative of an accuracy of proposals in enhancing cost efficiency, cell lifetime and minimizing degradation may be used to modify the efficiency assessment module1116to produce better results.

Operating with profile information from one or more profile sources, an embodiment routinely evaluates the constraints that are applicable to the cells and consumer of the large-scale energy storage facility. The embodiment adds new constraints/input data when found in profile information analysis, modifies existing constraints when justified by the profile information analysis, and diminishes the use of past constraints depending on the feedback, the observed usage of the constraint and/or presence of support for the past constraint in the profile information. A past constraint may be diminished or aged by deprioritizing the constraint by some degree, including removal/deletion/or rendering ineffective the past constraint. More generally, profile information may be obtained from any source available to the large-scale energy storage facility.

The intelligent operational efficiency enhancement proposals and techniques described herein generally are unavailable in the conventional methods in the technological field of endeavor pertaining to cell manufacturing and energy storage systems. A method of an embodiment described herein, when implemented to execute on a device or data processing system, comprises substantial advancement of the functionality of that device or data processing system in proposals by obtaining constraints and using an advanced tower architecture that enables control of input and output currents while ensuring maximization of the safety, life and capacity attributes.

In further embodiments, a machine learning engine may be provided to increase the resolution and efficacy of predictions made by a controller based on a comparison of sensed and received information. The machine learning engine may detect patterns and weigh the probable outcomes and energy demand profiles based on these patterns. As a consumer engages with the cells of the large-scale energy storage facility, data regarding the consumption may be collected and stored for analysis by the controller or another network-connected computerized device. The data may be aggregated to allow additional resolution in detecting patterns and predicting behavior. The machine learning engine may perform an analysis on time series data gathered at the cells510or environment, supplemental information such as that provided over a network, and/or other information to draw correlations. For example, the machine learning engine may perform a linear algebra regression analysis on the time series step data to find the best-fit parameter values. The machine learning engine may additionally return operational parameters, for example, that may be used by a controller in energy management.

Client application120ofFIG.1, dashboard application ofFIG.1, server application116ofFIG.1or any other application such application1104implements an embodiment described herein. Any of the applications can use data from the large-scale energy storage facility124, cells510and profile sources to propose operational efficiency enhancements. The applications can also obtain data from storage unit110for predictive analytics. The applications can also execute in any data processing systems (server106or server108, client112, dashboard114).

ConcerningFIG.11, this figure depicts a diagram of an example configuration for intelligent operational efficiency enhancements in accordance with an illustrative embodiment. The intelligent operational efficiency enhancements can be implemented using application1104inFIG.11. Application1104may be an example of server application116, client application120or dashboard application122, for example. The application1104receives or monitors, for example in real time, a set of input data1102. The input data comprises subject large-scale energy storage facility parameters1120such as measured cell parameters, including, for example, temperature of individual cells and that of their neighbors, voltages of the cells, impedances of the cells, state of health of the cells, capacity of the cells, computed polarization curves or charge discharge curves of the cells identifying graphitization plateaus, a required cell lifetime, a required cell-end-of-manufacturing capacity, a maximum cell-end-of-manufacturing degradation, an age of the cell, and a cell chemistry. The subject large-scale energy storage facility parameters1120may also include facility parameters such as number of energy storage towers, speed of manufacturing, electricity consumption demand, a time period for providing proposals, an energy input into the large-scale energy storage facility, etc.

The input data may also comprise consumer and environmental characteristics from profile sources1124(consumer profile1122, environment profile1128) such as preferences, pre-planned energy storage, average daily driving distance, past driving energy consumption per mile, duration of stops, calendar data for a timelining procedure, and environment data such as terrain data, road slope angle, air drag coefficient, road rolling resistance coefficient and the like.

In one or more embodiments described herein, characteristics, properties, and/or preferences associated with a consumer, an environment, a cell, a facility etc. are referred to as “features”. In one or more embodiments, the configuration1100defines and configures an algorithm and/or rule to drive feature selection results. In particular embodiments an algorithm may include, for example, determining a lowest common value for a feature, and determining whether the value satisfies a best match within a threshold value (e.g., 90%) of the feature. In an embodiment, the system may prioritize certain features so that features such as cell degradation at an end-of-manufacturing time, cell capacity at the end-of-manufacturing time, cost efficiency of operating cells of the large-scale energy storage facility, amount of capacity available to store energy and a revenue from arbitrage and sale of manufactured cells carry different weights. In an embodiment, after a common denominator in a plurality of consumers is found, the configuration1100understands the problems with individual consumers, and extracts and derives the best feature values that will help in intelligent operational efficiency enhancements proposals.

In an embodiment, features may be selected or extracted from outside the machine learning model. However, in another embodiment, features may additionally be extracted inside the machine learning model/deep neural network and thus may be integral to the model. Feature extraction/selection is therefore generally used interchangeably herein.

In an embodiment, feature selection/extraction component1114is configured to generate relevant features, based on contents of a request from application1104, using data from all the different available features (e.g., subject large-scale energy storage facility parameters1120, consumer profile1122, environment profile1128). In the embodiment, feature selection/extraction component1114may receive a request from application1104which includes at least an identification of a subject large-scale energy storage facility parameters as well as instructions to propose a cell and manufacturing operations in to enhance efficiency. Using the subject large-scale energy storage facility parameters and/or profile source1124, feature selection/extraction component1114may obtain a combination of specific subject large-scale energy storage facility parameters1120, profile information from consumer profile1122, and environmental data from environment profile1128. In the embodiment, feature selection/extraction component1114may use a defined algorithm of prioritization to generate the features as feature profile. In a particular embodiment, the feature profile includes each feature (e.g., 1. cell current, 2. cell temperature, 3. cell voltage, 4. cell impedance, 5. weather forecast, 8. consumption requirements, 9. state of health audit report indicative of a safety, capacity and remaining life cycles of the cells and 10. weights given to each feature). Using the extracted features and a trained M/L model1106that has been trained using a large number of different datasets, efficiency assessment module1116may determine an operational efficiency enhancement proposal1112for the subject large-scale energy storage facility1130. The operational efficiency enhancement proposal1112may comprise a cell operation proposal1202(FIG.12) and/or a cell manufacturing proposal1204(FIG.12). These may contain information indicative of a predicted state of one or more components of the large-scale energy storage facility and instructions to mitigate the predictions.

In an embodiment herein, the operational efficiency enhancement proposal is a cell operation proposal, and the cell operation proposal comprises information about when and how to charge and discharge the cell. The instructions may further be dependent on cell chemistry. Another cell operation proposal may comprise an indication of a number of cells needed in the large-scale energy storage facility and when to charge and/or discharge them. Said instructions may be at a cell-level or may be at a cell array-level or may be at an autonomous energy storage tower-level. Yet another cell operation proposal may comprise instructions for controlling a rate of charging and/or discharging of the at least one cell. A maximum number of charge-discharge cycles may also be proposed to preserve cell capacity and minimize cell degradation. This may ensure the cells may still meet requirements for use in a battery pack after cell manufacturing is completed. In another embodiment, a cell operation proposal comprises an inventorying/storage time period for at least one cell. This time period may be a factor of a performance of the cell. The cell operation proposal may also comprise instructions for determining a movement parameter of the energy storage tower. This may be used to drive a corresponding mobile drive unit of an autonomous energy storage tower in the subject large-scale energy storage facility. The cell operation proposal may also comprise instructions for grading the cell into a specified performance bin. The cell operation proposal may further comprise instructions for performing a series of energy storage and retrieval operations at defined times to maximize grid-scale arbitrage profits.

In an embodiment, the operational efficiency enhancement proposal may be a cell manufacturing proposal and comprise information about an optimum cell formation parameter for a cell formation procedure in the subject large-scale energy storage facility. This may thus serve as feedback information about an SEI layer thickness and how said thickness may be more precisely controlled to manufacture higher or different grade of cells in the future. The cell manufacturing proposal may also be is dependent on a chemistry the cell.

The proposals may be provided in real time as the input changes. User feedback concerning an accuracy of the proposals may also be used in modifying the machine learning model. By providing one or more of these cell operation and manufacturing operation proposals, and executing said proposals, a highly energy efficient, self-supporting and cost-efficient large-scale energy storage facility may be obtained. These examples are not meant to be limiting and any combination of these and other example power output proposals are possible in light of the descriptions.

The efficiency assessment module1116can be based, for example, on a neural network such as a recurrent neural network (RNN), although it is not meant to be limiting. An RNN is a type of artificial neural network designed to recognize patterns in sequences of data, such as numerical times series prediction and numerical time series anomaly detection using data emanating from sensors, generating image descriptions and content summarization. RNNs may use recurrent connections (going in the opposite direction that the “normal” signal flow) which form cycles in the network's topology. Computations derived from earlier input are fed back into the network, which gives an RNN a “short-term memory”. Feedback networks, such as RNNs, are dynamic; their ‘state’ is changing continuously until they reach an equilibrium point. For this reason, RNNs are particularly suited for detecting relationships across time in a given set of data. Recurrent networks take as their input not just the current input example they see, but also what they have perceived previously in time. The decision a recurrent net reached at time step t−1 may affect the decision it will reach one moment later at time step t. Thus, recurrent networks have two sources of input, the present and the recent past, which combine to determine how they respond to new data. Alternatively or in addition, other machine learning systems and techniques may be used, including both supervised and unsupervised techniques. Different algorithms or combinations of algorithms may be selected, for example, based on the specific cell chemistry, physical arrangement, or desired properties of the cells being evaluated and used in the systems disclosed herein. Moreover, predetermined logic may be used, for example, in cases where analyses of a large amount of data is not needed.

In an illustrative embodiment, the operational efficiency enhancement proposals1112may be presented, by a presentation component1108of application1104. An adaptation component1110may be configured to receive input from a user to adapt the operational efficiency enhancement proposals1112if necessary. For example, changing a tolerated cell degradation proposed by the efficiency assessment module1116causes a recalculation of operational efficiency enhancement proposal1112that takes the new tolerated degradation into consideration.

Feedback component1118optionally collects user or consumer feedback relative to the operational efficiency enhancement proposals1112. In one embodiment, application1104is configured not only to compute operational efficiency enhancement proposals1112but also to provide a method for a user to input feedback, where the feedback is indicative of an accuracy of the computed operational efficiency enhancement proposals1112. Feedback component1118applies the feedback in a machine learning technique such as to profiles or to M/L model1106in order to modify the M/L model1106for better proposals. In an illustrative embodiment, the application analyzes said feedback input and the application reinforces the M/L model1106of the efficiency assessment module1116. If the feedback is satisfactory or unsatisfactory as to the accuracy of the proposal, the application strengthens or weakens parameters of the M/L model1106respectively.

The input layer of the neural network model can be, for example, a vector representative of a current, voltage or impedance values of cells, contextual weather or calendar data provided by an NLP engine1126, etc. In an example, a CNN (convolutional neural network) uses convolution to extract features from an input. In an embodiment, upon receiving a request to provide a proposal, the application creates an array of values that are input to the input neurons of the M/L model1106to produce an array that contains the operational efficiency enhancement proposals1112.

The neural network M/L model1106may be trained using various types of training data sets including stored profiles and a large number of sample cell measurements. As shown inFIG.13, which depicts a block diagram of an example training architecture1302for machine-learning based recommendation generation in accordance with an illustrative embodiment, program code extracts various features1306from training data1304. The components of the training data1304have labels L. The features are utilized to develop a predictor function, H(x) or a hypothesis, which the program code utilizes as an M/L model1308. In identifying various features in the training data1304, the program code may utilize various techniques including, but not limited to, mutual information, which is an example of a method that can be utilized to identify features in an embodiment. Other embodiments may utilize varying techniques to select features, including but not limited to, principal component analysis, diffusion mapping, a Random Forest, and/or recursive feature elimination (a brute force approach to selecting features), to select the features. “P” is the output that can be obtained, which when received, could further trigger the large-scale energy storage facility124to perform other steps such steps of a stored instruction. The program code may utilize a machine learning m/l algorithm1312to train M/L model1308, including providing weights for the outputs, so that the program code can prioritize various changes based on the predictor functions that comprise the M/L model1308. The output can be evaluated by a quality metric1310.

By selecting a diverse set of training data1304, the program code trains M/L model1308to identify and weight various features. To utilize the M/L model1308, the program code obtains (or derives) input data or features to generate an array of values to input into input neurons of a neural network. Responsive to these inputs, the output neurons of the neural network produce an array that includes the operational efficiency enhancement proposal to be presented or used contemporaneously.

FIG.14is a flowchart depicting a summary of the machine learning process1400described herein. In step1402, process1400independently measures, by at least one cell array controller, parameters of one or more cells of a plurality of cells disposed in a cell array of an energy storage tower. In step1404, process1400receives the measured parameters as at least a part of a set of subject large-scale energy storage facility parameters, indicative of one or more characteristics of a subject large-scale energy storage facility, for use by an efficiency assessment module. In step1406, process1400generates input data using at least the set of subject large-scale energy storage facility parameters. In step1408, process1400extracts one or more features from the input data, the one or more features representative of a characteristic of a request for completing an operational efficiency enhancement proposal operation. In step1410, process1400proposes, using the efficiency assessment module, at least one operational efficiency enhancement proposal for the subject large-scale energy storage facility. The efficiency assessment module operates as a machine learns engine.

Concerning step1408, the one or more features may also represent attributes obtained from an attributes prioritization1502step, as shown inFIG.15. In the attributes prioritization, one or more attributes1510to consider for an output proposal operation are obtained. The one or more attributes may have different assigned priorities or weights or may have the same or even unassigned priority or weight. By training the M/L model1306with a large set of different datasets that consider the attributes1510, different scenarios can be handled by the efficiency assessment module1116. In an illustrative and non-limiting embodiment, the attributes1510include instructions to maximize or enforce a defined cell capacity1504, maximize revenue1506from cells sales and grid-scale arbitrage, and minimize cell degradation1508. Other attributes may include, for example, a defined cost efficiency of cells of the large-scale energy storage facility, a defined amount of capacity available in the entire large-scale energy storage facility to store energy and a defined a large-scale energy storage facility revenue at a specified time.

In an embodiment, maximizing cell capacity1504may maximize cell lifetime. Maximizing life may comprise maximizing the health of cells i.e., a cell's capability to discharge current. By observing an increase in a battery's impedance, the energy management system514of an autonomous energy storage tower may cause a change in the maximum current of the cells to avoid overheating or “over-stressing” the cells to maximize the life of the cells. Thus, a defined discharge power may be determined to complement the state of health of the cells. In the embodiment, impedance may be measured based on a discharge and recharge of cells in a quality assessment process and a comparison of the discharge parameters and recharge parameters to an ideal standard, wherein the cells may be graded in a SOH grading operation. The cells510may be graded, for example, as A, B, C, D, and E, with A representing a high SOH and E representing a low SOH. Thus, in the embodiment, all modules with cells510that are graded D and E may be operated by the power control module at a C-rate of, for example, C/10, and modules having cells that are graded B and C may be operated at a C-rate of, for example, of C/5 and modules with cells510that are graded A may be operated at a C-rate of, for example, C/3, the C-rates being a discharge power limit of the respective cell arrays508. The efficiency assessment module1116may keep learning and adjusting according to these limits in conjunction with the safety and capacity attributes. Thus, if one cell510graded A and cell array is taken offline because of a safety issue, another cell510may be operated to be upgraded from B to A keep in line with a quality demand.

Thus, maximizing capacity recognizes an impedance problem of a cell. For a cell having a high impedance, the efficiency assessment module1116may operate the corresponding cell array at the lowest C-rate, providing energy over the longest time.

Thus, in an illustrative embodiment, the efficiency assessment module1116operates based on a system of merits and demerits that functions to maximize life, safety, capacity, and other attributes while also considering other external non-cellular input data.

Thus, a computer-implemented method, system or apparatus, and computer program product are provided in the illustrative embodiments for intelligent operational efficiency enhancements and other related features, functions, or operations. Where an embodiment of a portion thereof is described with respect to a type of device, the computer-implemented method, system or apparatus, the computer program product, or a portion thereof, are adapted or configured for use with a suitable and comparable manifestation of that type of device.

Aspects of the present disclosure are described herein concerning flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that computer readable program instructions can implement each block of the flowchart illustrations and/or block diagrams and combinations of blocks in the flowchart illustrations and/or block diagrams.