Energy Management System

Abstract

An energy management system including a universal energy flow manager having a housing, an energy storage device installed within the housing, a power electronics module installed within the housing and adapted to convert and manage electrical power, and a connection interface. Also included is a distribution and communication module having an HVDC bus (high voltage direct current bus) with variable power limits that powers the overall load requirements of one or more coupled electrical loads up to a defined power limit determined by aggregation of power from one or more coupled energy sources. The one or more coupled electrical loads and the one or more coupled energy sources are external to the universal energy flow manager and connect to the HVDC bus through the connection interface.

Description

[Technical Field]

[0001] The present disclosure relates generally to energy management systems and, more particularly, to modular, scalable energy management systems with integrated electronics for simultaneously supplying and managing multiple loads. [Background technology]

[0002] Electric vehicles are becoming increasingly popular as more people become interested in using renewable and environmentally friendly energy resources, such as solar panels. In most situations, such technology may be connected to and work in conjunction with the power grid or home electrical wiring. Furthermore, in areas with high energy costs, consumers may find it more attractive to use electric vehicles and / or renewable energy sources to control costs. Summary of the Invention [Means for solving the problem]

[0003] An energy management system comprising a general-purpose energy flow manager, the general-purpose energy flow manager comprising: Housing and an energy storage device disposed within the housing; a power electronics module disposed within the housing and configured to convert and manage electrical power; A connection interface; a distribution and communication module comprising at least an HVDC bus (high voltage direct current bus) having a variable power limit, the distribution and communication module being configured to power the entire load requirements of one or more coupled electrical loads up to a defined power limit determined by aggregation of power from one or more coupled energy sources; Equipped with The one or more coupled electrical loads and the one or more coupled energy sources are external to the universal energy flow manager and connect to the HVDC bus through the connection interface.

[0004] To easily identify the description of any particular element or operation, one or more leading digits in a reference number refer to the figure number in which that element first appears. [Brief explanation of the drawings]

[0005] [Figure 1] FIG. 1 is a block diagram of an energy management environment in which exemplary embodiments may be implemented. [Figure 2] 1 is a block diagram of a data processing system in which illustrative embodiments may be implemented; [Figure 3] FIG. 1 is a block diagram of a generic energy flow manager in which exemplary embodiments may be implemented. [Figure 4A] FIG. 1 illustrates a schematic diagram of a parallel configuration of a generic energy flow manager in which exemplary embodiments may be implemented. [Figure 4B] FIG. 1 illustrates a schematic diagram of a series configuration of generic energy flow managers in which exemplary embodiments may be implemented. [Figure 5] FIG. 1 is a block diagram of an application-specific hardware environment in which illustrative embodiments may be implemented. [Figure 6] FIG. 1 is a block diagram of an electric vehicle application-specific hardware environment in which illustrative embodiments may be implemented. [Figure 7] FIG. 1 is a block diagram of an in-home application-specific hardware environment in which exemplary embodiments may be implemented. [Figure 8] FIG. 1 is a block diagram of a general-purpose application-specific hardware environment in which exemplary embodiments may be implemented. [Figure 9] FIG. 1 illustrates an overview of an energy management environment in which exemplary embodiments may be implemented. [Figure 10]1 is a flowchart of an energy management process in which example embodiments may be implemented. [Figure 11] FIG. 1 illustrates an overview of an energy management environment in which exemplary embodiments may be implemented. [Figure 12] FIG. 1 is a block diagram of a machine learning architecture in which exemplary embodiments may be implemented. [Figure 13] FIG. 1 is a block diagram of a training architecture in which exemplary embodiments may be implemented. [Figure 14] 1 is a flowchart of a power delivery process in which exemplary embodiments may be implemented. [Figure 15] FIG. 10 is a block diagram of an example prioritization of attributes in which example embodiments may be implemented. [Figure 16] FIG. 1 illustrates a system configured for charging arrangements, according to one or more implementations. [Figure 17] 1 is a flowchart of a process by which an exemplary embodiment may be implemented. [Figure 18] 1 is a flowchart of a cloud charging arrangement process in which exemplary embodiments may be implemented. [Figure 19] 1 is a flowchart of an electric vehicle charging dispatch process in which exemplary embodiments may be implemented. [Figure 20] 1 is a flowchart of a residential charging arrangement process in which exemplary embodiments may be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0006] The illustrative embodiments recognize that energy sources such as electric vehicles and solar panels are becoming increasingly popular as people become more interested in using renewable and environmentally friendly energy sources. In some situations, such technology may be connected to and work in conjunction with the power grid or residential electrical wiring. Furthermore, in areas with variable electricity rates for different times of day, consumers may find it more attractive to use electric vehicles and / or solar energy to control their energy usage and production to take advantage of relatively low energy prices.

[0007] For example, exemplary embodiments recognize that solar panels may have distinct advantages as an energy source that produces DC electricity without emitting pollutants or emissions. Inverters may be utilized to use this energy with home appliances. Because solar energy production may be unavailable at night, exemplary embodiments recognize that it may be desirable to store energy for later use, for example, in a battery or other storage system. Exemplary embodiments recognize that it may be prudent to manage energy storage and consumption automatically and / or intelligently.

[0008] The exemplary embodiments further recognize that electric vehicles may use large amounts of power that may not be readily available in a home energy system. For example, Level 3 power consumption (e.g., greater than 12 kW or greater than 15 kW) by an electric vehicle connected to a home energy system may use all available grid power in the home energy system or exceed the entire power budget of an electric panel. This may also be true for other high-power appliances, such as air conditioners. Furthermore, supplying multiple electric vehicles and high-power appliances with all their power requirements may be very difficult, if not impossible, to accomplish in a home environment. As a result, the exemplary embodiments recognize that users may have to prioritize energy usage and stagger charging times to accommodate these limitations.

[0009] Exemplary embodiments may disclose a general-purpose, modular, and scalable energy management system with integrated electronics for simultaneously meeting and managing the overall load requirements of multiple connected loads in a home or other environment. Exemplary embodiments may disclose optimization of energy harvesting and high-voltage direct current (HVDC) distribution for various applications, which may include residential and / or automotive applications. The energy management system may automatically detect and configure its operating parameters when interfacing to multiple loads and sources in an effort to reduce dependency on utility services, avoid capacity limitations, and reduce downtime costs. The energy management system may also introduce DC (direct current) fast charging to the residential market and facilitate the development of smart homes. Not only can electric vehicles or high-power appliances be DC fast charged in a home energy system, but multiple electric vehicles and high-power appliances can also be DC fast charged simultaneously. Exemplary embodiments may disclose a general-purpose energy flow manager that can receive any number of energy sources for routing to any number of loads. The ability to harvest large amounts of power may be limited only by the energy sources that can be connected. This can increase customer adoption of renewable energy sources by offering lower cost options and by eliminating the complexities that arise from different manufacturers, component specifications, and installation. Energy management systems can therefore offer smart home security and automation, energy recycling and resale, backup energy optimization, extended supply duration, convenience, and self-installation.

[0010] In one aspect, an energy management system may include a generic energy flow manager including a housing, an energy storage device installed within the housing, a power electronics module installed within the housing and configured to convert and manage electrical power, and a connection interface. The generic energy flow manager may also include a distribution and communication module including at least an HVDC bus (high-voltage direct current bus) with a variable power limit configured to power the entire load requirements of one or more combined electrical loads up to a defined power limit determined by aggregation of power from one or more combined energy sources. This aggregation may be accomplished, for example, by parallel connection of all low-voltage application-specific hardware (ASH) for the low-voltage DC bus or all high-voltage application-specific hardware for the high-voltage DC bus, as discussed hereinafter. The one or more combined electrical loads and one or more combined energy sources may be external to the generic energy flow manager and may connect to the HVDC bus through a connection interface. By configuring the energy flow manager to power the entire load requirements of one or more combined electrical loads up to a defined power limit determined by the aggregation of power from any number of combined energy sources and types, the energy flow manager can be "generic" to provide power to any type of connected load from any type of connected source, regardless of the load or source architecture. Thus, energy sources may not be required to have output energy / power specifications that match those of the generic energy flow manager.

[0011] In another aspect, EV-to-EV DC (electric vehicle to electric vehicle direct current) fast charging may be provided. Additionally, multiple EVs may be charged simultaneously using energy time division multiplexing, where the routing of energy / power from an energy source to an EV load via a common connection may be implemented by allocating all transmission activity to one source and / or one EV at a time for a fixed duration. This may be implemented to enable power transfer between pairs of sources and loads without requiring a dedicated connection for each pair.

[0012] In another embodiment, a portable energy bank may be provided where DC fast charging for low voltage DC appliances and emergency / roadside assistance capabilities may also be available through the on-board energy storage device of the energy flow manager.

[0013] In another aspect, intelligent suggestions of one or more power delivery suggestion actions may be disclosed. The intelligent suggestions may include receiving energy demand states of one or more coupled electric loads coupled to the generic energy flow manager via load application-specific hardware (load ASH), where the energy demand states indicate a desired amount of energy needed by the one or more coupled electric loads in the energy management environment. The intelligent suggestions may further include receiving available energy states of one or more coupled energy sources coupled to the generic energy flow manager via source application-specific hardware (source ASH), where the available energy states indicate an amount of energy available from the one or more coupled energy sources in the energy management environment. Input data may be generated using at least the energy demands and available energy states for use by the power delivery module, and one or more features may be extracted from the input data, where the one or more features represent characteristics of a request for the power delivery suggestion action. At least one power delivery suggestion may be proposed by the power delivery module and implemented for one or more coupled electric loads. The power delivery module may operate a machine learning engine.

[0014] The architecture and manner of managing energy sources and loads is not available in currently available methods in the challenging art of battery energy storage systems and electric vehicles. The term electric vehicle is used herein collectively to refer to vehicles and appliances, such as automobiles, rail cars, watercraft, household appliances, and aircraft, that are configured to use rechargeable electric batteries as a primary energy source for powering component systems or for propulsion. The exemplary embodiments are also described by way of example only, with respect to certain types of data, functions, algorithms, equations, model configurations, locations, additional data, devices, data processing systems, environments, components, and applications of the embodiments. Any particular implementation of these and other similar artifacts is not intended to be a limitation on the present disclosure. Any suitable implementation of these and other similar artifacts may be selected within the scope of the exemplary embodiments.

[0015] Furthermore, exemplary embodiments may be implemented with respect to any type of data, data source, or access to a data source via a data network. Any type of data storage device may provide data to embodiments of the present disclosure within the scope of the present disclosure, either locally at a data processing system or via a data network. Where embodiments are described using a client device, any type of data storage device suitable for use with the client device may provide data to such embodiments within the scope of exemplary embodiments, either locally at the client device or via a data network.

[0016] The exemplary embodiments are described using specific code, designs, architectures, protocols, layouts, schematics, and tools as examples only and are not limitations on the exemplary embodiments. Furthermore, the exemplary embodiments are described in some instances using specific software, tools, and data processing environments as examples only for clarity of explanation. The exemplary embodiments may be used in conjunction with other comparable or similar purpose structures, systems, applications, or architectures. For example, other comparable mobile devices, structures, systems, applications, or architectures therefor may be used in conjunction with such embodiments of the present disclosure within the scope of the present disclosure. The exemplary embodiments may be implemented in hardware, software, or a combination thereof.

[0017] The examples in this disclosure are used for clarity of explanation only and are not limiting to the exemplary embodiments. Additional data, operations, actions, tasks, activities, and operations are contemplated by this disclosure and are contemplated within the scope of the exemplary embodiments.

[0018] Any advantages listed herein are examples only and are not intended to be limitations on example embodiments. Additional or different advantages may be realized by particular example embodiments. Furthermore, a particular example embodiment may have some, all, or none of the advantages listed above.

[0019]

[0023] Referring now to the drawings, and in particular to Figure 1, this figure is an exemplary diagram of an energy management environment 100 in which illustrative embodiments may be implemented. Figure 1 is merely an example and is not intended to express or imply any limitations with regard to the environments in which various embodiments may be implemented. Specific implementations may make many modifications to the depicted environment based on the following description.

[0020] Energy management environment 100 is a network of energy management systems that includes a generalized energy flow manager 126 with an application 136. Energy management environment 100 may also include a computer on which an exemplary embodiment may be implemented, and energy sources such as a power grid 128, a renewable energy source 130, and an electric vehicle 132. Energy management environment 100 also includes electric loads (including, for example, other electric vehicles 132 and home appliances) and a network / communications infrastructure 102. Network / communications infrastructure 102 may be the medium used to provide communication links between the various devices, databases, and computers connected to each other within energy management environment 100. Network / communications infrastructure 102 may include connections such as a CAN (Controller Area Network) bus connection, a PLC (Programmable Logic Controller), wired or wireless communication links, or fiber optic cables.

[0021] Client or server are merely exemplary roles for some data processing systems connected to the network / communications infrastructure 102 and are not intended to exclude other configurations or roles for these data processing systems. The server 104 and the server 106, along with the storage unit 108 comprising the database 118, are coupled to the network / communications infrastructure 102. Software applications may run on any computer in the energy management environment 100. The client 110 and the dashboard 112 are also coupled to the network / communications infrastructure 102. The client 110 may be a remote computer with a display or a mobile device configured with an application for sending or receiving information, e.g., for receiving the charging condition of the energy management system 124 or its components. The dashboard 112 may be located within the residence 134 and configured to send or receive any of the information discussed herein. A data processing system, such as the server 104 or the server 106, or a client (client 110, dashboard 112), may contain data and have software applications or tools running thereon.

[0022] 1 illustrates several components that may be used in an exemplary implementation of an embodiment. For example, the servers and clients are merely examples and do not imply any limitation to a client-server architecture. As another example, an embodiment may be distributed across several data processing systems and data networks shown, while another embodiment may be implemented on a single data processing system within the scope of the exemplary embodiment. The data processing systems (server 104, server 106, client 110, dashboard 112) also represent exemplary nodes in clusters, partitions, and other configurations suitable for implementing embodiments.

[0023] The energy management system 124 may include one or more application-specific hardware 114 that can electrically couple energy sources or electrical loads to the generic energy flow manager 126. The architecture may enable a residence to avoid complete reliance on the utility to meet all EV and other load demands with Level 2 AC charging. The generic energy flow manager and connection interface design discussed herein may provide the residence 134 with the ability to set charging targets for multiple EVs. This may be accomplished simultaneously or by setting priority levels for various loads. Available power may be limited by available energy sources rather than by the existing specifications of the residence's circuits. The energy management system 124 may obtain energy from, for example, the utility grid, energy storage, solar panels, wind energy, fuel cells, and EVs in the garage. In some embodiments, when available energy should be used wisely, the system may monitor the residence's loads and redirect maximum AC power to charge the EVs with the highest priority. Furthermore, all EVs in the residence may have access to DC fast charging, rather than swapping vehicles for closer to a single-port DC charger. Furthermore, home energy may be scalable, where power can be multiplied by connecting another system in parallel.

[0024] Any other application, such as application 136, client application 120, dashboard application 122, or server application 116, may implement the embodiments described herein. Any of the applications may use data from the general energy flow manager 126, energy sources, or loads to calculate power or energy requirements. The application may also retrieve data from the storage unit 108 for predictive analysis. The application may also run on any of the data processing systems (server 104 or server 106, client 110, dashboard 112).

[0025] The server 104, the server 106, the storage unit 108, the client 110, and the dashboard 112 may be coupled to the network / communications infrastructure 102 using wired connections, wireless communication protocols, or other suitable data connections.

[0026] In the illustrated example, server 104 may provide data such as boot files, operating system images, and applications to client 110 and dashboard 112. Client 110 and dashboard 112 may be clients to server 104 in this example. Client 110 and dashboard 112, or some combination thereof, may include their own data, boot files, operating system images, and applications. Energy management environment 100 may include additional servers, clients, and other devices not shown.

[0027] The server 106 may include a search engine configured to search for information such as weather conditions, grid consumption data, power outage history data, total energy available from energy sources, required charge duration, user preferences, user feedback, or other energy management system data, either automatically or in response to a request from a utility for power dispatch as described herein with respect to various embodiments.

[0028] In the depicted example, the network / communications infrastructure 102 may include the Internet. The network / communications infrastructure 102 may represent a collection of networks and gateways that use Transmission Control Protocol / Internet Protocol (TCP / IP), Controller Area Network Bus (CAN Bus), Local Area Network (LAN), Wide Area Network (WAN), and / or other protocols to communicate with each other. At the heart of the Internet is a backbone of data communication links between major nodes or host computers, including thousands of commercial, residential, educational, and other computer systems, that route data and messages. Of course, energy management environment 100 may be implemented as a number of different types of networks, such as, for example, an intranet. Figure 1 is intended as an example, and not as an architectural limitation for different illustrative embodiments.

[0029] Among other uses, energy management environment 100 can be used to implement a client-server environment in which exemplary embodiments can be implemented. A client-server environment allows software applications and data to be distributed across a network, such that applications function using interactivity between client and server data processing systems. Energy management environment 100 may utilize a service-oriented architecture in which interoperable software components distributed across a network can be packaged together as a coherent business application. Energy management environment 100 may utilize a cloud computing model of service delivery to enable 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 administrative effort or interaction with a service provider.

[0030] Referring to Figure 2, this figure shows a block diagram of a data processing system in which exemplary embodiments may be implemented. Data processing system 200 is an example of a computer, such as client 110, dashboard 112, server 104, or server 106 of Figure 1, or another type of device in which computer-usable program code or instructions implementing processes may be located for exemplary embodiments.

[0031] Data processing system 200 is described as a computer by way of example only and not limitation, and implementations in the form of other devices may modify data processing system 200, for example, by adding a touch interface, and may also eliminate some of the illustrated components from data processing system 200, without departing from the general operational and functional aspects of data processing system 200 described herein.

[0032] In the illustrated example, data processing system 200 utilizes a hub architecture including a northbridge and memory controller hub (NB / MCH) 202 and a southbridge and input / output (I / O) controller hub (SB / ICH) 204. A processing unit 206, a main memory 208, and a graphics processor 210 are coupled to northbridge and memory controller hub (NB / MCH) 202. Processing unit 206 may include one or more processors and may be implemented using one or more heterogeneous processor systems. Processing unit 206 may be a multi-core processor. In some implementations, graphics processor 210 may be coupled to northbridge and memory controller hub (NB / MCH) 202 through an advanced graphics bus (AGP).

[0033] In the illustrated example, a local area network (LAN) adapter 212 is coupled to southbridge and input / output (I / O) controller hub (SB / ICH) 204. An audio adapter 216, a keyboard and mouse adapter 220, a modem 222, a read-only memory (ROM) 224, a universal serial bus (USB) and other ports 232, and PCI / PCIe devices 234 are coupled to southbridge and input / output (I / O) controller hub (SB / ICH) 204 through bus 218. A hard disk drive (HDD) or solid state drive (SSD) 226a and a CD-ROM 230 are coupled to southbridge and input / output (I / O) controller hub (SB / ICH) 204 through bus 228. PCI / PCIe devices 234 may include, for example, an Ethernet adapter, an add-in card, and a PC card for a notebook computer. PCI uses a card bus controller, while PCIe does not. The read-only memory (ROM) 224 may be, for example, a flash binary input / output system (BIOS). The hard disk drive (HDD) or solid-state drive (SSD) 226a and CD-ROM 230 may use, for example, an integrated drive electronics (IDE), a serial advanced technology attachment (SATA) interface, or variations such as external SATA (eSATA) and micro SATA (mSATA). A super I / O (SIO) device 236 may be coupled to the southbridge and input / output (I / O) controller hub (SB / ICH) 204 through bus 218.

[0034] Memory such as main memory 208, read-only memory (ROM) 224, or flash memory (not shown) are some examples of computer-usable storage devices. Hard disk drives (HDD) or solid-state drives (SSD) 226a, CD-ROM 230, and other similarly usable devices are some examples of computer-usable storage devices that include computer-usable storage media.

[0035] An operating system runs on processing unit 206. The operating system coordinates and provides control of various components within data processing system 200 of FIG. 2. The operating system may be a commercially available operating system for any type of computing platform, including, but not limited to, server systems, personal computers, and mobile devices. An object-oriented or other type of programming system may work in conjunction with the operating system and provide calls to the operating system from programs or applications executing on data processing system 200.

[0036] Instructions for the operating system, the object-oriented programming system, and applications or programs such as application 116 and client application 120 may be located on storage devices, for example, in the form of code 226b on a hard disk drive (HDD) or solid-state drive (SSD) 226a, and loaded into at least one of one or more memories, such as main memory 208, for execution by processing unit 206. The processes of the exemplary embodiments may be performed by processing unit 206 using computer-executable instructions, which may be located in a memory, such as main memory 208, read-only memory (ROM) 224, or in one or more peripheral devices.

[0037] Furthermore, in some cases, code 226b may be downloaded from a remote system 214b via network 214a, where similar code 214c is stored on storage device 214d, and in other cases, code 226b may be downloaded to a remote system 214b via network 214a, where the downloaded code 214c is stored on storage device 214d.

[0038] The hardware in Figures 1 and 2 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives, may be used in addition to or in place of the hardware depicted in Figures 1 and 2. Additionally, the processes of the illustrative embodiments may be applied to multiprocessor data processing systems.

[0039] In some illustrative examples, data processing system 200 may be a personal digital assistant (PDA), which is generally configured with flash memory to provide non-volatile storage for storing operating system files and / or user-generated data. The bus system may comprise one or more buses, such as a system bus, an I / O bus, and a PCI bus. Of course, the bus system may be implemented using any type of communications fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture.

[0040] The communications unit may include one or more devices used to send and receive data, such as a modem or network adapter. The memory may be, for example, main memory 208 or a cache, such as that found in northbridge and memory controller hub (NB / MCH) 202. The processing unit may include one or more processors or CPUs.

[0041] 1 and 2, as well as above-described examples, are not meant to imply architectural limitations. For example, data processing system 200 may be a tablet computer, a laptop computer, or a telephone device in addition to taking the form of a mobile or wearable device.

[0042] When a computer or data processing system is described as a virtual machine, virtual device, or virtual component, the virtual machine, virtual device, or virtual component operates in the manner of data processing system 200 using virtual implementations of some or all of the components shown in data processing system 200. For example, in a virtual machine, virtual device, or virtual component, processing unit 206 is implemented as a virtualized instance of all or some of the hardware processing units 206 available in the host data processing system, main memory 208 is implemented as a virtualized instance of all or some portion of main memory 208 that may be available in the host data processing system, and hard disk drive (HDD) or solid state drive (SSD) 226 a is implemented as a virtualized instance of all or some portion of hard disk drive (HDD) or solid state drive (SSD) 226 a that may be available in the host data processing system. The host data processing system in such a case is represented by data processing system 200.

[0043] Energy Management System 3, a generic energy flow manager is shown. The generic energy flow manager 126 may be part of the energy management system 124 and may include a housing 304, an energy storage device 306 (battery) installed within the housing and configured to provide on-board energy, a power electronics module 308 installed within the housing and configured to convert and manage electrical power, a connection interface 312 configured to accept external application-specific hardware 114, and a distribution and communication module 310 including at least an HVDC bus (high-voltage direct current bus) having variable power limits and configured to power the entire load requirements of one or more coupled electrical loads up to a defined power limit determined by aggregation of power from one or more coupled energy sources. The one or more coupled electrical loads and the one or more coupled energy sources are external to the generic energy flow manager and connect to the HVDC bus or the LVDC bus through their respective application-specific hardware 114 and connection interface 312. Multiple energy sources and loads may be connected to the universal energy flow manager, and multiple universal energy flow managers 126 may be connected in parallel as shown in Figure 4A or in series as shown in Figure 4B, so that various combinations can be obtained to meet the overall load requirements.

[0044] The general-purpose energy flow manager 126 may also include an application 136 that includes control software configured to communicate with and / or control external devices that provide or receive power from the energy flow manager.

[0045] The general-purpose energy flow manager 126 may also include a thermal management component 314 configured to regulate the temperature of the general-purpose energy flow manager 126, for example, by heat exchange through one or more heat exchange processes involving a gas or liquid medium.

[0046] The power electronics module 308 may include components for bidirectional high-voltage conversion, bidirectional low-voltage conversion, and electronics for energy storage management. As shown in FIG. 5 , a DC-DC converter, such as a bidirectional DC-DC converter 520, may be installed between the HVDC bus and the energy storage device and configured to convert power between the energy storage device and the HVDC bus. Here, for example, a 48V output of the energy storage device is converted to a 400-900V input for the HVDC bus, or vice versa. Another DC-DC converter, such as another bidirectional DC-DC converter 522, may be installed between the LVDC bus 514 (a low-voltage direct current bus) and the energy storage device 306 and configured to convert power between the energy storage device and the LVDC bus. Thus, a 48V output of the energy storage device may be converted to a 12-48V input for the LVDC bus, or vice versa. This may enable stack / energy flow manager balancing during charging or discharging and may also serve as an additional low-voltage power source for external loads. The energy storage device 306 may be backup storage or may be used to charge the vehicle. Furthermore, its presence in the universal energy flow manager 126 may allow the universal energy flow manager 126 to be used as a single or multiple vehicle 12V, 24V, or 48V battery for long-term storage. The power electronics module may include one or more controllers that can monitor the status of the HVDC bus and the LVDC bus. The power electronics module may also rapidly adjust the HVDC bus voltage level to match a particular EV. Thanks to single or multiple interleaved converters, flexible power limiting, which may be adjustable to allow a full range of power from several hundred milliwatts to full power, can be designed to improve load efficiency. Flexible power limiting may be enabled by the ability to automatically couple multiple ASHs to each other and automatically detect and adjust time-division energy multiplexing operation to manage power flow and conversion, as discussed herein.Integrated overvoltage, undervoltage, overtemperature, and undertemperature protection may also be implemented. To ensure proper isolation, the energy management system may perform automatic self-checks by applying safe voltage level output lines to ensure proper isolation. Constant monitoring of the HVDC signal may be performed with respect to ground to flag fast or slow drifts. Additionally, sensors may be integrated into the energy flow manager to provide automatic alert active load control during hazardous conditions. These may include closing the main water control valve, shutting down individual circuit breakers, clearing air control during carbon monoxide detection or indoor air quality alerts, detecting air quality, detecting smoke and fire, and measuring temperatures on the battery stack, PCBs, and connectors. Additionally, HVDC may be operated with a frequency flyback converter.

[0047] The power electronics may further include a battery management system (BMS) configured to implement active measurement and cell balancing, charge and discharge control, temperature measurement, and safety logic with redundant sensing and supply.

[0048] In one embodiment, the distribution and communications module 310 may further include components for flexible power-limited DC distribution, a communications hub for the energy management system 124 with a master digital controller (not shown), components for voltage and current measurement, monitoring, and protection, and a contactor-failure detection disconnect unit (contactor) configured to disconnect the distribution and communications module 310 from the application-specific hardware 114. The distribution and communications module 310 may also include components for DC bus pre-charging and control electronics for full system integration. For example, the pre-charging circuit may be controlled by the HVDC bus controller to match the voltage on both sides of the contactor 512 prior to closing the contactor 512. Furthermore, with respect to the components for voltage and current measurement, the generic energy flow manager receives information about the voltage or power needed at the ASH through communication with the ASH. For example, in the case of an EV ASH, the EV may command the current and voltage from the charger, which may then be transmitted to the ASH and then to the energy flow manager. In other cases, the energy flow manager may use internal measurements to ensure operation does not exceed design ratings.

[0049] The housing 304 may comprise a structure that can enclose other components of the generic energy flow manager 126. The housing may have HVIL (high-voltage interlock loop) and safety features, as well as "open case" detection that can shut off power when the housing is open. Leakage current measurement may be performed in a GFCI (ground fault circuit interrupter) implementation, and HVDC voltage detection can recognize an increase in voltage drop that may indicate a connection failure. Backup isolation measurement may be implemented in the generic energy flow manager 126 and / or each ASH. An application may collect feedback from all ASHs and perform short-to-chassis or leakage detection via individual HDVC (positive or negative) measurements. An application may perform comparison of HVDC measurements from all network-connected HW. An application may run among multiple controllers as backups, and voting logic may be implemented to ensure a safe state is always reached.

[0050] The housing may also include a quick connect 316 that may enable connection from one universal energy flow manager 126 to another universal energy flow manager in parallel and series configurations. In an embodiment, the housing may have a display, such as a touchscreen display or dashboard 112, configured to enable utility input. In an exemplary embodiment, the energy flow manager's display may receive load shedding configurations from a user and provide power management of the energy source based on the user configuration. Additionally, the display may output a power flow analysis representative of the status of the energy source and / or electrical load.

[0051] Connected to the high-voltage DC bus 516 of the generic energy flow manager 126 through the connection interface 312 and contactors 512 may be one or more application-specific hardware 114. The application-specific hardware may include an application-specific hardware controller (ASH controller 510) that may receive instructions from the generic energy flow manager, as shown in FIG. 5 . The ASH may also include a power conversion module 502 configured to convert electrical energy from a first input form to a defined second output form in response to instructions from the ASH controller. The power conversion module 502 may be optional. The module 502 may also include contactors operated to connect the input to the output. The defined second output form may be the form required by the generic energy flow manager 126. For example, the high-voltage DC bus 516 may accept 400V, and thus an ASH dedicated to solar input may be instructed to convert a 48V input to a 400V output for the high-voltage DC bus 516.

[0052] The ASH may be configured to operate in a first mode of operation as a source ASH, where the ASH connects an energy source to the connection interface and thus provides energy to the generic energy flow manager. The ASH may operate in a second mode of operation as a load ASH, where the ASH connects a load to the connection interface and thus receives energy from the generic energy flow manager for distribution to the load.

[0053] The energy management system may include multiple ASHs. For example, the ASH may be an EV ASH 604 ( FIG. 6 ), an EV ASH with DC fast charging 902 ( FIG. 9 ), or an in-home ASH 704 ( FIG. 7 ). The ASH may be a renewable energy source ASH, such as a PV / Wind ASH 906 (photovoltaic / wind ASH). It may also be a fuel cell ASH 910 or a hydrogen reformer ASH 912. Furthermore, based on the design of the ASH's power conversion module 502 (which may include multiple different converters), one or more different types of sources may be connected to the same ASH, as shown in FIG. 8 . The ASH may further include isolation detection and protection components, as well as an interface connector and a housekeeping power supply (HKPS, not shown).

[0054] In an embodiment, the ASH can be configured to automatically connect any energy source or any electrical load to the universal energy flow manager.

[0055] In the in-home ASH of FIG. 7 , the in-home ASH 704 can provide power from a grid energy source (power grid 128) to the generic energy flow manager 126 in a first operating mode. This energy can be provided to the load device 508 through the load ASH. The energy can be provided as is, converted to another form (e.g., to a relatively lower voltage), or combined with another source in any suitable configuration of one or more coupled generic energy flow managers 126 and provided to the load ASH 506. The in-home ASH 704 can alternatively receive power from the generic energy flow manager in a second operating mode to provide to a home appliance or tool or to sell to the grid / utility. The home appliance or tool can receive energy through a smart circuit breaker panel 922 in communication with the generic energy flow manager 126 and in electrical connection with the in-home ASH 704 acting as the load ASH. In some cases, the smart circuit breaker panel 922 can be configured as an ASH with a direct connection to the HVDC bus.

[0056] In an exemplary embodiment, the energy flow manager may configure one or more ASHs to operate in their first operating mode to aggregate energy for direct current (DC) fast charging of one or more electric vehicles or one or more electric loads, where DC fast charging may include Level 3 DC fast charging or, in some cases, a 400V-900V charging specification. For electric vehicles, the charging speed provided for 400V-900V may be 3 to 20 miles per minute. With the ability to aggregate multiple energy sources through a corresponding ASH, which is external to the generic energy flow manager 126 and further managed locally by the respective ASH controller 510, as opposed to directly managed by the generic energy flow manager 126 architecture, any load requirement can be met as long as the energy sources can be aggregated. Thus, there may be no need to redesign the generic energy flow manager 126 for new types of energy sources.

[0057] In embodiments where DC fast charging is used, multiple electric vehicles or electric loads may be DC fast charged simultaneously. In another embodiment, the in-home ASH 704 may use multiple universal energy flow managers 126 to charge electric vehicles in parallel or in a time-multiplexed manner. The in-home ASH 704 may allow EV charging not only from the grid but also from other sources, including solar, wind, fuel cells, another EV, a grid battery, or a combination thereof, through connection from other sources to the in-home ASH 704. The in-home ASH 704 may also have a standard 240V plug 904 and / or a 120V plug. The power conversion module 502 of the in-home ASH 704 may include an inverter or rectifier to convert DC to AC or AC to DC, respectively, depending on the operating mode. It may also include a DC-DC converter to supply DC loads.

[0058] In an electric vehicle ASH (EV ASH 604) as shown in FIG. 6 , the EV ASH 604 may be configured in a first mode of operation to send power from an electric vehicle 132 energy source (e.g., an EV battery) to a generic energy flow manager. This energy may be routed to a load device 508 through a corresponding load ASH 506. Thus, the electric vehicle 132 may power, for example, a home or another EV. In a second mode of operation of the EV ASH, the electric vehicle 132 may be a load and may receive power from the generic energy flow manager via the EV ASH 604. Thus, another device (such as the grid, a renewable energy source, or another EV) may power the subject electric vehicle 132.

[0059] The ASH controller 510 of the EV ASH 604 may be or interface with an electric vehicle communication controller (EVCC). The EVCC may have built-in CAN and PLC communication protocols and may act as a communication gateway between the vehicle and the rest of the energy management system 124. The power conversion module 502 may include a DC-DC converter configured to convert a first input voltage to a second output voltage. The contactors of the EV ASH 604 may be used to implement a time-division multiplexed charging algorithm and for protection during insulation fault detection. In a vehicle-to-vehicle implementation where the EV ASH 604 provides energy from one vehicle to another, the EV ASH may communicate with a universal flow manager to set the appropriate HVDC bus voltage, where, for example, a 400V EV may supply an 800V EV. This may enable vehicle-to-vehicle power transfer.

[0060] The ASH may also be a renewable energy source ASH or a fuel cell ASH configured to provide power to the universal energy flow manager from a renewable energy source or a fuel cell in a first operating mode, where the renewable energy source is a photovoltaic / solar energy source, a wind energy source, or another renewable energy source. As shown in Figure 9, the ASH may be a PV / Wind ASH 906 (a photovoltaic / wind ASH), a fuel cell ASH 910, or a hydrogen reformer ASH 912.

[0061] In embodiments herein, the universal energy flow manager 126 of the energy management system may be portable and may act as a stand-alone energy source by using the on-board energy storage device 306 as a battery to charge one or more loads. For example, the universal energy flow manager 126 may be used as a range extender battery in an electric vehicle power system.

[0062] 9 and 10 , an energy management environment 100 and method are shown. The environment 100 may include a network / communications infrastructure 102, multiple generic energy flow managers 126, renewable energy sources 130, multiple electric vehicles 132, a residential ASH 704, multiple EV ASHs 902 with DC fast charging, a 240V plug 904 for the residential ASH 704, a PV / Wind ASH 906, a DC bus 908, fuel cells ASH 910, a hydrogen reformer ASH 912, a service outlet 914, an AC line 916, a DC-DC converter 918, a DC line 920, and a smart circuit breaker panel 922. In this environment, multiple electric vehicles 132 may be charged simultaneously. Multiple energy sources may be provided through connections with one or more generic energy flow managers 126. An application 136 of one or more generic energy flow managers 126 may execute the energy management process 1000 of FIG. 10 . The energy management process may begin in step 1002, where the process provides one or more generic energy flow managers. In the case of multiple generic energy flow managers, a controller of one primary manager (primary controller) may control the controllers of the remaining secondary managers (secondary controllers). Here, the primary controller may determine where to route energy, routing the energy to the DC / HVDC bus of the connected secondary manager for provision to one or more loads. Thus, the primary controller may detect or measure the overall load requirements of one or more coupled electrical loads in step 1004. This may be done by detecting that one or more ASHs are connected. The controller may combine energy from one or more coupled energy sources coupled to the energy flow manager in step 1006 through instructions relayed to the ASH controllers of each of the one or more coupled energy sources.The controller may then power the entire load requirements of one or more coupled electric loads through the ASH in step 1008, up to the defined power limit determined by the aggregation. The power supply may further comply with multiple defined logics related to providing EV charging, providing backup charging, reducing costs, maximizing battery life, and allowing off-grid use. As shown in step 1010, this may be accomplished by operating a first ASH in a first operating mode as a source ASH for connecting an energy source of one or more coupled energy sources to the connection interface of the generic energy flow manager 126 and providing energy to the generic energy flow manager. Powering the entire load requirements may be accomplished by operating a second ASH in a second operating mode as a load ASH for connecting one or more coupled electric loads to the connection interface and receiving energy from the generic energy flow manager, as shown in step 1012. By doing so, the energy flow manager may route power from EV1 to charge EV2, as shown in FIG. 9 . The energy flow manager may also route power from the renewable energy source 130 to charge EV3. EV4 may be used as a source to power a home load via home ASH 704 acting as load ASH. Furthermore, all of this may occur simultaneously as available power may not be limited solely by available grid energy from service outlet 914.

[0063] FIG. 11 illustrates energy management in a residential setting with a residence 134 having multiple EV ASH902 with DC fast charging. A utility underground feeder 1104 may provide, for example, 200 A of current to a meter 1102 of the residence 134. Power may flow bidirectionally between the meter 1102 of the residence 134 and the utility underground feeder 1104, which is connected to a generic energy flow manager 126. The residence's generic energy flow manager 126 may also be connected to multiple other energy sources (130, 128). Three electric vehicles 132 may be connected to the generic energy flow manager 126, for example. Here, each of the three electric vehicles 132 may have an EV ASH902 with DC fast charging. To charge the electric vehicles 132 for a selected time period of approximately 1.5 hours, 75 kWh of energy may be required, assuming the EVs consume 50 kW of power. When three vehicles EV5, EV6, and EV7 are being charged simultaneously, for example, 125 A of current may each be required for electric vehicle 132. Because utility underground feeder 1104 may be limited to 200 amps and 70 amps may be required for the domestic loads, the remaining current required for DC fast charging may be taken from other energy sources to simultaneously DC fast charge the vehicles. Thus, Level 3 charging of one or more vehicles may be introduced in a residential setting without compromising the ability to simultaneously provide energy to other domestic electrical loads.

[0064] Meanwhile, another residence 1110 may not have a general-purpose energy flow manager 126 and thus may be limited to 200 amps of current from the utility underground feeder 1104. The other residence 1110 may have an AC Level 2 charger (AC L2 charger 1106) and an AC Level 1 charger (AC L1 charger 1108). The AC charger may provide power to the vehicle's onboard charger, which converts the AC power to DC for the battery. The onboard charger's acceptance rate may vary, but traditionally, a full Level 2 charge may take from 4 or 5 hours to over 12 hours. Thus, in the other residence 1110, which may require 70 amps to power the in-home loads, the remaining 130 amps may be split among the electric vehicles 132, where charging may be limited to only Level 2 charging at 50 amps and Level 1 charging at 30 amps. Thus, for example, EV8 and EV9 may be fully charged in 6.25 hours, and EV10 may be fully charged in 22 hours.

[0065] Intelligent Energy Management The exemplary embodiments further recognize that conventional residential energy management systems may often interface with a grid for energy distribution and, at best, may rely on predefined logic for energy management. The exemplary embodiments recognize that conventional systems may be limited to distributing energy due to an underlying architecture that cannot predict energy consumption needs and does not allow for variable energy sources to be used. The exemplary embodiments recognize that while energy and power needs may be estimated to provide for incoming energy, this can be highly error-prone and may not account for external influences such as power outages, unexpected fluctuations in load, and changing weather / environmental conditions. Moreover, conventional systems may, at best, rely on connecting energy sources with predetermined, matching voltage and power specifications. The exemplary embodiments recognize that this may limit the control a utility may have over the power management system and lead to inaccurate management.

[0066] As far as power metering of individual sources and loads of an energy storage system is concerned, currently, conventional energy management systems may charge and discharge all individual modules together without providing access to detailed statistics for each individual source and load. Exemplary embodiments recognize that it may be critical to monitor the energy of individual modules in a larger system and control them individually to ensure the efficiency and safety of the system as a whole. For example, by being able to safely connect and disconnect individual loads based on analysis of detailed information about the system as a whole, rather than being limited to predetermined load specifications, energy management systems may be made more efficient, modular, and scalable, and the availability of energy for different predictive operations may be greatly increased.

[0067] The exemplary embodiments used to describe this disclosure generally address and solve the above-described problems and other related complications through intelligent suggestion of power dispatch actions that can increase utility costs, available backup energy, charge times, and battery life. The exemplary embodiments may solve these problems with a proactive or “proactive” process that not only anticipates the power / energy demands of domestic loads, electric vehicles, and other appliance loads, but also anticipates the availability of energy from connected renewable energy sources, fuel cells, EV batteries, and / or other energy sources, and operates to meet said load demands by leveraging the modular architecture of the universal energy flow manager 126 and connected ASHs.

[0068] With respect to intelligent suggestions, some operations are described in certain embodiments as occurring at a particular component or location. Such locality of operations is not intended to be limiting to the exemplary embodiments. Operations described herein as occurring at or performed by a particular component, e.g., predictive analysis of load and source data and / or natural language processing (NLP) analysis of historical calendar or weather data, may be implemented as a component-specific function causing the operation, or may be performed in another component, e.g., a local or remote machine learning (ML) or NLP engine, respectively.

[0069] One embodiment implements automatic power flow routing to multiple loads from multiple sources, such as multiple batteries, grid, solar, wind fuel cells, and other EVs. Another embodiment allows detailed power metering of all sources and loads. A further embodiment implements intelligent load shedding to increase residential backup time. Yet another embodiment allows direct billing to the utility to eliminate the need for an additional meter if one is required to be installed by the utility company.

[0070] In one aspect, an embodiment monitors and manages the accumulated energy of an energy management system. This embodiment may make decisions about which sources to extract energy from based on attributes such as minimizing EV charging time, maximizing available backup during power outages, etc. Another embodiment may monitor various profile sources configured for an energy management system that may need to use or store energy during highly renewable energy production times, such as a renewable energy storage plant, or an electric vehicle may require DC fast charging. A profile source may be an electronic data source from which information usable to determine a consumer's profile characteristics can be obtained. For example, a profile source may be an operator profile 1222 that provides operator information such as, for example, a preference configuration including a ranking of the importance of loads to be charged, a calendar application including the operator's activity history and associated energy demand, energy generation events, feedback from an operator or operator group, or other operator data. A profile source may be an environmental profile 1228 that provides data such as past weather conditions, predicted future weather conditions, historical power outage data, or other environmental data. A profile source may be a device, apparatus, software, or platform that can provide information from which energy / power delivery or withdrawal characteristics can be derived. For example, dashboard 112 may act as a profile source within the scope of exemplary embodiments. Additionally, a community, such as a group of businesses in a residential facility or a group of businesses in a group of residential facilities, may be a profile source from which storage and distribution characteristics can be obtained to derive preferences, tastes, sentiments, or energy usage.Additionally, measured power, voltage, and health metrics or parameters for sources and loads of the generic energy flow manager 126 and the energy management system 124 as a whole, generally referred to herein as energy management system parameters 1220, may be input data and may be used to learn from and derive patterns for providing and drawing energy in the energy management system 124. Operator / environment profile data, information, and preferences are terms used interchangeably herein to refer to one or more user / environment constraints that may affect power / energy routing.

[0071] Additionally, information / data about the components of the energy management system 124 (e.g., voltage, power, current, number of connected sources and loads, temperature, battery state of health (SOH), battery state of charge (SOC), average energy consumption, vehicle energy demand profile, home energy demand profile, etc., or other energy management system parameters 1220) may form part of the constraints or be separate therefrom and may be obtained for use as input to the intelligent power dispatch module 1216 for predictive analysis, as described hereinafter. Thus, the profile source 1224 information and the energy management system parameters 1220 may collectively form at least part of the input data 1202 or constraints for the intelligent power dispatch module 1216 to predict optimal power dispatch operations and schedules, implement said operations, and observe said schedules.

[0072] Operating on profile information from one or more profile sources, embodiments routinely assess constraints applicable to the system and operator. Embodiments add new constraints / input data as discovered in the profile information analysis, modify existing constraints as justified by the profile information analysis, and reduce the use of past constraints depending on feedback, observed constraint usage, and / or the presence of support for past constraints in the profile information. Past constraints may be reduced or made obsolete by some degree of deprioritization of the constraint, including removal / erasure or disabling of past constraints. More generally, profile information may be obtained from any source available to the energy management system 124.

[0073] The input data 1202 determined by an embodiment may vary over time. For example, a load may have time-varying parameters that may be measured and used as inputs. This may provide real-time suggestions for efficiently operating the energy management system 124. Similarly, a grid energy source may indicate an outage; however, energy may still be needed. Thus, the power dispatch module 1216 may suggest options for routing energy from the solar panel through the solar panel ASH to power the loads within the residence.

[0074] Additionally, based on predictive analysis of inclement weather or certain seasons when renewable energy sources may not be readily available, the power dispatch module 1216 may suggest reducing the usage of certain in-house loads to meet the predicted DC fast charging demand, or vice versa. Herein, the ability to automatically detect the power / energy metrics of connected energy sources or loads, as well as the ability to connect other energy sources, may inhibit power sharing limitations typically found in systems that require energy sources with specific specifications.

[0075] The intelligent power dispatch proposals and techniques described herein are generally not available in conventional methods in the field of technical activities related to residential energy management systems. The method of the embodiments described herein, when implemented to run on a device or data processing system, captures constraints and includes in the proposal a significant advancement in the functionality of that device or data processing system by using an advanced modular general-purpose energy flow manager architecture.

[0076] In further embodiments, a machine learning engine may be provided to increase the resolution and effectiveness of predictions made by the generic energy flow manager based on a comparison of detected and received information. The machine learning engine may detect patterns and, based on these patterns, weigh likely outcomes and energy demand profiles. When a business engages the generic energy flow manager 126, data regarding consumption may be collected and stored for analysis by a controller in the generic energy flow manager 126 or another networked computerized device. The data may be aggregated to detect patterns and enable additional resolution in predicting behavior. The machine learning engine may perform analysis and draw correlations on time-series data collected at the source, load, or environment, supplemental information such as that provided via the network, and / or other information. For example, the machine learning engine may perform linear algebraic regression analysis on the time-series step data to find best-fit parameter values. The machine learning engine may further return operating parameters that can be used, for example, by the controller in energy management.

[0077] Any other application, such as the client application 120 of FIG. 1, the dashboard application of FIG. 1, the server application 116 of FIG. 1, or the application 1204, may implement the embodiments described herein. Any of the applications can use data from the energy management system 124 and profile sources to propose and implement power dispatch suggestions. The applications can also retrieve data from the storage unit 108 for predictive analysis. The applications can also run on any data processing system (server 104 or server 106, client 110, dashboard 112).

[0078] In one aspect, a computer-implemented method is disclosed that includes receiving energy demand states of one or more coupled electric loads coupled to a generic energy flow manager via load application-specific hardware (load ASH), where the energy demand states indicate a desired amount of energy needed by the one or more coupled electric loads in an energy management environment. The method may further include receiving available energy states of one or more coupled energy sources coupled to the generic energy flow manager via source application-specific hardware (source ASH), where the available energy states indicate an amount of energy available from the one or more coupled energy sources in the energy management environment; generating input data using at least the energy demands and available energy states; extracting one or more features from the input data, where the one or more features indicate characteristics of a request for a power dispatch proposal action; proposing, using a power dispatch module, at least one power dispatch proposal for the one or more coupled electric loads; and performing a power dispatch action based on the power dispatch proposal.

[0079] With reference to FIG. 12 , this figure shows a diagram of an example configuration for intelligent power dispatch according to an example embodiment. Intelligent power dispatch can be implemented using application 1204 of FIG. 12 . Application 1204 may be, for example, an example of application 136, server application 116, client application 120, or dashboard application 122. Application 1204 may receive or monitor a set of input data 1202, for example, in real time. The input data includes energy management system parameters 1220. The input data may also include operator and environmental characteristics from profile sources 1224 (operator profile 1222, environmental profile 1228), such as preferences, pre-planned energy storage, average daily mileage, historical driving energy consumption per mile, calendar data for time-lining procedures, and weather data.

[0080] In one or more embodiments described herein, characteristics, properties, and / or preferences associated with operators, environments, loads, sources, etc. are referred to as “features.” In one or more embodiments, configuration 1200 defines and configures algorithms and / or rules to drive feature selection results. In particular embodiments, the algorithm may include, for example, determining the lowest common value for the features and determining whether the value satisfies a best match within a threshold (e.g., 90%) of the features. In an embodiment, the system may prioritize some features such as battery life, charging time, utility costs, and available backup energy, so that features have different weights. In an embodiment, after a common denominator across multiple operators is found, configuration 1200 extracts and derives best feature values ​​to help understand the individual operator's problems and make intelligent suggestions.

[0081] In some embodiments, features may be selected or extracted from outside the machine learning model, although in other embodiments, features may be further extracted within the machine learning model / deep neural network and thus may be integral to the model. Feature extraction and selection may therefore be generally used interchangeably herein.

[0082] In one embodiment, the feature selection / extraction component 1214 is configured to use data from all the different available features (e.g., energy management system parameters 1220, enterprise profile 1222, environmental profile 1228) to generate relevant features based on the content of a request from the application 1204. In this embodiment, the feature selection / extraction component 1214 may receive a request from the application 1204 that may include at least an identification or detection of a load and instructions to propose and implement a corresponding power dispatch proposal. Using the energy management system parameters 1220 and / or the profile source 1224, the feature selection / extraction component 1214 may obtain a combination of specific energy management system parameters 1220, profile information from the enterprise profile 1222, and environmental data from the environmental profile 1228. In this embodiment, the feature selection / extraction component 1214 may use a defined algorithm of prioritization to generate the features as a feature profile. In particular embodiments, the feature profile includes each feature (e.g., 1. load voltage, 2. load power, 3. weather forecast, 4. connected secondary energy flow manager, 5. source power, 6. source power, 7. EV charging time, 8. operator preference, 9. remaining life cycle of EV battery, and 10. weighting assigned to each feature). Using the extracted features and a trained M / L model 1206 trained using multiple different datasets, the power dispatch module 1216 can determine a power dispatch proposal 1212 for the system.

[0083] The power dispatch proposal 1212 may include instructions for automatically routing a defined amount of power from a particular source to a particular load. The automatic power flow routing may be from multiple sources, including, for example, multiple batteries, the grid, solar panels, wind energy sources, fuel cells, and other EVs, to multiple loads, such as a domestic load or an EV nearing the end of its regular use life and with depleted battery energy.

[0084] The power dispatch proposal 1212 may include detailed reports on energy consumption that may allow the utility company to understand how much energy is being used for the EV or other specific loads, thereby eliminating or reducing the need for individual meters and therefore reducing utility bills.

[0085] The power dispatch proposal 1212 may also include instructions for automatically routing power to specific loads based on user preferences. For example, the user preferences may include minimizing energy transfer to or from a battery pack. In an embodiment, the power dispatch proposal 1212 may include instructions for maximizing EV charging speed, where the connected EV ASH 604 may be operated close to its contactors. Unnecessary loads such as AC and pool appliances may be shut down, grid power usage may be maximized, and power from renewable sources may be maximized and routed to the high-voltage DC bus 516, from which power may be dispatched to the EV through the EV ASH 604.

[0086] The power dispatch proposal 1212 may further include instructions for operating the energy management system 124 in an intelligent backup operating mode, whereby residential loads may be operated for a defined period of time (e.g., three hours) after a grid power outage. After the defined period of time, non-essential loads may be shut down based on utility preferences or learned shutdown parameters, and remaining energy sources may be used to automatically and progressively manage the system. Thus, the power dispatch proposal may include load shedding instructions to increase residential backup time. Additionally, the system or energy flow manager may have power inverters that provide seamless power transfer. The power inverters may be configured to be always on or may switch power during a cycle. The power dispatch proposal 1212 may include intelligent energy forecasting that estimates the required amount of energy to be stored overnight and can offset changing weather conditions that may hinder photovoltaic and wind energy availability. Additionally, with an automatic grid outage interface to the utility, the energy management system may anticipate required dropout times.

[0087] The power dispatch proposal 1212 may further include instructions for maximizing payback and usage of renewable energy sources during defined time periods, such as during the day. For example, excess power may be sold back to the utility or stored in the energy storage device 306 of the universal energy flow manager 126. During nighttime or off-peak hours, renewable energy may be used to power residential loads and charge batteries. Power may be harvested from EVs while maintaining a minimum charge desired by the EV operator. One or more of these strategies may be selected to minimize or eliminate peak charging rates. Further instructions may include optimizing system management to achieve net-zero or near-net-zero utility energy transfer.

[0088] Still further, the power dispatch proposal 1212 may include instructions to maximize battery life, and for operators interested in lowest maintenance costs, battery usage may be minimized to achieve a relatively long battery life.

[0089] The power dispatch proposal 1212 may further include instructions for rebalancing energy storage among the batteries or energy storage devices 306 of the connected universal energy flow manager 126 through active charge / discharge curtailment.

[0090] Suggestions may be provided in real time as inputs change, and implementation of the suggestions may be performed in real time or upon receiving operator confirmation. User feedback regarding the accuracy of the suggestions may be used in modifying the machine learning model. By providing one or more of these cell operation and manufacturing operation suggestions and implementing said suggestions, a highly energy-efficient, self-supporting, and cost-effective energy management system and environment may be obtained. These examples are not intended to be limiting, and any combination of these and other exemplary power output suggestions is possible in light of this description.

[0091] The power delivery module 1216 may be based on a neural network, such as, but not limited to, a recurrent neural network (RNN). RNNs are a type of artificial neural network designed to recognize patterns in data sequences, such as numerical time series prediction and numerical time series anomaly detection using data emanating from sensors, and to generate image descriptions and content summaries. RNNs may use feedback paths (going in the opposite direction to the "normal" signal flow) that form cycles in the network's topology. Computations derived from earlier inputs are fed back into the network, giving the RNN "short-term memory." Feedback networks such as RNNs are dynamic; their "state" is constantly changing until an equilibrium point is reached. For this reason, RNNs are particularly well-suited to detecting relationships across time in a given dataset. Recurrent networks take as their input not only the current input example they see, but also what they perceived earlier in time. A decision a recurrent net reaches at time step t-1 may influence a decision it reaches momentarily later at time step t. Thus, a recurrent network has two sources of input, current and recent, which combine to determine how to respond to new data.

[0092] In an example embodiment, the power delivery suggestion 1212 may be presented by a presentation component 1208 of the application 1204. The adaptation component 1210 may be configured to receive input from a user to adapt, if necessary, the power delivery suggestion 1212. For example, changing the minimum allowable charging time proposed by the power delivery module 1216 causes a recalculation of the power delivery suggestion 1212 to take into account the new minimum allowable charging time.

[0093] A feedback component 1218 optionally collects user or consumer feedback on the power delivery suggestions 1212. In one embodiment, the application 1204 may be configured not only to calculate the power delivery suggestions 1212 but also to provide a way for the user to input feedback, where the feedback indicates the accuracy of the calculated power delivery suggestions 1212. The feedback component 1218 applies the feedback in machine learning techniques, for example, to the profile or to the M / L model 1206, to modify the M / L model 1206 for better suggestions. In an exemplary embodiment, the application analyzes the feedback input, and the application strengthens the M / L model 1206 of the power delivery module 1216. If the feedback is satisfactory or unsatisfactory regarding the accuracy of the suggestions, the application strengthens or weakens the parameters of the M / L model 1206, respectively.

[0094] The input layer of the neural network model may be, for example, vectors representing current, voltage, or power, before and after weather or calendar data provided by the NLP engine 1226, etc. In one example, a CNN (convolutional neural network) uses convolutions to extract features from the input. In one embodiment, upon receiving a request to provide recommendations, the application creates an array of values, which are input to the input neurons of the M / L model 1206 to generate an array containing power dispatch recommendations 1212.

[0095] The neural network M / L model 1206 can be trained using various types of training data sets, including stored profiles and multiple sample cell measurements. As shown in FIG. 13 , which illustrates a block diagram of an exemplary training architecture 1302 for machine learning-based recommendation generation according to an exemplary embodiment, program code extracts various features 1306 from training data 1304. Components of the training data 1304 have labels L. The features are used to develop a predictor function, H(x), or hypothesis, which the program code uses as the M / L model 1308. In identifying various features in the training data 1304, the program code may use various techniques, including, but not limited to, mutual information, which is an example of a method that may be used to identify features in one embodiment. Other embodiments may use various techniques for feature selection, including, but not limited to, principal component analysis, diffusion mapping, random forests, and / or recursive feature elimination (a brute force approach to feature selection). "P" is an output that may be obtained and, when received, may further trigger the energy management system 124 to perform other steps, such as steps of stored instructions. The program code may use a machine learning m / l algorithm 1312 to train the M / L model 1308, including weighting the outputs, so that the program code can prioritize various changes based on a predictor function that comprises the M / L model 1308. The outputs may be evaluated by a quality metric 1310.

[0096] By selecting diverse sets of training data 1304, the program code trains M / L model 1308 to identify and weight various features. To use M / L model 1308, the program code obtains (or derives) input data or features to generate an array of values ​​to input to the input neurons of the neural network. In response to these inputs, the output neurons of the neural network generate an array containing power delivery suggestions to be contemporaneously presented or implemented.

[0097] Turning now to FIG. 14 , an M / L process 1400 is disclosed. The process may begin at step 1402, where process 1400 may receive an energy demand state of one or more coupled electric loads coupled to a generic energy flow manager via load application-specific hardware (load ASH), where the energy demand state indicates a desired amount of energy needed by the one or more coupled electric loads in the energy management environment. In step 1404, process 1400 may receive an available energy state of one or more coupled energy sources coupled to the generic energy flow manager via source application-specific hardware (source ASH), where the available energy state indicates an amount of energy available from the one or more coupled energy sources in the energy management environment. In step 1406, process 1400 generates input data using at least the energy demand and available energy states. The available energy demand state and available energy state may include a voltage level, a current level, a power level, and / or a charge time. At step 1408, process 1400 may extract one or more features from the input data, where the one or more features represent characteristics of a request for a power dispatch proposal action. At step 1410, process 1400 may use a power dispatch module to propose at least one power dispatch proposal for one or more coupled electric loads. At step 1412, process 1400 performs a power dispatch action based on the power dispatch proposal. The power dispatch module may operate a machine learning engine. The power dispatch action may be performed by operating a source ASH to obtain power for routing to the load ASH of one or more coupled electric loads. The power dispatch action may be performed automatically, where multiple loads, e.g., multiple EVs, may be powered by time-division multiplexing of load-source pairs.

[0098] With respect to step 1408, the one or more features may also represent attributes obtained from the attribute prioritization 1502 step, as shown in FIG. 15 . In attribute prioritization, one or more attributes 1510 may be obtained for consideration in the output suggestion operation. The one or more attributes may have different assigned priorities or weights, or may have the same or even no assigned priorities or weights. By training the M / L model 1308 with a large set of different datasets that consider the attributes 1510, different scenarios can be handled by the power dispatch module 1216. In an exemplary and non-limiting embodiment, the attributes 1510 include instructions for minimizing charging time 1504, maximizing available backup energy from the energy source 1506, and minimizing utility costs 1508. Other attributes may include, for example, maximizing payback or excess energy sales, maximizing system safety, and maximizing battery life.

[0099] Thus, in one exemplary embodiment, the power delivery module 1216 operates based on a system of advantages and disadvantages that serves to maximize lifespan, safety, utility costs, and other attributes while also taking into account other input parameters.

[0100] Charging arrangement service The exemplary embodiments recognize that as electric vehicle use occurs, owners may be periodically presented with the task of finding available chargers for their electric vehicles, especially when traveling away from their residence. In some new environments, owners may not be able to easily find chargers that meet their requirements. The exemplary embodiments recognize that even when public chargers are available, they may be limited in number, capacity, and may be subject to lengthy wait times, especially for in-demand chargers that can meet fast charging requirements. Furthermore, providing chargers that are fast enough to meet growing demand can be a challenging task that may be insurmountable without novel approaches.

[0101] Example embodiments may disclose residential battery-based charging networks for passenger and maritime services to facilitate charging accessibility and reduce downtime costs. Charging may include DC fast charging, and public DC fast charging networks may be expanded to include privately owned and operated DCFC networks, which may reduce downtime costs for providing, for example, maritime and ride-hailing services. By utilizing multiple universal energy flow managers 126 and corresponding distributed energy sources in a charging dispatch network, a modular, scalable charging architecture may be provided to greatly increase charger availability and reduce wait and charging times. While more remote locations may increase charger availability, grid outages may not significantly impede EV charging, less load may be placed on the utility system, and DC fast charging may become more accessible. Furthermore, the universal energy flow manager's ability to connect with independent energy sources without the requirement to meet specifications may not require site or feeder upgrades.

[0102] 16 , computing platform 1602 may be configured with application 1606 or a set of applications. Application 1606 may be an example of server application 116 of FIG. 1 or other application and may include one or more instruction modules or communicate with one or more remote, external, or client instruction modules, for example, client 110, dashboard 112, personal mobile device, and home charger 1618. The instruction modules may include computer program modules. The instruction modules may include one or more of charging dispatch module 1608, charging reporting module 1610, constraint analysis module 1612, presentation module 1614, charging module 1616, and / or other instruction modules. In an aspect, an EV operator / owner / customer / client may have a device that needs charging. A home charger 1618 equipped with one or more universal energy flow managers 126 may periodically update the central application through the charging reporting module 1610 with home charger status parameters including, for example, remaining energy, availability, location, DC fast charger, connected energy source, state of charge, cost, state of health, or other home charger status parameters. An operator may request charging needs through the charging dispatch module 1608, and the location and charge desired (e.g., 10 minute charge, 20 miles worth of charge, etc.) may be shared with the application 1606 through the charging dispatch module 1608.

[0103] Based on updates from the home chargers 1618 and the operator requests, the application 1606 may obtain a set of available home chargers 1618 via the constraint analysis module 1612. Constraint analysis may include identifying the most significant limiting factors (i.e., constraints) that may prevent the goal from being achieved and then systematically refining the constraints until they are no longer limiting factors, i.e., they may become a constraint on the degree of freedom in providing a solution. For example, an operator may request DC fast charging that can be completed within the next two hours. The constraint analysis module 1612 may determine distance and DC fast charging capability as constraints. The constraint analysis module 1612 may determine the operator's distance from one or more available home chargers 1618 and the power level of the HVDC bus of the generic energy flow manager 126 of the one or more available home chargers 1618. In another example, a home charger owner may have a preference that available reverse charging must not fall below a defined threshold level after the completion of any charging arrangement service. Based on the amount of charge required by the client / charging cue and the number of energy sources connected to the home charger 1618, the constraint analysis may determine that a threshold level may be exceeded upon completion of a future charging arrangement operation, and therefore, may remove the home charger from consideration. In some other examples, the constraint analysis may weigh the importance of the identified constraints and address them in order of importance. In some cases, the determined set of available home chargers may be presented along with an indication of how well each satisfies the identified constraints. This example, and others, may be implemented through defined instructions or by machine learning using the example constraints as inputs to a trained deep neural network. Of course, these are merely examples, and other constraints and other analysis techniques, such as charging cost, may be determined or required.

[0104] A set of home chargers 1618 that satisfy the constraints may be presented to the operator by the presentation module 1614. From that set, a home charger may be selected, and the operator may transport the electric vehicle to the home charger for charging based on instructions from the charging module 1616. Alternatively, the owner of the home charger may transport a standalone home charger 1618 having at least an on-board energy storage device 306 to the EV operator for charging. Other factors, such as the EV owner's reputation and the home charger owner's reputation, may be considered as discussed herein.

[0105] In some implementations, the computing platform 1602, the remote platform 1620, the home charger 1618, and / or the external resource 1622 may be operably linked by one or more electronic communication links. For example, such electronic communication links may be established at least in part via the network / communications infrastructure 102 of FIG. 1 , such as the Internet and / or other networks. This is not intended to be limiting, and it will be appreciated that the scope of the present disclosure includes implementations in which the computing platform 1602, the remote platform 1620, and / or the external resource 1622 may be operably linked via some other communication medium. The home charger 1618 may comprise a general-purpose energy flow manager capable of connecting to multiple energy sources through application-specific hardware herein and may be portable. The home charger may be used in any location with a conventional grid supply to augment available energy / power that may otherwise be limited when relying on grid power.

[0106] A given remote platform 1620 may include one or more processors configured to execute computer program modules that enable a professional or user associated with the given remote platform 1620 to interface with the system 1600 and / or external resources 1622 and / or provide other functionality that is attributed to the remote platform 1620 herein. As a non-limiting example, the given remote platform 1620 and / or the given computing platform 1602 may be a server, but may alternatively include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a netbook, a smartphone, a gaming console, and / or other computing platforms.

[0107] External resources 1622 may include information sources outside of system 1600, external entities engaging with system 1600, and / or other resources. In some implementations, some or all of the functionality identified herein as being in external resources 1622 may be provided by resources included in system 1600.

[0108] Computing platform 1602 may include electronic storage unit 108, one or more processors 1604, and / or other components. Computing platform 1602 may include communication lines or ports to enable exchange of information with networks and / or other computing platforms. The illustration of computing platform 1602 in FIG. 16 is not intended to be limiting. Computing platform 1602 may include multiple hardware, software, and / or firmware components that work together to provide the functionality attributed to computing platform 1602 herein. For example, computing platform 1602 may be implemented by a cloud of computing platforms operating together with computing platform 1602.

[0109] The storage unit 108 may include a non-transitory storage medium that electronically stores information. The electronic storage medium of the storage unit 108 may include one or both of system storage integral with (i.e., substantially non-removable from) the computing platform 1602 and / or removable storage removably connectable to the computing platform 1602 via, for example, a port (e.g., a USB port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage unit 108 may include one or more of an optically readable storage medium (e.g., an optical disk, etc.), a magnetically readable storage medium (e.g., a magnetic tape, a magnetic hard drive, a floppy drive, etc.), a charge-based storage medium (e.g., an EEPROM, a RAM, etc.), a solid-state storage medium (e.g., a flash drive, etc.), and / or other electronically readable storage media. The electronic storage 130 may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and / or other virtual storage resources). The storage unit 108 may store software algorithms, information determined by processor information received from the computing platform 1602, information received from the remote platform 1620, and / or other information that enables the computing platform 1602 to function as described herein.

[0110] The processor 1604 may be configured to provide information processing capabilities in the computing platform 1602. Thus, the processor 1604 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information. While the processor 1604 is depicted in FIG. 16 as a single entity, this is for illustrative purposes only. In some implementations, the processor 1604 may include multiple processing units. These processing units may be physically located within the same device, or the processor 1604 may represent the processing functions of multiple devices operating in concert. The processor 1604 may be configured to execute one or more of a charging arrangement module 1608, a charging reporting module 1610, a constraint analysis module 1612, a presentation module 1614, and a charging module 1616. The processor 1604 may be configured to execute modules in software, hardware, firmware, some combination of software, hardware and / or firmware, and / or other mechanisms for configuring processing power on the processor 1604. As used herein, the term "module" may refer to a component or set of components that implements the functionality attributed to the module, which may include one or more physical processors in execution of processor-readable instructions, processor-readable instructions, circuitry, hardware, storage media, or other components.

[0111] 16 as being implemented within a single processing unit, it should be appreciated that in implementations in which the processor 1604 includes multiple processing units, one or more of the modules 1608, 1610, 1612, 1614, and / or 1616 may be implemented remotely from the other modules. The descriptions of functionality provided by the various modules 1608, 1610, 1612, 1614, and / or 1616 described below are for purposes of illustration and not intended to be limiting, as any of the modules 1608, 1610, 1612, 1614, and / or 1616 may provide more or less functionality than described. For example, one or more of modules 1608, 1610, 1612, 1614, and / or 1616 may be excluded, and some or all of its functionality may be provided by others of modules 1608, 1610, 1612, 1614, and / or 1616. As another example, processor 1604 may be configured to execute one or more additional modules that may implement some or all of the functionality attributed to one of the following modules 1608, 1610, 1612, 1614, and / or 1616:

[0112] 17 illustrates a process for arranging charging for an electric vehicle, according to one or more implementations. The operations of process 1700 presented below are intended to be exemplary. In some implementations, process 1700 may be performed with one or more additional operations not described and / or without one or more of the operations discussed.

[0113] Process 1700 may begin at step 1702 by receiving a charging request from a first user device by a charging dispatch service of charging dispatch module 1608. In some implementations, the charging request may include at least a measurement of a desired charge for electric vehicle 132. The user device may be, for example, a personal mobile device or an electric vehicle dashboard. At step 1704, process 1700 may receive charging capacity updates from a plurality of home chargers, including at least the first home charger, by a charging reporting service. At step 1706, process 1700 may calculate a set of home chargers for the first user device by constraint analysis using a charging management service. At step 1708, process 1700 may present the set of home chargers to the first user device by a presentation service. At step 1710, process 1700 may receive a selection of a first home charger from the presented set of home chargers. The selection may be made by the EV operator or may be made automatically based on finding the charger that best meets the EV operator's constraints. The charging procedure may be performed later in step 1712.

[0114] In an aspect, process 1700 may include DC fast charging a first electric vehicle with a first home charger. Process 1700 may also include DC fast charging a plurality of electric vehicles with the first home charger based on allocation, by the charging management service, of a plurality of first user devices to the first home charger. In some implementations, the home charger 1618 may provide Level 3 charging power greater than 15 kW, e.g., greater than 20 kW, or greater than 50 kW, or between 20 kW and 200 kW, or between 40 kW and 150 kW, or a voltage between 400 V and 900 V with a charging rate of 3 to 20 miles per minute.

[0115] In another aspect, the first user device may be operated by a user of the first electric vehicle and the first home charger may be operated by an owner of the first home charger.

[0116] Process 1700 may perform a constraint analysis, for example, by performing a calculation of a set of home chargers that meet DC fast charging requirements. Accordingly, real-time charging parameter information about the home chargers may be used in the constraint analysis. Information about the charging parameters, as well as information about the electric vehicle and the electric vehicle owner, may be used in the constraint analysis, as needed. In an aspect, the constraint analysis may include performing a calculation of the likelihood of arriving at the home charger before a time limit or low-charge threshold is reached.

[0117] Process 1700 may further include, by the charging management service of charging management module 1624 and in response to receiving a selection of the first home charger, receiving an acceptance of the selection from the first home charger and automatically transmitting, by the charging management service, a location of the first home charger to the first user device. A first electric vehicle of the user device may be transported to the location for charging. In an aspect, the location may be obtained by triangulation.

[0118] In other implementations, the process may include, by the charging management service and in response to receiving a selection of the first home charger, receiving from the first home charger an acceptance of the selection, and automatically transmitting, by the charging management service, a location of the first user device to the first home charger, where the first home charger may be transported to the electric vehicle for charging. This may be particularly useful in situations where the electric vehicle is energy depleted and immobile. The charging management service may also receive the location automatically or by triangulation.

[0119] Furthermore, through the use of a generic energy flow manager 126 capable of communicating with multiple application-specific hardware 114, an electric vehicle load can be charged by another electric vehicle acting as a source. Still further, the low-voltage DC bus 514 may be used to provide low-voltage DC fast charging directly or indirectly to appliances as needed. Thus, any amount of power needed can be provided by a charging dispatch service by configuring the home charger with a sufficient energy source to meet the demand.

[0120] 18 , a cloud charging dispatch process 1800 is shown. The process may illustrate a charging dispatch process managed from a server, according to an example embodiment. The charging management module 1624 may implement at least some of the server processes. The process may begin at step 1802, where process 1800 may wait for a request for charging from a client. In step 1804, process 1800 may determine a new request for charging, and in step 1806, may perform a constraint analysis based on inputs including client / customer / EV charging parameters and home charger status parameters.

[0121] If, at step 1808, it is determined that a charger that meets the request is available, the process may send the charger information to the client at step 1812. This information may be stripped of location data for privacy reasons. However, if no chargers are available, process 1800 may send feedback to the client at step 1810 that no chargers are available and wait for another request.

[0122] In step 1814, the process may determine that the client has selected a charger. The selection may be automatic or manual. In step 1816, the process may send the client information to the home charger 1618 for manual or automatic acceptance. The acceptance may be based on a reliability score, e.g., a rating of the client. In step 1818, the process may determine that the charger has accepted the client for future charging procedures. In step 1820, process 1800 may send the charger location to the client and reserve the charger for a defined timeout period. In step 1822, process 1800 may determine that the client has arrived within the timeout period, and then in step 1824, may verify the client's identity based on predefined verification logic.

[0123] If the verification is successful in step 1826, process 1800 may instruct the residential charger 1618 to perform a charging process to satisfy the client request in step 1828. Upon completion (step 1830), post-charging operations may be performed in step 1832. This may include, for example, paying for the charging operation and / or rating the client or the charger. In step 1834, the charger may be marked as available, and the process may return to the beginning.

[0124] FIG. 19 illustrates an electric vehicle charging arrangement process 1900 that may be implemented from a client device, such as a personal mobile phone and / or an EV dashboard. Of course, the process is illustrative and not intended to be limiting. Other similar processes may be possible in light of the description herein. The electric vehicle charging arrangement process 1900 may begin at step 1902, where a client may submit a charging request. Upon determining that a defined timeout period has been reached (step 1904), the client may be notified that service is unavailable (step 1908). However, the client may be presented with a set of home chargers 1618 that can meet the demands of the charging request at step 1906. The client may select a charger from the set of home chargers at step 1910, and the selected charger may accept the proposed charging service within the timeout period at step 1914 (step 1912). Acceptance by the selected charger may be based, for example, on the client's reliability score.

[0125] In step 1916, the EV may be transported to the selected charger for the charging operation. However, in some embodiments, the charger may be a portable charger and may be transported to the client for the charging operation. In step 1918, process 1900 performs a verification operation in step 1920 to verify the client and / or the selected charger. This may be performed, for example, by checking the client and / or residential charger ID (identification). If verification is passed, the charging operation may be performed in step 1922 until completion in step 1924. The process may perform post-charging operations in step 1926 and optional feedback operations in step 1928.

[0126] 20 shows an example residential charging arrangement process 2000 that may be implemented from a home charger 1618. The process may begin at step 2002, where process 2000 may send updates on home charger parameter status at regular time intervals (e.g., every 1 second or every 30 seconds), including, for example, the total number of connected energy sources and corresponding energy parameters and owner preferences. Upon receiving a request for charging at step 2004, the process may automatically or manually accept the request at step 2006, for example, based on the client's reliability score associated with the request. Process 2000 may wait a defined timeout period (step 2010) at step 2008 until the client arrives at step 2012. Of course, in some embodiments, the charger may be transported to the client.

[0127] In step 2014, process 2000 may verify the charging process to determine whether charging is authorized, and if verification passes, the charging operation may be performed in step 2016. Once charging is completed in step 2018, charging statistics may be reported back to the server in step 2020.

[0128] Thus, computer-implemented methods, systems or apparatus, and computer program products for intelligent power delivery suggestion and other related features, functions, or operations are provided in exemplary embodiments, and where an embodiment of a portion thereof is described with respect to a certain type of device, the computer-implemented method, system or apparatus, computer program product, or portions thereof, is adapted or configured for use in an appropriate and comparable implementation of that type of device.

[0129] Where embodiments are described as being implemented in an application, provision of the application in a software-as-a-service (SaaS) model is contemplated within the scope of exemplary embodiments. In the SaaS model, the capabilities of an application implementing an embodiment are provided to users by running the application on a cloud infrastructure. Users can access the application using a variety of client devices through a thin-client interface, such as a web browser (e.g., web-based email) or other lightweight client application. Users do not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, or storage of the cloud infrastructure. In some cases, users may not even manage or control the capabilities of the SaaS application. In some other cases, a SaaS implementation of an application may allow for the possible exception of limited user-specific application configuration settings.

[0130] The present disclosure may be integrated into systems, methods, and / or computer program products at any possible level of technical detail. A computer program product may include one or more computer-readable storage media having computer-readable program instructions for causing a processor to perform aspects of the present disclosure.

[0131] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. A non-exhaustive list of more specific examples of computer-readable storage media includes portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded devices such as punch cards or ridge structures in grooves that record instructions, and any suitable combination of the above. As used herein, computer-readable storage media, including but not limited to computer-readable storage devices, should not be construed as being signals that are transitory in nature, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted over a wire.

[0132] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to a computer-readable storage medium within the respective computing / processing device for storage.

[0133] Computer-readable program instructions for carrying out the operations of the present disclosure may be either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and the like, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer as a standalone software package, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or a connection to an external computer may be made (e.g., through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions by using state information of the computer-readable program instructions to individualize the electronic circuitry to implement aspects of the present disclosure.

[0134] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present 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.

[0135] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine whereby the instructions, executed by the processor of the computer or other programmable data processing apparatus, create means for performing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may be stored on a computer-readable storage medium and can direct a computer, programmable data processing apparatus, and / or other device to function in a particular manner such that a computer-readable storage medium having instructions stored thereon includes an article of manufacture containing instructions that perform an aspect of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0136] The computer-readable program instructions may be loaded onto a computer, other programmable data processing apparatus, or other device and cause the computer, other programmable apparatus, or other device to execute a series of operational steps to generate a computer-implemented process, whereby the instructions executing on the computer, other programmable apparatus, or other device perform the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0137] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions, which includes one or more executable instructions for performing a specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or operations or implement a combination of special-purpose hardware and computer instructions. [Explanation of symbols]

[0138] 100 Energy Management Environment 102 Network / Communications Infrastructure 104 Server 106 Server 108 Memory Unit 110 clients 112 Dashboard 114 Application Specific Hardware 116 Server Applications 118 databases 120 client applications 122 Dashboard Applications 124 Energy Management Systems 126 General-purpose energy flow manager, Energy flow manager 128 Power Grid 130 renewable energy sources 132 Electric Vehicles 134 Residence 136 Applications 200 Data Processing System 202 North Bridge and Memory Controller Hub (NB / MCH) 204 South Bridge and Input / Output (I / O) Controller Hub (SB / ICH) 206 Processing Unit 208 main memory 210 graphics processor 212 Local Area Network (LAN) Adapter 214a Network 214b Remote System 214c Similar Code 214d Storage Device 216 Audio Adapter 218 Bus 220 Keyboard and Mouse Adapter 222 modem 224 read-only memory (ROM) 226a Hard Disk Drive (HDD) or Solid State Drive (SSD) 226b code 228 Bus 230 CD-ROM 232 Universal Serial Bus (USB) and other ports 234 PCI / PCIe devices 236 Super I / O (SIO) Device 304 Housing 306 Energy Storage Devices 308 Power Electronics Module 310 Distribution and Communication Module 312 connection interface 314 Thermal Management Components 316 Quick Connect 502 Power Conversion Module 506 Load ASH 508 Load Device 510 ASH Controller 512 Contactor 514 LVDC Bus 516 High Voltage DC Bus 520 Bidirectional DC-DC Converter 604 EV ASH 704 In-home ASH 902 EV ASH with DC fast charging 904 Standard 240V plug 906 PV / Wind ASH 908 DC Bus 910 Fuel Cell ASH 912 Hydrogen Reforming Unit ASH 914 Inlet 916 AC Line 918 DC-DC converter 920 DC Line 922 Smart Circuit Breaker Panel 1000 Energy Management Process 1102 meters 1104 Utility Underground Feeder 1106 AC L2 Charger 1108 AC L1 Charger 1200 configuration 1202 Input Data 1204 Applications 1206 M / L model 1208 Presentation Component 1210 Adaptive Components 1212 Power transmission proposal 1214 Feature Selection / Extraction Component 1216 Intelligent power transmission module, power transmission module 1218 Feedback Components 1220 Energy Management System Parameters 1222 Business Profile 1224 Profile Source 1226 NLP Engine 1228 Environmental Profile 1302 Training Architecture 1304 training data 1306 Features 1308 M / L model 1310 Quality Metrics 1312 Machine Learning m / l Algorithms 1400 M / L Process, Process 1502 Attribute Prioritization 1510 attributes 1600 System 1602 Computing Platform 1604 processor 1606 Applications 1608 Charging arrangement module, module 1610 Charge reporting module, module 1612 Constraint Analysis Module, Module 1614 Presentation Module, Module 1616 Charging module, module 1618 Residential Charger 1620 Remote Platform 1622 External Resources 1700 processes 1800 Cloud charging arrangement process, process 1900 Electric vehicle charging arrangement process, process 2000 Residential Charging Arrangement Process, Process

Claims

1. An energy management system comprising a general-purpose energy flow manager, the general-purpose energy flow manager comprising: Housing and an energy storage device disposed within the housing; a power electronics module disposed within the housing and configured to convert and manage electrical power; A connection interface; a distribution and communication module comprising at least an HVDC bus (high voltage direct current bus) having a variable power limit, the distribution and communication module being configured to power the entire load requirements of one or more coupled electrical loads up to a defined power limit determined by aggregation of power from one or more coupled energy sources; Equipped with and an energy management system, wherein the one or more coupled electrical loads and the one or more coupled energy sources are external to the universal energy flow manager and connect to the HVDC bus through the connection interface.

2. and one or more application specific hardware (ASH) external to the generic energy flow manager, wherein an ASH of the one or more ASHs: an application specific hardware controller (ASH controller) in communication with the generic energy flow manager; an optional power conversion module configured to convert electrical energy from a first input form to a second defined output form in response to receiving instructions from the ASH controller; Equipped with the ASH is configured to operate in a first mode of operation as a source ASH, the source ASH connecting an energy source of the one or more coupled energy sources to the connection interface and providing energy to the universal energy flow manager; and / or 2. The energy management system of claim 1, wherein the ASH is configured to operate in a second operating mode as a load ASH, the load ASH being configured to connect a load of the one or more coupled electrical loads to the connection interface and receive energy from the universal energy flow manager.

3. The energy management system of claim 2 , wherein the ASH is configured to automatically connect any energy source or any electrical load to the universal energy flow manager.

4. 3. The energy management system of claim 2, wherein the ASH is an in-home ASH configured to provide power from a grid energy source in the first operating mode to provide to the general-purpose energy flow manager, or to receive power from the general-purpose energy flow manager in the second operating mode to provide to a home appliance or tool that is a load.

5. The energy management system of claim 4 , wherein the in-home ASH provides energy to the home appliances or tools through a circuit panel configured to receive operational instructions from the universal energy flow manager.

6. 3. The energy management system of claim 2, wherein the universal energy flow manager is configured to operate one or more ASHs in the first operating mode to aggregate energy for direct current (DC) fast charging of one or more electric vehicles or one or more electric loads, wherein DC fast charging includes charging using a Level 3 charging specification of 400V to 900V.

7. The energy management system of claim 6 , wherein multiple electric vehicles or electric loads are DC fast charged simultaneously.

8. 3. The energy management system of claim 2, wherein the ASH is an electric vehicle (EV) ASH configured to provide power to the generic energy flow manager from an energy source of an electric vehicle in the first operating mode, or to receive power from the generic energy flow manager for providing to a load, an electric vehicle battery pack, in the second operating mode.

9. The energy management system of claim 8 , wherein the electric vehicle is configured to charge another electric vehicle through the EV ASH in the first mode of operation.

10. The energy management system of claim 8 , wherein the electric vehicle is configured to power one or more household appliances through the EV ASH in the first mode of operation.

11. the ASH is a renewable energy source ASH or a fuel cell ASH configured to provide power from a renewable energy source or a fuel cell to the universal energy flow manager in the first mode of operation; The energy management system of claim 2 , wherein the renewable energy source is a photovoltaic / solar energy source, a wind energy source, or another renewable energy source.

12. The energy management system of claim 1 , comprising a plurality of generic energy flow managers.

13. The energy management system of claim 1 , wherein the general-purpose energy flow manager is portable.

14. The energy management system of claim 1 , wherein the universal energy flow manager is configured to act as a stand-alone energy source based on energy provided by the energy storage device.

15. 10. The energy management system of claim 1, further comprising a bidirectional DC-DC converter installed between the HVDC bus and the energy storage device and configured to convert power between the energy storage device and the HVDC bus.

16. 2. The energy management system of claim 1, further comprising an LVDC bus (Low Voltage Direct Current bus) installed in the universal energy flow manager, and another bidirectional DC-DC converter inserted between the LVDC bus and the energy storage device, the another bidirectional DC-DC converter configured to convert power between the energy storage device and the LVDC bus.

17. 10. The energy management system of claim 1, wherein the one or more coupled energy sources are energy sources selected from the list consisting of a photovoltaic / solar energy source, a wind energy source, another renewable energy source, an electric vehicle, a power grid, a battery, and a fuel cell.

18. 1. A method comprising: providing an energy flow manager comprising a housing, an energy storage device disposed within the housing, a power electronics module disposed within the housing, and a connection interface; Detecting an overall load requirement of one or more coupled electrical loads; aggregating energy from one or more coupled energy sources coupled to an HVDC bus of the energy flow manager; powering the overall load requirements of the one or more combined electrical loads up to a defined power limit determined by the aggregation; Including, The method, wherein the one or more coupled electrical loads and the one or more coupled energy sources are installed external to the energy flow manager and connected to the HVDC bus through the connection interface.

19. connecting one or more application specific hardware (ASH) external to the generic energy flow manager to the generic energy flow manager, wherein an ASH of the one or more ASHs comprises an application specific hardware controller (ASH controller) in communication with the generic energy flow manager and an optional power conversion module configured to convert electrical energy from a first input form to a second defined output form in response to receiving instructions from the ASH controller; and / or operating the ASH in a first operating mode as a source ASH to connect an energy source of the one or more coupled energy sources to the connection interface and to provide energy to the universal energy flow manager; and / or operating the ASH in a second operating mode as a load ASH to connect a load of the one or more coupled electrical loads to the connection interface and to receive energy from the universal energy flow manager; 20. The method of claim 18, further comprising:

20. the ASH automatically connects any energy source to the universal energy flow manager to provide energy from the energy source to the energy flow manager; or automatically connecting any electrical loads to the universal energy flow manager via the ASH to provide energy from the universal energy flow manager to the electrical loads.

20. The method of claim 19, further comprising:

21. Steps to obtain ASH in your home, or providing power from a grid energy source to the general-purpose energy flow manager in the first operating mode of the residential ASH; or receiving power from the general energy flow manager for providing to a home appliance or tool in the second operating mode of the home ASH; 20. The method of claim 19, further comprising:

22. 22. The method of claim 21, further comprising operating the in-home ASH in the first operating mode to provide energy to the energy flow manager for DC fast charging of a plurality of electric vehicles or a plurality of electric loads.

23. Steps to obtain EV ASH, or providing power from an electric vehicle energy source to the universal energy flow manager in the first operational mode of the EV ASH; or receiving power from the universal energy flow manager for providing to an electric vehicle battery pack in the second operating mode of the EV ASH; 20. The method of claim 19, further comprising:

24. 20. The method of claim 19, wherein the one or more combined energy sources are energy sources selected from the list consisting of a photovoltaic / solar energy source, a wind energy source, another renewable energy source, an electric vehicle, a power grid, a battery, and a fuel cell.

25. 20. The method of claim 18, wherein powering the entire load requirement is based on a defined command from a utility.

26. 26. The method of claim 25, wherein the defined instructions include at least two instructions selected from the list consisting of: performing an EV charging operation, performing a backup charging operation, reducing utility costs, maximizing battery life, and performing off-grid energy usage.

27. 26. The method of claim 25, further comprising powering multiple loads by time division multiplexing load and source pairs.

28. 1. A computer system comprising a processor, the processor comprising: configuring the HVDC bus of the distribution and communication module to have a variable power limit by aggregating energy from one or more coupled energy sources coupled to the energy flow manager; configured to power the overall load requirements of the one or more combined electrical loads up to a defined power limit determined by said aggregating; A computer system, wherein the one or more coupled electrical loads and the one or more coupled energy sources are installed external to the energy flow manager and connected to the HVDC bus through a connection interface.

29. A non-transitory computer-readable storage medium storing a program, the program causing the computer system to: configuring an HVDC bus of the distribution and communication module to have a variable power limit by aggregating energy from one or more coupled energy sources coupled to the energy flow manager; powering the overall load requirements of the one or more combined electrical loads up to a defined power limit determined by said aggregating; A non-transitory computer-readable storage medium, wherein the one or more coupled electrical loads and the one or more coupled energy sources are installed external to the energy flow manager and connected to the HVDC bus through a connection interface.

30. 1. A computer-implemented method comprising: receiving an energy demand state of one or more coupled electrical loads coupled to a generic energy flow manager via load application specific hardware (load ASH), the energy demand state indicating a desired amount of energy needed by the one or more coupled electrical loads in an energy management environment; receiving an available energy state of one or more coupled energy sources coupled to the generic energy flow manager via source application specific hardware (source ASH), the available energy state indicating an amount of energy available from the one or more coupled energy sources in the energy management environment; generating input data using at least the energy demand state and the available energy state; extracting one or more features from the input data, the one or more features representing characteristics of a request for power dispatch proposal action; using a power delivery module to propose at least one power delivery proposal for the one or more coupled electrical loads; performing a power dispatch action based on the power dispatch proposal; A method comprising:

31. 31. The computer-implemented method of claim 30, wherein the power delivery module is a machine learning engine.

32. 31. The computer-implemented method of claim 30, further comprising: performing the power delivery operation through a load ASH of the one or more coupled electrical loads.

33. 33. The computer-implemented method of claim 32, wherein the power delivery operation is performed automatically.

34. generating a set of attributes for the energy management environment to be implemented by attribute prioritization; proposing the at least one power dispatch proposal based on one or more attributes of the set of attributes; 31. The computer-implemented method of claim 30, further comprising:

35. 35. The computer-implemented method of claim 34, wherein the attributes include attributes selected from the list consisting of minimizing utility costs, minimizing charging time, maximizing backup energy availability, maximizing excess energy sales, and maximizing battery life.

36. 31. The computer-implemented method of claim 30, wherein the power dispatch proposal includes instructions for implementing DC fast charging for one or more electric vehicles.

37. 31. The computer-implemented method of claim 30, wherein the power dispatch proposal includes instructions for maximizing use of renewable energy sources during a defined time interval.

38. 31. The computer-implemented method of claim 30, wherein the power dispatch suggestions are provided in real time.

39. 31. The computer-implemented method of claim 30, wherein the power dispatch suggestions include load shedding commands.

40. 31. The computer-implemented method of claim 30, wherein the power dispatch proposal includes powering multiple loads by time division multiplexing load-source pairs.

41. 31. The computer-implemented method of claim 30, wherein the input data further includes data selected from the list consisting of historical device usage data, fast charging requirements, weather forecasts, calendar data, current electrical consumption demands, vehicle energy demand profiles, in-home energy demand profiles, and battery life.

42. 31. The computer-implemented method of claim 30, further comprising providing feedback on the power delivery module indicating accuracy of suggestions for enhancing the power delivery module.

43. 31. The computer-implemented method of claim 30, further comprising providing the power dispatch suggestions in real time.

44. 1. A computer system comprising a processor, the processor comprising: receiving an energy demand state of one or more coupled electrical loads coupled to a generic energy flow manager via load application specific hardware (load ASH), the energy demand state indicating a desired amount of energy needed by the one or more coupled electrical loads in an energy management environment; receiving an available energy state of one or more coupled energy sources coupled to the generic energy flow manager via source application specific hardware (source ASH), the available energy state indicating an amount of energy available from the one or more coupled energy sources in the energy management environment; generating input data using at least the energy demand state and the available energy state; extracting one or more features from the input data, the one or more features representing characteristics of a request for power dispatch proposal action; using a power delivery module to propose at least one power delivery proposal for the one or more coupled electrical loads; performing a power dispatch operation based on the power dispatch proposal; and A computer system configured to:

45. A non-transitory computer-readable storage medium storing a program, the program causing the computer system to: receiving an energy demand state of one or more coupled electrical loads coupled to a generic energy flow manager via load application specific hardware (load ASH), the energy demand state indicating a desired amount of energy needed by the one or more coupled electrical loads in an energy management environment; receiving an available energy state of one or more coupled energy sources coupled to the generic energy flow manager via source application specific hardware (source ASH), the available energy state indicating an amount of energy available from the one or more coupled energy sources in the energy management environment; generating input data using at least the energy demand state and the available energy state; extracting one or more features from the input data, the one or more features representing characteristics of a request for power dispatch proposal action; using a power delivery module to propose at least one power delivery proposal for the one or more coupled electrical loads; performing a power dispatch operation based on the power dispatch proposal; and A non-transitory computer-readable storage medium that causes

46. receiving, by a charging arrangement service, a charging request from a first user device; receiving, via a charging reporting service, charging capacity updates from a plurality of home chargers, including at least the first home charger; computing a set of home chargers for the first user device through constraint analysis using a charging management service; presenting the set of home chargers to the first user device; receiving a selection of the first home charger from the set of presented home chargers; performing a charging procedure based on the received selection; A method comprising:

47. The performing step comprises:

47. The method of claim 46, comprising DC fast charging a first electric vehicle with the first residential charger.

48. The performing step comprises:

48. The method of claim 47, further comprising simultaneously DC fast charging a plurality of electric vehicles with the first home charger based on the allocation of a plurality of first user devices to the first home charger by the charging management service.

49. 48. The method of claim 47, wherein the first user device is operated by a user of the first electric vehicle and the first home charger is operated by an owner of the first home charger.

50. 47. The method of claim 46, wherein the plurality of residential chargers are a plurality of generalized energy flow managers each having a plurality of independent energy sources connected thereto.

51. 47. The method of claim 46, wherein a home charger of the plurality of home chargers comprises one or more universal energy flow managers.

52. 47. The method of claim 46, wherein the constraint analysis includes performing a calculation of the set of residential chargers that meets DC fast charging requirements.

53. 47. The method of claim 46, wherein the constraint analysis includes performing a calculation of the likelihood of arriving at a residential charger before a time limit or undercharge threshold is reached.

54. receiving, by the charging management service and in response to receiving a selection of the first home charger, an acceptance of the selection from the first home charger; automatically transmitting, by the charging management service, the location of the first residential charger to the first user device; transporting a first electric vehicle of the user device to the location; 47. The method of claim 46, further comprising:

55. 55. The method of claim 54, wherein the charging management service receives the location automatically or by triangulation.

56. receiving, by the charging management service and in response to receiving a selection of the first home charger, an acceptance of the selection from the first home charger; automatically transmitting, by the charging management service, the location of the first user device to the first home charger; transporting the first residential charger to the location; 47. The method of claim 46, further comprising:

57. 57. The method of claim 56, wherein the charging management service receives the location automatically or by triangulation.

58. 47. The method of claim 46, wherein the charge request includes at least a measurement of a desired amount of charge.

59. 47. The method of claim 46, wherein the user device is an electric vehicle component or a personal mobile phone.

60. 47. The method of claim 46, wherein the charging procedure includes a high-voltage DC fast charge operation or a low-voltage DC fast charge operation.

61. 1. A system comprising: one or more home chargers; Processor and wherein the processor: receiving a charging request from a first user device; receiving charging capacity updates from a plurality of home chargers, including at least the first home charger; calculating a first set of home chargers for the first user device through constraint analysis; presenting the first set of home chargers to the first user device; receiving a selection of the first home charger from the first set of presented home chargers; The system is configured to perform a charging procedure based on the received selection.

62. The processor:

62. The system of claim 61, further configured to DC fast charge a first electric vehicle with the first residential charger.

63. The processor:

62. The system of claim 61, further configured to simultaneously DC fast charge a plurality of electric vehicles with the first home charger based on allocating a plurality of first user devices to the first home charger.

64. 62. The system of claim 61, wherein the residential charger comprises one or more universal energy flow managers.

65. The processor:

62. The system of claim 61, further configured to perform the constraint analysis by calculating the first set of residential chargers to meet DC fast charging requirements.

66. A non-transitory computer-readable storage medium storing a program, the program causing the computer system to: receiving a charging request from a first user device; receiving charging capacity updates from a plurality of home chargers including at least the first home charger; computing a first set of home chargers for the first user device through constraint analysis; presenting a first set of the home chargers to the first user device; receiving a selection of the first home charger from the first set of presented home chargers; A non-transitory computer-readable storage medium that causes a charging procedure to be performed based on the received selection.

67. When the program is executed by a computer system, the computer system further 67. The non-transitory computer-readable storage medium of claim 66, wherein the first residential charger provides DC fast charging to a first electric vehicle.

68. When the program is executed by a computer system, the computer system further 67. The non-transitory computer-readable storage medium of claim 66, wherein the first home charger simultaneously DC fast charges a plurality of electric vehicles based on allocating a plurality of first user devices to the first home charger.

69. the non-transitory computer-readable storage medium stores computer-readable code; 67. The non-transitory computer-readable storage medium of claim 66, wherein the computer-readable code is transferred over a network from a remote data processing system.

70. the non-transitory computer-readable storage medium stores computer-readable code in a server data processing system; 67. The non-transitory computer-readable storage medium of claim 66, wherein the computer-readable code is downloaded to a remote data processing system over a network for use in a second computer-readable storage medium associated with the remote data processing system.

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