Intelligent charging system and control logic for crowdsourced vehicle energy transfer

Abstract

The present invention relates to a smart charging system and control logic for crowdsourcing vehicle energy transfer. A smart charging system for transferring energy to and from motor vehicles, methods for manufacturing / operating such a system, and vehicles with V2V and V2G energy transfer capabilities are presented. A method for operating a smart charging system includes a system controller receiving a request from a user's user interface to schedule an electrical transfer to the user. The system controller broadcasts a call for energy transfer bids to a set of crowdsourced service providers to fulfill the user's transfer request. After receiving a plurality of bid submissions from a plurality of service providers to fulfill the transfer request, the system controller uses a multi-criteria selection strategy to select an optimized bid submission among the bid submissions. The multi-criteria selection strategy includes a joint utility maximization function and / or a trade-off Pareto solution. The system controller communicates a task assignment with instructions to provide the transfer request to the service provider associated with the optimized bid submission.

Description

Technical Field

[0001] introduction

[0002] This disclosure generally relates to electrical systems for charging motor vehicles. More specifically, aspects of this disclosure relate to systems and methods for supplying charging for crowdsourced vehicles and for vehicle grid integration operations. Background Technology

[0003] Currently manufactured motor vehicles (such as Hyundai cars) are initially equipped with a powertrain that operates to propel the vehicle and power its onboard electronics. In automotive applications, for example, a vehicle powertrain is typically represented by a prime mover that delivers drive torque to the vehicle's final drive system (e.g., differential, axle shaft, corner modules, wheels, etc.) via an automatic or manual transmission. Historically, automobiles have been powered by reciprocating piston internal combustion engine (ICE) components due to their readily available availability and relatively low cost, light weight, and overall efficiency. As some non-limiting examples, such engines include compression ignition (CI) diesel engines, spark ignition (SI) gasoline engines, two-stroke, four-stroke, and six-stroke architectures, and rotary engines. Hybrid electric and fully electric vehicles (collectively referred to as "electric vehicles"), on the other hand, utilize alternative power sources to propel the vehicle and thus minimize or eliminate reliance on fossil fuel-based engines for traction power.

[0004] All-electric vehicles (FEVs)—commonly referred to as "electric vehicles"—are a type of electric-drive vehicle configuration that completely omits the internal combustion engine and associated peripheral components from the powertrain system, instead relying on a rechargeable energy storage system (RESS) and traction motors for propulsion. The engine assembly, fuel supply system, and exhaust system of ICE-based vehicles are replaced by one or more traction motors, a traction battery pack, and battery cooling and charging hardware in battery-based FEVs. In contrast, hybrid electric vehicles (HEVs) use multiple traction power sources to propel the vehicle, most commonly combining traction motors powered by batteries or fuel cell units to operate the internal combustion engine assembly. Because hybrid, electric-drive vehicles can draw power from sources other than the engine, the HEV engine can be completely or partially shut off when the vehicle is propelled by the electric motors (multiple motors).

[0005] Many commercially available hybrid electric and all-electric vehicles employ rechargeable traction battery packs to store and supply the necessary power to operate the powertrain's multiple traction motors. To generate sufficient traction power for vehicle range and speed, traction battery packs are significantly larger, more powerful, and have a higher capacity than 12-volt starter, lighting, and ignition (SLI) batteries. Modern traction battery packs, for example, group stacks of battery cells (e.g., 8-16 cells / stack) into individual battery modules (e.g., 10-40 modules / pack), which are mounted to the vehicle chassis via a battery pack housing or support tray. The stacked electrochemical battery cells can be connected in series or parallel using an electrical interconnect board (ICB) or a front-end DC-DC bus assembly. A dedicated electronic battery control module (EBCM) regulates the operation of the battery pack through coordinated operation with the powertrain control module (PCM) and the traction power inverter module (TPIM).

[0006] As hybrid and electric vehicles become more prevalent, infrastructure is being developed and deployed to make their everyday use feasible and convenient. Electric Vehicle Supply Equipment (EVSEs) take many forms, including residential EVCSs (EVCSs) purchased and operated by vehicle owners (e.g., installed in the owner's garage), publicly available EVCSs deployed by utilities or private retailers (e.g., at independently owned or municipal charging stations), and high-voltage, high-current charging stations used by manufacturers, dealerships, and service stations. For example, plug-in hybrid and electric vehicles can be recharged by physically connecting the EVCS's charging cable to the vehicle's complementary charging port. By comparison, wireless charging systems utilize electromagnetic field (EMF) induction or other wireless power transfer (WPT) technologies to provide vehicle charging capabilities without the need for matching cables and cable ports.

[0007] Vehicle electrification presents an opportunity to enhance the capacity and reliability of the utility grid through bidirectional charging infrastructure and strategies. Intelligent vehicles can contribute by participating in Vehicle-to-Grid (VGI) activities, which involves regulating vehicle charging and supporting bidirectional reverse power flow (RPF) between the vehicle's battery system and the utility company. Vehicles with electrified powertrains or transporting mobile EV charging stations can also provide roadside assistance services to recharge other vehicles. Summary of the Invention

[0008] This paper presents intelligent charging systems for transferring energy to and from motor vehicles, along with accompanying control logic, methods for operating and manufacturing such systems, and electric vehicles with vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) energy transfer capabilities. For example, a computer-implemented system and method for crowdsourced V2V and V2G power supply, storage, and integration is presented through a market-based, agent-mediated approach. An "agent" middleware node acts as an intermediary between participating customers (such as registered EVs and utility companies) and crowdsourced service providers (such as heterogeneous EVs, roadside assistance personnel with portable EV chargers, home charging points, etc.). The agent facilitates battery boosting for EVs and energy transfer and optional storage for power facilities within predefined geo-fenced areas of interest (ROIs) surrounding the customers. A multi-stage procurement process can be executed to achieve closed-loop feedback, including energy request intake, request for bid (RFB) broadcasting, bid acceptance and selection, energy transfer contracting, transfer confirmation, and post-processing. Multi-criteria selection strategies can be used to coordinate optimal task assignments for both customers and service providers, maximizing the joint utility function or the best Pareto solution. This allows customers to manually or automatically select providers based on predefined weighting schemes or customer-defined preference information. Task bundling frameworks can be employed to assign multiple boost / transmission / storage tasks as bundles to a single service provider. The provider can then subsequently assign these tasks to other service providers, for example, through auctions.

[0009] At least some of the disclosed concepts offer additional benefits including crowdsourced V2V roadside energy assistance (e.g., enhanced vehicle charging and reduced range anxiety) and V2G integration (e.g., reduced peak and off-peak energy consumption on the public grid). Customer satisfaction and experience are also improved through market-based, agent-mediated crowdsourced V2V battery boosting and V2G energy storage / transfer. Other additional benefits may include reduced demand for and associated costs of large-capacity, grid-based EVCSs permanently attached to parking infrastructure. In addition to improved charging capabilities and customer experience, the disclosed concepts could help increase the driving range of electric vehicles and battery pack performance.

[0010] Various aspects of this disclosure relate to system control logic, closed-loop feedback control techniques, and computer-readable media (CRM) for managing energy transfer to / from motor vehicles. In the examples, a method for operating a smart charging system for transferring energy to users (such as motor vehicles and / or utility companies) is presented. This representative method, in any order and in any combination of any of the options and features disclosed above and below, includes, for example, receiving a transmission request arranged for electrical transmission to a user from a user interface (such as an in-vehicle telematics unit or a dedicated mobile software application operating on the user's smartphone) via the system controller of the smart charging system; broadcasting a solicitation for energy transmission bids to a group of crowdsourced service providers via a wireless communication network via the system controller to fulfill the user's transmission request; receiving multiple bid submissions from a subset of the solicited service providers via the system controller to fulfill the transmission request; selecting an optimal bid submission from the received bid submissions using a multi-criteria selection strategy, which includes a joint utility maximization function (weighted scalar method) and / or a trade-off Pareto solution (optimal frontier method); and, for example, transmitting a task assignment with instructions to provide the transmission request to the selected service providers associated with the optimal bid submission via the system controller.

[0011] Additional aspects of this disclosure relate to smart charging systems for crowdsourced energy transfer to / from vehicles, vehicles with V2V and V2G energy transfer capabilities, and “agent” middleware engines for supplying market-based, agent-mediated energy transfer. As used herein, the terms “vehicle,” “motor vehicle,” and “vehicle” are used interchangeably and synonymously to include any relevant vehicle platform, such as passenger vehicles (ICE, HEV, FEV, fuel cell, fully and partially autonomous, etc.), commercial vehicles, industrial vehicles, tracked vehicles, off-road and all-terrain vehicles (ATVs), motorcycles, agricultural equipment, boats, aircraft, etc. In the example, the smart charging system supplies energy transfer from a crowdsourced fleet of registered service providers to one or more registered vehicles, utility companies, and / or other electrified users.

[0012] Continuing the discussion of the previous example, the smart charging system includes: a resident or remote memory device storing various user and service provider registration information; a communication device operable to communicate with users and service providers; and a server-level computer with a controller (e.g., a microcontroller, control module, or a network of controllers / modules) that, among other things, regulates power transfer between users and service providers. The system controller is programmed to receive, for example, a transfer request to transfer electricity to a user via the communication device from a user interface of at least one user. In response, the system controller broadcasts a solicitation for energy transfer bids to a group of crowdsourced service providers to fulfill the transfer request; it then receives multiple bid submissions to fulfill the transfer request. The controller selects the optimal bid submission from the received bid submissions using a multi-criteria selection strategy (such as a joint utility maximization function and / or a trade-off Pareto solution). A task assignment with instructions to provide the transfer request is then transmitted to the service provider associated with the optimal bid submission.

[0013] For any disclosed system, vehicle, and method, a joint utility maximization function can be used to select the optimal bid submission, which maximizes joint utility as a function of the following: Calculations show that the transmission request task can be completed. t User-defined customer weighting factor User's client utility function Provider-defined service provider weighting factor and the provider utility function of the selected service provider. In this case, the client utility function can be calculated as a function of the following: Expected impact on users' energy storage devices The estimated arrival time of the selected service provider or the estimated time of arrival at the selected service provider. The task requested in the bid submission from the selected service provider. t Total cost and user-defined influence weights ETA weight and payment weight The provider utility function can be calculated as a function of the following: : User to complete task t Total remuneration paid The total distance the selected service provider must travel to reach the user. Expected impact on the energy storage facilities of the selected service providers and the payment weights defined by the provider. Distance weight and influence weight .

[0014] For any disclosed system, vehicle, and method, a tradeoff Pareto solution can be used to select the optimal bid submission. This can include performing bi-objective optimization techniques as a function of the objective value and objective tradeoffs to visualize the Pareto frontier tradeoff curve. In this case, the tradeoff Pareto solution can produce multiple optimal tradeoff recommendations, which can be plotted as a service provider utility function. Relative customer utility function The system controller presents the user with optimal trade-off recommendations and a prompt to select one of these recommendations before transmitting task assignments to the selected service provider. The system controller receives the selected optimal trade-off recommendation from the user and sets the optimized bid submission as the selected recommendation.

[0015] For any disclosed system, vehicle, and method, the user can be a single user or a combination of multiple heterogeneous users. In the latter case, the system controller can receive, process, and facilitate multiple transmission requests from multiple users. Furthermore, the service provider's bid submission may include a request to bundle multiple selected transmission requests from the received transmission requests. After selecting an optimized bid submission, the system controller can generate a task agreement to have the selected service provider fulfill the user's transmission requests. This task agreement is then transmitted to one or both parties and prompted for approval.

[0016] For any disclosed system, vehicle, and method, the system controller may receive geolocation data indicating the real-time location of a user before soliciting bids for energy transfer. A geofence of predefined size is then defined around the user's real-time location. The system controller then receives geolocation data indicating the real-time locations of multiple registered service providers. A subset of crowdsourced service providers may be obtained as the registered service providers within the geofence. Alternatively, the system controller may retrieve, for example, priori data of users and service providers associated with received bid submissions from a resident or remote storage device. The controller may then execute a tree data structure to locate predefined keys within the received priori data; optimized bid submissions may be selected further based on the predefined keys located within the received priori data using the tree data structure.

[0017] For any disclosed system, vehicle, and method, the system controller may receive a transmission acknowledgment verifying that the transmission request was provided to the user by the selected service provider. After the transmission task is assigned and after receiving the transmission acknowledgment thereafter, the system controller may prompt the user and / or the selected service provider to assess the availability of the transmission request. Alternatively, the system controller may receive, process, and store the corresponding registration data files for each user and each service provider.

[0018] This invention includes the following solutions:

[0019] Option 1. A method for operating a smart charging system for transferring energy to a user having an energy storage system, the method comprising:

[0020] The system controller of the intelligent charging system receives a transmission request from the user's user interface to arrange electrical transmission to the user;

[0021] The system controller broadcasts a call for bids for energy transfer to a group of crowdsourced service providers in order to complete the transfer request to the user.

[0022] The system controller receives multiple bid submissions from multiple service providers to complete the transmission request;

[0023] A multi-criteria selection strategy is used to select the optimal bid submission from the received bid submissions, the multi-criteria selection strategy including a joint utility maximization function and / or a Pareto solution with trade-offs; and

[0024] The system controller transmits a task assignment with instructions to provide the transmission request to the selected service provider associated with the optimized bid submission among the plurality of service providers.

[0025] Option 2. The method according to Option 1, wherein the optimized bid submission is selected using the joint utility maximization function, which is calculated as follows:

[0026] maximize

[0027] in It is as a task to be completed t The combined utility of the function for transmitting requests, It is a user-defined customer weighting factor. It is as the task described t The user's client utility function is the function of that user. It is the service provider weighting factor defined by the provider, and It is as the task described t The provider utility function of the selected service provider.

[0028] Option 3. The method described in Option 2, wherein the customer utility function u c ( t ) is calculated as:

[0029]

[0030] in It is as the task described t The expected impact of the function on the user's energy storage device. It is the estimated arrival time of the selected service provider or the estimated time of arrival at the selected service provider. It is an optimized bid submission from the selected service provider for performing the task. t The total cost, and , and These are user-defined impact weights, ETA weights, and payment weights.

[0031] Option 4. The method according to Option 3, wherein the provider utility function Calculated as:

[0032]

[0033] in The selected service provider is responsible for performing the task. t The total compensation received It is the total distance that the selected service provider will travel to reach the user. It is as the task described t The expected impact of the function on the energy storage devices of the selected service provider, and , and These are the payment weights, distance weights, and influence weights defined by the provider.

[0034] Option 5. The method according to Option 1, wherein the optimized bid submission is selected using a trade-off Pareto solution, including performing a bi-objective optimization technique as a function of the objective value and the objective trade-off to visualize the Pareto front trade-off curve.

[0035] Option 6. The method according to Option 5, wherein the trade-off Pareto solution generates multiple optimal trade-off recommendations, the method further comprising:

[0036] Before the task assignment is transmitted, the system controller presents the optimal trade-off recommendations to the user and prompts the user to select the best trade-off recommendation from among the recommendations; and

[0037] The system controller receives the selected optimal trade-off recommendation from the user, wherein the optimized bid submission is the selected optimal trade-off recommendation.

[0038] Option 7. The method according to Option 1, wherein the user includes multiple heterogeneous users, the transmission request includes multiple transmission requests from the heterogeneous users, and the optimized bid submission includes a request that bundles multiple selected transmission requests from the transmission requests.

[0039] Option 8. The method according to Option 1 further includes:

[0040] In response to the selection of the optimized bid submission, a task protocol is generated via the system controller to allow the selected service provider to complete the transmission request for the user; and

[0041] The task agreement is transmitted to the user and the selected service provider, and the user is prompted to approve the task agreement.

[0042] Option 9. The method according to Option 1 further includes:

[0043] Before broadcasting the solicitation of bids for energy transmission, geolocation data indicating the user's location is received via the system controller;

[0044] Define a geofence of predefined size around the user's location;

[0045] The system controller receives geolocation data indicating the corresponding locations of multiple registered service providers; and

[0046] The group of crowdsourced service providers is obtained as a subset of the registered service providers within the geofence.

[0047] Option 10. The method according to Option 1 further includes,

[0048] The system controller receives prior data of the user and the service provider associated with the plurality of bid submissions from the memory device; and

[0049] The tree data structure is executed to locate predefined keywords within the received prior data, wherein the selection of the optimized bid submission is also based on the predefined keywords located within the received prior data using the tree data structure.

[0050] Option 11. The method according to Option 1 further includes, after transmitting the task assignment, receiving, via the system controller, a transmission confirmation provided by the selected service provider to the user for the transmission request.

[0051] Option 12. The method according to Option 11, further comprising, in response to a received transmission acknowledgment, transmitting a prompt to the user and / or the selected service provider via the system controller to assess the availability of the transmission request.

[0052] Option 13. The method according to Option 1 further includes receiving a corresponding registration data file from the user and each of the group of crowdsourced service providers by the system controller.

[0053] Option 14. A smart charging system for transferring energy to a user with an energy storage system, the smart charging system comprising:

[0054] A memory device that stores registration information of multiple service providers;

[0055] A communication device operable to communicate with the service provider; and

[0056] A server-class computer with a system controller, the system controller being programmed to:

[0057] The communication device receives a transmission request from the user's user interface to schedule an electrical transmission to the user.

[0058] The call for bids for energy transmission is broadcast to a group of crowdsourced service providers via the communication device in order to complete the transmission request;

[0059] The communication device is used to receive multiple tender submissions from multiple service providers in order to complete the transmission request.

[0060] A multi-criteria selection strategy is used to select the optimal bid submission from the received bid submissions, the multi-criteria selection strategy including a joint utility maximization function and / or a Pareto solution with trade-offs; and

[0061] A task assignment with instructions to provide the transmission request is transmitted to the selected service provider associated with the optimized bid submission among the plurality of service providers.

[0062] Option 15. The intelligent charging system according to Option 14, wherein the optimized bid submission is selected using the joint utility maximization function, which is calculated as follows:

[0063] maximize

[0064] in It is as a task to be completed t The combined utility of the function for transmitting requests. It is a user-defined customer weighting factor. It is as the task described t The user's client utility function is the function of that user. It is the service provider weighting factor defined by the provider, and It is as the task described t The provider utility function of the selected service provider.

[0065] Option 16. The intelligent charging system according to Option 15, wherein the customer utility function is calculated as:

[0066]

[0067] in It is as the task described t The expected impact of the function on the user's energy storage device. It is the estimated arrival time of the selected service provider or the estimated time of arrival at the selected service provider. It is an optimized bid submission from the selected service provider for performing the task. t The total cost, and , and These are user-defined impact weights, ETA weights, and payment weights.

[0068] Option 17. The intelligent charging system according to Option 16, wherein the provider utility function is calculated as:

[0069]

[0070] in The selected service provider is responsible for performing the task. t The total compensation received It is the total distance that the selected service provider will travel to reach the user. It is as the task described t The expected impact of the function on the energy storage devices of the selected service provider, and , and These are the payment weights, distance weights, and influence weights defined by the provider.

[0071] Option 18. The intelligent charging system according to Option 14, wherein the optimized bid submission is selected using the trade-off Pareto solution, including performing a bi-objective optimization technique as a function of the objective value and the objective trade-off to visualize the Pareto frontier trade-off curve.

[0072] Option 19. The intelligent charging system according to Option 18, wherein the trade-off Pareto solution generates multiple optimal trade-off suggestions, and wherein the server-level computer is further programmed to:

[0073] Before the task assignment is transmitted, the system controller presents the optimal trade-off recommendations to the user and prompts the user to select the best trade-off recommendation from among the recommendations; and

[0074] The system controller receives the selected optimal trade-off recommendation from the user, wherein the optimized bid submission is the selected optimal trade-off recommendation.

[0075] Option 20. The intelligent charging system according to Option 14, wherein the user includes multiple heterogeneous users, the transmission request includes multiple transmission requests from the heterogeneous users, and the optimized bid submission includes a request that bundles multiple selected transmission requests from the transmission requests.

[0076] The foregoing description of the invention does not represent every embodiment or aspect of this disclosure. Moreover, the foregoing features and advantages, as well as other features and accompanying advantages, will readily become apparent from the following detailed description of illustrative examples and models for carrying out this disclosure when understood in conjunction with the accompanying drawings and appended claims. Furthermore, this disclosure expressly includes any and all combinations and sub-combinations of the elements and features described above and below. Attached Figure Description

[0077] Figure 1 It is a partial schematic, side view illustration of a representative motor vehicle for intelligent V2V and V2G energy storage and transmission based on various aspects of the disclosed concept, the motor vehicle having a network of in-vehicle controllers, sensing devices, input / output devices and communication devices, and an electrified powertrain.

[0078] Figure 2 The diagram illustrates a representative vehicle charging system and control protocol for transferring energy from one or more crowdsourcing service providers to one or more registered users, based on various aspects of the disclosed concept. It includes processes that correspond to instructions stored in memory, which can be executed by: a resident or remote controller, control logic circuitry, a programmable control unit, or other integrated circuit (IC) device or network of devices.

[0079] Figure 3 The diagram illustrates a flowchart of a representative tendering language protocol based on various aspects of the disclosed concept. This tendering language protocol has a middleware application that arbitrates energy transfer between users and service providers. This middleware application may correspond to instructions stored in memory, which may be executed by: resident or remote controllers, control logic circuits, programmable control units, or other IC devices or networks of devices.

[0080] Figure 4 This is a flowchart illustrating a representative data exchange sequence between wireless interconnected nodes for transmitting energy to / from a motor vehicle, based on various aspects of the disclosed concept. This may correspond to instructions stored in a memory, which may be executed by: a resident or remote controller, control logic circuitry, a programmable control unit, or a network of other IC devices or devices.

[0081] Figure 5 This is a flowchart illustrating a representative multi-objective optimization protocol for selecting the optimal bid to transfer energy to and from a motor vehicle, based on various aspects of the disclosed concept. It may correspond to instructions stored in memory, which may be executed by: a resident or remote controller, control logic circuitry, a programmable control unit, or a network of other IC devices or devices.

[0082] Representative embodiments of this disclosure are illustrated by way of non-limiting example in the accompanying drawings and are described in additional detail below. However, it should be understood that the novel aspects of this disclosure are not limited to the specific forms illustrated in the drawings listed above. Moreover, this disclosure is intended to cover all modifications, equivalents, combinations, sub-combinations, permutations, groupings, and alternatives that fall within the scope of this disclosure, such as that covered by the appended claims. Detailed Implementation

[0083] This disclosure allows for numerous different forms of embodiments. Representative examples of this disclosure are shown in the accompanying drawings and described in detail herein, wherein these embodiments should be understood as examples of the principles disclosed and not as limitations on the broad aspects of this disclosure. For this purpose, elements and limitations described, for example, in the abstract, introduction, summary, description of drawings, and detailed description sections but not expressly set forth in the claims should not be individually or collectively incorporated into the claims by implication, inference, or otherwise. Furthermore, the drawings discussed herein may not be drawn to scale and are provided for illustrative purposes only. Therefore, the specific and relative dimensions shown in the drawings are not to be construed as limiting.

[0084] For the purposes of this specific embodiment, unless explicitly waived, the singular includes the plural and vice versa; the words “and” and “or” should both be combined and disjunctive; the words “any” and “all” should both mean “any and all”; and the words “including,” “containing,” “comprising,” “having,” and their combinations should each mean “including but not limited to.” Furthermore, approximate words such as “about,” “almost,” “basically,” “approximately,” etc., may be used herein, for example, in the sense of “for…,” “close to…,” or “close to…” or “within 0% to 5% of…” or “within acceptable manufacturing tolerances,” or any logical combination thereof. Finally, directional adjectives and adverbs (such as front, rear, inside, outside, starboard, port, vertical, horizontal, up, down, forward, rear, left, right, etc.) may be relative to the motor vehicle, such as the forward driving direction of the motor vehicle when it is operatively oriented on a level driving surface.

[0085] Referring now to the accompanying drawings, in which similar reference numerals denote similar features throughout several views. Figure 1 A representative vehicle is shown, generally designated at 10 and depicted for the purposes of discussion as a van-type electric passenger vehicle. The illustrated vehicle 10 (also referred to herein as a "motor vehicle" or simply "vehicle") is merely an exemplary application by which aspects of this disclosure can be practiced. Similarly, the application of this concept to all-electric vehicle powertrains should be understood as a non-limiting implementation of the disclosed features. Therefore, it will be understood that aspects and features of this disclosure can be applied to other powertrain architectures, implemented for any logically related type of vehicle, and utilized to transfer energy between any combination of users and service providers. Furthermore, only selected components of the motor vehicle and charging system are shown and described in additional detail herein. Nevertheless, the vehicles and systems discussed below may include numerous additional and alternative features, as well as other available peripheral components, to implement the various methods and functions of this disclosure.

[0086] Figure 1 The representative vehicle 10 is initially equipped with a vehicle telecommunications and information (“telematics”) unit 14, which wirelessly communicates with remotely located or “non-vehicle” cloud computing host service 24 (e.g., ONSTAR®) via, for example, cellular towers, base stations, mobile switching centers, satellite services, etc. Generally speaking... Figure 1Some of the other vehicle hardware components 16 shown include, as non-limiting examples, a touchscreen video display device 18, a microphone 28, audio speakers 30, and a variety of input controls 32 (e.g., buttons, knobs, pedals, switches, touchpads, joysticks, touchscreens, etc.). These hardware components 16 partially serve as a human-machine interface (HMI) to enable users to communicate with the telematics unit 14 and other system components inside and outside the vehicle 10. The microphone 28 provides the vehicle occupants with a means to input speech or other auditory commands; the vehicle 10 may be equipped with an embedded voice processing unit utilizing audio filtering, editing, and analysis modules. Conversely, the speakers 30 provide auditory output to the vehicle occupants and may be either separate speakers dedicated to use with the telematics unit 14 or part of an audio system 22. The audio system 22 is operatively connected to a network connection interface 34 and an audio bus 20 to receive analog information via one or more speaker components, thereby presenting it as sound.

[0087] The telematics unit 14 is communicatively connected to a network interface 34, suitable examples of which include a twisted-pair / fiber Ethernet switch, a parallel / serial communication bus, a local area network (LAN) interface, a controller area network (CAN) interface, a media-oriented system transport (MOST) interface, a local interconnect network (LIN) interface, etc. Other suitable communication interfaces may include those conforming to ISO, SAE, and / or IEEE standards and specifications. The network interface 34 enables the vehicle hardware 16 to send and receive signals to each other and to various systems and subsystems within or "resident" in the vehicle body 12, as well as outside or "away" from the vehicle body 12. This allows the vehicle 10 to perform various vehicle functions, such as regulating powertrain output, controlling the operation of the vehicle's transmission, selectively engaging friction and regenerative braking systems, controlling vehicle steering, regulating the charging and discharging of the vehicle's battery modules, and other automated functions. For example, the telematics unit 14 can receive signals and data from and transmit signals and data to the following: powertrain control module (PCM) 52, advanced driver assistance system (ADAS) module 54, electronic battery control module (EBCM) 56, steering control module (SCM) 58, braking system control module (BSCM) 60, and a variety of other vehicle modules, such as transmission control module (TCM), engine control module (ECM), sensor system interface module (SSIM), navigation system control (NSC) module, etc.

[0088] Continue to refer to Figure 1The telematics unit 14 is a hybrid of an in-vehicle computing device and a service provider, both independently and through communication with other networked devices. The telematics unit 14 generally comprises one or more processors 40, each of which may be implemented as a discrete microprocessor, an application-specific integrated circuit (ASIC), or a dedicated control module. The vehicle 10 may provide centralized vehicle control via a central processing unit (CPU) 36, operatively coupled to a real-time clock (RTC) 42 and one or more electronic memory devices 38, each of which may take the form of a CD-ROM, disk, IC device, flash memory, semiconductor memory (e.g., various types of RAM or ROM), etc.

[0089] Long-range vehicle communication capabilities with remote, non-vehicle-connected devices can be provided via one or more of a cellular chipset / component, a navigation and positioning chipset / component (e.g., a Global Positioning System (GPS) transceiver), or a wireless modem (all of which are collectively represented at 44). Short-range wireless connectivity can be provided via a short-range wireless communication device 46 (e.g., a BLUETOOTH® unit or a Near Field Communication (NFC) transceiver), a dedicated short-range communication (DSRC) component 48, and / or dual antennas 50. Vehicle 10 may be without [certain features] if desired. Figure 1 One or more of the components described herein may be implemented in a form that may, alternatively, include additional components and functions desired for a particular end use. The various communication devices described above may be configured to exchange data as part of periodic broadcasts in vehicle-to-vehicle (V2V) communication systems or vehicle-to-everything (V2X) communication systems (e.g., vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), vehicle-to-device (V2D), vehicle-to-grid (V2G), etc.).

[0090] CPU 36 receives sensor data from one or more sensing devices that use, for example, optical detection, radar, laser, ultrasonic, optical, infrared, or other suitable technologies (including short-range communication technologies such as DSRC or ultra-wideband (UWB)) to perform autonomous driving operations. According to the illustrated example, vehicle 10 may be equipped with one or more digital cameras 62, one or more ranging sensors 64, one or more vehicle speed sensors 66, one or more vehicle dynamic sensors 68, and any necessary filtering, classification, fusion, and analysis hardware and software for processing the raw sensor data. The type, location, number, and interoperability of the distributed array of in-vehicle sensors can be individually or collectively adapted to a given vehicle platform to achieve a desired level of autonomous vehicle operation. Using data from sensing devices 62, 64, 66, and 68, CPU 36 can identify surrounding driving conditions, determine road characteristics and surface conditions, identify target objects within the vehicle's detectable range, and perform automated control maneuvers based on these actions.

[0091] To propel the electric vehicle 10, the electrified powertrain is operable to generate traction torque and deliver it to one or more of the vehicle's wheels 26. The powertrain is generally... Figure 1 The term is represented by a rechargeable energy storage system (RESS), which may have the property of a chassis-mounted traction battery pack 70 operatively connected to an electric traction motor 78. The traction battery pack 70 generally consists of one or more battery modules 72, each battery module 72 having a stack of battery cells 74, such as pouch, can, or prismatic lithium-ion, lithium polymer, or nickel-metal hydride battery cells. One or more motors (such as traction motor / generator (M) units 78) draw power from the battery pack 70 of the RESS and optionally deliver power to the battery pack 70 of the RESS. A dedicated power inverter module (PIM) 80 electrically connects the battery pack 70 to the motor / generator (M) units(M) 78 and regulates the current transfer therebetween.

[0092] The battery pack 70 can be configured such that module management (including battery cell sensing, thermal management, and module-to-host communication functions) is directly integrated into each battery module 72 and performed wirelessly via a wirelessly enabled battery cell monitoring unit (CMU) 76. The CMU 76 can be a microcontroller-based, printed circuit board (PCB) mounted sensor array. Each CMU 76 may have GPS transceiver and RF capabilities and may be packaged on or within the battery module housing. The battery modules, battery cells 74, CMU 76, housing, coolant lines, busbars, etc., collectively define the module assembly.

[0093] The electric traction motor 78 may have the characteristics of an electromechanical motor / generator unit (MGU), acting as a traction propulsion source for propelling the vehicle 10 and as a generator for generating electricity stored in the traction battery pack 70. For example, the MGU may operate to generate motor torque input to the drivetrain independently of the engine torque output from the internal combustion engine components. In a HEV powertrain configuration, the MGU may also function as a starter motor to crank-start the engine and to boost engine output by supplementing engine torque. During regenerative braking operation, the MGU may operate as a generator, converting the vehicle's kinetic energy into electrical energy that can be transferred to the vehicle's RESS. In a P0 or P1 hybrid powertrain, the MGU may operate as a high-voltage (HV) integrated starter generator (ISG), converting engine torque into electrical energy that can be stored locally or transferred to off-board loads.

[0094] During vehicle operation, the driver or other occupants of vehicle 10 may wish to identify available energy to recharge the traction battery pack 70, for example, to extend the driving range of vehicle 10 beyond the maximum range provided by the current charge of battery pack 70. Alternatively, the driver or occupants may wish to operate vehicle 10 as an energy source that can be used to recharge the EVB of another vehicle or deliver energy to a utility or residential power supply. According to various aspects of this disclosure, market-based, agent-mediated integrated systems and methods supply crowdsourced V2V battery boost and V2G integration for energy transfer and storage. A multi-criteria winning strategy can be employed to find the optimal task assignment for both registered users (customers) and service providers (energy). The winning strategy can maximize the joint utility function or the optimal trade-off Pareto solution and, if desired, allow customers to manually or automatically select a strategy based on a predefined weighted scheme or customer-defined preference information. An optional task bundling framework allows multiple tasks to be bundled and assigned to a single service provider. The service provider can then “act as an auctioneer” to triage these tasks to other service providers.

[0095] The Task Identification and Allocation (TIA) module uses a six-phase process to facilitate energy transfers from crowdsourced service providers to registered users. During the announcement phase, agent computing nodes act as coordinators, processing incoming power transfer requests and broadcasting associated tasks to a group of service providers, such as those available for bidding. After calculating individual utility values ​​based on an objective function, each service provider communicates its corresponding bid and request value to the coordinating agent during the submission phase. Following the receipt and processing of bids, a selection phase is implemented to evaluate the received bids based on a joint utility function or Pareto solution. When selecting a winning bid, as part of the contract phase, a contract is created and distributed for the corresponding service provider to execute the task. During the execution phase, the energy transfer task is completed by the service provider and confirmed by the user. For the post-execution phase, automatic payment is enabled once the task is completed and verified by the customer. Two-way ratings for users and service providers can also be implemented; these ratings can be considered in future task allocation bidding strategies.

[0096] Next reference Figure 2 The flowchart illustrates an intelligent charging system and accompanying control logic (collectively labeled at 100) for transferring energy from one or more crowdsourcing service providers 102 to one or more registered users 104, according to various aspects of this disclosure. Figure 2 Some or all of the operations illustrated and further described in detail below may represent algorithms corresponding to processor-executable instructions stored, for example, in main, secondary, or remote memory (e.g., ...). Figure 1 24 / 7 cloud-based storage or Figure 2 The database 106 is stored in and is, for example, by an electronic controller, processing unit, logic circuit, or other module or device or module / device (e.g., Figure 1 24-hour cloud computing or hosting services Figure 2 The network execution of the server-level computer 108 (managed by the computer) is used to perform any or all of the functions described above and below that are associated with the disclosed concept. (Modifiable) Figure 2-5 The execution sequence of the operations shown in the diagram can be modified, combined, or eliminated by adding additional operation boxes. For example, the following pairs represent control actions / operations that may be similar or identical within the diagram: 110 and 211; 107 and 203; 103 and 201; 123 and 215; 125 and 217; and 121 and 213.

[0097] Figure 2Method 100 begins at START terminal block 101, where memory-stored processor-executable instructions cause the programmable controller or control module, or similarly suitable processor, to invoke an initialization process for scheduling energy transfer events for a user (such as an electric vehicle C1 or a "smart grid" utility company C2). System evaluations for supplying this routine can be performed in real-time, near real-time, continuously, systematically, intermittently, and / or at regular intervals (e.g., every 10 or 100 milliseconds during normal and continuous operation of vehicle 10). Alternatively, terminal block 101 can be initialized in response to user command prompts, resident vehicle controller prompts, or broadcast prompts received from a "non-vehicle" centralized vehicle service system (e.g., host cloud computing service 24). Figure 1 The telematics unit 14 in the electric vehicle 10 can, for example, display a notification that the traction battery pack 70 has or is expected to have a low charge state; the driver can respond by pressing a soft button to schedule a recharging event. Figure 2 When performing the control operation presented in the diagram, method 100 may proceed to the END (end) terminal box 129 and temporarily terminate, or alternatively, it may return to the terminal box 101 and run continuously in a loop.

[0098] When submitting a request to schedule an energy transfer event, the user's HMI or other suitable input / output device may issue a charging / transfer call, as indicated in the CHARGE REQUEST transmission box 103. The charging request may include details about the user, the requested task, and other potentially relevant information such as user location, behavior, and rating data. Although Figure 2 Two representative customers, C1 and C2, within user group 104 are depicted, but it should be understood that any number and type of users utilizing electricity can register with the smart charging system 100. In this regard, Figure 2 The diagram illustrates six representative service providers within the crowdsourcing group of available service providers 102: an all-electric vehicle S1, a portable charger transported by a van S2, a workplace / public charging point S3, residential EVCS S4, an electric bus S5, and... a hybrid electric vehicle Sn. Nevertheless, it should be understood that the crowdsourcing group of available service providers 102 can consist of any number and type of providers capable of storing, transporting, and transmitting electricity.

[0099] Before, during, or after a charging / transfer call is sent, as part of the registration process box 105, each party wishing to operate as a service provider 102 and each party wishing to participate as a user 104 can be guided to register with the smart charging system 100. Individuals can register as service providers by completing a provider profile, which may require submitting personal information, payee processing information, and one or more intended operating locations. Potential service providers may also be required to confirm their eligibility and the types of services they intend to provide. Similarly, potential users can register with the system 100 by completing a user profile, which may also require submitting personal information, providing payee processing information, and indicating which types of services they intend to use. As some non-limiting examples, user and provider registration can be completed online via a portal accessed through a personal computing device, via a dedicated mobile software application operating on a personal smartphone, or via a touchscreen display and / or input controls in an in-vehicle HMI.

[0100] Upon receiving a charging / transfer request, the networked system controller of the server-level computer 108 within the smart charging system 100 broadcasts an announcement to a group of crowdsourced service providers 102, soliciting bids to fulfill the user's energy transfer request, as indicated in the ANNOUNCEMENT output process box 107. Before broadcasting bid submission requests, the smart charging system 100 may first filter potential service providers based on, for example, the nature of the service request, the user's location, the corresponding locations of available service providers, and other relevant criteria. By way of non-limiting example, the server-level computer 108 may collect geolocation data that specifically describes the user's real-time, near-real-time, or anticipated energy transfer location. Using this information, a virtual geofence of predefined size can be assigned to the user's location or dynamically constructed around the user's location. Simultaneously, geolocation data specifying the corresponding real-time, near-real-time, or anticipated locations of available service providers is collected; the group of crowdsourced service providers from which bids are solicited can be defined as a subset of registered service providers within the user's specific geofence. In the Bidding Data Submission (BIDS) input process box 109, the server-level computer 108 of the charging system then receives a single bid submission or multiple bid submissions to complete an energy transfer request from one or more of the crowdsourcing service providers 102.

[0101] Figure 2The intelligent charging system and method 100 responds to bid submissions received via input process box 109 by automating a market-based task allocation module 110. Market-based task allocation is typically used to allocate a set of tasks—battery boosting, energy storage, energy transfer, etc.—as requested by a set of customers from a set of service providers after an auction or transaction process. At the joint utility function process box 111, system 100 executes a joint utility function as an objective function to be maximized from the perspectives of both users and service providers. Generally, a set of objectives for customers may conflict with a set of objectives for service providers; the joint utility function attempts to reconcile these inherent differences using an optimal trade-off protocol, since there is potentially no universally optimal solution that maximizes both customer and service provider objectives. The following section discusses... Figure 5 A further detailed description of an example of the multi-objective optimization process used to select the “winning” bid.

[0102] As part of the market-based task allocation process, server-level computer 108 can retrieve non-experienced prior data on requesting users and bidding service providers from database 106. This prior data may include previous rating data, previous transaction details, previous request / bid details, etc., specific to the specified user or service provider. At the search tree process box 115, a tree data structure can be executed to locate predefined keywords within the received prior data, for example, in scenarios involving complex tasks. Within the tree structure, for example, a complex task (parent generation) can be decomposed into simpler tasks (child generation); a graph search algorithm can then be applied to determine the task execution sequence for the simplified child generation tasks. For white-box mathematical models with sufficient prior data, these keywords can facilitate joint utility function analysis implemented at process box 111 and when evaluating the terms outlined in each received bid at the bidding language process box 113.

[0103] Proceeding from process block 115, method 100 implements the search strategy process block 117 to perform a predefined search algorithm to traverse different depth levels of the tree data structure from the root to identify specific information (e.g., tree nodes that satisfy a given property). Several search strategies and algorithms exist that can be applied to process block 117, such as blind graph search techniques (e.g., depth-first search (DFS), breadth-first search (BFS), Dijkstra's search, etc.), heuristic search graphs (e.g., hill climbing, A-star, bidirectional A*, etc.), and metaheuristic search techniques (e.g., genetic algorithms, swarm intelligence search, etc.). Upon completion of the search strategy, the winner determination strategy process block 119 is executed to determine the winner based on the joint utility function. The optimal trade-off is used to identify the best or "best" bid submitted by the service provider.

[0104] After selecting the winning bid, the Task Assignment input / output box 121 transmits a task assignment with instructions for providing the user's transfer request to the selected service provider associated with the selected bid submission. The selected service provider may respond with confirmation and estimated arrival time; the user may also be informed of confirmation and ETA. The Charging / Energy Transfer process box 123 proceeds, whereby the service provider completes the user-requested task; confirmation of completion may be sent by the user and / or the service provider to the system server computer 108. Once the requested energy transfer task is completed, the user, as the payer, submits payment to the service provider, as the payee (either directly or through a proxy middleware node), as indicated in the Payment process box 125. The registered user and the selected service provider are then prompted to rate each other's performance in completing the task at the Two-Way Rating input / output box 127. Method 100 may then proceed to the End terminal box 129 and temporarily terminate.

[0105] Figure 3 The illustration depicts a representative tendering language method 200 for middleware applications to arbitrate energy transfers between users and service providers. In this specific example, one or more customers C1, C2, ..., Cn from a group of registered users 104 issue a solicitation for energy transfer at process box 201. For instance, four registered users may each submit a battery boost / energy transfer request; these four transfer requests can be transformed into tasks. t A , t B , tC and t D The need for charging / energy transfer is communicated to agent-mediated middleware applications (such as...). Figure 2 A server-level computer (108) is used to receive and process the data. At process block 203, a corresponding announcement is generated and broadcast to one or more of the service providers S1, S2, ... Sn in a group of crowdsourced service providers 102.

[0106] Upon receiving an announcement requesting energy transfer, decision boxes 205 can be used to ask service providers S1, S2, ... Sn whether they are interested in completing a single task or multiple tasks. Based on the illustrated example, service provider S1 can choose to undertake a single task. t A Then proceed to process box 209, or select to undertake a bundle of tasks. t A , t B , t C and / or t D Then proceed to decision box 207. At decision box 207, select to bundle the submitted task. t A , t B , t C and t D When one or more of these conditions are met, the service provider will define the language by which these tasks are bundled and performed. Non-limiting examples of the types of languages ​​that can be used include OR-bid (AB or ABC or B or BC or C / AB OR ABC OR B OR BC OR C) and XOR-bid (ABC AB AC BC ABC). This language is translated into a bid submission at process box 209; thereafter, the bid is transmitted to the agent-mediated middleware application.

[0107] Upon receiving all service provider bids or after the predetermined time window for accepting bids expires, the agent-mediated middleware application executes process box 211 and selects the optimal bid submission from the received bid submissions using a multi-criteria selection strategy, which includes a joint utility maximization function and / or a trade-off Pareto solution. Then, at process box 213, a task agreement is created to involve the selected service provider in completing a single task or a bundle of tasks identified at decision box 205. Method 200 proceeds to process box 215, where the selected service provider either completes the task / task bundle or reassigns one or more tasks within the task or bundle. After enabling and executing battery boost, energy transfer, or energy storage, the recipient user submits payment at process box 217. Then, at process box 219, registered users can rate the selected service provider, and optionally, the selected service provider can rate registered users.

[0108] Figure 4 The diagram illustrates a representative task identification and allocation module 300 that uses a six-stage review process to implement energy transfer from crowdsourced service providers to registered users: (1) announcement stage σ1; (2) submission stage σ2; (3) selection stage σ3; (4) contract stage σ4; (5) execution stage σ5; and (6) post-execution stage σ6. At a first time T1 during the announcement stage σ1, user 104, acting as a potential customer, issues a call for charging / energy transfer. At a second time T2 during the announcement stage σ1, system controller 108, acting as a market-based, agent-mediated application, publishes an announcement of the energy transfer call to all registered and available service providers within the geofenced area.

[0109] During the third time T3 of the submission phase σ2, interested service providers prepare bids to execute charging / energy transfer calls; these bids are submitted to the agent-mediated application. Upon completion of the submission phase σ2, the agent-mediated application determines the winning bidder at the fourth time T4 of the selection phase σ3. The selected winning bidder, or a set of optimal trade-offs recommendations with potential winning bidders available for selection, is then transmitted from the agent-mediated application to user 104. At the fifth time T5 within the selection phase σ3, the user approves the selected winning bidder or manually selects a winning bidder from the set of optimal trade-offs recommendations. The approved / selected winning bidder's bid is then transmitted by the user to the agent-mediated application.

[0110] Upon receiving an approved / selected bid, the agent-mediated application generates a task agreement at time T6 during the sixth time period of contract phase σ4. The task is then assigned to the selected service provider upon requesting acceptance of the task agreement. At time T7 during the seventh time period of execution phase σ5, the selected service provider accepts the assigned task. The selected service provider then travels to the requesting user, or the user travels to the service provider, to perform the battery boost / energy transfer task; subsequently, the energy transfer is completed at time T8 during the eighth time period of execution phase σ5.

[0111] For the post-execution phase σ6, at time T9, the user sends confirmation that the task has been completed, submits payment for the services provided, and rates the service provider. The agent-mediated application notifies the selected service provider that payment has been received and prompts the service provider to provide feedback. At time T10 during the post-execution phase σ6... 10 The selected service provider rates the user; the rating data is then transmitted to the agent-mediated application. Upon completion... Figure 4 During the later stages of execution, σ6 and TIA modules, the agent-mediated application notifies the user that the transaction has been completed.

[0112] Next turn Figure 5 This illustrates a representative multi-objective optimization method 400 for selecting the optimal bid from a variety of bid submissions to transfer energy from a service provider to a requesting user. In the first step of method 400, Figure 5 Process blocks 401, 403, and 405 may represent multi-objective optimization problems, such as those for simultaneously and mathematically optimizing multiple discrete objective functions within a set of feasible decision vectors. It may be claimed that for a “non-ineffective” multi-objective optimization problem, there may not be a single solution that simultaneously optimizes each objective in the set. In the second step of method 400, process blocks 407, 409, and 411 may represent decision-making processes for manually or automatically selecting a final solution based on a predefined weighted scheme or predefined preference information. Automatic selection may be performed by a resident vehicle controller, an agent-mediated middleware node, or a dedicated mobile software application, for example, subject to user-defined weighting and preference constraints.

[0113] For at least some implementations, optimization method 400 attempts to simultaneously maximize (rather than minimize) the utility of both the requesting user and the selected service provider within a predefined set of hard constraints and a predefined set of soft constraints, respectively. and As indicated in the MULTI-OBJECTIVE OPTIMIZATION PROBLEM input / output box 401. Unrestricted examples of hard constraints may include completing all announced tasks, while unrestricted examples of soft constraints may include completing a specific task within a specific time window. Proceeding to the MULTI-OBJECTIVE OPTIMIZER process box 403, method 400 executes an optimization algorithm or solver designed to solve the multi-objective optimization problem of box 401 to find the optimal trade-offs or a set of Pareto values. Output the solver's results from process box 403 at the MULTIPLE TRADE-OFFSOLUTIONS process box 405; this output may contain a single solution or multiple solutions, as there may not be a single, universally optimal solution for each scenario.

[0114] For the decision-making process portion of the multi-objective optimization method 400, the final solution can be automatically selected at process box 407 or manually selected at process box 409. The AUTO SELECTION process box 407 may include receiving preference data and weighted scheme data from database box 411. As described above, the indicated electronic controller or computing device can automate the selection of the final solution, wherein such selection is constrained by a set of predefined preferences and / or a set of predefined weighting parameters. At the DECISION output data box 413, the final manually / automatically selected solution is output, for example, for... Figure 2 Frame 119 Figure 3 The frame 211 and / or time T5.

[0115] Common Reference Figure 2 and Figure 5 Both can be addressed by defining the customer group. C = {EV, power grid} Initially, this customer group had requests for battery boost / energy transfer. c i Many of their customers, As mentioned above, each requesting user (customer) can be presented as any suitable electricity consumer, including EVs requiring battery boost or power facilities / grids requiring energy transfer. The problem statement can also be defined by the user / customer. C Requested battery boost or power transfer task t k Task group T ,in Energy transfer can take the following forms: transferring electrical energy from a service provider to the power grid or EV; storing energy from the power grid in the service provider's storage devices for later transfer; or transferring energy from one user to another. Provider Group It is also possible to define a number of available service providers. s j ,in Service providers may include any suitable mobility service provider (MSP) (such as roadside assistance professionals with portable chargers in other EVs and trucks), as well as those surrounding the customer. c Suitable fixed service providers (SSPs) (such as home EV charging stations or publicly available energy storage points) within a geofenced area of ​​interest.

[0116] It also defines the methods used to perform tasks. k The combined effect u k utility group U For a single V2V battery boost / V2G power transfer task, the problem may include finding the optimal allocation of tasks to service providers, which would be a set of service provider-task pairs. For multiple V2V battery boost / V2G power transfer tasks, these groups can be expanded to include the ability to bundle battery boost tasks, where a single service provider is capable of performing at least a subset of the available tasks. The total cost is:

[0117]

[0118] and f It represents the number of tasks within the bundle.

[0119] For the task allocation problem with joint utility functions, utility u It could be a customer c and service providers s Both are responsible for completing the battery boosting / energy transfer task. t The satisfaction level obtained in the process. For some implementations, the primary objective may include finding the task. t To the customer c and service providers s The optimal or "best" assignment of the two is to maximize utility.

[0120] For a service provider utility function, given a service provider s and battery boost / energy transfer tasks t ,if s Capable of execution t Then, utility can be defined on some standardized scales as:

[0121]

[0122] in The service provider is performing the task. tThe total payment received thereafter; It is the total distance that the mobile service provider will travel to reach the customer; Due to the execution of tasks t The resulting anticipated impact on the service provider's battery / storage devices or the anticipated reduction in the service provider's driving range; and , and These are the payment weight, distance weight, and impact weight from the service provider's perspective. Without loss of generality, this can be normalized to... .

[0123] For the customer utility function, given the need for a battery boost / energy transfer task... t Customers c ,if c From service providers s Upon receiving a bid, the utility can be defined on some standardized scales as follows:

[0124]

[0125] in This refers to the expected impact on customers' batteries or the expected increase in customers' driving range due to the boost voltage. This refers to the estimated arrival time of the MSP or the estimated arrival time of the customer at the SSP. The service provider requested the execution of the task. t Total payment; and , and These are the impact weights, ETA weights, and payment weights from the customer's perspective, respectively. Without loss of generality, this can be normalized such that if s is EV, then... And if s is a power grid, then .

[0126] Successor determination strategies may include performing Pareto optimality analysis. For example, using trade-off Pareto solutions to select the optimal bid submission involves performing bi-objective optimization techniques as a function of the objective value and objective trade-offs to visualize the Pareto frontier trade-off curve. Trade-off Pareto solutions can generate multiple optimal trade-off recommendations; these recommendations can be presented to the user with a prompt to select an available recommendation. Agent-mediated middleware nodes can receive the user's selected optimal trade-off recommendation and set the optimized bid submission as the selected recommendation. Alternatively, successor determination strategies may utilize a scalar approach, including:

[0127] maximize

[0128] It belongs to ,in It is a weight, and without loss of generality, it can be normalized such that... .

[0129] The preceding section discussed a market-based, agent-mediated, integrated framework for crowdsourcing, geofenced V2V battery boosting, and V2G energy transfer from mobile and fixed service providers. A combined auction protocol with a six-phase process (announcement, submission, selection, contract, execution, and post-execution phases) was also discussed for allocating simple and complex battery boosting / energy transfer or storage tasks. Multi-criteria bidding determination strategies can be employed to find the optimal task assignment for both the requesting client and the crowdsourcing service providers (multiple) that maximizes the joint utility function or the best trade-off Pareto solution. The clients (multiple) can be enabled to manually or automatically select the final solution based on preference information set by the clients (multiple) or a predefined weighting scheme. Optional task bundling allows multiple decomposable tasks to be bundled and assigned to a single service provider. To search for the "best" bid (multiple), submitted bids are aggregated, processed, and structured in the form of a data tree. After constructing the bid tree, standard deterministic or random search algorithms can be used to identify the winning service provider. If needed, the winning service provider can be assigned a set / bundle of tasks; they may be promoted to the role of auctioneer to allocate these tasks to other service providers. The framework also supports multiple bidding languages ​​(such as OR bidding or XOR bidding) to enable bidders to bid on multiple tasks.

[0130] In some embodiments, aspects of this disclosure may be implemented by a computer-executable program of instructions (such as a program module), wherein the computer-executable program of instructions is generally referred to as a software application or application executed by any of the controllers or variations thereof described herein. In non-limiting examples, the software may include routines, programs, objects, components, and data structures that perform a particular task or implement a particular data type. The software may form an interface to allow a computer to respond to an input source. The software may also cooperate with other code segments to initiate various tasks in response to data received from a source incorporating received data. The software may be stored on any of a variety of memory media, such as CD-ROMs, magnetic disks, and semiconductor memories (e.g., various types of RAM or ROM).

[0131] Furthermore, various computer system and computer network configurations, including multiprocessor systems, microprocessor-based or programmable consumer electronic devices, minicomputers, mainframes, etc., can be used to implement aspects of this disclosure. Additionally, aspects of this disclosure can be implemented in distributed computing environments where tasks are performed by resident and remote processing devices linked via communication networks. In distributed computing environments, program modules can reside on both local and remote computer storage media, including memory storage devices. Therefore, various hardware, software, or combinations thereof can be combined in computer systems or other processing systems to implement aspects of this disclosure.

[0132] Any of the methods described herein may include machine-readable instructions for execution by (a) a processor, (b) a controller, and / or (c) any other suitable processing device. Any algorithm, software, control logic, protocol, or method disclosed herein may be implemented as software stored on a tangible medium such as flash memory, solid-state drive (SSD) memory, hard disk drive (HDD) memory, CD-ROM, digital versatile optical disc (DVD), or other memory devices. Complete algorithms, control logic, protocols, or methods and / or portions thereof may alternatively be executed by a device other than a controller and / or implemented in firmware or dedicated hardware (e.g., by application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable logic devices (FPLDs), discrete logic, etc.). Furthermore, while specific algorithms may be described with reference to the flowcharts and / or workflow diagrams depicted herein, many other methods may be used alternatively to implement the example machine-readable instructions.

[0133] Various aspects of this disclosure have been described in detail with reference to the illustrated embodiments; however, those skilled in the art will recognize that many modifications can be made thereto without departing from the scope of this disclosure. This disclosure is not limited to the precise construction and composition disclosed herein; any and all modifications, alterations, and variations apparent from the foregoing description are within the scope of this disclosure as defined by the appended claims. Furthermore, this concept expressly includes any and all combinations and sub-combinations of the foregoing elements and features.

Claims

1. A method for operating a smart charging system for transferring energy to a user having an energy storage system, the method comprising: The system controller of the intelligent charging system receives a transmission request from the user's user interface to arrange electrical transmission to the user; The system controller broadcasts a call for bids for energy transfer to a group of crowdsourced service providers in order to complete the transfer request to the user. The system controller receives multiple bid submissions from multiple service providers to complete the transmission request; A multi-criteria selection strategy is used to select the optimal bid submission from the received bid submissions, the multi-criteria selection strategy including a joint utility maximization function and / or a Pareto solution with trade-offs; as well as The system controller transmits a task assignment with instructions to provide the transmission request to the selected service provider associated with the optimized bid submission among the plurality of service providers.

2. The method according to claim 1, wherein, The optimized bid submission is selected using the joint utility maximization function, which is calculated as follows: maximize in It is as a task to be completed t The combined utility of the function for transmitting requests, It is a user-defined customer weighting factor. It is as the task described t The user's client utility function is the function of that user. It is the service provider weighting factor defined by the provider, and It is as the task described t The provider utility function of the selected service provider.

3. The method according to claim 2, wherein, The client utility function u c ( t ) is calculated as: in It is as the task described t The expected impact of the function on the user's energy storage device. It is the estimated arrival time of the selected service provider or the estimated time of arrival at the selected service provider. It is an optimized bid submission from the selected service provider for performing the task. t The total cost, and , and These are user-defined impact weights, ETA weights, and payment weights.

4. The method according to claim 3, wherein, The provider utility function Calculated as: in The selected service provider is responsible for performing the task. t The total compensation received It is the total distance that the selected service provider will travel to reach the user. It is as the task described t The expected impact of the function on the energy storage devices of the selected service providers, where MSP is a mobile service provider and SSP is a fixed service provider, and , and These are the payment weights, distance weights, and influence weights defined by the provider.

5. The method according to claim 1, wherein, Using a trade-off Pareto solution to select the optimized bid submission, including performing a bi-objective optimization technique as a function of the objective value and objective trade-offs to visualize the Pareto front trade-off curve.

6. The method according to claim 5, wherein, The Pareto solution of the trade-offs generates multiple optimal trade-off recommendations, and the method further includes: Before the task assignment is transmitted, the system controller presents the optimal trade-off recommendations to the user and prompts the user to select the best trade-off recommendation from among the recommendations; and The system controller receives the selected optimal trade-off recommendation from the user, wherein the optimized bid submission is the selected optimal trade-off recommendation.

7. The method according to claim 1, wherein, The user includes multiple heterogeneous users, the transmission request includes multiple transmission requests from the heterogeneous users, and the optimized bid submission includes a request that bundles multiple selected transmission requests from the transmission requests.

8. The method according to claim 1, further comprising: In response to the selection of the optimized bid submission, a task protocol is generated via the system controller to enable the selected service provider to complete the transmission request for the user. as well as The task agreement is transmitted to the user and the selected service provider, and the user is prompted to approve the task agreement.

9. The method according to claim 1, further comprising: Prior to broadcasting the solicitation of bids for energy transmission, geolocation data indicating the user's location is received via the system controller; Define a geofence of predefined size around the user's location; The system controller receives geographic location data indicating the corresponding locations of multiple registered service providers. as well as The group of crowdsourced service providers is obtained as a subset of the registered service providers within the geofence.

10. The method of claim 1, further comprising: The system controller receives prior data of the user and the service provider associated with the plurality of bid submissions from the memory device. as well as The tree data structure is executed to locate predefined keywords within the received prior data, wherein the selection of the optimized bid submission is also based on the predefined keywords located within the received prior data using the tree data structure.

11. The method of claim 1, further comprising, after transmitting the task assignment, receiving, via the system controller, a transmission confirmation provided by the selected service provider to the user for the transmission request.

12. The method of claim 11, further comprising, in response to a received transmission acknowledgment, transmitting a prompt to the user and / or the selected service provider via the system controller to assess the availability of the transmission request.

13. The method of claim 1, further comprising the system controller receiving a corresponding registration data file from the user and each of the group of crowdsourced service providers.

14. A smart charging system for transmitting energy to a user having an energy storage system, the smart charging system comprising: A memory device that stores registration information of multiple service providers; A communication device operable to communicate with the service provider; as well as A server-class computer with a system controller, the system controller being programmed to: The communication device receives a transmission request from the user's user interface to be scheduled for electrical transmission to the user. The call for bids for energy transmission is broadcast to a group of crowdsourced service providers via the communication device in order to complete the transmission request; The communication device is used to receive multiple tender submissions from multiple service providers in order to complete the transmission request. A multi-criteria selection strategy is used to select the optimal bid submission from the received bid submissions, the multi-criteria selection strategy including a joint utility maximization function and / or a Pareto solution with trade-offs; as well as A task assignment with instructions to provide the transmission request is transmitted to the selected service provider associated with the optimized bid submission among the plurality of service providers.

15. The intelligent charging system according to claim 14, wherein, The optimized bid submission is selected using the joint utility maximization function, which is calculated as follows: maximize in It is as a task to be completed t The combined utility of the function for transmitting requests. It is a user-defined customer weighting factor. It is as the task described t The user's client utility function is the function of that user. It is the service provider weighting factor defined by the provider, and It is as the task described t The provider utility function of the selected service provider.

16. The intelligent charging system according to claim 15, wherein, The client utility function is calculated as follows: in It is as the task described t The expected impact of the function on the user's energy storage device. It is the estimated arrival time of the selected service provider or the estimated time of arrival at the selected service provider. It is an optimized bid submission from the selected service provider for performing the task. t The total cost, and , and These are user-defined impact weights, ETA weights, and payment weights.

17. The intelligent charging system according to claim 16, wherein, The provider utility function is calculated as follows: in The selected service provider is responsible for performing the task. t The total compensation received It is the total distance that the selected service provider will travel to reach the user. It is as the task described t The expected impact of the function on the energy storage devices of the selected service providers, where MSP is a mobile service provider and SSP is a fixed service provider, and , and These are the payment weights, distance weights, and influence weights defined by the provider.

18. The intelligent charging system according to claim 14, wherein, Using the trade-off Pareto solution to select the optimized bid submission, including performing a bi-objective optimization technique as a function of the objective value and objective trade-offs to visualize the Pareto front trade-off curve.

19. The intelligent charging system according to claim 18, wherein, The Pareto solution of the tradeoffs generates multiple optimal tradeoff recommendations, and wherein the server-level computer is further programmed to: Before the task assignment is transmitted, the system controller presents the optimal trade-off recommendations to the user and prompts the user to select the best trade-off recommendation from among the recommendations; and The system controller receives the selected optimal trade-off recommendation from the user, wherein the optimized bid submission is the selected optimal trade-off recommendation.

20. The intelligent charging system according to claim 14, wherein, The user includes multiple heterogeneous users, the transmission request includes multiple transmission requests from the heterogeneous users, and the optimized bid submission includes a request that bundles multiple selected transmission requests from the transmission requests.

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