阅读说明：本技术 自主无线电动交通工具充电系统 (Autonomous wireless electric vehicle charging system ) 是由 洋·刘 F·博雷利 于 2020-02-28 设计创作，主要内容包括：本发明涉及一种无线自主电动交通工具充电系统及方法,所述系统包含：充电站,其经配置以从外部源接收电能；多个充电垫,每一充电垫包含可再充电电池且经配置以由所述充电站进行充电,并被放置于EV下方以对所述EV的电池进行无线充电；及自主机器人,所述自主机器人经配置以从所述充电站取回一或多个充电垫且将所述充电垫递送到EV,其中在将所述充电垫放置于所述EV下方后,所述充电垫即刻对所述EV的所述电池进行无线充电。(The invention relates to a wireless autonomous electric vehicle charging system and method, the system comprising: a charging station configured to receive electrical energy from an external source; a plurality of charging pads, each charging pad including a rechargeable battery and configured to be charged by the charging station and placed under an EV to wirelessly charge a battery of the EV; and an autonomous robot configured to retrieve one or more charging pads from the charging station and deliver the charging pads to an EV, wherein upon placement of the charging pads under the EV, the charging pads wirelessly charge the battery of the EV.)

1. A wireless autonomous Electric Vehicle (EV) charging system, comprising:

a charging station configured to receive electrical energy from an external source;

a plurality of charging pads, each charging pad including a rechargeable battery and configured to be charged by the charging station and placed under an EV to wirelessly charge a battery of the EV; and

an autonomous robot configured to retrieve one or more charging pads from the charging station and deliver the charging pads to an EV, wherein upon placement of the charging pads under the EV, the charging pads wirelessly charge the battery of the EV.

2. The wireless autonomous EV charging system of claim 1, further comprising a server configured to receive a charge request and transmit instructions to a charging station, a charge mat, or an autonomous robot in response to the charge request.

3. The wireless autonomous EV charging system of claim 2, wherein the instructions identify one or more of: the type of EV, charging algorithm, location of the EV, duration of charging, desired charge level, or route to the EV.

4. The wireless autonomous EV charging system of claim 1, wherein each of the plurality of charging pads includes a height adjustment mechanism configured to bring a charging coil of the charging pad in proximity to a charging pad of the EV.

5. The wireless autonomous EV charging system of claim 1, wherein each of the plurality of charging pads includes a wireless charger configured to convert Direct Current (DC) electrical energy in the rechargeable battery to Alternating Current (AC) to enable wireless transmission of electrical energy to the EV.

6. The wireless autonomous EV charging system of claim 1, wherein the charging station includes one or more battery chargers configured to receive electrical energy from the external source and convert the electrical energy into a form suitable for storage in the rechargeable battery of the charging pad.

7. The wireless autonomous EV charging system of claim 1, wherein the charging station is configured to support and charge a plurality of charging pads.

8. The wireless autonomous EV charging system of claim 1, wherein one or more of a charging station, a charging pad, and an autonomous robot includes a central processing unit that includes a communication module and is configured to receive and transmit communications with the charging station, the charging pad, the autonomous robot, a server, or an EV.

9. The wireless autonomous EV charging system of claim 1, wherein the autonomous robot includes at least one robotic arm configured to engage and remove a charging pad from a charging station and place the charging pad on the autonomous robot.

10. The wireless autonomous EV charging system of claim 9, wherein the at least one robotic arm is configured to engage a charging pad on the autonomous robot and place the charging pad under an EV.

11. The wireless autonomous EV charging system of claim 10, wherein the at least one robotic arm is configured to engage a charging pad located on the ground and place the charging pad on the autonomous robot.

12. The wireless autonomous EV charging system of claim 1, wherein the autonomous robot further includes at least one sensor.

13. The wireless autonomous EV charging system of claim 12, wherein the at least one sensor is selected from the group consisting of: an ultrasound sensor, a camera, a light detection and ranging (LIDAR) sensor, and an Inertial Monitoring Unit (IMU).

14. The wireless autonomous EV charging system of claim 1, further comprising a server in communication with an application running on a mobile device, the application configured to enable a user to request charging services for an EV.

15. The wireless autonomous EV charging system of claim 14, wherein the application requires input to one or more of: a type of EV, a location of the EV, a charging algorithm, a location of the EV, a duration of charging, a desired charging level, a payment method, an urgency level, or a priority request.

16. The wireless autonomous EV charging system of claim 15, wherein the server communicates with one or more of the charging station, the charging pad, and the autonomous robot to receive a charging status of the EV.

17. The wireless autonomous EV charging system of claim 16, wherein the server communicates the received state of charge of the EV to the application running on the mobile device.

18. The wireless autonomous EV charging system of claim 1, wherein upon termination of charging, the autonomous robot retrieves the charging pad.

19. The wireless autonomous EV charging system of claim 18, wherein upon retrieval of the charging pad, if the charging pad requires charging, the autonomous robot returns the charging pad to the charging station.

20. The wireless autonomous EV charging system of claim 18, wherein upon retrieval of the charging pad, if the charging pad has sufficient charge to charge another EV, the charging pad is retained on the autonomous robot for further use.

21. The wireless autonomous EV charging system of claim 1, wherein the charging pad employs a closed-loop current approach to precisely place the charging coil of the charging pad in proximity to the charging coil of the EV for high efficiency inductive charging.

22. The wireless autonomous EV charging system of claim 1, further comprising a three-dimensional (3D) map formed from a drawing of a parking structure and sensed features from an ultrasonic sensor, a camera, a light detection and ranging (LIDAR) sensor, and an Inertial Monitoring Unit (IMU).

23. The wireless autonomous EV charging system of claim 1, wherein the autonomous robot employs an application that combines a hybrid a-path planner with an optimization-based collision avoidance (OBCA) algorithm to determine a path for placing the charging pad under an EV.

Technical Field

The present disclosure relates to Electronic Vehicle (EV) charging systems and methods. More particularly, the present disclosure relates to an automated robotic wireless EV charging system.

Background

Electric Vehicle (EV) charging stations or points of charge have become a common feature of many parking facilities, homes, and other infrastructures. These EV charging stations supply electrical energy for recharging of electric vehicles (e.g., plug-in electric vehicles, including electric cars, neighborhood electric vehicles, and plug-in hybrid vehicles). As electric vehicles and the amount of electric vehicle inventory continue to expand, there is an increasing need for widely distributed, accessible charging stations. However, various constraints tend to limit the ability of the charging infrastructure to keep pace with demand increases. For example, the availability of charging stations in a geographic area may be affected by limitations on the number and location of parking spaces, power grid capacity, and other factors. Furthermore, the necessary modifications to existing architectures and infrastructures can significantly increase the cost of building more charging stations. The present disclosure is directed to addressing the shortcomings of prior systems and methods.

Disclosure of Invention

One aspect of the present disclosure relates to a wireless autonomous electric vehicle (EV charging system, including: a charging station is arranged at the bottom of the charging box, configured to receive electrical energy from an external source; a plurality of charging pads, each charging pad including a rechargeable battery and configured to be charged by the charging station, and placed under an EV to wirelessly charge a battery of the EV; and an autonomous robot, and a robot control system, the autonomous robot is configured to retrieve one or more charging pads from the charging station and deliver the charging pads to an EV, other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods and systems described herein.

Implementations of this aspect of the disclosure may include one or more of the following features. The wireless autonomous EV charging system further includes a server configured to receive a charging request and transmit instructions to a charging station, a charging mat, or an autonomous robot in response to the charging request. The wireless autonomous EV charging system, wherein the instructions identify one or more of: the type of EV, charging algorithm, location of the EV, duration of charging, desired charge level, or route to the EV. The wireless autonomous EV charging system, wherein each of the plurality of charging pads includes a height adjustment mechanism configured to bring a charging coil of the charging pad into proximity with a charging pad of the EV. The wireless autonomous EV charging system, wherein each of the plurality of charging pads includes a wireless charger configured to convert Direct Current (DC) electrical energy in the rechargeable battery to Alternating Current (AC) to enable wireless transmission of electrical energy to the EV. The wireless autonomous EV charging system, wherein the charging station includes one or more battery chargers configured to receive electrical energy from the external source and convert the electrical energy into a form suitable for storage in the rechargeable batteries of the charging mat. The wireless autonomous EV charging system, wherein the charging station is configured to support and charge a plurality of charging pads. The wireless autonomous EV charging system, wherein one or more of a charging station, a charging pad, and an autonomous robot includes a central processing unit that includes a communication module and is configured to receive and transmit communications with the charging station, the charging pad, the autonomous robot, a server, or an EV. The wireless autonomous EV charging system, wherein the autonomous robot includes at least one robot arm configured to engage a charging pad and move the charging pad out of a charging station and place the charging pad on the autonomous robot. The wireless autonomous EV charging system, wherein the at least one robotic arm is configured to engage a charging pad on the autonomous robot and place the charging pad under an EV. The wireless autonomous EV charging system, wherein the at least one robot arm is configured to engage a charging pad located on the ground and place the charging pad on the autonomous robot. The wireless autonomous EV charging system, wherein the autonomous robot further includes at least one sensor. The wireless autonomous EV charging system, wherein the at least one sensor is selected from the group including: an ultrasound sensor, a camera, a light detection and ranging (LIDAR) sensor, and an Inertial Monitoring Unit (IMU). The wireless autonomous EV charging system further includes a server in communication with an application running on the mobile device, the application configured to enable a user to request charging services for the EV. The wireless autonomous EV charging system, wherein the application requires input to one or more of: a type of EV, a location of the EV, a charging algorithm, a location of the EV, a duration of charging, a desired charging level, a payment method, an urgency level, or a priority request. The wireless autonomous EV charging system, wherein the server communicates with one or more of the charging station, the charging pad, and the autonomous robot to receive a charging status of the EV. The wireless autonomous EV charging system, wherein the server communicates the received charging status of the EV to the application running on the mobile device. The wireless autonomous EV charging system, wherein upon termination of charging, the autonomous robot retrieves the charging pad. The wireless autonomous EV charging system, wherein upon retrieval of the charging pad, the autonomous robot returns the charging pad to the charging station if the charging pad requires charging. The wireless autonomous EV charging system, wherein upon retrieval of the charging pad, if the charging pad has sufficient charge to charge another EV, the charging pad is retained on the autonomous robot for further use. The wireless autonomous EV charging system wherein the charging pad employs a closed loop current approach to precisely place the charging coil of the charging pad in proximity to the charging coil of the EV for high efficiency inductive charging. The wireless autonomous EV charging system includes a three-dimensional (3D) map formed from a drawing of a parking structure and sensed features from an ultrasound sensor, a camera, a light detection and ranging (LIDAR) sensor, and an Inertial Monitoring Unit (IMU). The wireless autonomous EV charging system, wherein the autonomous robot employs an application that combines a hybrid a path planner with an optimization-based collision avoidance (OBCA) algorithm to determine a path for placing the charging pad under an EV.

Implementations of the described techniques may include hardware, methods, or processes, or computer software on a computer-accessible medium, including software, firmware, hardware, or combinations thereof installed on a system that in operation causes the system to perform actions. One or more computer programs may be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform actions.

Drawings

Fig. 1 is a schematic diagram of an autonomous wireless charging system according to the present disclosure;

fig. 2 is a charging station of an autonomous wireless charging system according to the present disclosure;

fig. 3A and 3B depict aspects of a charging pad of an autonomous wireless charging system according to the present disclosure;

FIG. 3C depicts two wireless charging modalities using the charging pad of FIGS. 3A and 3B;

fig. 4A and 4B depict aspects of an autonomous robot of an autonomous wireless charging system according to the present disclosure;

FIG. 5 is a schematic diagram of a CPU according to the present disclosure;

fig. 6 is a network diagram of an autonomous wireless charging system according to the present disclosure;

FIG. 7 is a schematic diagram depicting a mapping system and algorithm according to the present disclosure; and is

FIG. 8 is a schematic diagram depicting a pathway generation system and algorithm according to the present disclosure.

Detailed Description

The present disclosure relates to an Electric Vehicle (EV) charging system and a charging method. Instead of a fixed charging station dedicated to a single location in a parking garage or parking lot, the present disclosure relates to systems and methods that enable EV charging at any location in a parking facility. To accomplish this "charge on place" method, the system of the present disclosure utilizes a charging robot that brings a charging pad containing a battery to a parked EV. The charging robot places the charging pad battery close to the chassis of the EV and enables wireless charging of the battery in the EV mobile device regardless of where the EV is located in the parking facility. When the operator of the EV returns to the vehicle, the operator is free to drive away only, stopping charging. The charging robot may retrieve the charging pad after EV departure or at the end of the charging cycle. Furthermore, if another vehicle is parked on the charging pad and no charging is required or is actually a non-EV, the charging robot may also retrieve and use the charging pad for the other vehicle. Furthermore, parking spaces are not limited to EVs, but may be used by any vehicle without unduly burdening the EV. These and other methods and systems are described in more detail below.

In the coming years, EVs will play a more dominant role in meeting the traffic needs of the population. This transition from gasoline-based refueling to charging has shown that a gas station model in which a vehicle range of 3 miles to 400 miles can be added to a vehicle with a simple 10 minute top-up is neither practical nor currently technically possible to replicate for an EV. Current battery technology, while greatly improving from just a few years ago, still requires time to achieve a similar amount of range-extending charging of the battery of an EV.

The failed, currently attempted solution to the gas station model is the ubiquitous 3-10 charging stations that can exist in a variety of public places, such as parking lots and garages. But these solutions also present challenges. First, the result of using the current charging station is a dedicated EV-only parking space, which eliminates valuable resources used by all drivers. Furthermore, since resources are so valuable, and since there is little stigma associated with the resources as compared to improper parking in a disabled parking space, many drivers ignore the presence of a charging station altogether and park in the parking space regardless of whether they require actual charging, and often little or no parking restrictions. Relatedly, many locations that recognize the value of parking spaces and the infrastructure costs associated with installing charging stations are reluctant to install such charging stations on their facilities.

In any event, the future of EV charging will remain deployed primarily in public spaces. In accordance with the present disclosure, a solution to the above identified problem can be seen with reference to fig. 1. In fig. 1, an Autonomous Wireless EV Charging System (AWECS)100 is depicted. The AWECS100 consists of three major components, each of which is described in more detail below. Central charging station 200 is connected to a utility as a charging power source, or alternatively to a dedicated source (such as a solar array) or other alternative energy platform configured to charge one or more charging pads 300. The charging pad 300 is stored in a charging station and can be charged via a wired connection. The autonomous robot 400 is configured to retrieve one or more charging pads 300 from the charging station 200 and place the one or more charging pads under the EV 500. Once in place under the EV500, the charging pad 300 wirelessly couples to a battery located within the EV and releases its power to charge the battery of the EV. By placing the charging pad 300 under the selected EV500 as required, each parking space in the parking facility remains available for both the EV500 and the other vehicles 600. Furthermore, by having a large number of charging pads 300 stored in the charging station, the time to charge the charging pads can be delayed beyond peak energy usage times, and delayed until evening hours or other times when energy costs and grid demand are reduced, thereby increasing the efficiency of existing utility grids. Still further, using transfer switching technology, locally-producible solar or wind energy can be used as the primary source of charging energy during daytime and other peak utility grid uses to again limit the impact of the AWECS100 on the existing utility grid.

Fig. 2 depicts a charging station 200 of the present disclosure. In one embodiment, charging station 200 includes one or more racks 201. A plurality of charging pads 300 are supported on the frame 201. Each charging pad 300 includes at least two leads 302 that mate with corresponding contacts 204 on charging station 200. Each contact 204 is electrically connected to a battery charger 206. Although described herein as a physical connection between the battery charger 206 and the charging pad 300, the charging pad may also be charged wirelessly or inductively without departing from the scope of the present disclosure. The battery charger 206 is electrically connected to an energy source, such as a mains connection to a utility grid or an alternative energy source, such as a dedicated solar array or wind generator. As will be appreciated, both charging station 200 and charging pad 300 include a CPU 208, 314 that includes memory, one or more sets of instructions (including software, firmware, applications), and a communication module for communication between battery charger 206 and the batteries in charging pad 300. These communications include data such as state of charge (SOC), depth of discharge, state of health of the cells of the battery in the charging pad 300, temperature, and other information related to charging of the battery (e.g., lithium ion and other lithium chemical batteries).

The communication module in CPU 208 of charging station 200 also enables communication with a server system, described in more detail below. Communication with the server may allow for remote monitoring of the status of the charging pad 300 and charging station 200, as well as other related communications for the AWECS 100.

Fig. 3A and 3B depict a charging pad 300 according to the present disclosure. Charging pad 300 includes leads 302 for connecting to contacts 204 of charging station 200. The leads 302 are electrically connected to one or more batteries 304. Battery 304 is charged by charging station 200. A wireless charger 306 is electrically connected to the battery. The wireless charger 306 converts Direct Current (DC) electrical energy stored in the battery 304 to Alternating Current (AC). The AC signal output from the wireless charger 306 is received by the adjustable charging coil 308, and the wireless charging coil 308 generates an AC electric field. A similar charging coil exists in the EV and results in an AC signal being generated in the EV500 if an electric field is inductively coupled to the charging coil 308 and the wireless charger 306. This AC signal is received by a wireless charger in the EV500, which converts the AC signal to a DC signal for charging the battery of the EV.

As will be appreciated for any inductive charging circuit, the close proximity of the charging coil 308 to the charging coil in the EV500 is both desirable and improves the efficiency of energy transfer from the battery 304 in the charging pad 300 and the EV 500. To enable this proximity of the charging coil 308 to a corresponding charging coil on the EV500, the charging coil 308 may have a height adjustment mechanism. This height adjustment mechanism may be mechanical, such as a rack and pinion drive connected to a drive motor that moves the charging coil vertically away from the top surface 310 of the charging pad 300. Alternatively, the height adjustment may be pneumatic, where compressed air is used to drive a piston connected to an adjustable charging coil to adjust the height of the adjustable charging coil and proximity to the charging coil of the EV 500. Once a charging coil is determined to be proximate to the EV500, movement of the height adjustment may be automatically enabled.

Alternatively, activation of the height adjustment mechanism may be controlled by the autonomous robot 400 via wireless or wired communication with the charging pad 300. This communication may be done after the charging pad 300 is placed under the EV 500. In some embodiments, the charge pads remain in communication with the autonomous robot 400 during and immediately after placement of the charge pads 300 and throughout the charge initiation to ensure proper placement of the charge pads 300 under the EV500 for efficient charging of the EV batteries. Regardless of the mechanism, the final height of the adjustable charging coil 308 depends on the height of the vehicle being charged.

Still further, the charging pad 300 may include one or more detection systems for ensuring proper alignment of the charging solution 308 with the charging coil of the EV 500. For example, RF signals may be employed by the charging pad to achieve this alignment. This alignment in combination with height adjustment minimizes the distance between the charging coil 308 and the charging coil in the EV500 to maximize energy transfer efficiency.

To transfer energy from charging coil 308 to the charging coil of EV500, two methods are generally employed: inductive coupling and resonant coupling, both of which are shown in fig. 3C. Inductive coupling or inductive charging is much more efficient than resonant charging. Inductive coupling requires that the charging coils 308 and EV500 be tightly coupled to each other and that inductive charging efficiency be related to the distance and alignment from the charging coil 308 (transmission coil) to the charging coil (reception coil) of the EV 500. As described above, the telescoping arrangement can move the charging coil 308 vertically and horizontally to achieve this alignment with the charging coil of the EV 500.

The following describes a closed loop current method for accurately placing the charging coil 308 in proximity to the charging coil of the EV500 for high efficiency inductive charging. After initially placing the charging pad 300 under the EV500 proximate to the charging coil of the EV, the charging coil 308 sends a charging signal to the EV500 to begin charging. A high frequency voltage will build up on the charging coil of EV 500. The on-board charger in the EV500 will begin to rectify the high frequency current into a dc current and charge the battery inside the vehicle.

Parameters of the initial charging current are typically determined by an on-board charger in the EV500 based on the state of the battery in the EV 500. After charging has been initiated, the rate of change of the charging current quickly begins to slow. In a relatively short time, the charging rate becomes constant.

The CPU 314 in the charging pad 300 measures the charging current at the input of the charging coil 308. Based on transformer theory, Vin/Vout is N1/N2 is N, where N1 and N2 are turns ratios. The ratio is constant. Thus:

vin, Iin, Vout, Iout, η (equation 1)

Wherein:

vin is the input voltage on the charging coil

Iin is the input current on the charging coil

Vout is the output voltage on the receive coil

Iout is the current on the receiving coil

Eta is the energy transfer efficiency of the charging and receiving coil

Equation 1 can be rewritten as:

vin, Iin, Vin, N, Iout, η (equation 2)

This can be rewritten as:

iin ═ N ═ Iout [. eta ] (equation 3)

As described above, Iout is constant for a short time. Since the input current Iin is directly related to the energy transfer efficiency η, and further since the transformer efficiency η and the primary and secondary coil air gaps and alignment are strictly related, the CPU 314 can evaluate the air gap and alignment of the charging coil 308 and the charging coil in the EV500 based on a comparison of the detected efficiencies. With this information, the charging pad can adjust the height and alignment of the charging coil 308. The raised structure described above can adjust the height of the charging coil 308 to minimize the air gap from the z-direction, and a servo motor or other mechanism can be employed to adjust the x-y position of the charging coil. By continuing the adjustment, the CPU 314 can determine the location of maximum efficiency of energy transfer by searching for the minimum Iin. In some examples, the Iout parameter is determined by the on-board charger of the EV500 and communicated to the CPU 314 of the charge pad 300.

Additionally or alternatively, one or more temperature sensors may be used to evaluate the alignment of the charging coil 308 with the charging coil of the EV 500. A temperature sensor may be placed on or near the surface of the charging coil 308 to detect the temperature difference. When the charging coil 308 is misaligned with the charging coil of the EV500, the effective area of the magnetic flux through the two coils is the effective area of the charging coil (i.e., the receiving coil) that efficiently transfers energy from the charging coil 308 to the EV 500. This transferred loss will be in the form of heat generated on the common area of the charging coils. The temperature sensor in the aligned region will measure a higher temperature than the temperature sensor on the misaligned region. A closed loop control algorithm may be employed by the CPU 314 to optimize the maximum number of temperature sensors in the hot region in an effort to minimize losses during energy transfer.

The charging pad 300 is relatively thin and designed to fit under a vehicle, as depicted in fig. 1. The exterior of the charging pad 300 may be formed of metal or plastic (e.g., high density polyethylene) and other materials that can withstand the pressure of a vehicle driving over the charging pad 300. Although not intended to be driven upon, this scenario is most likely to occur in view of the abilities of the average driver. In this and other aspects, the charge pad 300 meets requirements set forth by, for example, the Society of Automotive Engineers (SAE) or other relevant governmental agencies that promulgate standards and requirements for automotive-related devices.

As with charging station 200, charging pad 300 includes one or more CPUs 314 including a processor, memory, one or more sets of instructions (including software, firmware, applications), and a communication module for communication between charging pad 300 and a communication module in EV500 or between charging pad 300 and autonomous robot 400. Before the charging is initiated, the communication module in the CPU 314 starts communicating with the EV 500. This communication may utilize a predefined protocol. In some examples, this protocol may be defined by the service provider of the AWECS or the standard for EV charging.

As will be described in more detail below, the state of charge of the EV500 may be monitored and data regarding the SOC of the battery of the EV500 may be collected by the charging mat 300. This data may be communicated to charging station 200, where the communication module sends the data to a server that communicates with the operator of EV500 via, for example, an application running on a smartphone or other connected device.

Fig. 4A and 4B depict an autonomous robot 400 carrying multiple charging pads 300. The autonomous robot 400 is a wheeled vehicle configured to navigate in a parking facility without direct human intervention. The autonomous robot 400 may include diversity or sensors 402, including light detection and ranging (lidar) sensors, ultrasonic sensors, and cameras that enable the autonomous robot 400 to detect objects such as vehicles, pedestrians, and others that may be traversing a parking facility. Furthermore, these sensors 402 enable the autonomous robot 400 to align itself with the EV500 so that the charging pad 300 may be removed from the autonomous robot 400 and properly placed under the EV, as shown in fig. 1. Like the charging pad 300 and charging station 200, the autonomous robot 400 includes one or more CPUs 404 that include a processor, memory, one or more sets of instructions (including software, firmware, applications), and a communication module that enables wireless communication with the charging pad 300, charging station 200, or directly with one or more servers, as described in more detail below.

One or more robotic arms 406 are operably connected to the autonomous robot 400. The robotic arm 406 is configured to select one of those charging pads 300 on the autonomous robot 400 and place the charging pad 300 under the EV500, as depicted in fig. 1. Using a combination of sensors 402 on the autonomous robot 400 and the techniques described above, the charging pad 300 is positioned below the EV500 such that the charging coil 308 is aligned with the charging coil of the EV 500.

Upon completion of charging of the EV500, the charging pad 300 may transmit a signal to the autonomous robot 400 or charging station 200 indicating that charging is complete and the charging pad 300 may be collected. Similarly, if charging is interrupted (e.g., the operator drives the EV500 away), such a signal may be transmitted. This charge completion or charge interruption, or charge failure signal in some instances, initiates a response from the autonomous vehicle 400, causing the autonomous vehicle to return to the location of the charging pad 300 with which it is communicating. The autonomous vehicle 400 again extends its robotic arm 406 to retrieve the charging pad 300. If sufficient charge remains in the charge pad 300, the charge pad 300 may remain on the autonomous robot 400 for use with the next EV 500. Alternatively, if the state of charge of the batteries 304 in the charging pad 300 is sufficiently depleted, the autonomous robot 400 returns the charging pad 300 to the charging station 200, where it is placed on one of the racks 201 and connected to the contacts 204 and recharging is initiated. Again, the robotic arm 406 manipulates the charging pad 300 to place the charging pad on the appropriate rack 201.

The sensors 402, along with GPS signals, Inertial Measurement Units (IMUs), and other position detection devices, enable the autonomous robot to detect the local environment and make decisions to autonomously drive around the parking structure. A continuously updated three-dimensional (3D) local map of the parking structure may be stored in memory and accessed by the processor of the CPU 404. This map may be synchronized with other maps stored on the server so that updates may also be pushed from the server to the autonomous robot 400. This combination helps the autonomous robot navigate the parking structure and minimizes undesirable interactions with vehicles (including EV 500) or pedestrians.

Upon receiving a set of instructions to charge a particular EV (as described in more detail below), the autonomous robot 400 will determine whether there are sufficient charge pads 300 on the board to accommodate the instructions. If so, the autonomous robot 400 responds to the instruction and performs the placement at the appropriate time according to the instruction. If not, the autonomous robot 400 proceeds to the charging station 200 to place a charging pad 300 with insufficient charge on the charging station 200 and to fetch the charged charging pad 300 to execute the instruction. Autonomous robot 400 and charging station 200 may communicate to determine which charging pads 300 to retrieve, or server system 700 (fig. 6) may include identification of a particular charging pad 300 to place for a particular EV500 based on the charging status of charging pad 300, which charging pad 300 or charging station 200 reports to the server via the communication modules in CPUs 208, 314, respectively. The instructions may also consider which charge pads 300 are equipped with the appropriate charging algorithm for a particular battery on the EV500 and also consider based on this factor which charge pad 300 identification to utilize.

Charging station 200, charging pad 300, and autonomous robot 400 are all described as including a CPU. Specifically, CPUs 208, 314, and 404. Each of these CPUs may include some or all of the hardware described in connection with CPU 1000 in fig. 5. Those skilled in the art will recognize that the methods and systems described herein may be embodied on one or more applications operable on CPU 1000 (fig. 5). As an initial matter, these systems and methods may be embodied on one or more firmware, software, or applications. These applications implement battery monitoring, battery charging, communications, navigation, mapping, object detection and avoidance, and others, without departing from the scope of the present disclosure. Of course, those skilled in the art will recognize a variety of additional and complementary uses of the image processing methods described herein.

Referring now to fig. 5, which is a schematic diagram of a CPU 1000 configured for use with the methods of the present disclosure. The CPU 1000 may be coupled with the sensor 402 directly or indirectly (e.g., through wireless communication). The CPU 1000 may include a memory 1002, a processor 1004, a display 1006, and an input device 1010. The processor or hardware processor 1004 may include one or more hardware processors. The CPU 1000 may optionally include an output module 1012 and a network interface 1008. Memory 1002 may store applications 1018 and image data 1014. The application programs 1018 may include instructions executable by the processor 1004 for performing the methods of the present disclosure.

The application 1018 may further include a user interface 1016. Data 1014 may include sensor data, map data, and other data that may be used herein. The processor 1004 may be coupled with the memory 1002, the display 1006, the input device 1010, the output module 1012, the network interface 1008, and the sensors 402.

The memory 1002 may include any non-transitory computer-readable storage medium for storing data and/or software including instructions that are executable by the processor 1004 and that control the operation of the CPU 1000 and, in some embodiments, may also control the operation of the imaging device 1015. In an embodiment, the memory 1002 may include one or more storage devices, such as solid state storage devices (e.g., flash memory chips). Alternatively, or in addition to one or more solid state storage devices, the memory 1002 may also include one or more mass storage devices connected to the processor 1004 through a mass storage controller (not shown) and a communication bus (not shown).

Although the description of computer-readable media contained herein refers to solid state storage, it should be appreciated by those skilled in the art that computer-readable storage media can be any available media that can be accessed by the processor 1004. That is, computer-readable storage media may include non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media may include RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, Blu-ray or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the workstation 1001.

The application 1018, when executed by the processor 1004, may cause the display 1006 to present a user interface 1016. The user interface 1016 may be configured to present a variety of images and models to a user as described herein. The user interface 1016 may be further configured to display and mark aspects of the images and 3D models in different colors depending on the purpose, function, importance, etc. of the images and 3D models.

The network interface 1008 may be configured to connect to a network, such as a Local Area Network (LAN), a Wide Area Network (WAN), a wireless mobile network, a bluetooth network, and/or the internet, which may be comprised of wired and/or wireless networks. A network interface 1008 may be used to connect between CPU 1000 and a server (fig. 6). The network interface 1008 can also be used to receive data 1014 from a server. Input device 1010 may be any device through which a user may interact with CPU 1000, such as a mouse, keyboard, foot pedal, touch screen, and/or voice interface. The output module 1012 may include any connection port or bus, such as a parallel port, serial port, Universal Serial Bus (USB), or any other similar connection port known to those of skill in the art.

Fig. 6 depicts a schematic diagram of an architecture of the system 100 including multiple EVs 500. Each operator of the EV500 is provided with access to applications running on their personal connected device 502 (e.g., a smartphone, etc.). The application uses a mobile connection or a wireless connection to enable a connection between the operator and the charging system server 700. Upon reaching the parking structure and determining that the operator would like to charge his EV500, the operator opens the application. The application may need to verify payment information, vehicle location, vehicle type, and other relevant information. The request may be for a timed charge request or a charge state timed request. Some of these requests may have been previously addressed, such as when the operator becomes a subscriber to the charging service via an application. The AWECS server 700, which may be a cloud-based server, receives a request from an operator via an application running on the mobile device 502. Upon verifying the details of the request and the availability of the charging pad 300 that is compatible with the EV500 and has sufficient charge to perform the desired charging, the server 700 transmits a signal to the charging station 200, which communicates the signal to the autonomous robot 400, alternatively this signal from the server may be sent directly to the autonomous robot 400. In either case, the requested details are received by the autonomous robot 400. If there is no charging pad 300 available to fulfill the request, the server 700 may transmit possible alternative arrangements, such as reducing charging or delaying the start of charging, or simply rejecting the request due to current unavailability, depending on the planned availability of the charging pad 300.

The details received by the autonomous robot 400 may include a route planned by an application running in the server that takes into account the location of the autonomous robot 400, other charging requests that have been received, the distance from the charger robot 400 and the current location of the EV500 for which a request has been made, and other data, such as emergency requests or surcharges paid for emergency services, or other factors. With this information and following a map or planned route to the EV500 for which a request has been made, the autonomous robot 400 proceeds to the EV 500. Once in place, the robotic arm 400 of the autonomous robot 400 moves out of the charging pad 300 and places the charging pad under the EV 500. The combination of the sensor 402 and other features in the charging pad 300 along with the communication between the charging pad 300 and the EV500 ensures proper alignment and height adjustment of the charging coil 308 with the corresponding charging coil on the EV 500. Once alignment is confirmed, charging of the EV500 battery may begin. All charge status, system status, and other relevant data from the EV500, the charging mat 300, the autonomous robot 400, and the charging station 200 are reported back to the AWECS server for tracking and monitoring, and pushed to the operator to verify that their EV500 is charging.

One of the issues that needs to be addressed before charging can begin is to determine the charging algorithm required to charge a particular battery installed in a particular EV 500. This data may be retrieved from the EV500 by the CPU 314 in the charging pad 300 using its communication module as described above.

As will be appreciated, one of the functions of the server 700, which is a cloud-based system, is to collect data from the sensors 402 of the autonomous robot 400. This sensor data can be used to update a 3D map of the parking facility. Initially, during installation of the system 100, the parking facility will have been scanned to create the original 3D map. To produce raw map images, laser scanning and GPS systems may have been deployed to identify the necessary information, including the number of parking spots, the orientation of the parking spots, the route inside the parking structure, the actual size of each parking spot, and all details about the local infrastructure. However, once the parking facility is open to the public, some of this information may be outdated. User parking difficulties, maintenance, and other factors may need to be considered and used to periodically update the 3D map to provide up-to-date information for the server 700 and autonomous robot 400. This data is preferably received from the autonomous robot 400 as it completes its daily routine of deploying the charging pad 300.

Although the environment in which the autonomous robot 400 operates is identified herein with reference to a 3D map, the creation of an accurate map will now be described. In autonomous robots, accurate maps are crucial because they allow autonomous vehicles to operate in complex environments using only local sensors. Synchronized positioning and mapping (SLAM) is one of the most important algorithms for autonomous robots. SLAM is the process by which a vehicle builds a map of its surroundings and simultaneously locates itself relative to the map. Commonly used SLAM algorithms can be divided into two categories: filter-based algorithms and optimization-based algorithms. Although their applications are different, both methods depend greatly on the characteristics (or landmarks) of the surrounding environment, such as buildings, traffic signs, and street infrastructure. Some of the widely used filter-based methods have been derived using extended Kalman (Kalman) filters (EKFs). However, the EKF-based SLAM algorithm requires a heavy computational burden to implement. To alleviate this problem, Extended Information Filter (EIF) methods have been developed. The EIF method uses an information matrix that is the inverse of the covariance matrix from the kalman filter. To further reduce the computation time of EIF, sparse information filter forms have been developed. While the information filter can quickly integrate newly detected landmarks, the state estimation process requires the inverse elements of the entire information matrix, which can still be computationally burdensome. When landmark covariance data is not readily available, a fast SLAM method has been introduced. Graph-based SLAM, one of the optimization-based methods, is an intuitive way to solve the SLAM problem. This approach constructs the SLAM problem as a graph consisting of nodes and edges. In general, the nodes represent poses of vehicles and landmarks, and the edges represent spatial constraints between the nodes. Graph-based SLAM solves the error minimization problem to reduce the mismatch between real-time observations and spatial constraints (edges). Another approach is to use a non-linear least squares method for optimization formulation, but this approach is challenging to implement due to its non-linearity. Another approach reduces the number of edges at the expense of map accuracy for improved convergence speed. Other non-linear optimization-based methods include gaussian-Seidel relaxation (Gauss-Seidel relaxation), which uses linearization of the SLAM problem. Still further, multi-stage relaxation algorithms have been proposed to improve the convergence speed of sparse approximation problems. Instead, random gradient descent (SGD) has been proposed to solve the SLAM problem by providing a quick update of the state vector, allowing the algorithm to escape local minima. Still further, the visual SLAM method, which is a branch of the SLAM system, uses cameras to build maps.

The visual SLAM method is currently of major interest because optical or visual sensors are the cheapest and easy to set up. Visual SLAM has two main approaches to solve the SLAM problem: filter based and key frame based. Filter-based SLAM collects information at each frame using probabilistic theory. The attitude of the vehicle is estimated by using the information of each frame. Instead of considering all the features of a frame, a particular past frame is selected based on the SLAM of the key frame to calculate the current pose of the vehicle. One of the most popular visual SLAM methods, ORB-SLAM, is keyframe based SLAM. In all of the foregoing works, the method uses pose-based landmarks.

Although a number of approaches have been proposed, none of these approaches alone provide the features of the proposed mapping. In part, all of the foregoing methods and algorithms rely on one or more of the following: (1) determination of characteristics of the surrounding environment, (2) high precision GPS, and (3) a repetitive path with closed loop. Determining the surroundings is often very time consuming, often requiring hundreds of hours of manual work to mark properly. In addition, in parking garages, the environment is constantly changing as cars enter and leave the garage. In addition, in indoor parking structures, high precision GPS is generally not available or only inconsistently available due to the nature of the structure. Finally, since the path of the autonomous robot is not regularly repeated, it is not easy to obtain a repeated path with a closed loop.

To address these issues, the present disclosure employs an application 800 that performs a multi-step process for generating a high-precision SLAM for an indoor parking facility. As an initial matter, a set of drawings 801 (such as those available from an architect, owner, or operator of a parking garage) and a feature list 802 are input into a fusion algorithm 804. The list of features 802 may include, but is not limited to, identification of columns, markings on columns, stop lane markings, stop number markings, and others. The fusion algorithm 804 receives live camera images and lidar images from the sensors 402 of the autonomous robot 400. The fusion algorithm 804 would then process, for example using an iterative closest point approach, to compensate for poor positioning and pose of the autonomous robot and fuse real-time and pre-recorded lidar images with the original camera images to match the images in conjunction with the schema 801, producing the actual indicia of, for example, a parking spot or other feature parking garage in the form of spatial constraints. The fusion algorithm 804 may employ a standard feature-based matching method for use on a set of tailored features identified for the parking structure.

Both the live camera image and the lidar from the sensor 402 are also fed to the SLAM algorithm 806. SLAM algorithm 806 also receives the output of fusion algorithm 804, particularly the spatial constraints. These data are processed to perform initial pose predictions. SLAM algorithm 806 compensates the initial attitude estimate to compensate for poor vehicle attitude to determine a set of trajectories calculated from the vision and lidar information. Next, spatial constraints are employed to construct a map by using random gradient descent optimization without any pose-based landmarks. The SLAM algorithm 806 then outputs both the real-time map and the real-time location of the autonomous robot 400.

In one embodiment, the application 800 runs locally in the CPU 404 of the autonomous robot 400. Moreover, this location and updated map may be transmitted to the AWECS server 700 (fig. 6) to track the real-time location of the autonomous robot 400 and update the map, which allows the AWECS server 700 to provide further guidance to the autonomous robot 400 according to the present disclosure. Additionally or alternatively, the application 800 may run on the AWECS server 700 and communicate updated maps and locations of the autonomous robot 400. Further, the application 800 may run on the CPU 208 of the charging station 200 and communicate with both the AWECS server 700 and the autonomous robot 400.

As described herein, one of the aspects of the present disclosure involves placing the charging pad 300 under the vehicle by the autonomous robot 400 such that the charging coil 308 is proximate to the charging coil of the EV 500. Generating trajectories for the autonomous robot 400 in a cluttered parking environment is a difficult task, especially in cramped environments. The main challenges come from non-linearity and non-complete vehicle dynamics and non-convexity in free space. In fact, it has been shown that the task of finding collision-free paths is generally NP-hard (non-deterministic polynomial time-hardness). Therefore, an ideal universal trajectory generation algorithm does not exist. Recently, optimization-based path planning algorithms, such as Model Predictive Control (MPC), have attracted attention, with applications ranging from (unmanned) airplanes to robots to autonomous cars. This can be attributed to the increase in computational resources, the availability of robust numerical algorithms to solve optimization problems, and the ability of the MPC to systematically encode system dynamics and safety constraints within its formulation. The main challenge of optimization-based approaches is that the obstacle avoidance constraints can cause non-convex optimization problems, typically in the form of integer variables, making the resulting optimization problem computationally difficult to solve. By introducing an auxiliary decision variable, the obstacle avoidance constraints can be reformulated into a set of smooth constraints, allowing the use of a mature gradient and Hessian-based numerical solver. Unfortunately, it has been observed that the quality of the solution of these optimization problems in parking problems depends heavily on the initial guess provided to the numerical solver due to incomplete dynamics and the non-convexity of the unobstructed space. In accordance with the present disclosure, the present application seeks to address these issues in the context of using an autonomous robot 400 to place or remove a charging station 300 under an EV 500.

The autonomous robot 400 requires a precise insertion path underneath the target vehicle that does not contact the target and that surrounds the vehicle. Furthermore, the path must be generated in real time. To achieve the desired result, application 900 employs a combination of algorithms. The first algorithm employed is a hybrid a path planner. The hybrid a-path planner is used in conjunction with a collision avoidance algorithm based on hierarchical optimization. Specifically, application 900 utilizes hybrid a and the simplified vehicle model to quickly generate a coarse path that substantially satisfies vehicle dynamics. This coarse path is then passed on for initializing the H-OBCA algorithm that uses the full vehicle model to generate a high quality collision-free insertion path. The collision-free path enables the CPU 404 of the autonomous robot 400 to begin inserting the charging pad 300 under the EV 500. When the charging pad 300 is inserted, the collision-free insertion path is adjusted in real-time based on data collected by sensors 402 (e.g., ultrasound, lidar, images, laser scanners, etc.) of the autonomous robot 400. The distance from the tire of EV500 as sensed by sensor 402 is used to adjust the collision-free insertion path in real time. Further, the generated and updated insertion path may be tracked by a path following controller in the application 900. Upon completion of placement, a collision-free path may also be employed by the autonomous robot 400 to enable the autonomous robot to continue away from the EV500 and return to the EV500 as appropriate to exit the charging pad 300 upon completion of charging. While several aspects of the disclosure have been shown in the drawings, it is not intended that the disclosure be limited thereto, as the scope of the disclosure is intended to be as broad and as the art will allow for reading the specification. Accordingly, the foregoing description is not to be construed in a limiting sense, but is made merely as illustrative of certain aspects.