Cloud-based optimal charging route estimation for electric vehicles

Abstract

The present disclosure provides "Cloud-based optimal charging route estimation for electric vehicles". A hybrid electric vehicle (HEV) includes a communication unit configured to periodically respond to charging signals and adjust the driving route and charging waypoints based on estimated charging station driving routes and waypoint charging times received from a remote fleet server. The estimation is received in response to periodic operating conditions generated and communicated to the server. The operating conditions include one or more of the following: charging stations, environmental and location data, vehicle data and battery performance data, and other data. The controller is further configured to respond to the driving route and / or charging completion signals, and generate and store an estimated error as the difference between the actual optimal charging route and charging time and the estimated optimal charging route and charging time. The controller readjusts at least one of the driving route and the charging waypoints in response to the updated estimated optimal charging route and waypoint charging time received from the server.

Description

Technical Field

[0001] The present disclosure relates to estimating optimized routes to high-power charging stations for electric vehicles using remote server data analytics generated from real-time trip route planning and vehicle performance data accumulated from a global fleet. Background Art

[0002] In electric and hybrid electric vehicles (HEVs), long and short distance trips or trips can be unnecessarily expensive and may be extended and / or delayed due to the need to recharge the batteries. With the increasing availability of high-power and more efficient roadside charging stations, such trip times and costs can be reduced, provided that the HEVs can be optimally routed to the most efficient, cheapest, and most readily available roadside charging stations. The charging performance of HEVs and charging stations is affected by the surrounding environment, vehicle performance, charging station capacity and efficiency, availability due to usage and maintenance issues, electricity costs, and other factors, which can introduce undesirable errors into travel route planning and charging time estimates on such planned routes. Given the stochastic nature of the ever-changing charging station locations, efficiency, availability, and costs, such trip planning and charging time estimation errors persist despite some attempts to improve accuracy.

[0003] Some such attempts have involved predicting HEV range estimates, predicting optimal charging station locations, and / or predicting real-time HEV performance. Such attempts appear to have leveraged known HEV and charging station performance and lookup table algorithms, which appear to have been employed in various ways by controllers located on the HEV. Given the vastly increasing number of variables and parameters that define the ever-changing performance of HEVs within the global fleet, as well as the location and efficiency of charging stations, the ability to accurately estimate the optimal route to a high-power charging station has clearly exceeded the technical capabilities of such onboard HEV processors. Summary of the Invention

[0004] Hybrid, plug-in hybrid, and battery electric vehicles (HEV, PHEV, BEV) include navigation systems and one or more high-voltage traction batteries that are utilized with other HEV systems to enable travel over routes of varying distances. The present disclosure relates to improved systems and methods for more accurately estimating optimized travel routes having waypoints that include high-power charging stations, such that HEV performance over such travel routes can be maximized while minimizing range and battery recharging time due to utilization of such high-power charging stations.

[0005] The HEV according to the present disclosure utilizes, among other capabilities, communication capabilities with a cloud-based, neural network-based remote fleet server system. The HEV utilizes the remote fleet server to offload analytical and estimation computing resources and capabilities required to determine, estimate, and / or predict optimal charging station travel routes and / or charging waypoints and time estimates, with charging stations automatically detected and selected as waypoints along such travel routes. Such charging waypoints and charging waypoint charging time estimates or optimal charging routes are generated by the remote fleet server and include the evolving locations and capabilities of fast, lowest-cost, high-power, and / or high-efficiency charging stations along and / or proximate to the HEV's travel route.

[0006] The new remote server system receives and aggregates HEV battery and vehicle performance data from a global fleet of operating HEVs, and continuously updates high-power charging station location, capacity, availability, and performance data. A cloud-based, neural network-based remote server system is configured to ingest and digest this data, discovering and leveraging otherwise unknown patterns in HEV optimal charging routes and battery charging performance to more accurately predict more efficient high-power charging stations as waypoints for any individual HEV, as well as waypoint charging time estimates. In this way, responsive to charging signals and operating conditions received from the global fleet of operating HEVs, the remote server delivers continuously updated and more accurate estimates of such optimal charging routes, charging waypoints, and times to individual HEVs.

[0007] The present disclosure contemplates the use of real-time HEV data by a remote server to generate optimal charging routes and battery charge estimates. The remote server utilizes aggregated "big data" describing actual charging station performance, vehicle performance data, battery performance, and driver behavior, which is analyzed by one or more remote, cloud-based deep learning neural network engines trained to detect otherwise indiscernible patterns. The remote server engine utilizes continuously updated data and improved accuracy to predict / estimate optimal driving routes, charging waypoints, and / or waypoint charging times for individual HEVs, based on the needs of any individual HEV in communication with the server.

[0008] In operation, HEVs operating in a global fleet transmit location, environmental conditions, vehicle performance data, battery performance data, and charging station performance data in real time to a remote server. The remote server retains route and recharge / charging event data from the global EV fleet. The received and aggregated data is analyzed using a deep learning neural network to discover hidden patterns between the data and estimated and / or predicted and actual driving routes to charging stations, as well as charging waypoints and times. The neural network is trained to estimate and / or predict optimal driving routes to high-power charging stations based on demand from individual HEVs. Waypoints and / or recharge times along the driving routes are generated for individual HEVs. The estimated and / or predicted routes and / or waypoints and charging times can be periodically updated and communicated to individual HEVs in real time and as needed during driving and / or recharge / charging events to improve driving route optimization and / or charging waypoint and time estimation / prediction.

[0009] In the configuration and operating method of the present disclosure, an HEV / PHEV / BEV (hereinafter collectively referred to as "HEV") incorporates one or more controllers coupled to at least one battery and a communication unit, the one or more controllers being configured to periodically monitor and respond to charging signals from the HEV indicating that a charging / recharging event has begun and / or will soon be required while navigating to each of various waypoints along a current travel route. The periodic monitoring and responding can be configured to occur at discrete time and / or distance intervals, and / or when certain HEV, charging station, and / or travel route parameters change beyond predetermined and / or preferred thresholds.

[0010] The controller adjusts the driving route and / or charging time based on an estimated or predicted optimal charging route and / or charging waypoints received from a remote fleet server. The remote fleet server generates and transmits the optimized, estimated, and / or predicted optimal charging route and / or charging waypoints and times in response to operating conditions generated by the HEV controller and communication unit and communicated to the remote global fleet server. The operating conditions include various local HEV driving route and charging station data, such as at least one and / or one or more of the following: driving route weather and HEV performance along the driving route, as well as charging stations, environmental and location data, vehicle performance data, and related performance data and parameters, among other data.

[0011] In another embodiment, the controller is further configured to respond to at least one of a driving route and / or a charge completion signal indicating one or more of the following: completion of the driving route and / or completion of recharging of the HEV battery. In response, the controller generates and stores an estimated error, calculated as the difference between a previously received, estimated optimal charging route and / or charging waypoints and times and the actual driving route performance and / or charging times established at the time of driving route and / or recharging completion and at the time of generating the driving route and / or charge completion signal, and communicates the estimated error to a remote server. The estimated error is also communicated to a remote, cloud-based global fleet server to enable a neural network-based big data learning engine of the remote global fleet server to refine its expected estimate and / or prediction of the optimized optimal charging route and / or charging waypoints and times based on various data and performance parameters accumulated during the current driving route and / or charging / recharging event.

[0012] In other modifications, the controller is further configured to periodically and / or at discrete time and / or distance intervals readjust the corresponding driving routes and / or charging waypoints based on updated optimized optimal charging route and / or waypoint charging time predictions and / or estimates, received by the communication unit from a remote global fleet server. The remote fleet server transmits updated optimal charging routes and / or charging waypoint and time estimates in response to and when the HEV controller generates and transmits new real-time operating conditions, which may also include estimation errors. Such operating condition and vehicle performance data may further include vehicle identification number (VIN) and on-board diagnostic (OBD) codes and data, driving route performance data, vehicle power and related parameters and data, and other data.

[0013] Additionally, operating conditions may include battery performance data and parameters, which may also include battery pack capacity and chemistry, battery health and state of charge, battery temperature, charging station availability and power and performance, and low-voltage battery conditions, among other data. Charging station data sent by one or more of the global HEVs and / or charging stations to the remote global fleet server may also include charging station power cost and capacity data, utilization and availability, and charging station performance data generated and accumulated during the current HEV charging event. This charging station data may be sent in real time, only during discrete time intervals, and / or during intervals when various parameters and charging station data change.

[0014] The present disclosure contemplates that the controller is further configured to readjust the HEV driving route and / or charging waypoints and times based on an initially determined and estimated driving route and / or charging time to the charging station, which may be initially estimated when the vehicle is connected to the charging station power source, and may also be generated and received from a remote fleet server in response to new operating conditions generated by the HEV controller and communicated to the server, and may include the initially estimated and determined driving route and / or charging waypoints and times.

[0015] The present disclosure also includes a method for controlling individual HEVs in a global fleet using the described capabilities and improvements. For example, the method includes a controller coupled to a battery and a communication unit and periodically adjusting a driving route and / or charging waypoints and times in response to a charging signal. The controller performs the adjustments based on an optimal charging route and / or charging waypoints and time estimate / prediction generated by and received from a remote fleet server. Adjusting the driving route and / or charging waypoints and times is also performed in response to operating conditions generated by the HEV controller periodically and / or at discrete time and / or distance intervals and communicated to the remote server. The operating conditions include one or more of the following: driving route performance, and / or charging stations, environmental and location data, vehicle performance data, and battery performance data and parameters, as well as other data and parameters.

[0016] The controller also controls optimization of the driving route and / or recharging of the HEV battery in response to the driving route and / or charging completion signal, wherein the controller generates and stores an estimated error as the difference between the estimated optimal charging route and performance and / or charging waypoints and times and the actual driving route performance and / or waypoint charging times. The controller also readjusts the charging waypoints and times and the driving route based on updated estimated optimal charging routes and / or waypoint charging times received by the communication unit from the remote global fleet server, the updated estimated optimal charging routes and / or waypoint charging times also being responsive to operating conditions generated by the HEV controller and communicated to the remote global fleet server, which also include the estimated error.

[0017] The present disclosure also contemplates operating conditions generated by the HEV controller to include: environmental and location data incorporating geographic location as well as ambient temperature, humidity, and atmospheric pressure; vehicle performance data incorporating vehicle identification number and on-board diagnostic codes and data, vehicle power; and battery performance data and parameters including battery pack capacity and chemistry, battery health and state of charge, battery temperature, charging station power and performance, and low voltage battery status, among other parameters and data.

[0018] The methods may also include readjusting, by the controller, the travel route and / or charging waypoints and times based on updated estimated optimal charging routes and / or waypoint charging times received by the communication unit from the remote fleet server, and in response to operating conditions generated and communicated to the remote server, wherein the operating conditions include charging station data including electricity cost and capacity and availability data, and charging station performance data accumulated during discrete time intervals and during the current charging event.

[0019] The present summary of implementations and configurations of HEVs and described components and systems introduces selected exemplary implementations, configurations, and arrangements in a simplified and less technically detailed arrangement, and these are further described in greater detail below in the detailed description in conjunction with the accompanying drawings and the claims below.

[0020] This Summary is not intended to identify key features or essential features of the claimed technology, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The features, functions, capabilities, and advantages discussed herein may be implemented independently in various example implementations, or may be combined in other example implementations as further described elsewhere herein, and may also be understood by those skilled in the relevant art with reference to the following description and illustrations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] A more complete understanding of example implementations of the present disclosure may be obtained by referring to the detailed description and claims when considered with respect to the following drawings, in which like reference numerals refer to similar or identical elements throughout the drawings. The accompanying drawings and annotations thereon are provided to facilitate understanding of the present disclosure and do not limit the breadth, scope, scale, or applicability of the present disclosure. The drawings are not necessarily made to scale.

[0022] Figure 1 is a diagram of a hybrid electric vehicle and its systems, components, sensors, actuators, and methods of operation; and

[0023] Figure 2 Shown Figure 1 Certain aspects of the disclosure are shown with components removed and rearranged for illustration purposes. DETAILED DESCRIPTION

[0024] As requested, detailed embodiments of the present invention are disclosed herein; however, it should be understood that the disclosed embodiments are merely illustrative of the invention, which may be embodied in various and alternative forms. The drawings are not necessarily drawn to scale; some features may be exaggerated or minimized to illustrate details of particular components. Therefore, the specific structural and functional details disclosed herein should not be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art how to variously employ the present invention.

[0025] As will be understood by those skilled in the art, the various features, components, and processes shown and described with reference to any one of the accompanying drawings may be combined with the features, components, and processes shown in one or more other drawings to obtain embodiments that should be apparent to those skilled in the art but may not be explicitly shown or described. The combinations of features shown are representative embodiments for typical applications. However, various combinations and modifications of features consistent with the teachings of this disclosure may be desirable for specific applications or implementations and should be clearly within the knowledge, skill, and ability of those working in the relevant art.

[0026] Reference is now made to the various drawings and diagrams and Figure 1 and Figure 2 , with special reference to Figure 1 , shows a schematic diagram of a hybrid electric vehicle (HEV) 100 and illustrates representative relationships between the components of the HEV 100, which may also be a battery electric vehicle (BEV), a plug-in hybrid electric vehicle (PHEV), and combinations and modifications thereof, collectively referred to herein as "HEV." The physical placement and orientation of the components within the vehicle 100 may vary. The vehicle 100 includes a drivetrain 105 having a powertrain system 110 that includes a combustion engine (CE) 115 and an electric motor or electric motor / generator / starter (EM) 120 that generate power and torque to propel the vehicle 100. The engine or CE 115 is a combustion engine powered by gasoline, diesel, biofuel, natural gas, or an alternative fuel that generates output torque and, in addition, generates other forms of electricity, cooling, heating, vacuum, pressure, and hydraulic power through a front engine attachment device (FEAD) described elsewhere herein. The CE 115 is coupled to the electric motor or EM 120 by a disconnect clutch 125. The CE 115 generates this power and associated engine output torque for transmission to the EM 120 when the disconnect clutch 125 is at least partially engaged.

[0027] The EM 120 can be any of a variety of types of electric machines, and can be, for example, a permanent magnet synchronous motor, a generator, and an engine starter 120. For example, when the disconnect clutch 125 is at least partially engaged, power and torque can be transferred from the engine 115 to the EM 120 to enable operation as a generator and operation of other components of the vehicle 100. Similarly, in a vehicle that includes or does not include a separate engine starter 135, the EM 120 can operate as a starter for the engine 115 with the disconnect clutch 125 partially or fully engaged to transfer power and torque to the engine 115 via the disconnect clutch drive shaft 130 to start the engine 115.

[0028] Additionally, the EM or electric motor 120 can assist the engine 115 in a "hybrid electric mode" or "electric assist mode" by delivering additional power and torque to rotate the drive shafts 130 and 140. Furthermore, the EM 120 can operate in a pure electric mode, wherein the engine 115 is decoupled by the disconnect clutch 125 and can be shut down, thereby enabling the EM 120 to deliver positive or negative (reverse) mechanical torque to the EM drive shaft 140 in both forward and reverse directions. While in generator mode, the EM 120 can also be commanded to generate negative electric torque (when driven by the CE 115 or other driveline components) and thereby generate electricity to charge the battery and power the vehicle electrical system while the CE 115 is generating propulsion power for the vehicle 100. As described in greater detail below, the EM 120 can also achieve regenerative braking while in generator mode by converting rotational kinetic energy from the driveline 110 and / or wheels 154 during deceleration into negative electric torque and regenerated electrical energy for storage in one or more batteries 175, 180.

[0029] The disconnect clutch 125 can be disengaged to enable the engine 115 to be stopped or operated independently to power engine accessories, while the EM 120 generates drive power and torque to propel the vehicle 100 via the EM drive shaft 140, the torque converter drive shaft 145 and the transmission output drive shaft 150. In other arrangements, the engine 115 and the EM 120 can both be operated with the disconnect clutch 125 fully or partially engaged to cooperatively propel the vehicle 100 via the drive shafts 130, 140, 150, the differential 152 and the wheels 154. Each component or any such component can also be partially and / or fully combined in a comparable transaxle configuration (not shown). The driveline 105 can be further modified using selectable and / or controllable differential torque capabilities to achieve regenerative braking to one or any or all of the wheels 154. Although Figure 1 Two wheels 154 are schematically illustrated, but the present disclosure contemplates that the drivetrain 105 will include additional wheels 154 .

[0030] Figure 1 The schematic diagram also contemplates alternative configurations having more than one engine 115 and / or EM 120 that may be offset from the drive shafts 130, 140 and in which one or more of the engine 115 and EM 120 are positioned in series and / or in parallel at other locations in the driveline 105, such as between or as part of a torque converter and transmission, and / or a transaxle off-axis relative to the drive shaft, and / or at other locations and in other arrangements. Other variations are contemplated without departing from the scope of this disclosure. The driveline 105 and drivetrain 110 also include a transmission that includes a torque converter (TC) 155 that couples the engine 115 and EM 120 of the driveline 110 together and / or to the transmission 160. The TC 155 may further incorporate a bypass clutch and clutch safety lock 157 , which may also operate as a launch clutch, to enable further control and regulation of power and torque transmitted from the powertrain 110 to other components of the vehicle 100 .

[0031] The powertrain 110 and / or drive train 105 also includes one or more batteries 175, 180. One or more of these batteries may be one or more high-voltage DC batteries 175 operating in a range of about 48 to 600 volts, and sometimes in a range of about 140 to 300 volts or more or less, used to store and supply power to the EM 120 and to capture and store energy during regenerative braking, and to power and store energy from other vehicle components and accessories. The other battery may be a low-voltage DC battery 180 operating in a range of about 6 to 24 volts or more or less, used to store and supply power to the starter 135 for starting the engine 115, and to other vehicle components and accessories.

[0032] like Figure 1 As shown, batteries 175, 180 are coupled to the engine 115, EM 120, and vehicle 100, respectively, through various mechanical and electrical interfaces and vehicle controllers as described elsewhere herein. The high-voltage EM battery 175 is also coupled to the EM 120 through one or more of a motor control module (MCM), a battery control module (BCM), and / or power electronics 185 configured to convert and condition direct current (DC) power provided by the high-voltage (HV) battery 175 to the EM 120.

[0033] The MCM / BCM / power electronics 185 are also configured to condition, transform, and convert the DC battery power into three-phase alternating current (AC), as typically required to drive the motor or EM 120. The MCM / BCM 185 / power electronics are also configured to charge one or more batteries 175, 180 with energy generated by the EM 120 and / or front-end accessory drive components, and receive power from, store, and supply power to other vehicle components as needed. Such controllers, including, for example, those incorporating power electronics 185, are configured to: monitor battery sensors to detect voltage, current, state of charge (SoC); charge the battery; adjust and control the charge rate and charge time of the battery; monitor and estimate charge time; monitor recharge; and discharge power from the battery and deliver power from the battery, among other capabilities.

[0034] Continue to refer Figure 1 In addition to the MCM / BCM / power electronics 185, the vehicle 100 also includes one or more controllers and computing modules and systems that implement various vehicle capabilities. For example, the vehicle 100 may incorporate a vehicle system controller (VSC) 200 and a vehicle computing system (VCS) and controller 205, which communicate with the MCM / BCM 185, other controllers, and vehicle networks such as a controller area network (CAN) 210, as well as larger vehicle control systems including other microprocessor-based controllers as described elsewhere herein and other vehicle networks. In addition to communication links between controllers, sensors, actuators, and vehicle systems and components, the CAN 210 may also include a network controller.

[0035] This type of CAN 210 is known to those skilled in the art and is described in more detail by various industry standards, including, for example, Society of Automotive Engineers International™ (SAE) J1939, entitled “Serial Control and Communications Heavy Duty Vehicle Network” and available from standards.sae.org, and automotive information standards available from the International Organization for Standardization (ISO) 11898, entitled “Road vehicles—Controller area network (CAN)”, and ISO 11519, entitled “Road vehicles—Low-speed serial data communication”, available from www.iso.org / ics / 43.040.15 / x / .

[0036] Follow again Figure 1 The VCS 205 included in the vehicle 100 may also be the SYNC onboard vehicle computing system manufactured by Ford Motor Company (see, for example, SmartDeviceLink.com, www.ford.com, U.S. Patent Nos. 9,080,668, 9,042,824, 9,092,309, 9,141,583, 9,141,583, 9,680,934, and other patents). The vehicle 100 may also include a powertrain control unit / module (PCU / PCM) 215, which is coupled to the VSC 200 or another controller, and is coupled to the CAN 210 and the engine 115, the EM 120, and the TC 155 to control each powertrain component. A transmission control unit may also be coupled to the VSC 200 and other controllers via the CAN 210, and is coupled to the transmission 160 and optionally to the TC 155 to implement operational control.

[0037] The VCS 205 may include, be configured with and / or cooperate with one or more communication, navigation and other systems, units, controllers and / or sensors, such as a vehicle-to-vehicle communication system (V2V) 201, and a road and cloud-based network infrastructure-to-vehicle communication system (I2V) 202, a LIDAR / SONAR (light and / or sound detection and ranging) and / or camera road proximity imaging and obstacle sensor system 203, a GPS or global positioning system 204, and a navigation and moving map display and sensor system 206.

[0038] Such communication systems, units, controllers may be configured with, as part of, and be part of other communication units and enable bidirectional communication via wired and wireless communications, which may include cellular, wireless Ethernet, and access points, such as WiFi™ wireless capabilities, near field communications such as Bluetooth™, and many others. The VCS 205 may cooperate with the VSC 200 and other controllers in parallel, serial, and distributed fashion to manage and control the HEV 100 and such other controllers and / or actuators in response to sensor and communication signals, data, parameters, and other information identified by, established by, communicated to, and received from these vehicle systems, controllers, and components, as well as other systems external to and / or remote from the HEV 100.

[0039] Although shown here as discrete for purposes of example, the individual controllers, MCM / BCM 185, VSC 200, and VCS 205 can control, be controlled by, communicate signals to, and from, and exchange data with other controllers and other sensors, actuators, signals, and components that are part of larger vehicle and control systems, external control systems, and internal and external networks. The capabilities and configurations described in conjunction with any particular microprocessor-based controller as contemplated herein may also be embodied in one or more other controllers and distributed across more than one controller, such that multiple controllers can enable any such capabilities and configurations individually, cooperatively, in combination, and collaboratively. Thus, references to "a controller" or "controllers" are intended to refer to such controllers in both the singular and plural senses and individually, collectively, and in various suitable collaborative and distributed combinations.

[0040] Additionally, communications over the network and CAN 210 are intended to include responding to, sharing, transmitting, and receiving commands, signals, data, embedded data in signals, control logic, and information between the controller and sensors, actuators, controls, and vehicle systems and components. The controller communicates with one or more controller-based input / output (I / O) interfaces, which can be implemented as a single integrated interface that enables communication of raw data and signals, and / or signal conditioning, processing, and / or conversion, short-circuit protection, circuit isolation, and the like. Alternatively, one or more dedicated hardware or firmware devices, controllers, and systems on a chip can be used to pre-condition and pre-process specific signals during communication and before and after they are communicated.

[0041] In the following description, the MCM / BCM 185, VSC 200, VCS 205, CAN 210, and other controllers may include one or more microprocessors or central processing units (CPUs) that communicate with various types of computer-readable storage devices or media. Computer-readable storage devices or media may include volatile and non-volatile storage in the form of read-only memory (ROM), random access memory (RAM), and non-volatile or keep-alive memory (NVRAM or KAM). NVRAM or KAM is persistent or non-volatile memory that can be used to store various commands, executable control logic and instructions, as well as code, data, constants, parameters, and variables required to operate the vehicle and systems when the vehicle and systems, as well as the controllers and CPU, are not powered on or powered off. Computer-readable storage devices or media may be implemented using any of a number of known memory devices, such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combination memory device capable of storing and communicating data.

[0042] Follow again Figure 1 The HEV 100 may also include a powertrain control unit / module (PCU / PCM) 215, which is coupled to the VSC 200 or another controller and is coupled to the CAN 210 and the engine 115, the EM 120, and the TC 155 to control each powertrain component. A transmission control unit (TCU) 220 is also coupled to the VSC 200 and other controllers via the CAN 210, and is coupled to the transmission 160 and optionally to the TC 155 to implement operational control. An engine control module (ECM) or unit (ECU) or energy management system (EMS) 225 may also be included, which has a corresponding integrated controller and communicates with the CAN 210, and cooperates with the PCU 215 and TCU 220 and other controllers to couple to the engine 115 and the VSC 200.

[0043] In this arrangement, the VSC 200 and VCS 205 cooperatively manage and control vehicle components and other controllers, sensors, and actuators, including, for example, but not limited to, the PCU 215, the TCU 220, the MCM / BCM 185, and / or the ECU / EMS 225, as well as various other components. For example, the controller can communicate control commands, logic, and instructions and codes, data, information, and signals to and from the engine 115, the disconnect clutch 125, the EM 120, the TC 155, the transmission 160, the battery 175, 180, and the MCM / BCM / power electronics 185. Although not shown in the figures, the controller can also control and communicate with other vehicle components known to those skilled in the art. Figure 1 The embodiment of the vehicle 100 in FIG. 1 also illustrates exemplary sensors and actuators in communication with the vehicle network and CAN 210 that can transmit and receive signals to and from the VSC 200, VCS 205, and other controllers. These control commands, logic, and instructions, as well as code, data, information, signals, settings, and parameters (including driver favorite settings and preferences) can be captured and stored in and communicated from a repository of driver controls and profiles 230.

[0044] In further examples, various other vehicle functions, actuators, and components may be controlled by and in cooperation with controllers within the HEV 100 systems and components, and may receive signals from other controllers, sensors, and actuators, which may include, for purposes of illustration and not limitation, front end accessory drive (FEAD) components and various sensors for battery charging or discharging, including sensors for detecting and / or determining maximum charge, state of charge or state of charge (SoC), voltage and current, battery chemistry and lifecycle parameters, and discharge power limits, external ambient air temperature (TMP), pressure, humidity, and component temperature, voltage, current, and battery discharge power and rate limits, among other components. Such sensors are configured to communicate with the controller and CAN 210, and, for further example, may establish or indicate ignition switch position (IGN), external ambient temperature and pressure, engine and thermal management system sensors, charging receptacle sensors, and external power supply voltage, current, and related data communication sensors, among others.

[0045] The HEV 100 also includes at least one external power outlet and sensor 235 that couples to various controllers including, for example, the BCM / MCM / power electronics 185 and the HV battery 175. When the HEV 100 is stationary and adjacent to an external power source (XPS) ( Figure 1), such as when parked at a residence, office, or other electrical charging station or location, utilizing a receptacle 235, which is also referred to by those skilled in the art as electric vehicle supply equipment (EVSE). These controllers are configured to detect the presence of an XPS when connected to the receptacle 235 and initiate a charge / recharge cycle or event of the HV battery 175, battery 180, and enable power to be supplied to the HEV 100 for various purposes.

[0046] Such a controller can also enable bidirectional communication between the HEV 100 and an external XPS / EVSE to establish power capacity, power cost, power usage authorization, compatibility, and other parameters and information related to and from the external XPS. Such communication between the HEV 100 and the external XPS can enable automatic charging, power purchase over a period of time, and communication between the external XPS and the VSC 200 and VCS 205, as well as communication with remote systems external to the HEV 100 and its various controllers. This configuration can further enable occupants of the HEV 100 to interact via a display in the HEV 100 to communicate power purchase authorization. In addition, the HEV 100 can autonomously interact with the external XPS and one or more of the VSC 200 and VCS 205 to communicate information to enable automatic charging of the HEV 100 and charging time estimates, as well as communication of various vehicle and system data and parameters to such external systems.

[0047] To enable charging of the HV battery 175 and / or other batteries, one or more of the controllers, such as those included in the BCM / MCM / power electronics 185, are configured to detect that an external XPS is connected to the receptacle 235 and generate and communicate an external power signal or DC charge signal (DS), which may include the previously described information indicating connection to the XPS, power available from the XPS, cost of such power, compatibility data, and authorization and authentication data for use, as well as related information. In response, the power electronics 185 and / or other controller initiates charging at the charge rate of the battery 175, 180, or other battery.

[0048] Typically, a charge rate and charge time are initially estimated and determined when the HEV 100 is initially connected to and / or plugged into the XPS / EVSE. Additionally, a charge time is also initially determined during operation when the HEV 100 detects an anticipated need for battery recharging and generates a charging signal CHS 190. Such initial estimates and determinations vary depending on EVSE and grid capabilities, costs, and availability, as well as various parameters of the HEV 100, including, for example, the current state of charge (SoC) of the corresponding battery 175. During charging operations and during normal use, both the initially estimated and determined charge rate and charge time can be automatically varied by the controller due to potential lifecycle and performance variations in charge capacity and power delivery capabilities, which can be detected by the controller in the battery 175 and power electronics 185 and remotely estimated by the RFS for a global fleet of HEVs 100, as well as for individual HEVs 100.

[0049] If included Figure 1 and Figure 2 As described and illustrated in the various figures of the present invention, signals and data including, for example, an external power signal DS and associated control logic and executable instructions, as well as other signals and data, may also include other signals (OS) 240, and control or command signals (CS) 245 received from, sent to, and between the controller and vehicle components and systems. The external power signal DS, OS 240, and CS 245, as well as other signals, associated control logic and executable instructions, parameters, and data, may and / or may be predicted, generated, established, received, communicated to, and communicated between any of the following: vehicle controllers, sensors, actuators, components, and internal, external, and remote systems. Any and / or all of these signals may be raw analog or digital signals and data, or pre-conditioned, pre-processed, combined, and / or derived data and signals generated in response to other signals, and may be encoded, embedded, represented, and represented as voltages, currents, capacitances, inductances, impedances, and digital data representations thereof, as well as digital information that encodes, embeds, and / or otherwise represents such signals, data, and analog, digital, and multimedia information.

[0050] The communication and manipulation of the described signals, commands, control instructions and logic, as well as data and information, by the various contemplated controllers, sensors, actuators and other vehicle components may be schematically illustrated as follows: Figure 1 and 2 As shown in Figure 2The flowcharts or similar diagrams exemplified in the methods of the present disclosure as specifically shown in the accompanying drawings are represented. Such flowcharts and diagrams illustrate exemplary command and control processes, control logic and instructions, and operating strategies that can be implemented using one or more computing, communication, and processing technologies that can include real-time, event-driven, interrupt-driven, multi-tasking, multi-threading, and combinations thereof. The steps and functions shown can be executed, communicated, and performed in the order shown, in parallel, repeatedly, and in a modified order, and in some cases can be combined with other processes and / or omitted. The commands, control logic, and instructions can be executed in one or more of the described microprocessor-based controllers, in external controllers, and in systems, and can be embodied primarily in hardware, software, virtualized hardware, firmware, virtualized hardware / software / firmware, and combinations thereof.

[0051] Continued references include Figure 1 , the present disclosure contemplates an HEV 100 that includes at least one and / or one or more of a controller coupled to batteries 175, 180, which controller may be any of a VSC 200, a VCS 205, a PCU 215, a TCU 220, an MCM / BCM 185, and / or an ECU / EMS 225; and one or more communication units, such as a VSC 200, a V2V 201, an I2V 202, and / or a VCS 205, wherein any of the controllers is further configured to generate and communicate a charging signal (CHS) 190 that identifies a need for recharging of the battery 175 of the HEV 100. In response to the CHS 190 , the controller adjusts the charging waypoints and time (CWT) 250 and the driving route (TR) 255 and its waypoints based on a charging time estimate (CTE) 260 and an estimated optimal charging route (OCR) 265 , both received from a remote, off-board global fleet server (RFS) external to and remote from the HEV 100 .

[0052] Such communication between the controller of the HEV 100 and the RFS is and / or may be accomplished via the EVSE / XPS and through other communication pathways, connections, and / or capabilities, including, for example, but not limited to, contemplated cellular, WiFi, Bluetooth, near-field, and / or mobile or nomadic communication devices that may be located or situated in the passenger compartment of the HEV 100 and connected to a controller of a SYNC system, such as described elsewhere herein, that may communicate and display the adjusted CWT 250 and / or TR 255 to the driver in cooperation with the VCS 205 or another controller or communication unit that enables display of the adjusted CWT 250 and / or TR 255 within the cabin of the HEV 100, and / or via a mobile or nomadic device, or other mobile communication device.

[0053] The CWT 250 determines which charging station and / or charging waypoint is recommended and how much time remains to complete the required charging / recharging event and is initially calculated by the controller of the HEV 100 based on operating conditions (OC) 270 and other data and information. The OC 270 includes, for example, one or more of the following: TR 255, charging station (XPS, EVSE) data, environmental data and location data, vehicle performance data (VPD) 294 such as current power and cooling requirements, and battery performance data and parameters, as well as instantaneous vehicle operating parameters and data.

[0054] Such parameters and data are generated, stored, and communicated by the respective vehicle controllers as described elsewhere herein, and may also be stored locally in the HEV 100 in the profile repository 230. The initially determined CWT 250 and subsequently adjusted CWT 250 may be time-stamped and stored in the VPD 294 for each charge / recharge event, along with other mentioned data and parameters (e.g., voltage, current, charge rate, temperature, etc.) that may be utilized to generate the CWT 250, and as adjustments to the VPD 294, charging station, and battery performance data.

[0055] In another example, the VPD 294 may also include various battery charge / recharge profiles that may establish voltage, current, charge and discharge rates, minimum and maximum limits for SoC under various operating conditions and environmental circumstances, and battery state of health, any or all of which may change over the lifecycle / accumulation of recharging events. Furthermore, the VPD 294 may include such established rates for various types of residential, public, and / or commercial charging stations (XPS, EVSE), and may also include authentication, authorization, and / or payment data required to implement automated, unattended, and / or manually initiated / operated charging events.

[0056] In another example, the VPD 294 may also include the HEV 100 power demands for cooling various components during such anticipated charging / recharging events, as well as other HEV 100 power demands that may be issued by the driver (e.g., cabin cooling, media system usage, interior lighting, etc.). The VPD 294 and other data, information, settings, and parameters may be stored in and communicated from the driver control and profile repository 230.

[0057] In response to the current location and a predicted destination that can be derived from the previous trips and current trajectory of the HEV 100, the TR 255 is initially generated by the vehicle user and / or automatically generated by the controller of the HEV 100 to include various driving route waypoints, including charging station waypoints. In either case, the TR 255 includes a starting waypoint and a destination waypoint, and can include various navigation and charging station waypoints in between. The TR 255 can be configured automatically or by the vehicle user using the VCS 205 (e.g., Ford's SYNC system), the navigation and moving map system 206, and / or other vehicle controllers, and further implements various related capabilities, including, in variations, manually selected, automatically selected, and / or automatically recommended and / or suggested waypoints, including charging stations and / or high-power, high-efficiency charging stations; such waypoints; and other types of intermediate driving route waypoints.

[0058] The CTE 260 is received from the RFS in response to the OC 270 generated by the vehicle controller and communicated to the RFS. For each charging / recharging event, the CTE 260 and OCR 265 may also be time-stamped and stored in the VPD 294. The CTE 260 identifies an estimate of how much time remains to complete the required charging / recharging event, which is established by the RFS individually for the HEV 100, but varies based on accumulated and aggregated data and parameters received by the cloud-based RFS from a global fleet of similar and / or identical HEVs 100.

[0059] In response to the OC 270, an OCR 265 is also received from the RFS, and the OCR 265 includes recommended updates to the TR 255, which may include recently identified high-power and high-efficiency charging stations proximate to the current location of the HEV 100 and / or proximate to the trajectory and waypoints established by the current TR 255. The RFS may utilize the OC 270 received from each individual HEV 100 to identify improvements to the TR 255 through updated CWT 250 and CTE 260, which may take into account weather and road conditions along the TR 255 that would otherwise be unknown to the HEV 100, as well as EVSE availability, efficiency, and / or current conditions reported by other HEVs 100 that may have previously been located along the current TR 255 of the HEV 100 reporting the OC 270.

[0060] The RFS includes a remote big data analytics engine and computing resources that can leverage neural networks, artificial intelligence, and other analytical techniques to identify otherwise indiscernible patterns in energy usage based on the environmental factors of all HEVs in the global fleet, as well as the environmental factors of the HEV 100 as reported by OC 270. Furthermore, the RFS is configured to observe, digest, and predict the utilization, congestion, and / or availability of high-power charging stations along the TR 255 based on data received from other vehicles. Furthermore, the RFS is able to identify and predict the energy cost at each charging station and / or EVSE at waypoints and trajectories along the TR 255. With each of these and other capabilities, the RFS is equipped with more powerful computing resources to more accurately generate, estimate, and predict each CWT 250, CTE 260, and OCR 265 communicated to the HEV 100, in response to received OC 270, and on demand and in real time.

[0061] The utilization of the off-board RFS generated, estimated, and / or predicted CWT 250, CTE 260, and OCR 265 improves the accuracy of the CWT 250 and TR 255 by adjusting the CWT 250 with updated charging station waypoints and charging times. This improvement would not otherwise be available given the limited processing power, data storage, and computing resources available onboard most HEVs 100 and the controllers contemplated therein. Additionally, the utilization of the RFS generated, predicted, and estimated CWT 250, CTE 260, and OCR 265 without extensive on-board processing reduces the drain on the HEV 100's limited computing power and resources required to implement the various inherent systems and subsystems thereof by enabling the adjustment of the CWT 250 and / or TR 255 through the RFS's significantly greater resources and aggregated data from the HEV's global fleet.

[0062] During a charging event, and in response to at least one and / or one or more of a charge completion signal (CCS) 275 and / or a drive route completion signal (TRC) 277, the vehicle controller generates an estimated error (EE) 280. The CCS 275 is generated by the vehicle controller after completion of the current charging event for the HEV 100. The TRC 277 is generated by the vehicle controller after completion of the TR 255. The estimated error EE 280 is generated by the vehicle controller to include various data elements, including the difference between the CTE 260 and the actual charge time (ACT) 285, and the drive route performance (TRP) 290.

[0063] More specifically, once the SoC of the batteries 175, 180 reaches a predetermined maximum charge limit, a CCS 275 is generated by the controller and can be established by any desired data in conjunction with data stored in the TR 255 and / or other controllers. In response to the CCS 275 and EE 280, other data, parameters, settings, and information may also be updated and modified by updates received from the RFS, which may also be established based on the accumulated and aggregated global fleet data received by the RFS and analyzed to identify previously unknown battery charge / recharge event lifecycle performance patterns. Similar to other signals, settings, parameters, data, and information generated by the controller, each of the EE 280 and / or ACT 285, etc., can be time-stamped and, in addition to being communicated to the RFS and other systems and controllers, recorded and / or stored in the VPD 294 and stored in the repository 230.

[0064] The TRP 290 is generated by the vehicle controller and includes one or more vehicle performance data that identifies the time, energy consumed, and / or efficiency data for vehicle travel along the trajectory established by the TR 255, as adjusted by the OCR 265, based on various VPDs 294 and / or vehicle preferences and profiles stored in the repository 230. For example, if the TR 255 is established to minimize travel between an origin and a destination, and the TR 255 is periodically adjusted by the vehicle controller using the OCR 265, the varied TRP 290 will include the time difference between the original or initial predicted or estimated time to complete the TR 255 and / or the adjusted TR 255 time and / or the OCR 265 estimated time to complete the TR 255 trip.

[0065] In another variation, if the TR 255 and associated waypoints are initially established to minimize the energy consumed by the HEV 100 between the starting waypoint and the destination waypoint, the TRP 290 is modified to generate a difference between the predicted energy consumption of the original TR 255, and / or the adjusted TR 255 energy consumption and / or the OCR 265 energy consumption. In either exemplary variation, whether minimizing time or energy consumption is intended, the corresponding differences are embedded in the EE 280 so that the RFS can receive and analyze such error differences and continue to improve the accuracy of the expected charging waypoint CWT 250, CTE 260, and OCR 265.

[0066] In variations of the present disclosure, the controller will also readjust the CWT 250 and TR 255 as needed and / or at periodic intervals, which may be discrete time intervals, time intervals established by changing HEV 100 conditions or parameters, and / or discrete or variable distance intervals that vary in response to changes in certain vehicle parameters or conditions. In variations of the present disclosure, such changing vehicle parameters or conditions include, for example, but are not limited to: updated estimated charging station waypoints CWT 250, CTE 260, and / or OCR 265 received from the RFS; changes in vehicle performance and energy consumption, such as additional cooling for the batteries 175, 180, and / or BCM 185; power demands issued by the driver for cabin cooling, lighting, media system (SYNC) utilization, and other power demands; and / or new waypoints identified by the driver, etc.

[0067] Any such varying or discrete time and / or distance intervals may be predetermined and / or automatically established by the controller in response to HEV 100 performance characteristics, and a plurality of such intervals may be established and utilized to readjust various parameters, settings, CWT 250, and / or TR 255. Such intervals may be, for example, but not limited to, on the order of milliseconds, seconds, and larger units of time, as well as kilometers, miles, and / or fractions or multiples thereof as may be suitable for use with the systems and components of the HEV 100.

[0068] In other arrangements, updated CWT 250 and CTE 260 are received from the RFS by the communication units VSC 200, V2V 201, I2V 202, and / or VCS 205. Such updated CWT 250 and CTE 260 may be pushed from the RFS during a charging event and / or during travel prior to charging, and may also be received from the HEV 100 as needed, and / or in real time and / or periodically at the discrete time and distance intervals mentioned and / or as the OC 270 changes. Such updated CWT 250 and CTE 260 enable the HEV 100 to update the waypoints of the TR 255, which may reduce the overall travel time of the TR 255 and / or reduce the overall energy consumption on the TR 255 if energy consumption reduction compared to travel time is desired.

[0069] As with other adjustments described herein, a new and / or real-time OC 270 is generated by the controller and communicated to the server via the vehicle communication unit. Additionally, in some variations, the original, initial, new, and / or updated OC 270 also includes, embeds, encodes, and / or incorporates an EE 280 and an ACT 285, and the EE 280 and ACT 285 are communicated to the RFS, which enables the RFS to further improve the accuracy of the CWT 250 and CTE 260, among other capabilities. As the RFS continues to improve the accuracy of such CWT 250 and CTE 260, the vehicle controller adjusts and readjusts the CWT 250 so that the ACT 285 is optimized, thereby optimizing and / or minimizing the discrepancies identified in the EE 280, or even reducing them to an ideal zero discrepancy condition in some cases.

[0070] In another variation, updated OCRs 265 are received by the vehicle controller from the RFS during travel along the TR 255. Such OCRs 265 may be received by the controller of the HEV 100 as needed, and / or may be pushed from the RFS to the HEV 100, in response to the OC 270 being communicated to the RFS. The OCRs 265 include updated suggestions for adjustments to the TR 255, which may include new, updated, or changed waypoints, and the OCRs 265 include, in modifications, suggested changes to the waypoints of the TR 255, including previously unidentified high-power and / or high-efficiency charging stations as waypoints along the TR 255, which were identified by the RFS to meet time or energy minimization requirements that may have been established for the original or initial TR 255.

[0071] Additionally, such OCRs 265 may be updated by the RFS to change or recommend new such charging stations and associated waypoints due to emergency situations, charging station congestion or availability, environmental conditions, energy costs, and other data identified by the RFS from continuously updated data received by the RFS from other HEVs in the global fleet. As the RFS continuously improves the accuracy of the OCRs 265 generated in response to the aggregated OCs 270 received from the global HEV fleet, the generated OCRs 265 result in an adjusted TR 255 for the HEV 100, which optimizes the TRP 290, such that the EE 280 is optimized and / or minimized.

[0072] Additionally, in other variations of the present disclosure, the original, initial, new and / or updated OC 270 may include ACT 285, TRP 290, battery performance data and parameters (BPD), charging station data (CSD) 292, vehicle performance data (VPD) 294, environmental data (END) 296 and / or location data (LOD) 298, which may also be stored in, received from, and / or communicated by, and / or communicated between other controllers, and remotely communicated to and communicated by the RFS. The ACT 285, TRP 290, BPD may also include any of a variety of instantaneous and / or historical vehicle performance information from other sensors and controllers, such as for example purposes but not limitation: battery pack capacity and chemistry, energy consumption rate and total amount, environmental conditions of the HEV 100, battery state of health and state of charge (SoH, SoC), battery temperature, low voltage battery status, charge and discharge voltages and currents, and charge and discharge rates, and / or EVSE performance data and other data.

[0073] Current and / or historical charging station data or CSD 292 about the charging station captured and / or received from the XPS / EVSE by the power outlets and sensors 235 may also include charging station power and performance availability, charging station power cost and capacity data, and charging station performance data, among other data. Additional modifications of the present disclosure include current and / or historical vehicle data or VPD 294, including vehicle identification number (VIN), power and cooling requirements, vehicle power availability and requirements, cabin climate control profile, entertainment and infotainment system power consumption, on-board diagnostic (OBD) codes and data, vehicle power and climate control profile, and other data. Utilization of the VIN enables the RFS to aggregate the OC 270 by manufacturer, vehicle make and model, and associated vehicle identifiers, allowing the RFS to further enhance its analytical capabilities relative to HEVs in the corresponding global fleet.

[0074] In other adjustments, current and / or historical environmental data or END 296 from controllers, which may include VSC 200, VCS 205, may include ambient temperature, humidity, and barometric pressure, among other information, along TR 255 and at LOD 298. Current and / or historical geographic location data or LOD 298 may be obtained from GPS 204 and navigation system 206 and included in the data from other controllers along with TR 255.

[0075] The present disclosure contemplates additional modifications of the controller that are configured to readjust the CWT 250 and TR 255 based on an initial estimated and determined charging time and recharging profile and an initial TR 255. Such initial CWT 250 may be and / or is a function of current or expected EVSE and / or charging station capabilities and the current condition of the HEV 100, and may also be and / or received from the RFS in response to a new OC 270 generated by the controller and / or communication unit and communicated to the RFS. When such an update is determined to be desirable, such as when the RFS's analytics engine discovers new patterns and performance capabilities of fleet-wide battery performance and TR 255 from continuously accumulated and aggregated data from each of the global fleet of HEVs 100, the initial estimated and determined CWT 250 and TR 255 may be pushed from the RFS to replace the stored initial, prior, and / or previous CWT 250 and TR 255 for all HEVs 100 in the global fleet.

[0076] Continue to refer Figure 1 , and now also refer to Figure 2, the operating methods of the present disclosure include methods of controlling the HEV 100. In view of the components, controllers, systems, and capabilities that have been described, such methods are contemplated to be implemented by a controller generally designated herein as controller 300 and, for purposes of illustration and not limitation, may include: at least one and / or one or more of the controllers VSC 200, VCS 205, PCU 215, TCU 220, MCM / BCM 185, and / or ECU / EMS 225, and communication units VSC 200, V2V 201, I2V 202, and / or VCS 205.

[0077] The method begins at step 305 and includes detecting the CHS 190 at step 310 and detecting vehicle data changes and / or elapsed time and / or distance intervals at step 315, which results in the controller 300 generating the OC 270 and communicating it to the RFS at step 320. At step 325, in response to the detected CHS 190 and intervals, the controller 300 adjusts the CWT 250 and TR 255 based on the CWT 250, CTE 260, and OCR 265 received from the RFS at step 330. The adjusted CWT 250 and / or TR 255 may be communicated and reported to the driver in cooperation with the VCS 205 or another controller or communication unit that enables display of the adjusted CWT 250.

[0078] As with various other arrangements of the present disclosure, OC 270 is generated in response to vehicle data changes and / or periodically and / or at discrete time and / or distance intervals and communicated to the RFS. OC 270 includes at least one and / or one or more of the following: TRP 290, BPD, CSD 292, VPD 294, END 296, and / or LOD 298, as well as other data and parameters. The present disclosure also contemplates a method comprising: adjusting the CWT 250 and / or charging batteries 175, 180 of the HEV 100; and / or adjusting the TR 255 in response to the CCS 275 and TRC 277, wherein the controller 300 detects at step 335 whether charging and trip are complete, and if not, repeats the method loop and returns control to step 305. If charging and / or traveling is complete, then at step 340, ACT 285, TRP 290, and EE 280 are generated and stored to include TRP 290 and the difference between CWT 250 and / or CTE 260 and / or ACT 285. Additionally, such OC 270 is generated and communicated to the RFS at step 345, and includes at least one and / or one or more of the following: EE 280, ACT 285, TRP 290, BPD, CSD 292, VPD 294, END 296, and / or LOD 298.

[0079] At step 350, the method ends the loop and returns control to start step 305 to continue monitoring and processing. Although exemplary embodiments have been described above, it is not intended that these embodiments describe all possible forms of the present invention. Rather, the words used in this specification are descriptive rather than restrictive terms, and it should be understood that various changes can be made without departing from the spirit and scope of the present invention. In addition, the features of the various embodiments of implementation can be combined to form other embodiments of the present invention.

[0080] According to the present invention, a vehicle is provided having a controller coupled to a communication unit and, responsive to a charging signal, configured to adjust a driving route to include charging waypoints based on estimated optimal charging routes and waypoint charging times received from a remote fleet server in response to operating conditions communicated to the server, and such that actual charging time is reduced and the driving route and driving route performance are optimized.

[0081] According to one embodiment, the controller is further configured to generate and store as one of the vehicle performance data an estimated error in response to one or more of the charging and driving route completion signals, the estimated error including driving route performance and a difference between an actual charging time and an estimated waypoint charging time generated by a remote fleet server.

[0082] According to one embodiment, the controller is further configured to readjust at least one of the driving route and the charging waypoints at one or more of discrete time and distance intervals based on updated estimated optimal charging routes and waypoint charging times received by the communication unit from the remote fleet server and in response to new real-time operating conditions generated by the communication unit and communicated to the remote fleet server and including estimation errors.

[0083] According to one embodiment, the controller is further configured to readjust at least one of the driving route and the charging waypoints at one or more of discrete time and distance intervals based on updated estimated optimal charging routes and waypoint charging times received by the communication unit from the remote fleet server in response to the charging signal and in response to new real-time operating conditions generated and communicated to the remote fleet server.

[0084] According to one embodiment, the controller is further configured to readjust at least one of the driving route and the charging waypoints based on updated estimated optimal charging routes and waypoint charging times received by the communication unit from the remote fleet server, and in response to new real-time operating conditions generated and communicated to the remote fleet server and including vehicle data including a vehicle identification number.

[0085] According to one embodiment, the controller is further configured to generate operating conditions to include: vehicle environment and location data, wherein the vehicle environment and location data includes geographic location and ambient temperature, humidity, and atmospheric pressure; vehicle data, wherein the vehicle data includes a vehicle identification number and on-board diagnostic codes and data; and vehicle power and battery performance data, wherein the vehicle power and battery performance data includes battery pack capacity and chemistry, battery health and state of charge, battery temperature, charging station power and performance, and low voltage battery status.

[0086] According to one embodiment, the controller is further configured to readjust at least one of the driving route and the charging waypoints based on updated estimated optimal charging routes and waypoint charging times received by the communication unit from the remote fleet server, and in response to new real-time operating conditions generated and communicated to the server and including charging station data including electricity cost and capacity data, and charging station performance data accumulated during the current charging event.

[0087] According to one embodiment, the controller is further configured to readjust at least one of the travel route and the charging waypoints at one or more of discrete time and distance intervals based on updated estimated optimal charging routes and waypoint charging time estimates received by the communication unit from a remote fleet server, and in response to new real-time operating conditions generated and communicated to the server and including charging station data including electricity cost and capacity data, and charging station performance data accumulated during the discrete time intervals.

[0088] According to one embodiment, the controller is further configured to adjust charging waypoints and travel routes based on initially determined waypoint charging times and optimal charging routes generated and received from a remote fleet server and in response to operating conditions generated and communicated to the server, the operating conditions including one or more of: charging stations, environmental and location data, vehicle data, and battery performance data.

[0089] According to one embodiment, the controller is further configured to readjust at least one of the waypoint charging time and the driving route at one or more of discrete time and distance intervals based on initially determined waypoint charging times and optimal charging routes generated and received from a remote fleet server, in response to new operating conditions generated and communicated to the server.

[0090] According to the present invention, a vehicle is provided having a controller coupled to a battery and a communication unit and configured to periodically respond to a charging signal, adjust charging waypoints and a driving route based on estimated optimal charging routes and waypoint charging times received from a remote fleet server and in response to periodic operating conditions generated and communicated to the server, such that charging time is reduced and the driving route is optimized.

[0091] According to one embodiment, the controller is further configured to generate and store an estimated error in response to one or more of the charging and driving route completion signals, the estimated error comprising the optimized driving route performance and the difference between the estimated waypoint charging time and the actual charging time.

[0092] According to one embodiment, the controller is further configured to readjust at least one of the charging waypoints and the driving route based on updated estimated driving route and waypoint charging times received by the communication unit from the remote fleet server and in response to operating conditions and estimated errors communicated by the communication unit to the server.

[0093] According to one embodiment, the controller is further configured to generate operating conditions to include: environmental and location data to include geographic location and ambient temperature, humidity, and barometric pressure; vehicle data to include vehicle identification number and on-board diagnostic codes and data, vehicle power, and climate control profile; and battery performance data to include battery pack capacity and chemistry, battery health and state of charge, battery temperature, and charging station power and performance.

[0094] According to one embodiment, the controller is further configured to readjust at least one of the charging waypoints and the driving route based on updated estimated optimal charging routes and waypoint charging times received by the communication unit from the remote fleet server, and in response to operating conditions generated and communicated to the server, and the operating conditions also include charging station data to include electricity cost and capacity data, and charging station performance data accumulated during the current charging event.

[0095] According to one embodiment, a controller is coupled to the battery and periodically responds to the charging signal to adjust the charging waypoints and the driving route based on estimated optimal charging routes and waypoint charging times received from a remote fleet server and in response to periodic operating conditions generated and communicated to the server, such that the driving route and charging times are optimized.

[0096] According to one embodiment, the present invention is further characterized in that the controller generates and stores an estimated error in response to one or more of the driving route and the charging completion signal, the estimated error including the driving route performance and the difference between the estimated waypoint charging time and the actual charging time.

[0097] According to one embodiment, the present invention is further characterized in that the controller readjusts at least one of the driving route and the charging waypoints based on updated estimated optimal charging routes and waypoint charging times received by the communication unit from the remote fleet server and in response to estimated errors and operating conditions communicated by the communication unit to the server.

[0098] According to one embodiment, the present invention is further characterized in that the controller generates operating conditions to include: environmental and location data to include geographic location and ambient temperature, humidity, and atmospheric pressure; vehicle data to include vehicle identification number and on-board diagnostic codes and data; and vehicle power and battery performance data to include battery pack capacity and chemistry, battery health and state of charge, battery temperature, charging station power and performance, and low voltage battery status.

[0099] According to one embodiment, the present invention is further characterized in that the controller readjusts one or more of the driving route and the charging waypoints based on updated estimated optimal charging routes and waypoint charging times received by the communication unit from the remote fleet server, and in response to operating conditions generated and communicated to the server, and the operating conditions also include charging station data to include power cost and capacity data, and charging station performance data accumulated during the current charging event.

Claims

1. A vehicle comprising: a controller coupled to the communication unit, and configured to: in response to the charging signal, adjusting the travel route to include a charging waypoint in accordance with or in response to: based on an estimated optimal charging route and estimated waypoint charging times received from a remote fleet server, in response to operating conditions communicated to the remote fleet server, and such that an actual charging time is reduced; generating and storing as one of the vehicle performance data an estimated error in response to one or more of the charging and driving route completion signals, the estimated error comprising driving route performance and a difference between the actual charging time and the estimated waypoint charging time generated by the remote fleet server; as well as At least one of the driving route and the charging waypoints is readjusted at one or more of discrete time intervals and distance intervals based on or in response to updated estimated optimal charging routes and estimated waypoint charging times received by the communication unit from the remote fleet server, and in response to new real-time operating conditions generated by the communication unit and communicated to the remote fleet server and including the estimated error.

2. The vehicle according to claim 1, wherein: The controller is further configured to readjust at least one of the driving route and the charging waypoint in response to a charging signal.

3. The vehicle according to claim 1, wherein: The new real-time operating conditions also include vehicle data incorporating a vehicle identification number.

4. The vehicle according to claim 1, wherein: The controller is further configured to generate the operating conditions including: vehicle environment and location data incorporating geographic location as well as ambient temperature, humidity and atmospheric pressure, vehicle data incorporating a vehicle identification number and on-board diagnostic codes and data and vehicle power, and Battery performance data including battery pack capacity and chemistry, battery health and state of charge, battery temperature, charging station power and performance, and low voltage battery status.

5. The vehicle according to claim 1, wherein: The new real-time operating conditions also include charging station data including electricity cost and capacity data, and charging station performance data accumulated during the current charging event.

6. The vehicle according to claim 1, wherein: The new real-time operating conditions also include charging station data including power cost and capacity data, and charging station performance data accumulated during the discrete time intervals.

7. The vehicle according to claim 1, wherein: The operating conditions include one or more of: charging station data, environmental data, location data, vehicle data, and battery performance data.

8. The vehicle of claim 1, wherein: The controller is further configured to, at one or more of discrete time and distance intervals: Re-adjusting at least one of the waypoint charging time and the driving route according to: Based on an initially determined waypoint charging time and an optimal charging route generated by the remote fleet server and received from the remote fleet server in response to new operating conditions generated and communicated to the remote fleet server.

9. A method of controlling a vehicle, comprising: adjusting, by a controller coupled to the battery, periodically in response to the charging signal, the charging waypoints and the driving route in accordance with or in response to: an estimated optimal charging route and estimated waypoint charging times received from a remote fleet server, and in response to periodic operating conditions generated and communicated to the remote fleet server, such that the driving route and charging times are optimized; generating and storing, by the controller, an estimated error in response to one or more of a driving route and a charging completion signal, the estimated error comprising a driving route performance and a difference between the estimated waypoint charging time and an actual charging time; and At least one of the driving route and the charging waypoints is readjusted, by the controller, based on or in response to an updated estimated optimal charging route and estimated waypoint charging times received by the communication unit from the remote fleet server, and in response to the estimated error and the operating condition communicated by the communication unit to the remote fleet server.

10. The method according to claim 9, further comprising: The controller generates the operating conditions including the following: environmental and location data incorporating geographic location as well as ambient temperature, humidity and atmospheric pressure, vehicle data incorporating a vehicle identification number and on-board diagnostic codes and data and vehicle power, and Battery performance data including battery pack capacity and chemistry, battery health and state of charge, battery temperature, charging station power and performance, and low voltage battery status.

11. The method according to claim 9, wherein: The operating conditions also include charging station data including electricity cost and capacity data, and charging station performance data accumulated during a current charging event.

12. A vehicle comprising: a controller coupled to the battery and the communication unit, and configured to: periodically responsive to the charging signal, adjusting the charging waypoints and the driving route based on an estimated optimal charging route and estimated waypoint charging times received from a remote fleet server and in response to periodic operating conditions generated and communicated to the remote fleet server, such that the charging time is reduced and the driving route is optimized; generating and storing an estimated error in response to one or more of the charging and driving route completion signals, the estimated error comprising an optimized driving route performance and a difference between the estimated waypoint charging time and an actual charging time; as well as At least one of the driving route and the charging waypoints is readjusted based on or in response to: based on an updated estimated driving route and estimated waypoint charging times received by the communication unit from a remote fleet server, and in response to the operating conditions and the estimated errors communicated by the communication unit to the remote fleet server.

13. The vehicle of claim 12, wherein: The controller is further configured to generate operating conditions to include: environmental and location data to include geographic location and ambient temperature, humidity, and barometric pressure; vehicle data to include vehicle identification number and onboard diagnostic codes and data, vehicle power, and climate control profile; and battery performance data to include battery pack capacity and chemistry, battery health and state of charge, battery temperature, and charging station power and performance.

14. The vehicle of claim 12, wherein: The operating conditions further include charging station data to include power cost and capacity data, and charging station performance data accumulated during a current charging event.

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