DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-12 are cancelled.
Claims 13-32 are pending.
Claims 21-32 are new.
Response to Amendments
The amendment filed August 11th, 2025 has been entered. Claims 13-32 are pending in the application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 13-32 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claim 13 is objected to because of the following informalities:
In claim 13, "a local renewal energy system" should read "a local renewable energy system".
Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claim 27 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “optimal” in claim 27 is a relative term which renders the claim indefinite. The term “optimal” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. "period of time" is rendered indefinite by the use of "optimal".
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 13-32 are rejected under 35 U.S.C. 103 as being unpatentable over BERGER DE 102022108574 A1 (hereinafter “BERGER”), in view of Lu et al. USPGPUB 2023/0356614 (hereinafter “Lu”).
Regarding claim 1, BERGER teaches a vehicle-to-home power supply and energy storage system ([Description] “The charging plan can be drawn up in such a way that it also achieves the following goals: - particularly battery-friendly charging, especially with low charging currents; - Stopping charging at times when it makes more monetary sense to feed the surplus into the public power grid than to use it to charge an electric vehicle; - the possibility of using the battery(s) of the electric vehicle as (possibly additional) intermediate storage with the possibility of feeding back into the local power grid and/or into the public power grid, if such recovery capability, e.g. B. V2G”, wherein examiner interpreted feeding power back into local power grid using the batteries of electric vehicle as a vehicle-to-home power supply and energy storage system, and see Fig. 1 description), comprising:
a vehicle including a traction battery pack ([Description] “The electric vehicle can be a plug-in hybrid vehicle (also referred to as a “PHEV”) or a fully electric vehicle (also referred to as a “BEV”). Charging the electric vehicle includes charging an electrical energy storage device, hereinafter referred to as a “battery” without limiting generality, in particular at least one drive battery of the electric vehicle”, and [Description] “The fact that an electric vehicle is coupled to the charging point can in particular include that the electric vehicle or its battery(s) can be charged through the charging point, e.g. because a charging cable is connected or the electric vehicle is parked on an inductive charging surface” wherein examiner interpreted battery as a traction battery pack); and
a control module programmed to: (1) prepare a charging/discharging schedule for transferring energy from a local renewal energy system to the traction battery pack ([Abstract] “a charging plan is drawn up for the coupled electric vehicle (BEV) based on the predicted time distribution of the excess power in such a way that the electric vehicle (BEV) has a desired state of charge at the expected departure time and the excess power is used to charge the electric vehicle (BEV), and that Electric vehicle (BEV) is charged according to the charging plan”, wherein examiner interpreted charging plan as a charging/discharging schedule for transferring energy from a local renewal energy system to the traction battery pack); and
(2) schedule a drive route of the vehicle to occur during a time period in which an estimated renewable energy generation of the local renewable energy system is expected to exceed a predefined threshold (([Abstract] “a charging plan is drawn up for the coupled electric vehicle (BEV) based on the predicted time distribution of the excess power in such a way that the electric vehicle (BEV) has a desired state of charge at the expected departure time and the excess power is used to charge the electric vehicle (BEV), and that Electric vehicle (BEV) is charged according to the charging plan”, and [Description] “The departure time can, for example, be specified by a user, estimated by specifying a destination and a destination time together with route planning, or can be estimated from history data”, and [Description] “the charging requirement and its expected departure time can be stored in a data processing device, which is in particular also set up to create the charging plan”, wherein examiner interpreted charging plan drawn up based on predicted time distribution of excess power, and estimating departure time based on destination, destination time, and route planning as scheduling a drive route of the vehicle to occur during a time period in which an estimated renewable energy generation of a local renewable energy system is expected to exceed a predefined threshold, and wherein examiner interpreted data processing device as a control module).
BERGER does not explicitly teach schedule a drive route of the vehicle.
However, Lu teaches (2) schedule a drive route of the vehicle ([Abstract] “An example operation includes one or more of providing, by a battery of a vehicle, energy to a location, departing, by the vehicle, when a condition at the location is above or below a threshold, receiving a charge at the battery of the vehicle, from a charging station until the condition is at the threshold, and returning, by the vehicle, to the location when the condition is at the threshold and a capacity of the battery is above a level”, wherein examiner interpreted departing by vehicle to a charging station when a condition at a location is above a threshold as scheduling a drive route of the vehicle to occur during a time period in which a condition is above a threshold, wherein examiner interpreted departing a vehicle based on a condition in combination with charging plan of BERGER that is based on predicted time distribution of excess power, estimated departure time based on destination, destination time, and route planning as scheduling a drive route of the vehicle to occur during a time period in which an estimated renewable energy generation of the local renewable energy system is expected to exceed a predefined threshold).
BERGER, and Lu are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to power systems.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above vehicle-to-home power supply and energy storage system as taught by BERGER, and incorporating scheduling a drive route of vehicle, as taught by Lu.
One of ordinary skill in the art would have been motivated to improve Paragraph [0055] “instruct[ing] the vehicle to leave and maneuver to a charging location to obtain an additional charge”, as suggested by Lu.
Regarding claim 14, BERGER, and Lu teaches all of the features with respect to claim 13 as outlined above.
BERGER further teaches wherein the control module is an on-board component of the vehicle ([Description] “It is a further development that the local power grid has a data processing device (also referred to as an “IT system”), which can be connected to the electric vehicle”, wherein examiner interpreted data processing device connected to vehicle as including a control module being an on-bard component of the vehicle).
Regarding claim 15, BERGER, and Lu teaches all of the features with respect to claim 13 as outlined above.
BERGER further teaches wherein the estimated renewable energy generation of the local renewable energy system is derived from a crowd sourced renewable power generation data ([Description] “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system only based on system data (e.g. comprising performance, orientation, installation location, etc.) and sun position data (e.g. comprising solar radiation, in particular Radiation intensity) is predicted. Such a forecast is particularly easy to provide. The system data is created when planning or setting up the photovoltaic system, and the sun position data is easily available for the installation location of the photovoltaic system”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”, “It is an embodiment that the predicted temporal distribution of the excess power of the local power grid is determined based on historical data from a local or external energy measuring device installed at a grid connection point between the local power grid and a public supply grid, to which no electric vehicle has been charged”, “it is an advantageous further development that additional measurement data about an energy or power flow from the local power grid to the public power grid and vice versa is available, be it through the presence of a local grid energy measuring device or through an external “smart meter” house meter from the energy supplier/ Measuring point operator at the grid connection point, whose measurement data is available to the user of the local power grid”, “The at least one renewable energy source can include or be, for example, at least one photovoltaic system, at least one wind turbine, at least one geothermal system, etc. An electrical power of the at least one regenerative energy source corresponds to the “regenerative” electrical power fed into the local power grid by the at least one regenerative energy source. The (DC) photovoltaic system can be connected to the rest of the local (AC) power grid via a unidirectional inverter”, wherein examiner interpreted the various data described that are used to predict renewable energy temporal distribution as estimating renewable energy generation of the local renewable energy system being derived from a crowed sourced renewable power generation data, wherein examiner interpreted local grid system comprising various renewable energy sources as including crowd sourced renewable power generation data).
Regarding claim 16, BERGER, and Lu teaches all of the features with respect to claim 13 as outlined above.
BERGER further teaches wherein the estimated renewable energy generation of the local renewable energy system is further derived from a weather data ([Description] “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”).
Regarding claim 17, BERGER, and Lu teaches all of the features with respect to claim 13 as outlined above.
BERGER further teaches wherein the weather data includes wind information or cloud coverage information ([Description] “It is an embodiment that the at least one renewable energy source is a wind turbine, the predicted temporal distribution of the electrical power of the wind turbine is predicted based on a wind forecast and a model of the wind turbine”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system only based on system data (e.g. comprising performance, orientation, installation location, etc.) and sun position data (e.g. comprising solar radiation, in particular Radiation intensity) is predicted”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”, wherein examiner interpreted wind forecast, sun position data including solar radiation, and weather forecasts in general as including wind information and cloud coverage information).
Regarding claim 18, BERGER, and Lu teaches all of the features with respect to claim 13 as outlined above.
BERGER further teaches wherein the control module is programmed to identify a target departure time for the vehicle to travel away from a home location of the vehicle and a target return time for the vehicle to return to the home location as part of scheduling the drive route ([Description] “For example, the charging requirement and its expected departure time can be stored in a data processing device, which is in particular also set up to create the charging plan”, “The departure time can, for example, be specified by a user, estimated by specifying a destination and a destination time together with route planning, or can be estimated from history data”, and [Abstract] “A method is used to charge an electric vehicle (BEV) on a local power grid (1), which has at least one renewable energy source (2) and at least one charging point (EVSE) for charging the electric vehicle (BEV), wherein in the method for a with Charging point coupled electric vehicle (BEV) whose charging requirement and its expected departure time are provided, a predicted temporal distribution of excess power from the local power grid for the expected parking period of the electric vehicle (BEV) is determined based on a predicted temporal distribution of an electrical power of the at least one renewable energy source (2). a charging plan is drawn up for the coupled electric vehicle (BEV) based on the predicted time distribution of the excess power in such a way that the electric vehicle (BEV) has a desired state of charge at the expected departure time and the excess power is used to charge the electric vehicle (BEV), and that Electric vehicle (BEV) is charged according to the charging plan”, and [Fig. 1 Description] “Alternatively, the departure time can be transmitted from or via the BEV electric vehicle to the IT system 8 directly or via the EVSE wallbox. The electric vehicle BEV can also, for example, have an ID, the arrival time and required and available amounts of energy, in particular a charging requirement at the time of departure is transmitted to the IT system 8 directly or via the EVSE wallbox”, wherein examiner interpreted charging electric vehicle on local power grid based on the departure time as identifying a target departure time for the vehicle to travel away from a home location of the vehicle and a target, and wherein examiner interpreted arrival time that is part of the data processing unit that is used to create a charge plan as being the target return time for the vehicle to return to the home location, both being part of the scheduling the drive route, and Lu teaches a vehicle departing and returning to a location based on a condition and thresholds, wherein the combination teaches identify a target departure time for the vehicle to travel away from a home location of the vehicle and a target return time for the vehicle to return to the home location as part of scheduling the drive route).
Lu further teaches vehicle to travel away from a home location of the vehicle and for the vehicle to return to the home location as part of the scheduling the drive route ([Abstract] “An example operation includes one or more of providing, by a battery of a vehicle, energy to a location, departing, by the vehicle, when a condition at the location is above or below a threshold, receiving a charge at the battery of the vehicle, from a charging station until the condition is at the threshold, and returning, by the vehicle, to the location when the condition is at the threshold and a capacity of the battery is above a level”, wherein examiner interpreted a vehicle departing and returning to a location based on a condition and thresholds as control module being programmed to identify a target departure time for the vehicle to travel away from a home location of the vehicle and a target return time for the vehicle to return to the home location as part of scheduling the drive route, additionally BERGER teaches identifying a target departure time and arrival time is used create a charge plan).
Regarding claim 19, BERGER, and Lu teaches all of the features with respect to claim 18 as outlined above.
BERGER further teaches wherein the control module is programmed to identify a charging location along the drive route as part of scheduling the drive route ([Description] “The charging plan can be drawn up in such a way that it also achieves the following goals: - particularly battery-friendly charging, especially with low charging currents; - Stopping charging at times when it makes more monetary sense to feed the surplus into the public power grid than to use it to charge an electric vehicle; - the possibility of using the battery(s) of the electric vehicle as (possibly additional) intermediate storage with the possibility of feeding back into the local power grid and/or into the public power grid, if such recovery capability, e.g. B. V2G”, and [Abstract] “A method is used to charge an electric vehicle (BEV) on a local power grid (1), which has at least one renewable energy source (2) and at least one charging point (EVSE) for charging the electric vehicle (BEV)”, wherein examiner interpreted charging point in a grid as charging location along the drive route as part of scheduling the drive route, wherein departure time is part of scheduling the drive route, and a charging point is a charging location along the drive route).
Lu further teaches identify a charging location along the drive route as part of scheduling the drive route ([Abstract] “An example operation includes one or more of providing, by a battery of a vehicle, energy to a location, departing, by the vehicle, when a condition at the location is above or below a threshold, receiving a charge at the battery of the vehicle, from a charging station until the condition is at the threshold, and returning, by the vehicle, to the location when the condition is at the threshold and a capacity of the battery is above a level”, wherein examiner interpreted vehicle departing from a location to a charging station to receive charge as identifying a charging location along the drive route as part of scheduling the drive route).
Regarding claim 20, BERGER, and Lu teaches all of the features with respect to claim 13 as outlined above.
BERGER further teaches wherein the time period correlates to a period of relatively low cloud coverage at a location of the local renewable energy system ([Description] “It is an embodiment that the at least one renewable energy source is a wind turbine, the predicted temporal distribution of the electrical power of the wind turbine is predicted based on a wind forecast and a model of the wind turbine”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system only based on system data (e.g. comprising performance, orientation, installation location, etc.) and sun position data (e.g. comprising solar radiation, in particular Radiation intensity) is predicted”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”, wherein examiner interpreted time of sun position and weather forecasts as including the time period correcting to relatively low cloud coverage at location of the local renewable energy, wherein weather forecasts are known to include forecasting cloud coverage).
Regarding claim 21, BERGER, and Lu teaches all of the features with respect to claim 15 as outlined above.
BERGER further teaches wherein the crowd sourced renewable power generation data is derived from a plurality of renewable energy sources that are located within a common geographical region as the local renewable energy system ([Description] “It is an embodiment that the local power grid is a power grid of a private household, e.g. a private residential building or an apartment”, “The at least one renewable energy source can include or be, for example, at least one photovoltaic system, at least one wind turbine, at least one geothermal system, etc. An electrical power of the at least one regenerative energy source corresponds to the “regenerative” electrical power fed into the local power grid by the at least one regenerative energy source. The (DC) photovoltaic system can be connected to the rest of the local (AC) power grid via a unidirectional inverter”, wherein examiner interpreted local power grid including a household that includes renewable energy sources as renewable energy sources that are located within a common geographical region as the local renewable energy system).
Regarding claim 22, BERGER, and Lu teaches all of the features with respect to claim 21 as outlined above.
BERGER further teaches wherein the crowd sourced renewable power generation data includes a location of each of the plurality of renewable energy sources, an amount of renewable power generation occurring at each of the plurality of renewable energy sources, and a date and a time when the amount of renewable power generation occurring at each of the plurality of renewable energy sources is recorded ([Description] “It is a further development that the external energy measuring device is a smart meter that automatically measures power and transmits it to a recording point. If this is the case, in a further development these measurement data can be forwarded to the user of the local power grid via a smart meter gateway, e.g. via an EEBus protocol”, “the following additional measured values can be used to set up and/or update the charging plan: - Measured values of the PV power fed into the local power grid from the photovoltaic system, measured for example by the associated inverter; - Measured values of the amount of energy exchanged with the buffer, measured for example by the associated power converter; - Measured values of the amount of energy exchanged with the charging point or the actual amount of charging or recovery energy to or from the electric vehicle, measured for example by a rectifier or power converter assigned to the charging point”, “the forecast about the electrical energy expected to be fed in by the photovoltaic system 2 can be compared by a PV energy measuring device 11 present between the photovoltaic system 2 and the rest of the local power grid 1. The PV energy measuring device 11 can be integrated into the inverter 2a in a further development, so that z. B. the current performance of the photovoltaic system 2 can be measured via the inverter 2a”, wherein examiner interpreted measuring PV power of a household as the crowd sourced renewable power generation data including a location of each of the plurality of renewable energy sources, an amount of renewable power generation occurring at each of the plurality of renewable energy sources, and a date and a time when the amount of renewable power generation occurring at each of the plurality of renewable energy sources is recorded).
Regarding claim 23, BERGER, and Lu teaches all of the features with respect to claim 13 as outlined above.
BERGER further teaches wherein the control module is programmed to calculate the estimated renewable energy generation of the local renewable energy system for each of a plurality of future time periods ([Description] “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system only based on system data (e.g. comprising performance, orientation, installation location, etc.) and sun position data (e.g. comprising solar radiation, in particular Radiation intensity) is predicted”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”, “It is an embodiment that the predicted temporal distribution of the excess power of the local power grid is determined based on historical data from a local or external energy measuring device installed at a grid connection point between the local power grid and a public supply grid, to which no electric vehicle has been charged”, “It is an embodiment that the predicted temporal distribution of the excess power is determined based on a difference between the predicted temporal distribution of the electrical power of the at least one renewable energy source and a predicted temporal distribution of an electrical power consumed by the local power grid”, wherein examiner interpreted predicted temporal distribution of the electrical power of photovoltaic system as to calculate the estimated renewable energy generation of the local renewable energy system for each of a plurality of future time periods).
Regarding claim 24, BERGER, and Lu teaches all of the features with respect to claim 23 as outlined above.
BERGER further teaches wherein the plurality of future time periods includes at least 10 minutes, 30 minutes, 60 minutes, 12 hours, 24 hours, and 1 week ([Description] “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system only based on system data (e.g. comprising performance, orientation, installation location, etc.) and sun position data (e.g. comprising solar radiation, in particular Radiation intensity) is predicted”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”, “It is an embodiment that the predicted temporal distribution of the excess power of the local power grid is determined based on historical data from a local or external energy measuring device installed at a grid connection point between the local power grid and a public supply grid, to which no electric vehicle has been charged”, “It is an embodiment that the predicted temporal distribution of the excess power is determined based on a difference between the predicted temporal distribution of the electrical power of the at least one renewable energy source and a predicted temporal distribution of an electrical power consumed by the local power grid”, wherein examiner interpreted predicted temporal distribution of the electrical power of photovoltaic system as to include estimating for the plurality of future time periods includes at least 10 minutes, 30 minutes, 60 minutes, 12 hours, 24 hours, and 1 week).
Regarding claim 25, BERGER, and Lu teaches all of the features with respect to claim 16 as outlined above.
BERGER further teaches wherein the weather data includes cloud coverage information that includes at least a direction and a speed of cloud movement ([Description] “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”, wherein examiner interpreted sun position data and weather forecasts as including cloud coverage information that includes at least a direction).
Regarding claim 26, BERGER, and Lu teaches all of the features with respect to claim 18 as outlined above.
Lu further teaches wherein the control module is programmed to reduce an energy consumption of the home location while the vehicle is away from the home location (Paragraph [0067] “The current application continues to monitor the location 106 condition, such as the internal temperature at the location. Monitoring may occur through a reception of condition data messages 125, which may be sent periodically, at an interval, responsive to a query from a processor, and the like. When the condition at location 106 is below a threshold and/or the vehicle battery capacity is above a level 128, a message, such as a maneuver message 130, directs the vehicle to maneuver from the charging station to location 132. The vehicle may connect, once again, to location 106 and provide a charge as previously described”, wherein examiner interpreted monitoring the location condition such as internal temperature of the location, wherein monitoring condition at location includes whether internal temperature is below a threshold, therefore, examiner interpreted the temperature dropping as reducing energy consumption of the home location while the vehicle is away from the home location).
Regarding claim 27, BERGER, and Lu teaches all of the features with respect to claim 20 as outlined above.
BERGER further teaches wherein the time period is during a period of time in which it is considered optimal for the vehicle to travel away from its home location due to the relatively low cloud coverage ([Description] “It is an embodiment that if the charging requirement is greater than or equal to the excess energy forecast up to the time of departure, the electric vehicle is charged with maximum power from the forecast excess power. This has the advantage that a particularly large amount of excess energy can be used for charging or a particularly high proportion of the charging requirement is covered by excess energy. If desired or required, any remaining portion of the charging requirement can be supplied from the stationary power storage system, if available, and/or from the public power supply network”, wherein examiner interpreted vehicle charging using excess energy power and remaining portion from a public power supply network as a period of time in which it is considered optimal for the vehicle to travel away from its home location due to the relatively low cloud coverage).
Regarding claim 28, BERGER, and Lu teaches all of the features with respect to claim 18 as outlined above.
BERGER further teaches wherein the control module is further programmed to cause the target departure time and the target return time to be displayed within a user interface of a human machine interface associated with the vehicle ([Description] “The electric vehicle and/or a user terminal, in particular a mobile user terminal, for example a smartphone, are networked with the data processing device. Information or preferences regarding the vehicle status and charging process are displayed and controlled in the electric vehicle and/or the user terminal; - The electric vehicle and/or the user terminal communicates, for example, an ID, arrival time, desired or estimated departure time and required and available amounts of energy from the vehicle battery to the data processing device”, wherein examiner interpreted user terminal displaying vehicle status including arrival time and departure time as control module causing the target departure time and the target return time to be displayed within a user interface of a human machine interface associated with the vehicle).
Regarding claim 29, BERGER, and Lu teaches all of the features with respect to claim 28 as outlined above.
BERGER further teaches wherein the user interface includes a countdown timer that indicates in real time an amount of time left before the vehicle needs to return to the home location ([Description] “The electric vehicle and/or a user terminal, in particular a mobile user terminal, for example a smartphone, are networked with the data processing device. Information or preferences regarding the vehicle status and charging process are displayed and controlled in the electric vehicle and/or the user terminal; - The electric vehicle and/or the user terminal communicates, for example, an ID, arrival time, desired or estimated departure time and required and available amounts of energy from the vehicle battery to the data processing device”, wherein examiner interpreted user terminal displaying vehicle status including arrival time and departure time as including countdown timer that indicates in real time an amount of time left before the vehicle needs to return to the home location).
Regarding claim 30, BERGER, and Lu teaches all of the features with respect to claim 13 as outlined above.
BERGER further teaches wherein the control module is a cloud-based control module ([Description] “The data processing device can be a data processing device located locally in the household, for example a computer or an IT system. Alternatively, the data processing device can be an external entity such as a cloud computer or a network server”, wherein examiner interpreted data processing device that can be a cloud computer as a control module being a cloud-based control module).
Regarding claim 31, BERGER teaches a vehicle-to-home power supply and energy storage system ([Description] “The charging plan can be drawn up in such a way that it also achieves the following goals: - particularly battery-friendly charging, especially with low charging currents; - Stopping charging at times when it makes more monetary sense to feed the surplus into the public power grid than to use it to charge an electric vehicle; - the possibility of using the battery(s) of the electric vehicle as (possibly additional) intermediate storage with the possibility of feeding back into the local power grid and/or into the public power grid, if such recovery capability, e.g. B. V2G”, wherein examiner interpreted feeding power back into local power grid using the batteries of electric vehicle as a vehicle-to-home power supply and energy storage system, and see Fig. 1 description), comprising:
a vehicle including a traction battery pack ([Description] “The electric vehicle can be a plug-in hybrid vehicle (also referred to as a “PHEV”) or a fully electric vehicle (also referred to as a “BEV”). Charging the electric vehicle includes charging an electrical energy storage device, hereinafter referred to as a “battery” without limiting generality, in particular at least one drive battery of the electric vehicle”, and [Description] “The fact that an electric vehicle is coupled to the charging point can in particular include that the electric vehicle or its battery(s) can be charged through the charging point, e.g. because a charging cable is connected or the electric vehicle is parked on an inductive charging surface” wherein examiner interpreted battery as a traction battery pack); and
a control module programmed to schedule a drive route of the vehicle to occur during a time period in which an estimated renewable energy generation of a local renewable energy system is expected to exceed a predefined threshold (([Abstract] “a charging plan is drawn up for the coupled electric vehicle (BEV) based on the predicted time distribution of the excess power in such a way that the electric vehicle (BEV) has a desired state of charge at the expected departure time and the excess power is used to charge the electric vehicle (BEV), and that Electric vehicle (BEV) is charged according to the charging plan”, and [Description] “The departure time can, for example, be specified by a user, estimated by specifying a destination and a destination time together with route planning, or can be estimated from history data”, and [Description] “the charging requirement and its expected departure time can be stored in a data processing device, which is in particular also set up to create the charging plan”, wherein examiner interpreted charging plan drawn up based on predicted time distribution of excess power, and estimating departure time based on destination, destination time, and route planning as scheduling a drive route of the vehicle to occur during a time period in which an estimated renewable energy generation of a local renewable energy system is expected to exceed a predefined threshold, and wherein examiner interpreted data processing device as a control module),
wherein the time period correlates to a period of relatively low cloud coverage at a home location of the vehicle ([Description] “It is an embodiment that the at least one renewable energy source is a wind turbine, the predicted temporal distribution of the electrical power of the wind turbine is predicted based on a wind forecast and a model of the wind turbine”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system only based on system data (e.g. comprising performance, orientation, installation location, etc.) and sun position data (e.g. comprising solar radiation, in particular Radiation intensity) is predicted”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”, wherein examiner interpreted time of sun position and weather forecasts as including the time period correlating to a period of relatively low cloud coverage at a home location of the vehicle),
wherein the estimated renewable energy generation of the local renewable energy system is derived from a crowd sourced renewable power generation data ([Description] “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system only based on system data (e.g. comprising performance, orientation, installation location, etc.) and sun position data (e.g. comprising solar radiation, in particular Radiation intensity) is predicted. Such a forecast is particularly easy to provide. The system data is created when planning or setting up the photovoltaic system, and the sun position data is easily available for the installation location of the photovoltaic system”, “It is an embodiment that the at least one renewable energy source comprises a photovoltaic system and the predicted temporal distribution of the electrical power of the photovoltaic system is predicted based on system data and sun position data and additionally based on historical data and/or weather forecasts”, “It is an embodiment that the predicted temporal distribution of the excess power of the local power grid is determined based on historical data from a local or external energy measuring device installed at a grid connection point between the local power grid and a public supply grid, to which no electric vehicle has been charged”, “it is an advantageous further development that additional measurement data about an energy or power flow from the local power grid to the public power grid and vice versa is available, be it through the presence of a local grid energy measuring device or through an external “smart meter” house meter from the energy supplier/ Measuring point operator at the grid connection point, whose measurement data is available to the user of the local power grid”, wherein examiner interpreted the various data described that are used to predict renewable energy temporal distribution as estimating renewable energy generation of the local renewable energy system being derived from a crowed sourced renewable power generation data) that is received from a plurality of renewable energy sources that are located within a common geographical region as the home location ([Description] “It is an embodiment that the local power grid is a power grid of a private household, e.g. a private residential building or an apartment”, “The at least one renewable energy source can include or be, for example, at least one photovoltaic system, at least one wind turbine, at least one geothermal system, etc. An electrical power of the at least one regenerative energy source corresponds to the “regenerative” electrical power fed into the local power grid by the at least one regenerative energy source. The (DC) photovoltaic system can be connected to the rest of the local (AC) power grid via a unidirectional inverter”, wherein examiner interpreted local power grid including a household that includes renewable energy sources as renewable energy sources that are located within a common geographical region as the local renewable energy system),
wherein the control module is programmed to identify a target departure time for the vehicle to travel away from the home location and a target return time for the vehicle to return to the home location as part of scheduling the drive route ([Description] “For example, the charging requirement and its expected departure time can be stored in a data processing device, which is in particular also set up to create the charging plan”, “The departure time can, for example, be specified by a user, estimated by specifying a destination and a destination time together with route planning, or can be estimated from history data”, and [Abstract] “A method is used to charge an electric vehicle (BEV) on a local power grid (1), which has at least one renewable energy source (2) and at least one charging point (EVSE) for charging the electric vehicle (BEV), wherein in the method for a with Charging point coupled electric vehicle (BEV) whose charging requirement and its expected departure time are provided, a predicted temporal distribution of excess power from the local power grid for the expected parking period of the electric vehicle (BEV) is determined based on a predicted temporal distribution of an electrical power of the at least one renewable energy source (2). a charging plan is drawn up for the coupled electric vehicle (BEV) based on the predicted time distribution of the excess power in such a way that the electric vehicle (BEV) has a desired state of charge at the expected departure time and the excess power is used to charge the electric vehicle (BEV), and that Electric vehicle (BEV) is charged according to the charging plan”, and [Fig. 1 Description] “Alternatively, the departure time can be transmitted from or via the BEV electric vehicle to the IT system 8 directly or via the EVSE wallbox. The electric vehicle BEV can also, for example, have an ID, the arrival time and required and available amounts of energy, in particular a charging requirement at the time of departure is transmitted to the IT system 8 directly or via the EVSE wallbox”, wherein examiner interpreted charging electric vehicle on local power grid based on the departure time as identifying a target departure time for the vehicle to travel away from a home location of the vehicle and a target, and wherein examiner interpreted arrival time that is part of the data processing unit that is used to create a charge plan as being the target return time for the vehicle to return to the home location, both being part of the scheduling the drive route, and Lu teaches a vehicle departing and returning to a location based on a condition and thresholds, wherein the combination teaches identify a target departure time for the vehicle to travel away from a home location of the vehicle and a target return time for the vehicle to return to the home location as part of scheduling the drive route).
BERGER does not explicitly teach schedule a drive route of the vehicle, and vehicle to travel away from a home location of the vehicle and for the vehicle to return to the home location as part of the scheduling the drive route.
However Lu teaches schedule a drive route of the vehicle to occur during a time period ([Abstract] “An example operation includes one or more of providing, by a battery of a vehicle, energy to a location, departing, by the vehicle, when a condition at the location is above or below a threshold, receiving a charge at the battery of the vehicle, from a charging station until the condition is at the threshold, and returning, by the vehicle, to the location when the condition is at the threshold and a capacity of the battery is above a level”, wherein examiner interpreted departing by vehicle to a charging station when a condition at a location is above a threshold as scheduling a drive route of the vehicle to occur during a time period in which a condition is above a threshold, wherein examiner interpreted departing a vehicle based on a condition in combination with charging plan of BERGER that is based on predicted time distribution of excess power, estimated departure time based on destination, destination time, and route planning as scheduling a drive route of the vehicle to occur during a time period in which an estimated renewable energy generation of the local renewable energy system is expected to exceed a predefined threshold)
vehicle to travel away from a home location of the vehicle and for the vehicle to return to the home location as part of the scheduling the drive route ([Abstract] “An example operation includes one or more of providing, by a battery of a vehicle, energy to a location, departing, by the vehicle, when a condition at the location is above or below a threshold, receiving a charge at the battery of the vehicle, from a charging station until the condition is at the threshold, and returning, by the vehicle, to the location when the condition is at the threshold and a capacity of the battery is above a level”, wherein examiner interpreted a vehicle departing and returning to a location based on a condition and thresholds as control module being programmed to identify a target departure time for the vehicle to travel away from a home location of the vehicle and a target return time for the vehicle to return to the home location as part of scheduling the drive route, additionally BERGER teaches identifying a target departure time and arrival time is used create a charge plan).
BERGER, and Lu are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to power systems.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above vehicle-to-home power supply and energy storage system as taught by BERGER, and incorporating scheduling a drive route of vehicle, as taught by Lu.
One of ordinary skill in the art would have been motivated to improve Paragraph [0055] “instruct[ing] the vehicle to leave and maneuver to a charging location to obtain an additional charge”, as suggested by Lu.
Regarding claim 32, BERGER teaches a method for controlling a vehicle-to-home power supply and energy storage system in a manner that minimizes energy consumed from a grid power source ([Description] “The charging plan can be drawn up in such a way that it also achieves the following goals: - particularly battery-friendly charging, especially with low charging currents; - Stopping charging at times when it makes more monetary sense to feed the surplus into the public power grid than to use it to charge an electric vehicle; - the possibility of using the battery(s) of the electric vehicle as (possibly additional) intermediate storage with the possibility of feeding back into the local p