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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 12/21/2023 and 3/25/2025 have been considered by the examiner.
Claim Objections
Claims 14 and 16 are objected to because of the following informalities:
“The system of claim of claim 10” should read “The system of claim 10”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 17 is rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Sartipizadeh et al. (US 20230256855 A1) (hereinafter referred to as “Sarti”).
Regarding Claim 17, Sarti teaches a site controller (202) of a work site that includes microgrid system having multiple types of energy assets (¶7] “selecting at least one of a plurality of power sources for the plurality of chargers from at least a utility grid and a subset of fleet vehicles having stored charge capacity exceeding an associated threshold … The charging strategy may include charging at least some of the fleet vehicles using power from the fixed, stationary batteries. The fleet charging system may also include a photovoltaic power source. The charging strategy may include charging at least some of the fleet vehicles using power from the voltaic power source”), the site controller comprising:
processing circuitry configured to:
calculate energy demand on the microgrid system by multiple work machines over a predetermined time window (¶[7] “a fleet charging system includes a plurality of chargers and a controller programmed to predict charge demand for fleet vehicles over a predetermined time interval”);
generate a charging schedule to pair chargers of the work site to the work machines for the predetermined time window (¶[7] “and to generate a charging strategy for the predetermined time interval including selecting at least one of a plurality of power sources for the plurality of chargers”);
generate a power dispatch schedule of activating and deactivating energy assets of the microgrid system and apportioning power levels of the energy assets to supply the calculated energy demand (see ¶[7] quoted above);
determine a difference between energy to be supplied by the energy assets during the predetermined time window and the energy demand for the predetermined time window (¶[29] “Controller 202 may also use grid historical data, predicted power factor, and utility rate information 214, which may also include rate schedules and surcharges associated with connected loading and associated power factor of connected loads to perform an associated grid/microgrid demand analysis 226. Based on the available information, controller 202 may maximize the use of off-peak and low-rate energy hours for charging the fleet BEVs 240, as well as fixed/stationary energy units 246”); and
operate the energy assets according to the power dispatch schedule and activate one or more energy storage systems according to the determined difference between the energy demand and the energy supplied (¶[44] “Based on the optimization strategy, the controller then controls dispatching, charge scheduling, energy source selection, and battery SOC requirements for one or more fleet vehicles as represented at 460”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sartipizadeh et al. (US 20230256855 A1) (hereinafter referred to as “Sarti”) in view of Nakada et al. (JP2024071183A).
Regarding Claim 1, Sarti teaches a method of controlling a work site (204) with multiple work machines (240) to schedule the work machines to perform work and to provide energy to the work site, the method comprising:
calculating, by a site controller of the work site (202), energy demand by the work machines to perform the work over a predetermined time window (¶[7] “a fleet charging system includes a plurality of chargers and a controller programmed to predict charge demand for fleet vehicles over a predetermined time interval”);
generating a charging schedule for the predetermined time window (¶[7] “and to generate a charging strategy for the predetermined time interval including selecting at least one of a plurality of power sources for the plurality of chargers”),
generating a power dispatch schedule of activating and deactivating energy assets of a microgrid system of the work site and apportioning power levels of the energy assets to supply the calculated energy demand during the predetermined time window (¶7] “selecting at least one of a plurality of power sources for the plurality of chargers from at least a utility grid and a subset of fleet vehicles having stored charge capacity exceeding an associated threshold … the plurality of power sources may include fixed, stationary batteries. The charging strategy may include charging at least some of the fleet vehicles using power from the fixed, stationary batteries. The fleet charging system may also include a photovoltaic power source. The charging strategy may include charging at least some of the fleet vehicles using power from the voltaic power source”);
determining a difference between energy to be supplied by the energy assets during the predetermined time window and the energy demand for the predetermined time window (¶[29] “Controller 202 may also use grid historical data, predicted power factor, and utility rate information 214, which may also include rate schedules and surcharges associated with connected loading and associated power factor of connected loads to perform an associated grid/microgrid demand analysis 226. Based on the available information, controller 202 may maximize the use of off-peak and low-rate energy hours for charging the fleet BEVs 240, as well as fixed/stationary energy units 246”); and
operating the energy assets according to the power dispatch schedule and the chargers according to the charging schedule (¶[44] “Based on the optimization strategy, the controller then controls dispatching, charge scheduling, energy source selection, and battery SOC requirements for one or more fleet vehicles as represented at 460”), and
activating one or more energy storage systems of the microgrid system according to the determined difference between the energy demand and the energy supplied (¶[31] “when electricity demand is high, the electric utility may provide electricity at a relatively high price to discourage use. Also, when electricity demand is high, the electric utility may pay to receive electricity from the fleet charging system 204. The fleet charging system 204 may be configured to transfer power from the energy storage devices 246 and EVs 240 via connected charging stations 242 and power lines” and see ¶[44] quoted above).
Sarti does not explicitly teach wherein the charging schedule pairs chargers of the work site to the work machines and includes charging wait times for charging the multiple work machines;
Nakada teaches wherein the charging schedule pairs chargers of the work site to the work machines and includes charging wait times for charging the multiple work machines (¶[16] “Figure 2 is a graph showing an example of the time change in the charging state of the power storage devices 11 of three electric excavators 10, and a graph showing the time change in the number of electric excavators 10 that are being charged by the charging device 20 or waiting for the charging device 20 to be charged”);
It would be obvious to one of ordinary skill in the art to before the effective filing date of the claimed invention to have modified Sarti to incorporate the teachings of Nakada to provide wherein the charging schedule pairs chargers of the work site to the work machines and includes charging wait times for charging the multiple work machines in order to account for the availability of chargers and the amount of chargers being fewer than the number of work machines.
Regarding Claim 9, Sarti in view of Nakada teaches the method of claim 1.
Sarti further teaches wherein the generating the power dispatch schedule includes:
receiving, by the site controller in real time, one or more of a utility price for energy from a utility grid input to the microgrid system, a prediction of availability of energy from renewable energy assets of the microgrid system, and price of fuel for non-renewable energy assets of the microgrid system; and
scheduling activation, deactivation, and power level apportioning of the renewable and non-renewable energy assets in real time to meet the calculated energy demand and minimize cost of operating the microgrid system (¶[16] “Embodiments according to this disclosure may provide various advantages by decreasing the total expenses associated with ownership of EV fleets as well as grid infrastructure requirements by providing a system and method for power source selection and charge scheduling for EV fleets that considers the relatively long charging time, variable prices of power sources for charging, limited number of chargers, effect on power factor, and the effect of charging behavior on the battery life”).
Claim(s) 2-8 are rejected under 35 U.S.C. 103 as being unpatentable over Sartipizadeh et al. (US 20230256855 A1) (hereinafter referred to as “Sarti”) in view of Nakada et al. (JP2024071183A) further in view of Everly et al. (US 20240420518 A1).
Regarding Claim 2, Sarti in view of Nakada teaches the method of claim 1.
Sarti does not explicitly teach receiving material movement information related to an amount of material to be moved during the predetermined time window;
calculating, by the site controller, revenue from moving the material using the work machines, a cost of operating the work machines, and a cost of operating the microgrid system during the predetermined time window; and
wherein the generating the charging schedule includes the microgrid controller optimizing productivity from operating the work machines using the charging schedule for the work machines and the activation/deactivation schedule for the energy assets.
Everly teaches receiving material movement information related to an amount of material to be moved during the predetermined time window (¶[72] “Tons-kilometers was chosen as the key performance indicator (KPI) to represent production. Tons-kilometers is calculated by multiplying the load carried, in tons, by the distance traveled, in kilometers”);
calculating, by the site controller, revenue from moving the material using the work machines, a cost of operating the work machines (¶[4] “This need for improvements is only super-charged considering that haulage costs can often account for 50-60% of the total cost of mining at an operation”), and a cost of operating the microgrid system during the predetermined time window (¶[14] “Additionally, the modeling tools described herein may be used to lower operating costs by permitting scheduling of different types of vehicles depending on fuel and energy costs, which may periodically fluctuate. Additionally, inventive embodiments may allow operators to optimize on variables other than production (or to optimize on variable in addition to production)”); and
wherein the generating the charging schedule includes the microgrid controller optimizing productivity from operating the work machines using the charging schedule for the work machines and the activation/deactivation schedule for the energy assets (see ¶[14] quoted above).
It would be obvious to one of ordinary skill in the art to before the effective filing date of the claimed invention to have modified Sarti in view of Nakada to incorporate the teachings of Everly to provide receiving material movement information related to an amount of material to be moved during the predetermined time window;
calculating, by the site controller, revenue from moving the material using the work machines, a cost of operating the work machines, and a cost of operating the microgrid system during the predetermined time window; and
wherein the generating the charging schedule includes the microgrid controller optimizing productivity from operating the work machines using the charging schedule for the work machines and the activation/deactivation schedule for the energy assets,
in order to maintain productivity while still reducing charging costs.
Regarding Claim 3, the combination of Sarti, Nakada and Everly teaches the method of claim 2.
The combination of Sarti, Nakada and Everly further teaches wherein the generating the charging schedule includes:
calculating a number of the work machines to charge using the microgrid system according to the received material movement information (see Everly ¶[72] quoted above);
determining travel time of the work machines to the chargers; and determining charging times and charging wait times of the work machines with the chargers (see Nakada ¶[28]).
Regarding Claim 4, the combination of Sarti, Nakada and Everly teaches the method of claim 3.
Sarti further teaches wherein the generating the charging schedule includes:
determining a charge range and a charge rate of batteries of the work machines;
determining a charge rate of the chargers of the work site; and
pairing the work machines to the chargers using the determined charge range and charge rates (¶[37-38] “In a first option, controller 202 may schedule charging of V1 with a fast charger 340 which may require 5-10 minutes to increase V1 SOC to 30%, but this would be more expensive and may negatively affect the battery health and decrease battery life if used repeatedly. This option may also be limited based on the distance to an available Fast DC charger 340 and the associated energy and access charges/fees, particularly if charger 340 is owned or operated by a third party … As another option, controller 202 may consider charging V1 310 to 30% with a Level 1 or Level 2 charger that takes longer than option one, but is less expensive and has less of an effect on battery health”).
Regarding Claim 5, the combination of Sarti, Nakada and Everly teaches method of claim 2.
Sarti further teaches wherein the calculating the cost of operating the work machines includes:
calculating, by the microgrid controller, a battery degradation cost of cycling batteries of the work machines; and
generating the charging schedule to reduce the battery degradation cost to optimize the revenue from operating the work machines during the predetermined time window (¶[32] “Controller 202 performs battery life health analysis 222 for fleet EVs 240 using a battery model to forecast the degradation rate due to different types of charging behaviors to maximize the battery life by avoiding charging behaviors that have a greater effect on battery health/life (such as unnecessary charging via a fast DC charger, unnecessary depletion to minimum allowed SOC, or unnecessary charging to maximum allowed SOC) to meet fleet demand requirements and satisfy the requested services 206. A lower battery degradation rate decreases the overall maintenance and extends battery life for the fleet vehicles 240.”).
Regarding Claim 6, the combination of Sarti, Nakada and Everly teaches the method of claim 2.
The combination further teaches wherein the calculating the cost of operating the energy assets includes:
determining, by the site controller, a utility pricing schedule during the predetermined time window, a prediction of availability of renewable energy assets during the predetermined time window, and a cost of operating nonrenewable energy assets during the predetermined time window (Sarti ¶[29] “Controller 202 may also use storage depot data/information 212, which may include sustainable energy availability (such as from a photovoltaic (PV) source 248, wind source, etc.), fixed/stationary battery storage unit 246 capacity, fleet charging station 242 data (such as maximum charging rate, availability, location, connector compatibility, etc. Controller 202 may also use grid historical data, predicted power factor, and utility rate information 214, which may also include rate schedules and surcharges associated with connected loading and associated power factor of connected loads to perform an associated grid/microgrid demand analysis 226”); and
adjusting use of the utility, non-renewable energy assets, and the renewable energy assets in the power dispatch schedule to optimize the revenue from operating the work machines during the predetermined time window (Sarti ¶[29] “Based on the available information, controller 202 may maximize the use of off-peak and low-rate energy hours for charging the fleet BEVs 240, as well as fixed/stationary energy units 246”, see also Everly ¶[14]).
Regarding Claim 7, the combination of Sarti, Nakada and Everly teaches the method of claim 2.
Sarti does not explicitly teach wherein activating the one or more energy storage systems includes:
calculating, by the site controller, a degradation cost of using the one or more energy storage systems; and
activating the one or more energy storage systems according to the determined difference between the energy demand and the degradation cost of using the one or more energy storage systems.
However Sarti teaches calculating, by the site controller, a degradation cost of the batteries of the electric work machines (¶[32] “Controller 202 performs battery life health analysis 222 for fleet EVs 240 using a battery model to forecast the degradation rate due to different types of charging behaviors to maximize the battery life by avoiding charging behaviors that have a greater effect on battery health/life (such as unnecessary charging via a fast DC charger, unnecessary depletion to minimum allowed SOC, or unnecessary charging to maximum allowed SOC) to meet fleet demand requirements and satisfy the requested services 206. A lower battery degradation rate decreases the overall maintenance and extends battery life for the fleet vehicles 240”), and it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the same analysis to the fixed energy storage modules (which are also taught by Sarti in ¶[7]).
Regarding Claim 8, Sarti in view of Nakada teaches the method of claim 1.
Sarti as modified does not teach wherein the calculating the energy demand on the microgrid system includes:
receiving, by the site controller in real time, material movement information related to an amount of material to be moved using the electric work machines; and
computing the energy demand in real time using the received material movement information; and wherein the generating the charging schedule includes the site controller updating the charging schedule in real time.
Everly teaches wherein the calculating the energy demand on the microgrid system includes:
receiving, by the site controller in real time, material movement information related to an amount of material to be moved using the electric work machines; (¶[11] “The mine operation model simulates the movement of material and vehicles in the mine”); and
computing the energy demand in real time using the received material movement information; and wherein the generating the charging schedule includes the site controller updating the charging schedule in real time (¶[11] “uses the vehicle models to compute energy use and input energy required by the modeled vehicles, so as to predict a mine efficiency value as a function of various mine configuration parameters, including vehicle mix, road layout, and placement of trolley lines and refueling stations”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sarti in view of Nakada to incorporate the teachings of Everly to provide wherein the calculating the energy demand on the microgrid system includes:
receiving, by the site controller in real time, material movement information related to an amount of material to be moved using the electric work machines; and
computing the energy demand in real time using the received material movement information; and wherein the generating the charging schedule includes the site controller updating the charging schedule in real time, in order to maintain the required level of production.
Claim(s) 10-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sartipizadeh et al. (US 20230256855 A1) (hereinafter referred to as “Sarti”) in view of Everly et al. (US 20240420518 A1).
Regarding Claim 10, Sarti teaches a microgrid system (Figs 2-3) for a work site to provide energy to multiple work machines, the system comprising:
multiple energy assets including renewable and non-renewable energy assets (¶7] “selecting at least one of a plurality of power sources for the plurality of chargers from at least a utility grid and a subset of fleet vehicles having stored charge capacity exceeding an associated threshold … the plurality of power sources may include fixed, stationary batteries. The charging strategy may include charging at least some of the fleet vehicles using power from the fixed, stationary batteries. The fleet charging system may also include a photovoltaic power source. The charging strategy may include charging at least some of the fleet vehicles using power from the voltaic power source”);
a computing resource; and
a system optimizer application (202) to execute on the computing resource (¶[43] “When implemented in software, the control logic may be provided in one or more non-transitory computer-readable storage devices or media having stored data representing code or instructions executed by a computer to control the various resources of the smart fleet management system as described”) and configured to:
calculate energy demand on the microgrid system by the work machines over a predetermined time window (¶[7] “a fleet charging system includes a plurality of chargers and a controller programmed to predict charge demand for fleet vehicles over a predetermined time interval”);
wherein the schedule includes a charging schedule for the work machines and a power dispatch schedule for energy assets of the microgrid system (¶[7] “and to generate a charging strategy for the predetermined time interval including selecting at least one of a plurality of power sources for the plurality of chargers”); and
activate and deactivate the energy assets of the microgrid system, and apportion power levels of the energy assets during the predetermined time window according to the activation/deactivation schedule (¶[44] “Based on the optimization strategy, the controller then controls dispatching, charge scheduling, energy source selection, and battery SOC requirements for one or more fleet vehicles as represented at 460”).
Sarti does not explicitly teach to calculate a cost of operating the microgrid system and the work machines during the predetermined time window;
determine a schedule to optimize productivity of operating the work machines during the predetermined time window.
Everly teaches to calculate a cost of operating the microgrid system and the work machines during the predetermined time window (¶[4] “This need for improvements is only super-charged considering that haulage costs can often account for 50-60% of the total cost of mining at an operation”);
determine a schedule to optimize productivity of operating the work machines during the predetermined time window (¶[14] “Additionally, the modeling tools described herein may be used to lower operating costs by permitting scheduling of different types of vehicles depending on fuel and energy costs, which may periodically fluctuate. Additionally, inventive embodiments may allow operators to optimize on variables other than production (or to optimize on variable in addition to production)”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sarti to incorporate the teachings of Everly to provide to calculate a cost of operating the microgrid system and the work machines during the predetermined time window;
determine a schedule to optimize productivity of operating the work machines during the predetermined time window,
in order to maximize production and therefore revenue while lowering operating costs.
Regarding Claim 11, Sarti in view of Everly teaches the system of claim 10.
Sarti as modified does not explicitly teach wherein the system optimizer application is configured to:
receive material movement information related to an amount of material to be moved during the predetermined time window;
determine revenue information using the material movement information; and
generating the charging schedule and the power dispatch schedule to optimize the revenue of operating the electric work machines to move the amount of material.
Everly teaches wherein the system optimizer application is configured to:
receive material movement information related to an amount of material to be moved during the predetermined time window (¶[11] “The mine operation model simulates the movement of material and vehicles in the mine”) ;
determine revenue information using the material movement information (¶[72] “Tons-kilometers was chosen as the key performance indicator (KPI) to represent production. Tons-kilometers is calculated by multiplying the load carried, in tons, by the distance traveled, in kilometers”) and
generating the charging schedule and the power dispatch schedule to optimize the revenue of operating the electric work machines to move the amount of material (¶[14] “Additionally, the modeling tools described herein may be used to lower operating costs by permitting scheduling of different types of vehicles depending on fuel and energy costs, which may periodically fluctuate. Additionally, inventive embodiments may allow operators to optimize on variables other than production (or to optimize on variable in addition to production)”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sarti in view of Everly to further incorporate the teachings of Everly to provide wherein the system optimizer application is configured to:
receive material movement information related to an amount of material to be moved during the predetermined time window;
determine revenue information using the material movement information; and
generating the charging schedule and the power dispatch schedule to optimize the revenue of operating the electric work machines to move the amount of material, in order to meet production goals while minimizing operating costs.
Regarding Claim 12, Sarti in view of Everly teaches the system of claim 11.
Everly further teaches wherein the system optimizer application is configured to:
determine a number of the work machines to be powered by the microgrid system according to the material movement information; and
calculate the energy demand using the determined number of the work machines (¶[11] “The mine operation model simulates the movement of material and vehicles in the mine, and uses the vehicle models to compute energy use and input energy required by the modeled vehicles, so as to predict a mine efficiency value as a function of various mine configuration parameters, including vehicle mix, road layout, and placement of trolley lines and refueling stations”).
Regarding Claim 13, Sarti in view of Everly teaches the system of claim 10.
Sarti further teaches wherein the system optimizer application is configured to:
determine a charge range and a charge rate of batteries of the work machines;
determine a charge rate of chargers of the work site; and
pair the work machines to the chargers in the charging schedule using the determined charge range and charge rates (¶[37-38] “In a first option, controller 202 may schedule charging of V1 with a fast charger 340 which may require 5-10 minutes to increase V1 SOC to 30%, but this would be more expensive and may negatively affect the battery health and decrease battery life if used repeatedly. This option may also be limited based on the distance to an available Fast DC charger 340 and the associated energy and access charges/fees, particularly if charger 340 is owned or operated by a third party … As another option, controller 202 may consider charging V1 310 to 30% with a Level 1 or Level 2 charger that takes longer than option one, but is less expensive and has less of an effect on battery health”).
Regarding Claim 14, Sarti in view of Everly teaches the system of claim 10.
Sarti further teaches wherein the system optimizer application is configured to:
calculate a battery degradation cost of cycling batteries of the work machines; and
generate the charging schedule to reduce the battery degradation cost to optimize the revenue from operating the work machines during the predetermined time window (¶[32] “Controller 202 performs battery life health analysis 222 for fleet EVs 240 using a battery model to forecast the degradation rate due to different types of charging behaviors to maximize the battery life by avoiding charging behaviors that have a greater effect on battery health/life (such as unnecessary charging via a fast DC charger, unnecessary depletion to minimum allowed SOC, or unnecessary charging to maximum allowed SOC) to meet fleet demand requirements and satisfy the requested services 206. A lower battery degradation rate decreases the overall maintenance and extends battery life for the fleet vehicles 240.”).
Regarding Claim 15, Sarti in view of Everly teaches the system of claim 10.
Sarti further teaches wherein the system optimizer application is configured to:
receive information of utility pricing from a utility grid input to the microgrid system during the predetermined time window; predict availability of the renewable energy assets of the during the predetermined time window; determine a cost of operating the non-renewable energy assets during the predetermined time window (Sarti ¶[29] “Controller 202 may also use storage depot data/information 212, which may include sustainable energy availability (such as from a photovoltaic (PV) source 248, wind source, etc.), fixed/stationary battery storage unit 246 capacity, fleet charging station 242 data (such as maximum charging rate, availability, location, connector compatibility, etc. Controller 202 may also use grid historical data, predicted power factor, and utility rate information 214, which may also include rate schedules and surcharges associated with connected loading and associated power factor of connected loads to perform an associated grid/microgrid demand analysis 226”); and
schedule use of the utility, non-renewable energy assets, and renewable energy assets in the power dispatch schedule according to the utility pricing information, the availability of the renewable energy assets, and the cost of operating the non-renewable energy assets (Sarti ¶[29] “Based on the available information, controller 202 may maximize the use of off-peak and low-rate energy hours for charging the fleet BEVs 240, as well as fixed/stationary energy units 246”).
Regarding Claim 16, Sarti in view of Everly teaches the system of claim 10.
Sarti further teaches one or more energy storage systems (¶7] “the plurality of power sources may include fixed, stationary batteries”), and
Wherein the system optimizer application is configured to:
calculate a difference between the calculated energy demand and an amount of energy supplied according to the power dispatch schedule (¶[29] “Controller 202 may also use grid historical data, predicted power factor, and utility rate information 214, which may also include rate schedules and surcharges associated with connected loading and associated power factor of connected loads to perform an associated grid/microgrid demand analysis 226. Based on the available information, controller 202 may maximize the use of off-peak and low-rate energy hours for charging the fleet BEVs 240, as well as fixed/stationary energy units 246”);
Sarti does not explicitly teach wherein the system optimizer application is configured to:
calculate a degradation cost of using the one or more energy storage systems; and
activate the one or more energy storage systems according to the calculated difference between the energy demand and the degradation cost of using the one or more energy storage systems; however Sarti teaches calculating, by the site controller, a degradation cost of the batteries of the electric work machines (¶[32] “Controller 202 performs battery life health analysis 222 for fleet EVs 240 using a battery model to forecast the degradation rate due to different types of charging behaviors to maximize the battery life by avoiding charging behaviors that have a greater effect on battery health/life (such as unnecessary charging via a fast DC charger, unnecessary depletion to minimum allowed SOC, or unnecessary charging to maximum allowed SOC) to meet fleet demand requirements and satisfy the requested services 206. A lower battery degradation rate decreases the overall maintenance and extends battery life for the fleet vehicles 240”), and it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the same analysis to the fixed energy storage modules (which are also taught by Sarti in ¶[7]).
Regarding Claim 18, Sarti teaches the site controller of claim 17.
Sarti does not explicitly teach wherein the processing circuitry is configured to:
receive material movement information related to an amount of material to be moved during the predetermined time window;
calculate revenue from moving the material using the work machines, a cost of operating the work machines, and a cost of operating the microgrid system during the predetermined time window; and
optimize productivity from operating the electric work machines using the charging schedule for the electric work machines and the power dispatch schedule for the energy assets.
Everly teaches receive material movement information related to an amount of material to be moved during the predetermined time window (¶[72] “Tons-kilometers was chosen as the key performance indicator (KPI) to represent production. Tons-kilometers is calculated by multiplying the load carried, in tons, by the distance traveled, in kilometers”);
calculate revenue from moving the material using the work machines, a cost of operating the work machines (¶[4] “This need for improvements is only super-charged considering that haulage costs can often account for 50-60% of the total cost of mining at an operation”), and a cost of operating the microgrid system during the predetermined time window (¶[14] “Additionally, the modeling tools described herein may be used to lower operating costs by permitting scheduling of different types of vehicles depending on fuel and energy costs, which may periodically fluctuate. Additionally, inventive embodiments may allow operators to optimize on variables other than production (or to optimize on variable in addition to production)”); and
optimize productivity from operating the electric work machines using the charging schedule for the electric work machines and the power dispatch schedule for the energy assets (see ¶[14] quoted above).
It would be obvious to one of ordinary skill in the art to before the effective filing date of the claimed invention to have modified Sarti in view of Nakada to incorporate the teachings of Everly to provide wherein the processing circuitry is configured to:
receive material movement information related to an amount of material to be moved during the predetermined time window;
calculate revenue from moving the material using the work machines, a cost of operating the work machines, and a cost of operating the microgrid system during the predetermined time window; and
optimize productivity from operating the electric work machines using the charging schedule for the electric work machines and the power dispatch schedule for the energy assets,
in order to maintain productivity while still reducing charging costs.
Regarding Claim 19, Sarti in view of Everly teaches the site controller of claim 18.
Sarti further teaches wherein the processing circuitry is configured to:
calculate a battery degradation cost of cycling batteries of the work machines; and
generate the charging schedule to reduce the battery degradation cost to optimize the revenue from operating the work machines during the predetermined time window (¶[32] “Controller 202 performs battery life health analysis 222 for fleet EVs 240 using a battery model to forecast the degradation rate due to different types of charging behaviors to maximize the battery life by avoiding charging behaviors that have a greater effect on battery health/life (such as unnecessary charging via a fast DC charger, unnecessary depletion to minimum allowed SOC, or unnecessary charging to maximum allowed SOC) to meet fleet demand requirements and satisfy the requested services 206. A lower battery degradation rate decreases the overall maintenance and extends battery life for the fleet vehicles 240”).
Regarding Claim 20, Sarti teaches the site controller of claim 17.
Sarti teaches determining a charge range and a charge rate of batteries of the work machines;
determine a charge rate of the chargers of the work site; and
pair the work machines to the chargers in the charging schedule using the determined charge range and charge rates (¶[37-38] “In a first option, controller 202 may schedule charging of V1 with a fast charger 340 which may require 5-10 minutes to increase V1 SOC to 30%, but this would be more expensive and may negatively affect the battery health and decrease battery life if used repeatedly. This option may also be limited based on the distance to an available Fast DC charger 340 and the associated energy and access charges/fees, particularly if charger 340 is owned or operated by a third party … As another option, controller 202 may consider charging V1 310 to 30% with a Level 1 or Level 2 charger that takes longer than option one, but is less expensive and has less of an effect on battery health”).
Sarti does not explicitly teach wherein the processing circuitry is configured to:
receive material movement information related to an amount of material to be moved during the predetermined time window;
calculate a number of the work machines to charge using the chargers of the work site according to the received material movement information.
Everly teaches wherein the processing circuitry is configured to:
receive material movement information related to an amount of material to be moved during the predetermined time window;
calculate a number of the work machines to charge using the chargers of the work site according to the received material movement information (¶[11] “The mine operation model simulates the movement of material and vehicles in the mine, and uses the vehicle models to compute energy use and input energy required by the modeled vehicles, so as to predict a mine efficiency value as a function of various mine configuration parameters, including vehicle mix, road layout, and placement of trolley lines and refueling stations”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sarti to incorporate the teachings of Everly to provide wherein the processing circuitry is configured to:
receive material movement information related to an amount of material to be moved during the predetermined time window;
calculate a number of the work machines to charge using the chargers of the work site according to the received material movement information in order to use the minimum amount of resources (vehicles and charging sources) while still meeting production goals.
Conclusion
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/A.B./ Examiner, Art Unit 2859
/JULIAN D HUFFMAN/ Supervisory Patent Examiner, Art Unit 2859
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