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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 10 and 2 and 11 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 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.
Claim(s) 1-6, 9-15, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumaraswamy et al. (US 20240157850 A1, hereinafter Kumaraswamy) in view of Laine et al. (US 20250249906 A1, hereinafter Laine), and further in view of Wang et al. (US 20200391721 A1, hereinafter Wang).
Regarding claim 1, Kumaraswamy teaches:
A predictive supervisory energy management system for a fuel cell electric vehicle (FCEV) (at least as in paragraph 0055, “the FCEV 100 comprises a control input system 102, drivable systems 104, energy storage systems 106, and a power demand allocation system (PDAS) 108”), the predictive supervisory energy management system comprising:
a set of sensors configured to monitor driver inputs to the FCEV, states of the FCEV, and external inputs affecting the FCEV along a defined route (at least as in paragraph 0056, “The control input system 102 receives or determines a control input for the FCEV 100”; at least as in paragraph 0061, “The driving data processor 202 is configured to determine a current operating state of the FCEV 100…may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, a state of health (SoH) of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”); and
a control system connected to the set of sensors and configured to (at least as in paragraph 0060, “the PDAS 200 comprises a driving data processor 202, a predictor 204, and an optimizer 206. The PDAS 200 may be communicatively coupled to the control input system 102 and the energy storage systems 106”):
(at least as in paragraph 0064, “The predictor 204 is configured to receive the signal indicative of the current operating state of the FCEV 100 sent from the driving data processor 202 in the case that the power demand of the current operating state has a cyclical temporal evolution. The predictor 204 is then configured to determine a future operating state of the FCEV 100 based on the current operating state and a past operating state of the FCEV 100”; at least as in paragraph 0061, “The current operating state may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, a state of health (SoH) of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”; at least as in paragraph 0058, “the energy storage systems 106 may comprise at least a fuel cell and a battery”; at least as in paragraph 0089, “The optimizer 206 may be implemented as an MPC module. MPC is an optimization-based method that minimizes a desired cost function, subject to constraints on the states as well as input. This optimization is carried out over a finite horizon and the optimal input and state trajectory is calculated at every time instant… The prediction horizon is continuously shifted forward, and for this reason, MPC may also be referred to as receding horizon control”);
select, (at least as in paragraph 0076, “To determine the power demand allocation, the optimizer 206 solves an optimization problem at every time instant. This may involve solving a cost function or any other suitable type of optimization problem. The optimization problem may be subject to constraints in the operation of the fuel cell 208 and the battery 210, and be configured to minimize transients in fuel cell power delivery, thus minimizing degradation of the fuel cell 208”; at least as in paragraph 0080, “the optimizer 206 is configured to determine the power demand allocation for the fuel cell 208 and the battery 210 based on upper and/or lower limits for certain parameters. These limits may be implemented as constraints for the cost function, or as limits for some other optimization problem”; at least as in paragraph 0085-0086, wherein constraints may be implemented such as a rate constraint or end constraint; at least as in paragraph 0087, “The range defined by the upper and lower limits can be determined based on the confidence value of the correlation and/or the current operating state”; at least as in paragraph 0078-0079, wherein the cost function includes weighting coefficients; at least as in paragraph 0102, “At step 612, a confidence value for the correlation between the current operating state and the past operating state is determined. As discussed above, the confidence value is an auto-correlation coefficient normalized between −1 and 1”);
evaluate the cost function based on the determined weighting factors, boundary conditions, and the predicted energy consumption by the high voltage system across the future prediction horizon, (at least as in paragraph 0078, “the optimizer 206 is configured to determine the power demand allocation for the fuel cell 208 and the battery 210 based on a cost function”; at least as in paragraph 0107, “At step 622, the optimizer 206 determines a power demand allocation for the fuel cell 208 and the battery 210 based on the future operating state. As discussed above, the power demand allocation may be determined based on a cost function configured to minimize hydrogen consumption of the fuel cell and minimize the occurrence of transient loads on the fuel cell 208”); and
optimally control the fuel cell system and the high voltage battery system based on the evaluation of the cost function (at least as in paragraph 0108, “At step 624, the optimizer 206 sends a signal indicating the power demand allocation to the fuel cell 208 and the battery 210”).
But Kumaraswamy does not explicitly teach:
based on the driver inputs, the states of the FCEV. and the external inputs. classify an operational context of the FCEV into one of a plurality of predefined usage scenarios to determine a usage scenario for the FCEV…
from a usage scenario-specific parameter set corresponding to the determined usage scenario… the usage scenario-specific parameter set being distinct from parameter sets corresponding~ to all other usage scenario in the plurality of predefined usage scenarios…
wherein the cost function comprises a fuel cell wake-up and thermal management cost term, a terminal state of charge target term, and a terminal fuel level target term.
However, Laine, in the same field of endeavor of vehicle control systems with an optimization-based method of controlling the motion support system in accordance with a currently active drive mode, specifically teaches wherein:
based on the driver inputs, the states of the FCEV. and the external inputs. classify an operational context of the FCEV into one of a plurality of predefined usage scenarios to determine a usage scenario for the FCEV (at least as in paragraph 0045, “The method begins with a step 910 of selecting a current drive mode from at least a first drive mode and a second drive mode. The selection may be based, in the case of an automated vehicle, on the assigned mission, e.g., the mission's time criticality, the topography of the environment where the mission is to be completed. For a vehicle which a human driver, the drive mode can be selected by observing driver behaviors, such as the degree of regularity (smoothness) of acceleration and braking over time, peak values of acceleration and braking, the topography, a manual input indicating the driver's desired mode, and the like. Pattern-recognition techniques can be used to map observed driver behavior to a suitable drive mode. Common to the autonomous and human-driven case, an energy-optimizing one of the drive modes may be imposed when the vehicle's 100 battery level falls below a predetermined threshold, such as 20%”)…
from a usage scenario-specific parameter set corresponding to the determined usage scenario… the usage scenario-specific parameter set being distinct from parameter sets corresponding~ to all other usage scenario in the plurality of predefined usage scenarios (at least as in paragraph 0047, “The predefined drive modes include, generally speaking, a first drive mode which is performance-oriented and a second drive mode in which energy economy is a priority. The degree of specialization of the drive modes can vary between embodiments, notably, by defining a cost function to be optimized where different weighting coefficients are applied to a power-loss term and a term that represents an actuation error (e.g., difference between the actual vehicle behavior and a control request from the driver or ADS). In an extreme example, the first drive mode is a pure control allocation problem that neglects power-loss issues altogether. This may be achieved by defining a cost function that is independent of power loss, e.g., one which lacks a term that explicitly captures power losses; this directs more efforts at fulfilling the control request. The first and second drive modes may optionally include cost terms or constraints that act to ensure stability of the vehicle, in particular yaw stability”)…
Furthermore, Wang, in the same field of endeavor of model predictive control strategies for powertrain systems of electric-drive vehicles, specifically teaches:
wherein the cost function comprises a fuel cell wake-up and thermal management cost term, a terminal state of charge target term, and a terminal fuel level target term (at least as in paragraph 0044, “To summarize, a nonlinear MPC control protocol with SOC terminal cost is provided to optimize fuel consumption in power split output control for hybrid powertrains, while maintaining a desired SOC level. Calibration methods are also presented for estimating an MPC cost function weight that represents a fuel equivalence factor based on predicted preview route information. Also presented is calibration of MPC cost function weights using Dynamic Programming. Adaptation of MPC cost function weights may also be achieved using predicted and historic vehicle data, such as regenerative energy during a given trip”; at least as in paragraph 0045, “Other aspects of the disclosed concepts relate to novel MPC control of both HEV energy management and thermal management during a cold start operation. MPC cost function weights may be defined as equivalence fuel factors that are imposed on terminal conditions for SOC tracking and catalyst warm-up temperature tracking. In addition, unique calibration methods are provided for initial values of equivalent fuel factors based on solving original global optimization problems using the Hybrid Minimum Principle. Motor equivalence fuel factors may be designed using baseline MPC control for energy management. Also presented are catalyst heating equivalence fuel factors that are designed by solving a first order differential equation based on a catalyst model”; at least as in paragraph 0035, “Predictive, adaptive energy management for HEV/PHEV power split control may also be utilized in real-time implementations in which vehicle velocities for a predicted path of a given trip are estimated for a sequence of short time intervals [t, t.sub.p] for the next N.sub.p steps of a prediction horizon”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Kumaraswamy, to include Laine’s teaching of an optimization based method determining a current drive mode and determining the weighting of cost function and Wang’s teaching of predictive control strategies optimizing fuel consumption based on a cost function weighting on fuel factors, since Laine teaches wherein the system minimizes energy loss during operation and improves the lifespan of vehicle components and Wang teaches wherein the method increases fuel economy and reduces emissions with minimal additional cost and reduced powertrain calibration time.
Regarding claim 2, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management system of claim 1, wherein the cost function is defined as follows: J=.Math.(α1f1(Pb)+α2f2(Pfc)+α3f3(Pnet.Math.PEMd)+
α4f4(ζ.Math.ζmin*)+α5f5(ζ.Math.ζmax*)+α6f6(FCWU))Δt+
α7f7(ζf.Math.ζf*)+α8f8(LHTf,LHTf*),(1)
where J represents the cost function with weighting factors α.sub.1 to α.sub.8 for components f.sub.1 to f.sub.8, P.sub.b and P.sub.fc represent a battery terminal power and a fuel cell output power, respectively, PEMd represents a desirable power of one or more electric motors of the FCEV, P.sub.net represents a net power of the high voltage battery system, the fuel cell system, and a set of accessory systems, ζ represents a state of charge (SOC) of the high voltage battery system, ζmin*andζmax* represent target minimum and maximum bounds of ζ, FCWU represents a cost of waking up and thermally managing the fuel cell system, ζ.sub.f and ζf* represent a final remaining SOC and its target, respectively, and LHT.sub.f and LHTf* represent a final remaining level of fuel cell system fuel and its target, respectively (at least as in paragraph 0079, wherein “the cost function may be given by J.sub.E=C.sub.H.sub.2+C.sub.op+C.sub.FCS+R.sub.FCS+C.sub.s.sub.e+C.sub.s.sub.u … where k.sub.l is a weighting coefficient for hydrogen consumption, P.sub.B(t) is the power from the battery 210, P.sub.dem(t) is the power demand for the FCEV 100, s.sub.e and s.sub.u are slack variables, and k.sub.s.sub.e and k.sub.s.sub.u are slack penalties for the slack variables. C.sub.H.sub.2 and C.sub.op may also include a term related to the price of hydrogen”; at least as in paragraph 0081, “it may be desired to maintain SoC of the battery 210 between certain upper and lower limits in order to ensure the battery operates in its most efficient region of operation and prevent draining and overcharging of the battery 210. A slack variable may be introduced to the maximum SoC limit to enable the battery to be charged slightly above the maximum and maintain the SoC in that range, for example to give some leeway if the FCEV is charged throughout a long idle period”; at least as in paragraph 0078, “R.sub.FCS=k.sub.r*(P.sub.FCS(t)−P.sub.FCS(t−1)).sup.2 … k.sub.r is a weighting coefficient for the ramping rate of fuel cell power”)…
But Kumaraswamy does not explicitly teach:
wherein the weighting factors a, to aR are each independently and dynamically set to usage-specific values selected from the usage scenario-specific parameter set corresponding to the determined usage scenario such that a relative contribution of each of the components f, to fA to the cost function Jis tailed to operational objectives of the determined usage scenario.
However, Wang, in the same field of endeavor of model predictive control strategies for powertrain systems of electric-drive vehicles, specifically teaches:
wherein the weighting factors a, to aR are each independently and dynamically set to usage-specific values selected from the usage scenario-specific parameter set corresponding to the determined usage scenario such that a relative contribution of each of the components f, to fA to the cost function Jis tailed to operational objectives of the determined usage scenario (at least as in paragraph 0044, “To summarize, a nonlinear MPC control protocol with SOC terminal cost is provided to optimize fuel consumption in power split output control for hybrid powertrains, while maintaining a desired SOC level. Calibration methods are also presented for estimating an MPC cost function weight that represents a fuel equivalence factor based on predicted preview route information. Also presented is calibration of MPC cost function weights using Dynamic Programming. Adaptation of MPC cost function weights may also be achieved using predicted and historic vehicle data, such as regenerative energy during a given trip”; at least as in paragraph 0045, “Other aspects of the disclosed concepts relate to novel MPC control of both HEV energy management and thermal management during a cold start operation. MPC cost function weights may be defined as equivalence fuel factors that are imposed on terminal conditions for SOC tracking and catalyst warm-up temperature tracking. In addition, unique calibration methods are provided for initial values of equivalent fuel factors based on solving original global optimization problems using the Hybrid Minimum Principle. Motor equivalence fuel factors may be designed using baseline MPC control for energy management. Also presented are catalyst heating equivalence fuel factors that are designed by solving a first order differential equation based on a catalyst model”; at least as in paragraph 0035, “Predictive, adaptive energy management for HEV/PHEV power split control may also be utilized in real-time implementations in which vehicle velocities for a predicted path of a given trip are estimated for a sequence of short time intervals [t, t.sub.p] for the next N.sub.p steps of a prediction horizon”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Kumaraswamy, to include Wang’s teaching of predictive control strategies optimizing fuel consumption based on a cost function weighting on fuel factors, since Wang teaches wherein the method increases fuel economy and reduces emissions with minimal additional cost and reduced powertrain calibration time.
Regarding claim 3, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management system of claim 1, wherein the control system is configured to apply numerical and statistical methods to predict a vehicle state evolution through the future prediction horizon, wherein the predicted vehicle state evolution is used in the evaluating of the cost function (at least as in paragraph 0064-0065, “the predictor 204 is configured to determine a temporal evolution of a future power demand for the FCEV 100. To achieve this, the system works on the assumption that analysis of historical values can be useful in forecasting the future. In particular, the predictor 204 is configured to take the cyclical power demand of the current operating state, and determine if a matching or similar power demand was present in a past operating state of the FCEV 100”).
Regarding claim 4, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management system of claim 1, wherein the driver inputs, the states of the FCEV, and the external inputs affecting the FCEV include both historical and real-time information (at least as in paragraph 0068, “The temporal evolution of the power demand in a past state may be data stored from historical operation of the FCEV 100, or may be recent data taken “from earlier in a current operation period”; at least as in paragraph 0062, “the driving data processor 202 is configured to determine the current operating state of the FCEV 100 in real time”; at least as in paragraph 0072, “the predictor 204 can determine a future power demand based on the past power demand. In particular, a good correlation between a current and a past operating state allows the predictor 204 to study the power demand after the correlated past state and use this as a prediction for the future power demand”).
Regarding claim 5, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management system of claim 4, wherein the driver inputs include (i) route plan, (ii) accelerator pedal position, (iii) vehicle stoppage intervals, (iv) accessory loads, (v) fuel cell system refueling and battery charging patterns, (vi) vehicle driving mode, (vii) departure time, (viii) scheduled charging, and (ix) scheduled conditioning (at least as in paragraph 0062, “In examples where the operating conditions do not change and are nearly 100% repetitive, such as a forklift operating inside a warehouse on a fixed path, the power demand will be known beforehand and may be directly pre-programmed in the predictor 204 or the optimizer 206”; at least as in paragraph 0056, “an operator may input controls for example through a steering wheel, pedals, levers and the like to control operation of the FCEV 100”; at least as in paragraph 0054, “Examples of FCEVs having cyclical power demands include a forklift inside a factory that operates on a known route, a hauler that operates between common loading and drop-off points, a wheel loader that drives to a pile, picks up a load, drives to a stationary hauler, and unloads, or a bus that drives repeatedly along the same route… the FCEV 100 may be controlled by an “autopilot” system that determines control signals for the FCEV based on a programmed operation schedule or a user input”; at least as in paragraph 0098, “the current operating state may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, and a SoH of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”; at least as in paragraph 0057, “Drivable systems 104 include the power consuming systems of the FCEV 100. This may include the powertrain, hydraulics, brakes, and auxiliary systems such as cabin heating, lights, navigation/infotainment, and the like. In the context of a construction vehicle, the drivable systems 104 may include machine attachments such as components attached to excavator shovels, loaders, and the like”).
Regarding claim 6, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management system of claim 4, wherein the states of the FCEV include (i) subsystem temperatures, (ii) subsystem efficiencies, (iii) remaining battery energy, (iv) battery state of health (SOH), (v) accumulated vehicle run time, (vi) accumulated vehicle energy consumption, and/or (vii) vehicle weight and payload (at least as in paragraph 0061, “The current operating state may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, a state of health (SoH) of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”).
Regarding claim 9, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management system of claim 1, wherein the usage scenario is one of (i) maximizing regenerative braking capability, (ii) maintaining SOC for key-off functions, (iii) optimizing vehicle-to-load and/or vehicle-to-home functions, (iv) blending fuel cell system fuel and battery SOC based on availability of charging and refueling stations, or (v) managing trade-off between fuel cell system warm-up and other thermal conditioning loads (at least as in paragraph 0072, “In the case that the prediction is higher than the real demand, any extra power generated can be used to charge the battery 210 such that no power is wasted”; at least as in paragraph 0075, “a part 406 or 408 of the temporal evolution 400 offset from and subsequent to the correlated part 402 can be taken and used for a predicted future state. This may be advantageous in the case that it is desired to correlate the current state with an event at a certain time in the past, for example at the start of a lunch break and use that to predict what happens after the break”).
Regarding claim 10, Kumaraswamy teaches:
A predictive supervisory energy management method for a fuel cell electric vehicle (FCEV) (at least as in paragraph 0097, “method 600 of power demand allocation for an FCEV”), the predictive supervisory energy management method comprising:
monitoring, by a set of sensors of the FCEV, driver inputs to the FCEV, states of the FCEV, and external inputs affecting the FCEV along a defined route (at least as in paragraph 0056, “The control input system 102 receives or determines a control input for the FCEV 100”; at least as in paragraph 0061, “The driving data processor 202 is configured to determine a current operating state of the FCEV 100…may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, a state of health (SoH) of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”);
(at least as in paragraph 0064, “The predictor 204 is configured to receive the signal indicative of the current operating state of the FCEV 100 sent from the driving data processor 202 in the case that the power demand of the current operating state has a cyclical temporal evolution. The predictor 204 is then configured to determine a future operating state of the FCEV 100 based on the current operating state and a past operating state of the FCEV 100”; at least as in paragraph 0061, “The current operating state may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, a state of health (SoH) of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”; at least as in paragraph 0058, “the energy storage systems 106 may comprise at least a fuel cell and a battery”; at least as in paragraph 0089, “The optimizer 206 may be implemented as an MPC module. MPC is an optimization-based method that minimizes a desired cost function, subject to constraints on the states as well as input. This optimization is carried out over a finite horizon and the optimal input and state trajectory is calculated at every time instant… The prediction horizon is continuously shifted forward, and for this reason, MPC may also be referred to as receding horizon control”);
selecting, by the control system, (at least as in paragraph 0076, “To determine the power demand allocation, the optimizer 206 solves an optimization problem at every time instant. This may involve solving a cost function or any other suitable type of optimization problem. The optimization problem may be subject to constraints in the operation of the fuel cell 208 and the battery 210, and be configured to minimize transients in fuel cell power delivery, thus minimizing degradation of the fuel cell 208”; at least as in paragraph 0080, “the optimizer 206 is configured to determine the power demand allocation for the fuel cell 208 and the battery 210 based on upper and/or lower limits for certain parameters. These limits may be implemented as constraints for the cost function, or as limits for some other optimization problem”; at least as in paragraph 0085-0086, wherein constraints may be implemented such as a rate constraint or end constraint; at least as in paragraph 0087, “The range defined by the upper and lower limits can be determined based on the confidence value of the correlation and/or the current operating state”; at least as in paragraph 0078-0079, wherein the cost function includes weighting coefficients; at least as in paragraph 0102, “At step 612, a confidence value for the correlation between the current operating state and the past operating state is determined. As discussed above, the confidence value is an auto-correlation coefficient normalized between −1 and 1”);
evaluating, by the control system, the cost function based on the determined weighting factors, boundary conditions, and the predicted energy consumption by the high voltage system across the future prediction horizon, (at least as in paragraph 0078, “the optimizer 206 is configured to determine the power demand allocation for the fuel cell 208 and the battery 210 based on a cost function”; at least as in paragraph 0107, “At step 622, the optimizer 206 determines a power demand allocation for the fuel cell 208 and the battery 210 based on the future operating state. As discussed above, the power demand allocation may be determined based on a cost function configured to minimize hydrogen consumption of the fuel cell and minimize the occurrence of transient loads on the fuel cell 208”); and
optimally controlling, by the control system, the fuel cell system and the high voltage battery system based on the evaluation of the cost function (at least as in paragraph 0108, “At step 624, the optimizer 206 sends a signal indicating the power demand allocation to the fuel cell 208 and the battery 210”).
But Kumaraswamy does not explicitly teach:
based on the driver inputs, the states of the FCEV, and the external inputs, classifying, by a control system of the FCEV, an operational context of the FCEV into one of a plurality of predefined usage scenarios to determine a usage scenario for the FCEV…
and from a usage scenario-specific parameter set corresponding to the determined usage scenario… the usage scenario-specific parameter set being distinct from parameter sets corresponding to all other usage scenario in the plurality of predefined usage scenarios…
wherein the cost function comprises a fuel cell wake-up and thermal management cost term, a terminal state of charge target term, and a terminal fuel level target term.
However, Laine, in the same field of endeavor of vehicle control systems with an optimization-based method of controlling the motion support system in accordance with a currently active drive mode, specifically teaches wherein:
based on the driver inputs, the states of the FCEV, and the external inputs, classifying, by a control system of the FCEV, an operational context of the FCEV into one of a plurality of predefined usage scenarios to determine a usage scenario for the FCEV (at least as in paragraph 0045, “The method begins with a step 910 of selecting a current drive mode from at least a first drive mode and a second drive mode. The selection may be based, in the case of an automated vehicle, on the assigned mission, e.g., the mission's time criticality, the topography of the environment where the mission is to be completed. For a vehicle which a human driver, the drive mode can be selected by observing driver behaviors, such as the degree of regularity (smoothness) of acceleration and braking over time, peak values of acceleration and braking, the topography, a manual input indicating the driver's desired mode, and the like. Pattern-recognition techniques can be used to map observed driver behavior to a suitable drive mode. Common to the autonomous and human-driven case, an energy-optimizing one of the drive modes may be imposed when the vehicle's 100 battery level falls below a predetermined threshold, such as 20%”)…
and from a usage scenario-specific parameter set corresponding to the determined usage scenario… the usage scenario-specific parameter set being distinct from parameter sets corresponding to all other usage scenario in the plurality of predefined usage scenarios (at least as in paragraph 0047, “The predefined drive modes include, generally speaking, a first drive mode which is performance-oriented and a second drive mode in which energy economy is a priority. The degree of specialization of the drive modes can vary between embodiments, notably, by defining a cost function to be optimized where different weighting coefficients are applied to a power-loss term and a term that represents an actuation error (e.g., difference between the actual vehicle behavior and a control request from the driver or ADS). In an extreme example, the first drive mode is a pure control allocation problem that neglects power-loss issues altogether. This may be achieved by defining a cost function that is independent of power loss, e.g., one which lacks a term that explicitly captures power losses; this directs more efforts at fulfilling the control request. The first and second drive modes may optionally include cost terms or constraints that act to ensure stability of the vehicle, in particular yaw stability”)…
Furthermore, Wang, in the same field of endeavor of model predictive control strategies for powertrain systems of electric-drive vehicles, specifically teaches:
wherein the cost function comprises a fuel cell wake-up and thermal management cost term, a terminal state of charge target term, and a terminal fuel level target term (at least as in paragraph 0044, “To summarize, a nonlinear MPC control protocol with SOC terminal cost is provided to optimize fuel consumption in power split output control for hybrid powertrains, while maintaining a desired SOC level. Calibration methods are also presented for estimating an MPC cost function weight that represents a fuel equivalence factor based on predicted preview route information. Also presented is calibration of MPC cost function weights using Dynamic Programming. Adaptation of MPC cost function weights may also be achieved using predicted and historic vehicle data, such as regenerative energy during a given trip”; at least as in paragraph 0045, “Other aspects of the disclosed concepts relate to novel MPC control of both HEV energy management and thermal management during a cold start operation. MPC cost function weights may be defined as equivalence fuel factors that are imposed on terminal conditions for SOC tracking and catalyst warm-up temperature tracking. In addition, unique calibration methods are provided for initial values of equivalent fuel factors based on solving original global optimization problems using the Hybrid Minimum Principle. Motor equivalence fuel factors may be designed using baseline MPC control for energy management. Also presented are catalyst heating equivalence fuel factors that are designed by solving a first order differential equation based on a catalyst model”; at least as in paragraph 0035, “Predictive, adaptive energy management for HEV/PHEV power split control may also be utilized in real-time implementations in which vehicle velocities for a predicted path of a given trip are estimated for a sequence of short time intervals [t, t.sub.p] for the next N.sub.p steps of a prediction horizon”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Kumaraswamy, to include Laine’s teaching of an optimization based method determining a current drive mode and determining the weighting of cost function and Wang’s teaching of predictive control strategies optimizing fuel consumption based on a cost function weighting on fuel factors, since Laine teaches wherein the system minimizes energy loss during operation and improves the lifespan of vehicle components and Wang teaches wherein the method increases fuel economy and reduces emissions with minimal additional cost and reduced powertrain calibration time.
Regarding claim 11, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management method of claim 10, wherein the cost function is defined as follows: J=.Math.(α1f1(Pb)+α2f2(Pfc)+α3f3(Pnet.Math.PEMd)+
α4f4(ζ.Math.ζmin*)+α5f5(ζ.Math.ζmax*)+α6f6(FCWU))Δt+
α7f7(ζf.Math.ζf*)+α8f8(LHTf,LHTf*),(1)
where J represents the cost function with weighting factors α.sub.1 to α.sub.8 for components f.sub.1 to f.sub.8, P.sub.b and P.sub.fc represent a battery terminal power and a fuel cell output power, respectively, PEMd represents a desirable power of one or more electric motors of the FCEV, P.sub.net represents a net power of the high voltage battery system, the fuel cell system, and a set of accessory systems, ζ represents a state of charge (SOC) of the high voltage battery system, ζmin*andζmax* represent target minimum and maximum bounds of ζ, FCWU represents a cost of waking up and thermally managing the fuel cell system, ζ.sub.f and ζf* represent a final remaining SOC and its target, respectively, and LHT.sub.f and LHTf* represent a final remaining level of fuel cell system fuel and its target, respectively (at least as in paragraph 0079, wherein “the cost function may be given by J.sub.E=C.sub.H.sub.2+C.sub.op+C.sub.FCS+R.sub.FCS+C.sub.s.sub.e+C.sub.s.sub.u … where k.sub.l is a weighting coefficient for hydrogen consumption, P.sub.B(t) is the power from the battery 210, P.sub.dem(t) is the power demand for the FCEV 100, s.sub.e and s.sub.u are slack variables, and k.sub.s.sub.e and k.sub.s.sub.u are slack penalties for the slack variables. C.sub.H.sub.2 and C.sub.op may also include a term related to the price of hydrogen”; at least as in paragraph 0081, “it may be desired to maintain SoC of the battery 210 between certain upper and lower limits in order to ensure the battery operates in its most efficient region of operation and prevent draining and overcharging of the battery 210. A slack variable may be introduced to the maximum SoC limit to enable the battery to be charged slightly above the maximum and maintain the SoC in that range, for example to give some leeway if the FCEV is charged throughout a long idle period”; at least as in paragraph 0078, “R.sub.FCS=k.sub.r*(P.sub.FCS(t)−P.sub.FCS(t−1)).sup.2 … k.sub.r is a weighting coefficient for the ramping rate of fuel cell power”)…
But Kumaraswamy does not explicitly teach:
wherein the weighting factors a, to aR are each independently and dynamically set to usage-specific values selected from the usage scenario-specific parameter set corresponding to the determined usage scenario such that a relative contribution of each of the components f, to fA to the cost function Jis tailed to operational objectives of the determined usage scenario.
However, Wang, in the same field of endeavor of model predictive control strategies for powertrain systems of electric-drive vehicles, specifically teaches:
wherein the weighting factors a, to aR are each independently and dynamically set to usage-specific values selected from the usage scenario-specific parameter set corresponding to the determined usage scenario such that a relative contribution of each of the components f, to fA to the cost function Jis tailed to operational objectives of the determined usage scenario (at least as in paragraph 0044, “To summarize, a nonlinear MPC control protocol with SOC terminal cost is provided to optimize fuel consumption in power split output control for hybrid powertrains, while maintaining a desired SOC level. Calibration methods are also presented for estimating an MPC cost function weight that represents a fuel equivalence factor based on predicted preview route information. Also presented is calibration of MPC cost function weights using Dynamic Programming. Adaptation of MPC cost function weights may also be achieved using predicted and historic vehicle data, such as regenerative energy during a given trip”; at least as in paragraph 0045, “Other aspects of the disclosed concepts relate to novel MPC control of both HEV energy management and thermal management during a cold start operation. MPC cost function weights may be defined as equivalence fuel factors that are imposed on terminal conditions for SOC tracking and catalyst warm-up temperature tracking. In addition, unique calibration methods are provided for initial values of equivalent fuel factors based on solving original global optimization problems using the Hybrid Minimum Principle. Motor equivalence fuel factors may be designed using baseline MPC control for energy management. Also presented are catalyst heating equivalence fuel factors that are designed by solving a first order differential equation based on a catalyst model”; at least as in paragraph 0035, “Predictive, adaptive energy management for HEV/PHEV power split control may also be utilized in real-time implementations in which vehicle velocities for a predicted path of a given trip are estimated for a sequence of short time intervals [t, t.sub.p] for the next N.sub.p steps of a prediction horizon”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Kumaraswamy, to include Wang’s teaching of predictive control strategies optimizing fuel consumption based on a cost function weighting on fuel factors, since Wang teaches wherein the method increases fuel economy and reduces emissions with minimal additional cost and reduced powertrain calibration time.
Regarding claim 12, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management method of claim 10, further comprising applying, by the control system, numerical and statistical methods to predict a vehicle state evolution through the future prediction horizon, wherein the predicted vehicle state evolution is used by the control system in the evaluating of the cost function (at least as in paragraph 0064-0065, “the predictor 204 is configured to determine a temporal evolution of a future power demand for the FCEV 100. To achieve this, the system works on the assumption that analysis of historical values can be useful in forecasting the future. In particular, the predictor 204 is configured to take the cyclical power demand of the current operating state, and determine if a matching or similar power demand was present in a past operating state of the FCEV 100”).
Regarding claim 13, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management method of claim 10, wherein the driver inputs, the states of the FCEV, and the external inputs affecting the FCEV include both historical and real-time information (at least as in paragraph 0068, “The temporal evolution of the power demand in a past state may be data stored from historical operation of the FCEV 100, or may be recent data taken “from earlier in a current operation period”; at least as in paragraph 0062, “the driving data processor 202 is configured to determine the current operating state of the FCEV 100 in real time”; at least as in paragraph 0072, “the predictor 204 can determine a future power demand based on the past power demand. In particular, a good correlation between a current and a past operating state allows the predictor 204 to study the power demand after the correlated past state and use this as a prediction for the future power demand”).
Regarding claim 14, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management method of claim 13, wherein the driver inputs include (i) route plan, (ii) accelerator pedal position, (iii) vehicle stoppage intervals, (iv) accessory loads, (v) fuel cell system refueling and battery charging patterns, (vi) vehicle driving mode, (vii) departure time, (viii) scheduled charging, and (ix) scheduled conditioning (at least as in paragraph 0062, “In examples where the operating conditions do not change and are nearly 100% repetitive, such as a forklift operating inside a warehouse on a fixed path, the power demand will be known beforehand and may be directly pre-programmed in the predictor 204 or the optimizer 206”; at least as in paragraph 0056, “an operator may input controls for example through a steering wheel, pedals, levers and the like to control operation of the FCEV 100”; at least as in paragraph 0054, “Examples of FCEVs having cyclical power demands include a forklift inside a factory that operates on a known route, a hauler that operates between common loading and drop-off points, a wheel loader that drives to a pile, picks up a load, drives to a stationary hauler, and unloads, or a bus that drives repeatedly along the same route… the FCEV 100 may be controlled by an “autopilot” system that determines control signals for the FCEV based on a programmed operation schedule or a user input”; at least as in paragraph 0098, “the current operating state may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, and a SoH of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”; at least as in paragraph 0057, “Drivable systems 104 include the power consuming systems of the FCEV 100. This may include the powertrain, hydraulics, brakes, and auxiliary systems such as cabin heating, lights, navigation/infotainment, and the like. In the context of a construction vehicle, the drivable systems 104 may include machine attachments such as components attached to excavator shovels, loaders, and the like”).
Regarding claim 15, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management method of claim 13, wherein the states of the FCEV include (i) subsystem temperatures, (ii) subsystem efficiencies, (iii) remaining battery energy, (iv) battery state of health (SOH), (v) accumulated vehicle run time, (vi) accumulated vehicle energy consumption, and/or (vii) vehicle weight and payload (at least as in paragraph 0061, “The current operating state may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, a state of health (SoH) of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”).
Regarding claim 18, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management method of claim 10, wherein the usage scenario is one of (i) maximizing regenerative braking capability, (ii) maintaining SOC for key-off functions, (iii) optimizing vehicle-to-load and/or vehicle-to-home functions, (iv) blending fuel cell system fuel and battery SOC based on availability of charging and refueling stations, or (v) managing trade-off between fuel cell system warm-up and other thermal conditioning loads (at least as in paragraph 0072, “In the case that the prediction is higher than the real demand, any extra power generated can be used to charge the battery 210 such that no power is wasted”; at least as in paragraph 0075, “a part 406 or 408 of the temporal evolution 400 offset from and subsequent to the correlated part 402 can be taken and used for a predicted future state. This may be advantageous in the case that it is desired to correlate the current state with an event at a certain time in the past, for example at the start of a lunch break and use that to predict what happens after the break”).
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumaraswamy et al. (US 20240157850 A1, hereinafter Kumaraswamy) in view of Laine et al. (US 20250249906 A1, hereinafter Laine) and Wang et al. (US 20200391721 A1, hereinafter Wang), and further in view of Eschenbach et al. (US 20170179512 A1, hereinafter Eschenbach).
Regarding claim 7, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management system of claim 4, wherein the external inputs affecting the FCEV include (i) geographical location, (ii) route topological information, (iii) climatic information, (iv(at least as in paragraph 0061, “The current operating state may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, a state of health (SoH) of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”).
Kumaraswamy does not explicitly teach (iv) traffic information, and (v) location of battery charging and fuel cell system refueling stations.
However, Eschenbach, in the same field of endeavor of the predictive operation of a fuel cell or of a high-voltage accumulator of a vehicle, specifically teaches (iv) traffic information, and (v) location of battery charging and fuel cell system refueling stations (at least as in paragraph 0022, “External parameters which represent navigation information are, for example, navigation parameters, which include geographic information such as, for example, position information, route information and/or altitude profile information. Navigation information is also information about the driving cycle, i.e. the mix of the town cycle component, long inter-city component and/or freeway component in the overall route. Further navigation information is, for example, a relatively long journey with an increased positive gradient (uphill journey) which can often entail operation of the fuel cell in the upper load range. Further navigation information is also, for example, traffic information such as current or future traffic problems. For example, current traffic jam reports or predictable areas of dense traffic owing to large events, business traffic, particular events such as, for example, a large-scale gathering, etc. are included in the navigation information”; at least as in paragraph 0043, “The method can also include the step of reducing consumption and/or switching off of at least one energy consumer, in particular an energy consumer which is not relevant for driving the vehicle. If, for example, it is detected that the next refueling station cannot be reached, the controller of the vehicle can, as an emergency mode, switch off the secondary consumers which are not relevant for the driving mode, or reduce their consumption”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Kumaraswamy, to include Eschenbach's teaching of a system predicting potential operating parameters of the fuel cell based on external parameters, since Eschebach teaches wherein the system utilizing external parameters allows for the power requirement to be predicted more precisely and the fuel cell to be operated in a predictive fashion, thus improving the health of components and the efficient usage of energy.
Regarding claim 16, in view of the above combination of Kumaraswamy, Laine, and Wang, Kumaraswamy further teaches:
The predictive supervisory energy management method of claim 13, wherein the external inputs affecting the FCEV include (i) geographical location, (ii) route topological information, (iii) climatic information, (iv(at least as in paragraph 0061, “The current operating state may include information such as a power demand for the FCEV 100, an energy level of the fuel cell 208, a SoC of the battery 210, a state of health (SoH) of the battery 210, a temperature of the fuel cell 208 and/or the battery 210, a GPS location of the FCEV 100, an inclination of the FCEV 100, grip levels and available traction, for example based on weather data, and other suitable information”).
Kumaraswamy does not explicitly teach (iv) traffic information, and (v) location of battery charging and fuel cell system refueling stations.
However, Eschenbach, in the same field of endeavor of the predictive operation of a fuel cell or of a high-voltage accumulator of a vehicle, specifically teaches (iv) traffic information, and (v) location of battery charging and fuel cell system refueling stations (at least as in paragraph 0022, “External parameters which represent navigation information are, for example, navigation parameters, which include geographic information such as, for example, position information, route information and/or altitude profile information. Navigation information is also information about the driving cycle, i.e. the mix of the town cycle component, long inter-city component and/or freeway component in the overall route. Further navigation information is, for example, a relatively long journey with an increased positive gradient (uphill journey) which can often entail operation of the fuel cell in the upper load range. Further navigation information is also, for example, traffic information such as current or future traffic problems. For example, current traffic jam reports or predictable areas of dense traffic owing to large events, business traffic, particular events such as, for example, a large-scale gathering, etc. are included in the navigation information”; at least as in paragraph 0043, “The method can also include the step of reducing consumption and/or switching off of at least one energy consumer, in particular an energy consumer which is not relevant for driving the vehicle. If, for example, it is detected that the next refueling station cannot be reached, the controller of the vehicle can, as an emergency mode, switch off the secondary consumers which are not relevant for the driving mode, or reduce their consumption”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Kumaraswamy, to include Eschenbach's teaching of a system predicting potential operating parameters of the fuel cell based on external parameters, since Eschebach teaches wherein the system utilizing external parameters allows for the power requirement to be predicted more precisely and the fuel cell to be operated in a predictive fashion, thus improving the health of components and the efficient usage of energy.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICARDO I VISCARRA whose telephone number is (571)270-0154. The examiner can normally be reached M-F 9-12 & 2-4 PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Mott can be reached on (571) 270-5376. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RICARDO I VISCARRA/Examiner, Art Unit 3657 /ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
Prosecution Insights