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 .
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(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) 1-3, 10-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by ElBstat (hereinafter El) (USPGPUB US20180224814).
As per claim 1, 14, 15, El discloses, A method for controlling an energy storage system, the method comprising the steps of: a) obtaining information related to a state of the energy storage system; b) determining a control action for controlling the energy storage system; c) evaluating a cost function using the control action and the information; the controller is configured to perform the optimization by optimizing the cost defined by the modified cost function. In some embodiments, the controller is configured to perform the optimization by optimizing a financial metric including at least one of net present value, internal rate of return, or simple payback period. In some embodiments, the controller is configured to perform the optimization using mixed integer linear programming. In some embodiments, the controller is configured to carry over the optimal values of the asset size variables and use the optimal values of the asset size variables as a lower limit of the asset size variables to be determined over a next execution of the optimization. 0006-0007 d) determining whether the evaluated cost function has reached a minimum; and e) if the evaluated cost function has reached the minimum, applying the control action to the energy storage system; and if the evaluated cost function has not reached the minimum, adjusting the control action on a basis of the evaluated cost function and repeating step c) using the adjusted control action. low level optimizer 634 may determine on/off states and/or operating setpoints for various devices of the subplant equipment in order to optimize (e.g., minimize) the energy consumption of each subplant while meeting the resource allocation setpoint for the subplant. In some embodiments, low level optimizer 634 receives actual incentive events from incentive programs 602. Low level optimizer 634 may determine whether to participate in the incentive events based on the resource allocation set by high level optimizer 632. For example, if insufficient resources have been allocated to a particular IBDR program by high level optimizer 632 or if the allocated resources have already been used, low level optimizer 634 may determine that energy storage system 500 will not participate in the IBDR program and may ignore the IBDR event. 0131
As per Claim 2, El discloses, wherein the energy storage system comprises at least one module, wherein the control action is determined for each of the at least one module, and wherein the control action preferably relates to conduction times of the at least one module energy cost optimization system 550 is shown to include a controller 552. Controller 552 may be configured to control the distribution, production, and usage of resources in system 550. In some embodiments, controller 552 performs an optimization process determine an optimal set of control decisions for each time step within an optimization period. The control decisions may include, for example, an optimal amount of each resource to purchase from utilities 510, an optimal amount of each resource to produce or convert using generator subplants 520, an optimal amount of each resource to sell to energy purchasers 504, and/or an optimal amount of each resource to provide to building 502. In some embodiments, the control decisions include an optimal amount of each input resource and output resource for each of generator subplants 520. 0107
As per Claim 3, El discloses, wherein the control action relates to bypassing and/or reducing energy output of at least one module energy cost optimization system 550 is shown to include a controller 552. Controller 552 may be configured to control the distribution, production, and usage of resources in system 550. In some embodiments, controller 552 performs an optimization process determine an optimal set of control decisions for each time step within an optimization period. The control decisions may include, for example, an optimal amount of each resource to purchase from utilities 510, an optimal amount of each resource to produce or convert using generator subplants 520, an optimal amount of each resource to sell to energy purchasers 504, and/or an optimal amount of each resource to provide to building 502. In some embodiments, the control decisions include an optimal amount of each input resource and output resource for each of generator subplants 520. 0107
As per Claim 10, El discloses, wherein the control action is determined along a control horizon, wherein preferably the evaluation of the cost function includes evaluating a power demand predicted along the control horizon. The energy cost optimization system can be configured to determine the optimal size of an asset by considering the potential benefits and costs of the asset. Potential benefits can include, for example, reduced energy costs, reduced demand charges, reduced peak load contribution (PLC) charges, and/or increased revenue from participating in incentive-based demand response (IBDR) programs such as frequency regulation (FR) or economic load demand response (ELDR). Potential costs can include fixed costs (e.g., an initial purchase cost of the asset) as well as marginal costs (e.g., ongoing costs of using the asset) over the time horizon. 0042
As per Claim 11, El discloses, wherein the control action determined in step b) is based on the information obtained in step a). the controller is configured to perform the optimization by optimizing the cost defined by the modified cost function. In some embodiments, the controller is configured to perform the optimization by optimizing a financial metric including at least one of net present value, internal rate of return, or simple payback period. In some embodiments, the controller is configured to perform the optimization using mixed integer linear programming. In some embodiments, the controller is configured to carry over the optimal values of the asset size variables and use the optimal values of the asset size variables as a lower limit of the asset size variables to be determined over a next execution of the optimization. 0006-0007
As per Claim 12, El discloses, wherein in step e) the control action is also applied to the energy storage system if a stopping criterion is satisfied. the controller is configured to perform the optimization by optimizing the cost defined by the modified cost function. In some embodiments, the controller is configured to perform the optimization by optimizing a financial metric including at least one of net present value, internal rate of return, or simple payback period. In some embodiments, the controller is configured to perform the optimization using mixed integer linear programming. In some embodiments, the controller is configured to carry over the optimal values of the asset size variables and use the optimal values of the asset size variables as a lower limit of the asset size variables to be determined over a next execution of the optimization. 0006-0007
As per Claim 13, El discloses, wherein the stopping criterion is at least one of reaching a maximum number of iterations of step c), falling below a minimum difference between evaluated cost functions of two subsequent iterations of step c), a total computation time of the performed method, falling below a minimum difference between two subsequent iterations of the control action. the controller is configured to perform the optimization by optimizing the cost defined by the modified cost function. In some embodiments, the controller is configured to perform the optimization by optimizing a financial metric including at least one of net present value, internal rate of return, or simple payback period. In some embodiments, the controller is configured to perform the optimization using mixed integer linear programming. In some embodiments, the controller is configured to carry over the optimal values of the asset size variables and use the optimal values of the asset size variables as a lower limit of the asset size variables to be determined over a next execution of the optimization. 0006-0007
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) 4-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over ElBstat (hereinafter El) (USPGPUB US20180224814) and further in view of Chang et al [hereinafter Chang] (USPGPUB US20160075254)
As per Claim 4, Chang discloses, wherein the cost function is a function of a temperature of the energy storage system, wherein preferably the temperature is a predicted temperature That is, (SOC×SOH) is the product of the state of charge (SOC) and the state of health (SOH). In the formula 7 and the formula 8, the sorting controller 101 judges whether the temperature rise of the battery module is abnormal. Generally, the battery module whose battery core temperature is abnormally high has a lower module score than the battery module whose battery core temperature is normal. Moreover, if the battery core temperatures of some battery modules are nearly equal, the battery module with higher electric capacity has the priority to provide the electric energy (i.e., has the higher module score). From the above mathematic formulae, it is found that the module score of the battery module is positively related to the state of charge (SOC), related to the temperature rise curve of the battery module, and negatively related to the battery core temperature of the battery module. 0030
It would have been obvious to one of ordinary skill in the art, at the time of the invention, to modify the system of El to include the use and calculation of cost optimization using battery core temperature as taught by Chang. The motivation to combine the arts is taught by Chang “the sorting controller 101 calculates a corresponding module score of each battery module according to the state of charge, the state of health and the battery core temperature of each battery module, which are obtained by the vehicular computer 10” 0043
As per Claim 5, Chang discloses wherein the temperature of the energy storage system is calculated using a spatially discretized heat transfer equation, resulting in a plurality of spatially discretized temperature values, wherein preferably the cost function is a variance of the plurality of spatially discretized temperature values. the sorting controller 101 calculates a corresponding module score of each battery module according to the state of charge, the state of health and the battery core temperature of each battery module, which are obtained by the vehicular computer 10. Then, the battery modules of each configuration-variable series-type battery box are sorted according to the rank of the module scores, and thus a battery module sorting result is obtained. Moreover, the module score is defined according to a mathematic formula containing the state of charge, the state of health and/or the temperature information of each battery module 0029
As per Claim 6, Chang discloses, wherein the spatially discretized heat transfer equation takes into account cooling effects taking place in the energy storage system module score=SOC−((battery core temperature−average battery core temperature of all modules)×temperature rise compensation coefficient) Formula 7: module score=(SOC×SOH)−temperature rise compensation coefficient×(battery core temperature−∫((battery discharge quantity×heat loss proportion coefficient)−(heat dissipation coefficient)×(battery temperature−battery box internal temperature)))) Formula 8: module score=(SOC×SOH)−(temperature rise compensation coefficient×(battery core temperature−evaluated battery 0029
As per Claim 7, Chang discloses, wherein the energy storage system is a multilevel converter comprising a plurality of energy sources and a plurality of power converter modules, each power converter module comprising at least two switching elements. Consequently, a DC bus voltage of the motor drive 191 of the power structure 1 is adjusted to comply with the optimized setting of the target motor speed range. According to this setting, the duty cycle of each power transistor is not too short or too long and is close to the ideal duty cycle when the power structure 1 provides the electric energy to the motor drive 191. Moreover, the DC bus voltage of the motor drive 191 is related to the number of serially-connected battery modules of the four configuration-variable series-type battery boxes 11˜14 in the power supply mode. Consequently, in the step S11, the required DC bus voltage range is calculated according to the proportional relation between the motor speed and the required voltage, and the required number N of battery modules is calculated according to the required DC bus voltage range. On the other hand, the vehicular computer 10 also detects or forecasts a target motor torque of the electric vehicle. Since the accelerating capability of the motor of the electric vehicle is dependent on the magnitude of the current, the current of the motor drive 191 to drive the motor 192 is limited by the number of the parallel-connected configuration-variable series-type battery boxes 0027-0028
As per Claim 8, Chang discloses, wherein the control action relates to bypassing and/or reducing the energy output of at least one energy source. That is, after the sorting controller 101 sorts the battery modules of each configuration-variable series-type battery box, the sorting controller 101 will select N battery modules with the highest module scores according to the battery module sorting result and the required number N of battery modules calculated in the step S11. Moreover, the relays of these selected battery modules are controlled by the battery module monitoring boards of the corresponding battery modules. Consequently, the relays of these selected battery modules are connected with the battery core strings of the corresponding battery modules. In such way, the selected battery modules are added to the power supply loop of the corresponding configuration-variable series-type battery box, and the power supply voltage is adjusted 0031
As per Claim 9, Chang discloses, wherein the information comprises at least one of a terminal voltage of the energy storage system, a current of the energy storage system, an ambient temperature of the energy storage system, and a state of charge of the energy storage system. he sorting controller 101 calculates a corresponding module score of each battery module according to the state of charge, the state of health and the battery core temperature of each battery module, which are obtained by the vehicular computer 10. Then, the battery modules of each configuration-variable series-type battery box are sorted according to the rank of the module scores, and thus a battery module sorting result is obtained. Moreover, the module score is defined according to a mathematic formula containing the state of charge, the state of health and/or the temperature information of each battery module. 0029
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH M LO whose telephone number is (571)272-9774. The examiner can normally be reached M-F 830a - 6pm.
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KENNETH M. LO
Supervisory Patent Examiner
Art Unit 2136
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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