DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1 thru 19 and 35 have been examined. Claims 20 thru 34 and 36 thru 51 have been cancelled.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: From Figure 10, reference characters R, A, S, n0, n1, n2 and nk are not in the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
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)(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) 1 thru 6 and 8 thru 19 and 35 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Lima et al Patent Application Publication Number 2022/0200405 A1.
Regarding claim 1 Lima et al disclose the claimed method of controlling vehicle fan speed to regulate coolant temperature, “a method performed in a control unit for controlling the operation of the cooling arrangements” P[0013], the flow chart of Figure 7, and an operational parameter of the fan speed of rotation may be adjusted for cooling P[0049], the method comprising:
the claimed performing a present fan speed demand generation iteration, performing a method for predictive master cooling control P[0050], comprising:
the claimed acquiring previous coolant temperature data and previous thermal impact data wherein the previous thermal impact data is associated with the previous coolant temperature data, “The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle.” P[0063], “the method may also comprise adjusting S22 the cooling model in dependence of information about the cooling effect generated during prior operation of the vehicle” P[0068], “data on heat generation may be collected from a plurality of vehicles, for example vehicles of a similar type or equipped with similar components and stored in a data storage unit such as a server” P[0065], and “A vehicle 100 may be arranged to upload data associated with component temperatures and the like in different driving scenarios to the server 140, and optionally also receive updated models from the server 140. The uploaded data may, e.g., comprise vehicle speed, road incline angle, vehicle load and so on, along with temperatures of the different vehicle components being cooled.” P[0070], the components of the vehicle include the clamed coolant;
the claimed generating predicted coolant temperature data and predicted thermal impact data based on the previous coolant temperature data and the previous thermal impact data wherein the previous thermal impact data is associated with the previous coolant temperature data, “The method comprises obtaining S1 a predicted cooling requirement for a future time t and calculating S2 an operational parameter of the cooling arrangement such that the generated cooling effect meets the predicted cooling requirement. The method also comprises applying S3 the calculated operational parameter to the cooling arrangement 200.” (P[0050] and Figure 7), “The predicted cooling requirement may be obtained as a predicted amount of heat generated by a component that the cooling arrangement 200 is arranged to cool, measured, e.g., in Joules or some similar quantity. The predicted amount of heat generated by the component may be obtained from a heat estimation model arranged to estimate the amount of heat generated by the component under different operating conditions.” P[0053], and “The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle. The updated machine learning models can then be fed back to the vehicles, thereby improving the predictive cooling operation.” P[0065]; and
the claimed generating a currently predicted fan speed demand for a vehicle fan hardware based on the predicted coolant temperature data, the predicted thermal impact data and previously imposed fan speed demands for the vehicle fan hardware from a previous fan speed demand generation iteration or an initial fan speed demand for the vehicle fan hardware, “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.” (P[0060] and Figure 6), and “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061]; and
the claimed in response to performing the present fan speed demand generation iteration, performing a present fan speed demand selection and control signal generation iteration, comprising:
the claimed receiving a real time fan speed demand for the vehicle fan hardware, an operational parameter may be the fan speed of rotation P[0049], and “FIG. 6 shows an example driving scenario and a requested fan speed, or fan speed demand, during the driving scenario.” (P[0059] and Figure 6), and “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7.” (P[0060] and Figure 6), the beginning of the driving scenario in section A equates to the claimed real time fan speed demand;
the claimed comparing the currently predicted fan speed demand with the real time fan speed demand, “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope.” (P[0061] and Figure 6);
the claimed determining a highest fan speed demand for the vehicle fan hardware among the currently predicted fan speed demand and the real time fan speed demand, “At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum.” (P[0061] and Figure 6); and
the claimed generating a control signal for controlling vehicle fan speed of the vehicle fan hardware based on the highest fan speed demand, “obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature” P[0060], “proactively generating cooling before the temperature actually rises, over-heating is more efficiently prevented by the cooling system” P[0062], and “In addition to the heat estimation model, it may also be possible to measure the amount of heat generated by a component of the vehicle 100 under different driving conditions. For example, the vehicle may be equipped with temperature sensors arranged in proximity to the component. The heat estimation model can then be altered or adjusted in such a way that the difference between the measured and estimated amount of heat generated by the component is reduced. The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle. For example, if the predicted amount of generated heat is always below the actual generated heat, then the heat estimation model can be adjusted to reduce the discrepancy. Similarly, if the predicted amount of generated heat is constantly above the actual generated heat, the heat estimation model can be adjusted to predict a lower generated amount of heat.” (P[0063] and Figure 7).
Regarding claim 2 Lima et al disclose the claimed generating the predicted coolant temperature data and the predicted thermal impact data by using a coolant temperature and thermal impact prediction model, “obtaining S1 a predicted cooling requirement for a future time t may also comprise obtaining information about a speed of the vehicle at the future time t and using a heat estimation model to estimate S11 the amount of heat generated at the future time t based on the speed of the vehicle” P[0056], “In addition to the heat estimation model, it may also be possible to measure the amount of heat generated by a component of the vehicle 100 under different driving conditions. For example, the vehicle may be equipped with temperature sensors arranged in proximity to the component. The heat estimation model can then be altered or adjusted in such a way that the difference between the measured and estimated amount of heat generated by the component is reduced. The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle. For example, if the predicted amount of generated heat is always below the actual generated heat, then the heat estimation model can be adjusted to reduce the discrepancy.” P[0063], and “As with the generated heat, the cooling effect of the cooling arrangement may be measured during operation of the vehicle, for example using temperature sensors arranged in proximity to the cooling arrangement, air flow sensors, and the like. The measured cooling effect may then be used to improve the cooling model by adjusting the cooling model to reduce the difference between the estimated and measured cooling effect in a manner similar to the heat estimation model discussed above. Thus, the method may also comprise adjusting S22 the cooling model in dependence of information about the cooling effect generated during prior operation of the vehicle.” P[0068].
Regarding claim 3 Lima et al disclose the claimed method of claim 1, wherein
the claimed generating a thermal impact scenario based on the predicted thermal impact data, “FIG. 6 shows an example driving scenario and a requested fan speed, or fan speed demand, during the driving scenario. In this scenario the component that the cooling arrangement 200 is arranged to cool is a component that generates additional heat when the vehicle 100 encounters an incline, such as a main traction machine or a wheel-end traction machine.” P[0059], and “The server 140 may use this data to adjust models for predicting cooling requirements in different scenarios. The server 140 can maintain, e.g., trained neural networks for a plurality of different vehicle types, where each neural network is configured to take driving scenario as input and generate a predicted cooling requirement as output.” P[0070]; and
the claimed generating the currently predicted fan speed demand based on the predicted coolant temperature data, the thermal impact scenario and the previously imposed fan speed demands or the initial fan speed demand, “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061].
Regarding claim 4 Lima et al disclose the claimed generating the thermal impact scenario by using a clustering strategy model, “data on heat generation may be collected from a plurality of vehicles, for example vehicles of a similar type or equipped with similar components and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle.” P[0065], “The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle.” P[0063], and “Information about the cooling effect of a cooling arrangement 200 may also be collected for a plurality of vehicles 100 comprising cooling arrangements 200 and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The information may be used to improve a cooling model that can subsequently be used in any vehicle of the plurality of vehicles. This way cooling data can be collected from a plurality of different sources and used to build a model of cooling needs and cooling efficiency in various driving scenarios.” P[0069], the similar information about other vehicles, components, and driving scenarios equate to the claimed clustering strategy model. The clustering strategy model is interpreted as structured approach to grouping related elements based on shared characteristics, dependencies, or objectives (Google definition).
Regarding claim 5 Lima et al disclose the claimed method of claim 1, wherein
the claimed generating a thermal impact scenario based on the pervious thermal impact data, “FIG. 6 shows an example driving scenario and a requested fan speed, or fan speed demand, during the driving scenario. In this scenario the component that the cooling arrangement 200 is arranged to cool is a component that generates additional heat when the vehicle 100 encounters an incline, such as a main traction machine or a wheel-end traction machine.” P[0059], and “The server 140 may use this data to adjust models for predicting cooling requirements in different scenarios. The server 140 can maintain, e.g., trained neural networks for a plurality of different vehicle types, where each neural network is configured to take driving scenario as input and generate a predicted cooling requirement as output.” P[0070]; and
the claimed generating the predicted coolant temperature data and the predicted thermal impact data based on the previous coolant temperature data and the thermal impact scenario, “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061].
Regarding claim 6 Lima et al disclose the claimed generating the thermal impact scenario by using a clustering strategy model, “data on heat generation may be collected from a plurality of vehicles, for example vehicles of a similar type or equipped with similar components and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle.” P[0065], “The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle.” P[0063], and “Information about the cooling effect of a cooling arrangement 200 may also be collected for a plurality of vehicles 100 comprising cooling arrangements 200 and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The information may be used to improve a cooling model that can subsequently be used in any vehicle of the plurality of vehicles. This way cooling data can be collected from a plurality of different sources and used to build a model of cooling needs and cooling efficiency in various driving scenarios.” P[0069], the similar information about other vehicles, components, and driving scenarios equate to the claimed clustering strategy model. The clustering strategy model is interpreted as structured approach to grouping related elements based on shared characteristics, dependencies, or objectives (Google definition).
Regarding claim 8 Lima et al disclose the claimed present fan speed demand generation iteration starts before a previous fan speed demand selection and control signal generation iteration ends, “The method comprises obtaining S1 a predicted cooling requirement for a future time t and calculating S2 an operational parameter of the cooling arrangement such that the generated cooling effect meets the predicted cooling requirement. The method also comprises applying S3 the calculated operational parameter to the cooling arrangement 200.” P[0050], and “According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B.” P[0061].
Regarding claim 9 Lima et al disclose the claimed present fan speed demand generation iteration starts when or after the initial fan speed demand is received, “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.” P[0060] and “According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B.” P[0061].
Regarding claim 10 Lima et al disclose the claimed storing the currently predicted fan speed demand, “data on heat generation may be collected from a plurality of vehicles, for example vehicles of a similar type or equipped with similar components and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle.” P[0065], and “Information about the cooling effect of a cooling arrangement 200 may also be collected for a plurality of vehicles 100 comprising cooling arrangements 200 and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The information may be used to improve a cooling model that can subsequently be used in any vehicle of the plurality of vehicles. This way cooling data can be collected from a plurality of different sources and used to build a model of cooling needs and cooling efficiency in various driving scenarios.” P[0069], server storing the heating and cooling information would include the claimed predicted fan speed demand.
Regarding claim 11 Lima et al disclose the claimed performing a subsequent fan speed demand generation iteration, “The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle. The updated machine learning models can then be fed back to the vehicles, thereby improving the predictive cooling operation.” P[0065], “A vehicle 100 may be arranged to upload data associated with component temperatures and the like in different driving scenarios to the server 140, and optionally also receive updated models from the server 140. The uploaded data may, e.g., comprise vehicle speed, road incline angle, vehicle load and so on, along with temperatures of the different vehicle components being cooled. The server 140 may use this data to adjust models for predicting cooling requirements in different scenarios.” P[0070], and the fan speed is adjusted for changing driving scenarios to proactively adjust the cooling (P[0060] thru P[0062]), comprising:
the claimed retrieving the stored predicted fan speed demand, “. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061], “Information about the cooling effect of a cooling arrangement 200 may also be collected for a plurality of vehicles 100 comprising cooling arrangements 200 and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The information may be used to improve a cooling model that can subsequently be used in any vehicle of the plurality of vehicles. This way cooling data can be collected from a plurality of different sources and used to build a model of cooling needs and cooling efficiency in various driving scenarios.” P[0069], and “A vehicle 100 may be arranged to upload data associated with component temperatures and the like in different driving scenarios to the server 140, and optionally also receive updated models from the server 140.” P[0070], the received updated models equate to the claimed retrieving; and
the claimed providing the stored predicted fan speed demand as one of the previously imposed fan speed demands, “The uploaded data may, e.g., comprise vehicle speed, road incline angle, vehicle load and so on, along with temperatures of the different vehicle components being cooled. The server 140 may use this data to adjust models for predicting cooling requirements in different scenarios. The server 140 can maintain, e.g., trained neural networks for a plurality of different vehicle types, where each neural network is configured to take driving scenario as input and generate a predicted cooling requirement as output. These trained neural networks can be downloaded to vehicles in order to improve control of cooling operations.” P[0070].
Regarding claim 12 Lima et al disclose the claimed subsequent fan speed demand generation iteration starts before the present fan speed demand selection and control signal generation iteration ends, “The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle. For example, if the predicted amount of generated heat is always below the actual generated heat, then the heat estimation model can be adjusted to reduce the discrepancy. Similarly, if the predicted amount of generated heat is constantly above the actual generated heat, the heat estimation model can be adjusted to predict a lower generated amount of heat.” P[0063], “The method comprises obtaining S1 a predicted cooling requirement for a future time t and calculating S2 an operational parameter of the cooling arrangement such that the generated cooling effect meets the predicted cooling requirement. The method also comprises applying S3 the calculated operational parameter to the cooling arrangement 200.” P[0050], and “According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B.” P[0061].
Regarding claim 13 Lima et al disclose the claimed in response to subsequent fan speed demand generation iteration performing a subsequent fan speed demand selection and control signal generation iteration, “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061], and “This way, by proactively generating cooling before the temperature actually rises, over-heating is more efficiently prevented by the cooling system. This, in turn, means that vehicle performance does not become temperature-limited as easily as if the cooling arrangement is controlled using the RFC type of methods.” P[0062]. The proactive generation equates to the claimed subsequent fan speed demand selection and control signal generation iteration.
Regarding claim 14 Lima et al disclose the claimed method of claim 1, wherein
the claimed acquiring previous coolant temperature data and previous thermal impact data, “The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle.” P[0063], “the method may also comprise adjusting S22 the cooling model in dependence of information about the cooling effect generated during prior operation of the vehicle” P[0068], “data on heat generation may be collected from a plurality of vehicles, for example vehicles of a similar type or equipped with similar components and stored in a data storage unit such as a server” P[0065], and “A vehicle 100 may be arranged to upload data associated with component temperatures and the like in different driving scenarios to the server 140, and optionally also receive updated models from the server 140. The uploaded data may, e.g., comprise vehicle speed, road incline angle, vehicle load and so on, along with temperatures of the different vehicle components being cooled.” P[0070], the components of the vehicle include the clamed coolant,
the claimed generating predicted coolant temperature data and predicted thermal impact data in the present fan speed demand generation iteration, “The method comprises obtaining S1 a predicted cooling requirement for a future time t and calculating S2 an operational parameter of the cooling arrangement such that the generated cooling effect meets the predicted cooling requirement. The method also comprises applying S3 the calculated operational parameter to the cooling arrangement 200.” (P[0050] and Figure 7), “The predicted cooling requirement may be obtained as a predicted amount of heat generated by a component that the cooling arrangement 200 is arranged to cool, measured, e.g., in Joules or some similar quantity. The predicted amount of heat generated by the component may be obtained from a heat estimation model arranged to estimate the amount of heat generated by the component under different operating conditions.” P[0053], and “The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle. The updated machine learning models can then be fed back to the vehicles, thereby improving the predictive cooling operation.” P[0065],
the claimed generating the currently predicted fan speed demand in the present fans speed demand generation iteration, “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.” (P[0060] and Figure 6), and “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061],
the claimed receiving the real time fan speed demand and comparing the currently predicted fan speed demand with the real time fan speed demand in the present fan speed demand selection and control signal generation iteration, an operational parameter may be the fan speed of rotation P[0049], and “FIG. 6 shows an example driving scenario and a requested fan speed, or fan speed demand, during the driving scenario.” (P[0059] and Figure 6), and “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7.” (P[0060] and Figure 6), the beginning of the driving scenario in section A equates to the claimed real time fan speed demand, and “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope.” (P[0061] and Figure 6), and
the claimed determining a highest fan speed demand f, “At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum.” (P[0061] and Figure 6), and
the claimed generating the control signal in the present fan speed demand selection and control signal generation iteration, “obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature” P[0060], “proactively generating cooling before the temperature actually rises, over-heating is more efficiently prevented by the cooling system” P[0062], and “In addition to the heat estimation model, it may also be possible to measure the amount of heat generated by a component of the vehicle 100 under different driving conditions. For example, the vehicle may be equipped with temperature sensors arranged in proximity to the component. The heat estimation model can then be altered or adjusted in such a way that the difference between the measured and estimated amount of heat generated by the component is reduced. The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle. For example, if the predicted amount of generated heat is always below the actual generated heat, then the heat estimation model can be adjusted to reduce the discrepancy. Similarly, if the predicted amount of generated heat is constantly above the actual generated heat, the heat estimation model can be adjusted to predict a lower generated amount of heat.” (P[0063] and Figure 7).
The limitations of claim 14 merely assign names to the claim limitations of claim 1. The functions of the method steps remain the same, they are just given a new name.
Regarding claim 15 Lima et al disclose the claimed present coolant temperature data and thermal impact data generation process starts before a previous present fan speed demand generation process ends, “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.” P[0060], “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061], and “This way, by proactively generating cooling before the temperature actually rises, over-heating is more efficiently prevented by the cooling system.” P[0062].
Regarding claim 16 Lima et al disclose the claimed subsequent coolant temperature data and thermal impact data generation process starts before the present fan speed demand generation process ends, “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.” P[0060], “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061], and “This way, by proactively generating cooling before the temperature actually rises, over-heating is more efficiently prevented by the cooling system.” P[0062].
Regarding claim 17 Lima et al disclose the claimed present fan speed demand generation process starts when or after the initial fan speed demand is received, “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.” P[0060] and “According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B.” P[0061].
Regarding claim 18 Lima et al disclose the claimed system of controlling vehicle fan speed to regulate coolant temperature, the control unit of Figure 8 used for “a method performed in a control unit for controlling the operation of the cooling arrangements” P[0013], and an operational parameter of the fan speed of rotation may be adjusted for cooling P[0049], the system comprising:
the claimed processor, “This control unit 110 may be comprised in the vehicle 100, e.g., in the form of a vehicle motion management (VMM) unit. Processing circuitry 810 is provided using any combination of one or more of a suitable central processing unit CPU, multiprocessor, microcontroller, digital signal processor DSP, etc., capable of executing software instructions stored in a computer program product, e.g. in the form of a storage medium 830.” (P[0072] and Figure 8); and
the claimed sensor electrically couple with the processor, “the vehicle may be equipped with temperature sensors arranged in proximity to the component” P[0063], and “The processing circuitry 810 controls the general operation of the control unit 110, e.g., by sending data and control signals to the interface 820 and the storage medium 830, by receiving data and reports from the interface 820, and by retrieving data and instructions from the storage medium 830.” P[0076],
wherein the processor is configured to perform operations comprising:
the claimed performing a present fan speed demand generation iteration, performing a method for predictive master cooling control P[0050], comprising:
the claimed acquiring previous coolant temperature data and previous thermal impact data wherein the previous thermal impact data is associated with the previous coolant temperature data, “The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle.” P[0063], “the method may also comprise adjusting S22 the cooling model in dependence of information about the cooling effect generated during prior operation of the vehicle” P[0068], “data on heat generation may be collected from a plurality of vehicles, for example vehicles of a similar type or equipped with similar components and stored in a data storage unit such as a server” P[0065], and “A vehicle 100 may be arranged to upload data associated with component temperatures and the like in different driving scenarios to the server 140, and optionally also receive updated models from the server 140. The uploaded data may, e.g., comprise vehicle speed, road incline angle, vehicle load and so on, along with temperatures of the different vehicle components being cooled.” P[0070], the components of the vehicle include the clamed coolant;
the claimed generating predicted coolant temperature data and predicted thermal impact data based on the previous coolant temperature data and the previous thermal impact data wherein the previous thermal impact data is associated with the previous coolant temperature data, “The method comprises obtaining S1 a predicted cooling requirement for a future time t and calculating S2 an operational parameter of the cooling arrangement such that the generated cooling effect meets the predicted cooling requirement. The method also comprises applying S3 the calculated operational parameter to the cooling arrangement 200.” (P[0050] and Figure 7), “The predicted cooling requirement may be obtained as a predicted amount of heat generated by a component that the cooling arrangement 200 is arranged to cool, measured, e.g., in Joules or some similar quantity. The predicted amount of heat generated by the component may be obtained from a heat estimation model arranged to estimate the amount of heat generated by the component under different operating conditions.” P[0053], and “The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle. The updated machine learning models can then be fed back to the vehicles, thereby improving the predictive cooling operation.” P[0065]; and
the claimed generating a currently predicted fan speed demand for a vehicle fan hardware based on the predicted coolant temperature data, the predicted thermal impact data and previously imposed fan speed demands for the vehicle fan hardware from a previous fan speed demand generation iteration or an initial fan speed demand for the vehicle fan hardware, “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.” (P[0060] and Figure 6), and “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061]; and
the claimed in response to performing the present fan speed demand generation iteration, performing a present fan speed demand selection and control signal generation iteration, comprising:
the claimed receiving a real time fan speed demand for the vehicle fan hardware, an operational parameter may be the fan speed of rotation P[0049], and “FIG. 6 shows an example driving scenario and a requested fan speed, or fan speed demand, during the driving scenario.” (P[0059] and Figure 6), and “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7.” (P[0060] and Figure 6), the beginning of the driving scenario in section A equates to the claimed real time fan speed demand;
the claimed comparing the currently predicted fan speed demand with the real time fan speed demand, “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope.” (P[0061] and Figure 6);
the claimed determining a highest fan speed demand for the vehicle fan hardware among the currently predicted fan speed demand and the real time fan speed demand, “At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum.” (P[0061] and Figure 6); and
the claimed generating a control signal for controlling vehicle fan speed of the vehicle fan hardware based on the highest fan speed demand, “obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature” P[0060], “proactively generating cooling before the temperature actually rises, over-heating is more efficiently prevented by the cooling system” P[0062], and “In addition to the heat estimation model, it may also be possible to measure the amount of heat generated by a component of the vehicle 100 under different driving conditions. For example, the vehicle may be equipped with temperature sensors arranged in proximity to the component. The heat estimation model can then be altered or adjusted in such a way that the difference between the measured and estimated amount of heat generated by the component is reduced. The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle. For example, if the predicted amount of generated heat is always below the actual generated heat, then the heat estimation model can be adjusted to reduce the discrepancy. Similarly, if the predicted amount of generated heat is constantly above the actual generated heat, the heat estimation model can be adjusted to predict a lower generated amount of heat.” (P[0063] and Figure 7).
Regarding claim 19 Lima et al disclose the claimed generating the predicted coolant temperature data and the predicted thermal impact data by using a coolant temperature and thermal impact prediction model, “obtaining S1 a predicted cooling requirement for a future time t may also comprise obtaining information about a speed of the vehicle at the future time t and using a heat estimation model to estimate S11 the amount of heat generated at the future time t based on the speed of the vehicle” P[0056], “In addition to the heat estimation model, it may also be possible to measure the amount of heat generated by a component of the vehicle 100 under different driving conditions. For example, the vehicle may be equipped with temperature sensors arranged in proximity to the component. The heat estimation model can then be altered or adjusted in such a way that the difference between the measured and estimated amount of heat generated by the component is reduced. The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle. For example, if the predicted amount of generated heat is always below the actual generated heat, then the heat estimation model can be adjusted to reduce the discrepancy.” P[0063], and “As with the generated heat, the cooling effect of the cooling arrangement may be measured during operation of the vehicle, for example using temperature sensors arranged in proximity to the cooling arrangement, air flow sensors, and the like. The measured cooling effect may then be used to improve the cooling model by adjusting the cooling model to reduce the difference between the estimated and measured cooling effect in a manner similar to the heat estimation model discussed above. Thus, the method may also comprise adjusting S22 the cooling model in dependence of information about the cooling effect generated during prior operation of the vehicle.” P[0068].
Regarding claim 35 Lima et al disclose the claimed computer program product of controlling vehicle fan speed to regulate coolant temperature, “a computer readable medium 910 carrying a computer program comprising program code means 920 for performing, e.g., the methods” (P[0077] and Figure 9) for “a method performed in a control unit for controlling the operation of the cooling arrangements” P[0013], and an operational parameter of the fan speed of rotation may be adjusted for cooling P[0049], the computer program product comprising:
the claimed a non-transitory computer readable medium, computer readable medium 910 (Figure 9); and
the claimed program code stored in the computer readable medium executed by a system to perform operations, “a computer program comprising program code means 920 for performing, e.g., the methods illustrated in FIG. 7, when said program product is run on a computer. The computer readable medium and the code means may together form a computer program product 900.” P[0077], the operations comprising:
wherein the processor is configured to perform operations comprising:
the claimed performing a present fan speed demand generation iteration, performing a method for predictive master cooling control P[0050], comprising:
the claimed acquiring previous coolant temperature data and previous thermal impact data wherein the previous thermal impact data is associated with the previous coolant temperature data, “The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle.” P[0063], “the method may also comprise adjusting S22 the cooling model in dependence of information about the cooling effect generated during prior operation of the vehicle” P[0068], “data on heat generation may be collected from a plurality of vehicles, for example vehicles of a similar type or equipped with similar components and stored in a data storage unit such as a server” P[0065], and “A vehicle 100 may be arranged to upload data associated with component temperatures and the like in different driving scenarios to the server 140, and optionally also receive updated models from the server 140. The uploaded data may, e.g., comprise vehicle speed, road incline angle, vehicle load and so on, along with temperatures of the different vehicle components being cooled.” P[0070], the components of the vehicle include the clamed coolant;
the claimed generating predicted coolant temperature data and predicted thermal impact data based on the previous coolant temperature data and the previous thermal impact data wherein the previous thermal impact data is associated with the previous coolant temperature data, “The method comprises obtaining S1 a predicted cooling requirement for a future time t and calculating S2 an operational parameter of the cooling arrangement such that the generated cooling effect meets the predicted cooling requirement. The method also comprises applying S3 the calculated operational parameter to the cooling arrangement 200.” (P[0050] and Figure 7), “The predicted cooling requirement may be obtained as a predicted amount of heat generated by a component that the cooling arrangement 200 is arranged to cool, measured, e.g., in Joules or some similar quantity. The predicted amount of heat generated by the component may be obtained from a heat estimation model arranged to estimate the amount of heat generated by the component under different operating conditions.” P[0053], and “The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle. The updated machine learning models can then be fed back to the vehicles, thereby improving the predictive cooling operation.” P[0065]; and
the claimed generating a currently predicted fan speed demand for a vehicle fan hardware based on the predicted coolant temperature data, the predicted thermal impact data and previously imposed fan speed demands for the vehicle fan hardware from a previous fan speed demand generation iteration or an initial fan speed demand for the vehicle fan hardware, “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.” (P[0060] and Figure 6), and “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061]; and
the claimed in response to performing the present fan speed demand generation iteration, performing a present fan speed demand selection and control signal generation iteration, comprising:
the claimed receiving a real time fan speed demand for the vehicle fan hardware, an operational parameter may be the fan speed of rotation P[0049], and “FIG. 6 shows an example driving scenario and a requested fan speed, or fan speed demand, during the driving scenario.” (P[0059] and Figure 6), and “At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7.” (P[0060] and Figure 6), the beginning of the driving scenario in section A equates to the claimed real time fan speed demand;
the claimed comparing the currently predicted fan speed demand with the real time fan speed demand, “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope.” (P[0061] and Figure 6);
the claimed determining a highest fan speed demand for the vehicle fan hardware among the currently predicted fan speed demand and the real time fan speed demand, “At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum.” (P[0061] and Figure 6); and
the claimed generating a control signal for controlling vehicle fan speed of the vehicle fan hardware based on the highest fan speed demand, “obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature” P[0060], “proactively generating cooling before the temperature actually rises, over-heating is more efficiently prevented by the cooling system” P[0062], and “In addition to the heat estimation model, it may also be possible to measure the amount of heat generated by a component of the vehicle 100 under different driving conditions. For example, the vehicle may be equipped with temperature sensors arranged in proximity to the component. The heat estimation model can then be altered or adjusted in such a way that the difference between the measured and estimated amount of heat generated by the component is reduced. The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle. For example, if the predicted amount of generated heat is always below the actual generated heat, then the heat estimation model can be adjusted to reduce the discrepancy. Similarly, if the predicted amount of generated heat is constantly above the actual generated heat, the heat estimation model can be adjusted to predict a lower generated amount of heat.” (P[0063] and Figure 7).
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) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lima et al Patent Application Publication Number 2022/0200405 A1 in view of Wanjale et al Patent Application Publication Number 2022/0404882 A1.
Regarding claim 7 Lima et al teach the claimed generating the currently predicted fan speed demand, “In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.” P[0061]. Lima et al do not teach the claimed demand is generated using a reinforcement learning labeling model, but do recite that the server may maintain a plurality of machine learning models that are regularly updated P[0065]. A reinforcement learning labeling model would be included in the maintained and updated models.
Wanjale et al teach, the reinforcement learning model includes a fan speed term, a temperature overshoot term, a fan acceleration term (P[0036] thru P[0038]), and “the machine learning model 274 may be a reinforcement learning model and may be configured to receive, as inputs, the performance data 50, the temperature setpoint 64, and the value of the loss function 72. The loss function 72 may include a fan speed term 72A, a temperature overshoot term 72B, and a fan acceleration term 72C.” P[0046], the terms of Wanjale et al equate to the claimed labeling. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the method for controlling the operation of the cooling arrangements of Lima et al with the reinforcement learning model using terms for fan signal control of Wanjale et al in order to, with a reasonable expectation of success, control the fan to properly cool the device while inhibiting the generation of noise that a distraction or annoyance to users (Wanjale et al P[0002]).
Related Art
The examiner points to Farhat et al Patent Number 11,274,595 B1 as related art, but not relied upon for any rejection. Farhat et al is directed to a flow chart of an example method 200 for estimating effectiveness of a radiator of an engine cooling system. At 202, the method includes estimating and/or measuring vehicle and engine operating conditions. Operating conditions may include, for example, vehicle speed, engine speed and load, driver torque demand, and road conditions (e.g., road grade), weather conditions (e.g., presence of wind, rain, snow, etc.), the settings of grille shutters coupled to the front end of the vehicle, etc. The operating conditions may further include ambient conditions, such as ambient air temperature, pressure, and humidity; engine temperature; coolant temperature; transmission fluid temperature; engine oil temperature; cabin air settings (e.g., AC settings); boost pressure (if the engine is boosted); exhaust gas recirculation (EGR) flow; manifold pressure (MAP); manifold airflow (MAF); manifold air temperature (MAT); etc. When the vehicle is a HEV, operating conditions may further include a mode of operation, such as an engine-only mode (where all of the torque to propel the vehicle is supplied by the engine), an electric-only mode (where all of the torque to propel the vehicle is supplied by an electric machine), and an assist mode (where the torque to propel the vehicle is supplied by both the engine and the electric machine). Operating conditions may further include a temperature of the electric machine and/or a temperature of the system battery. At 204, temperature (T1) of coolant entering the radiator via a coolant line may be estimated via a temperature sensor (such as temperature sensor 104 in FIG. 1) coupled to a coolant inlet of the radiator. The temperature sensor may estimate temperature of coolant entering the radiator after circulating through the engine with heat from the engine being transferred to the coolant. Further, a thermal load on the cooling system may be estimated as a rate of change in temperature of coolant entering the radiator. The coolant temperature T1 may represent a resultant of the thermal load (heat rejected to the cooling system) and the cooling provided by the cooling system. Therefore, if T1 stabilizes over time, it may be inferred that the heat rejected into the cooling system is equal to the cooling power provided by the cooling system. At 205, a speed of a fan (such as fan 91 in FIG. 1) providing cooling air flow to the radiator and the cooling system may be adjusted based on the thermal load (rate of change of T1). In one example, the controller may use a look-up table to determine the fan speed corresponding to a measured rate of change of T1 with the rate of change of T1 as input and the fan speed as output. As an example, the fan speed may increase with an increase in the thermal load and the fan speed may decrease with a decrease in thermal load. At 206, inlet air temperature (T2) and outlet air temperature (T3) may be estimated. The temperature of air entering the radiator (T2) may be estimated via a first air temperature sensor (such as air temperature sensor 107 in FIG. 1) coupled to a first side of the radiator facing the grille. The temperature of air exiting the radiator (T3) may be estimated via a second air temperature sensor (such as air temperature sensor 108 in FIG. 1) coupled to a second side of the radiator facing the fan, the second side opposite to the first side. At 208, effectiveness (c) of the radiator may be estimated as a function of each of the sensed T1, T2, and T3. Effectiveness of the radiator is an estimation of an ability of the radiator to dissipate heat from the coolant circulated through the radiator. At 210, a speed of a water pump (such as pump 86 in FIG. 1) pumping coolant through the lines of the cooling system may be adjusted based on the estimated effectiveness of the radiator. By adjusting the fan speed based on the thermal load, and pump speed based on ε, engine cooling may be provided while reducing power consumption and undesired increase in pressure resistance without any effectiveness benefits. (Figure 2)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DALE W HILGENDORF whose telephone number is (571)272-9635. The examiner can normally be reached Monday - Friday 9-5:30.
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/DALE W HILGENDORF/Primary Examiner, Art Unit 3662
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