Device for identifying mounting in a heating and cooling system of a vehicle

Abstract

Embodiments include identifying improperly installed devices in a heating and cooling system of a vehicle. Aspects include obtaining an initial reading from each of a plurality of sensors disposed in a vehicle, activating one or more of a heating system and a cooling system disposed in the vehicle, and obtaining a series of readings from each of the plurality of sensors. Aspects also include, for each of the plurality of sensors, identifying an incremental value from the series of readings; for each of the plurality of sensors, comparing the delta value to an expected delta value; identifying, from the plurality of sensors, two or more sensors that have not matched based on a difference between the delta value and an expected delta value; and updating the mapping of the two or more sensors in software of the vehicle.

Description

Technical Field

[0001] The present disclosure relates to heating and cooling systems for vehicles. More particularly, the present disclosure relates to identifying improperly installed devices in heating and cooling systems for vehicles. Background Art

[0002] Typically, vehicles include multiple heating and cooling systems for regulating the temperature of various vehicle components. Heating and cooling systems utilize numerous components, such as compressors, condensers, evaporators, and motors. These systems include a series of fluid connections between these components and one or more valves that control the flow of fluid through these connections. Furthermore, the heating and cooling systems include controllers that control the operation of the components and valves to achieve heating and cooling of various vehicle components. The heating and cooling systems also include sensors that communicate with the controllers and monitor temperature, pressure, and other characteristics of various vehicle components.

[0003] Improper heating and cooling system operation is often caused by improperly installed valves or sensors in the heating and cooling system. Traditional methods of diagnosing improper heating and cooling system operation are often time-consuming. Summary of the Invention

[0004] In an exemplary embodiment, a method for identifying mismatched sensors in a vehicle is provided. The method includes obtaining an initial reading from each of a plurality of sensors disposed in the vehicle, activating one or more of a heating system and a cooling system disposed in the vehicle, and obtaining a series of readings from each of the plurality of sensors. The method further includes: for each of the plurality of sensors, identifying a delta value from the series of readings; and, for each of the plurality of sensors, comparing the delta value to an expected delta value. The method further includes identifying two or more sensors from the plurality of sensors that have become mismatched based on a difference between the delta value and the expected delta value, and updating a mapping for the two or more sensors in the vehicle's software.

[0005] In addition to one or more features described herein, the plurality of sensors are temperature sensors.

[0006] In addition to one or more features described herein, identifying two or more sensors from a plurality of sensors that have been mismatched based on a difference between a delta value and an expected delta value includes: identifying a first sensor from the plurality of sensors having a first delta value that is not within a first threshold range of a first expected delta value; identifying a second sensor from the plurality of sensors having a second delta value that is not within a second threshold range of a second expected delta value; and determining that the first delta value is within the second threshold range of the second expected delta value and the second delta value is within the first threshold range of the first expected delta value.

[0007] In addition to one or more features described herein, the first threshold range and the second threshold range are different.

[0008] In addition to one or more features described herein, a series of readings from each of the plurality of sensors are obtained during a predetermined period of time after starting one or more of the heating system and the cooling system.

[0009] In addition to one or more features described herein, the mapping in the vehicle software is a logical mapping between each of the plurality of sensors and a variable in the software.

[0010] In addition to one or more features described herein, one or more of the heating system and the cooling system are configured to control the temperature of a battery pack of the vehicle.

[0011] In one exemplary embodiment, a method for identifying mismatched sensors in a vehicle is provided. The method includes: activating one or more of a heating system and a cooling system provided in the vehicle; obtaining a series of readings from each of a plurality of sensors provided in the vehicle; and inputting the series of readings from each of the plurality of sensors and the operating status of the one or more of the heating system and the cooling system into a trained machine learning model. The method also includes receiving an indication from the trained machine learning model of an identification of two or more mismatched sensors from the plurality of sensors; and updating a mapping of the two or more sensors in the vehicle's software.

[0012] In addition to one or more features described herein, the plurality of sensors are temperature sensors.

[0013] In addition to one or more features described herein, the trained machine learning model is trained using historical readings from sensors disposed in a test vehicle and operating states of the heating system and the cooling system corresponding to the historical readings.

[0014] In addition to one or more features described herein, a series of readings from each of the plurality of sensors are obtained during a predetermined period of time after starting one or more of the heating system and the cooling system.

[0015] In addition to one or more features described herein, the mapping in the vehicle software is a logical mapping between each of the plurality of sensors and a variable in the software.

[0016] In addition to one or more features described herein, one or more of the heating system and the cooling system are configured to control the temperature of a battery pack of the vehicle.

[0017] In addition to one or more features described herein, the method also includes receiving, from the trained machine learning model, an identification of a check valve that has been improperly installed, wherein the check valve is disposed in a fluid path of one or more of the heating system and the cooling system.

[0018] In one exemplary embodiment, a method for identifying an improperly installed check valve in a vehicle is provided. The method includes: activating one or more of a heating system and a cooling system provided in the vehicle; obtaining a series of readings from one or more sensors provided in the vehicle; and, for each of the one or more sensors, identifying a delta value from the series of readings. The method also includes comparing the delta value to an expected delta value for each of the one or more sensors; identifying an improperly installed check valve provided in the vehicle based on a difference between the delta value and the expected delta value; and generating an alert identifying the check valve.

[0019] In addition to one or more features described herein, the one or more sensors include one or more of a pressure sensor, a temperature sensor, and a flow sensor.

[0020] In addition to one or more features described herein, one or more sensors are disposed in a fluid path of one or more of the heating system and the cooling system.

[0021] In addition to one or more features described herein, the check valve is configured to control a direction of flow in a fluid path.

[0022] In addition to one or more features described herein, a series of readings from each of the one or more sensors are obtained within a predetermined period of time after one or more of the heating system and the cooling system are activated.

[0023] In addition to one or more features described herein, one or more of the heating system and the cooling system are configured to control the temperature of a battery pack of the vehicle.

[0024] The above features and advantages and other features and advantages of the present disclosure will become apparent when the following detailed description is read in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Additional features, advantages, and details appear by way of example only in the following detailed description, which refers to the accompanying drawings, in which:

[0026] Figure 1 is a schematic diagram of a vehicle according to an exemplary embodiment;

[0027] Figure 2 depicts a block diagram of components of a machine learning training and inference system according to an exemplary embodiment;

[0028] Figure 3 is a flow chart illustrating a method for identifying mismatched sensors in a vehicle according to an exemplary embodiment;

[0029] Figure 4 A block diagram depicting mapping of physical sensor identification to logical sensor identification according to an exemplary embodiment;

[0030] Figure 5 is a flow chart illustrating a method for identifying an improperly installed check valve in a vehicle according to an exemplary embodiment;

[0031] Figure 6A 、 6B , 6C and 6D are flow charts illustrating a method for identifying unmatched sensors in a vehicle according to an exemplary embodiment; and

[0032] Figure 7A and 7B is a flow chart illustrating a method for identifying an improperly installed check valve in a vehicle according to an exemplary embodiment. DETAILED DESCRIPTION

[0033] The following description is merely exemplary in nature and is not intended to limit the present disclosure and its application or use. Various embodiments of the present disclosure are described herein with reference to the relevant drawings. Alternative embodiments of the present disclosure may be designed without departing from the scope of the claims. Various connections and positional relationships (e.g., above, below, adjacent, etc.) are set forth between the elements in the following description and drawings. Unless otherwise stated, these connections and / or positional relationships may be direct or indirect, and the present disclosure is not intended to be limited in this respect. Therefore, the connection of entities may refer to direct or indirect connection, and the positional relationship between entities may be direct or indirect positional relationship.

[0034] Turning now to an overview of various aspects of the present disclosure, embodiments of the present disclosure include methods and systems for identifying improperly installed devices in a heating and cooling system of a vehicle. In an exemplary embodiment, a processing system of a vehicle is configured to selectively operate the heating and cooling system of the vehicle and monitor readings of various sensors during operation of the heating and cooling system. Based on changes in the various monitored sensor readings, the processing system is configured to identify improperly installed devices (i.e., sensors or valves) of the heating and cooling system. In an exemplary embodiment, the processing system utilizes various algorithms to identify mismatched sensors or improperly installed valves. In one embodiment, the processing system includes a trained machine learning system that is configured to identify mismatched sensors or improperly installed valves based on the operating state of the heating and cooling system and changes in the monitored sensor readings.

[0035] Now refer to Figure 1, shows a schematic diagram of a vehicle 100 according to one or more embodiments. The vehicle 100 can be an electric vehicle, an internal combustion engine vehicle, or a hybrid vehicle. The vehicle 100 includes a processing system 102 that is configured to control the operation of a heating and cooling system 104. In an exemplary embodiment, the heating and cooling system 104 is configured to selectively heat and / or cool various parts of the vehicle 100. These various parts of the vehicle include, but are not limited to, a battery 106 of the vehicle 100 and a passenger compartment 108 of the vehicle 100. In an exemplary embodiment, the processing system 102 is one of a general-purpose processor, a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), and the like.

[0036] In an exemplary embodiment, the vehicle 100 also includes a user interface 114 configured to receive input from a user. For example, the user interface 114 can be used to selectively activate the heating and cooling system 104. The user interface 114 can also include a display configured to display information about the operating status of the heating and cooling system 104.

[0037] In an exemplary embodiment, vehicle 100 includes a plurality of sensors 112 configured to monitor one or more temperatures and pressures at various locations within vehicle 100. For example, one of sensors 112-1 is configured to monitor the temperature within passenger compartment 108, one of sensors 112 is configured to monitor the temperature of battery 106, and another sensor 112-2 is configured to monitor the ambient temperature of vehicle 100. In another example, one or more sensors 112 are configured to measure the temperature and pressure of various components of heating and cooling system 104.

[0038] In the exemplary embodiment, the heating and cooling system 104 of the vehicle 100 also includes a plurality of valves 110 configured to control the flow of fluid through the heating and cooling system 104. The operation of the valves 110 is controlled by the processing system 102 to enable operation of the heating and cooling system 104. In the exemplary embodiment, the processing system 102 is configured to receive input from sensors 112, receive input from a user interface 114, and responsively control the operation of the heating and cooling system 104, including the valves 110.

[0039] In an exemplary embodiment, processing system 102 is configured to execute various algorithms to identify mismatched sensors or improperly installed valves. In one embodiment, processing system 102 is configured to control the operation of heating and cooling system 104 of vehicle 100 and monitor changes in the output of sensors 112 during operation of the heating and cooling system. Based on the monitored changes in the output of various sensors, processing system 102 is configured to identify improperly installed devices (i.e., sensors or valves) in the heating and cooling system. In one example, processing system 102 receives temperature readings from a first sensor 112-1 that is logically associated with the interior of passenger compartment 108 of vehicle 100 and a second sensor 112-2 that is logically associated with the exterior of vehicle 100. However, when processing system 102 instructs heating and cooling system 104 to cool vehicle compartment 108, the reading from first sensor 112-1 remains constant, while the reading from second sensor 112-2 decreases. Similarly, when processing system 102 instructs heating and cooling system 104 to heat cabin 108 of vehicle 100, the reading from first sensor 112-1 remains constant while the reading from second sensor 112-2 increases. Thus, processing system 102 identifies that first sensor 112-1 and second sensor 112-2 are no longer matched (i.e., the mapping of the physical locations of the sensors has been improperly reversed). In an exemplary embodiment, processing system 102 is configured to update the mapping of the sensors identified as no longer matched in the vehicle 100's software.

[0040] In one embodiment, the processing system 102 includes a trained machine learning system 120 that is configured to identify mismatched sensors or improperly installed valves based on the operating status of the heating and cooling system 104 and changes in the output of the monitored sensors 112. Although the trained machine learning system 120 is described as part of the processing system 102, it will be apparent to those skilled in the art that the trained machine learning system 120 can be separate from the processing system 102 and can communicate with the processing system 102. For example, the trained machine learning system 120 can be provided on a diagnostic tool that can be selectively connected to the vehicle 100 to communicate with the processing system 102.

[0041] Now refer to Figure 2 , describes in more detail the system for training and using machine learning models. Specifically, Figure 2A block diagram depicts components of a machine learning training and inference system 200, according to one or more embodiments described herein. System 200 performs training 202 and inference 204. During training 202, training engine 216 trains a model (e.g., trained model 218) to perform a task, such as identifying improperly installed devices in a vehicle's heating and cooling system. Inference 204 is the process of implementing trained model 218 within the context of a larger system (e.g., system 226) to perform a task, such as identifying improperly installed devices in a vehicle's heating and cooling system. Figure 2 All or part of the system 200 shown may be provided by, for example Figure 1 This may be implemented by all or a subset of the processing system 102.

[0042] Training 202 begins with training data 212, which can be structured or unstructured data. According to one or more embodiments described herein, training data 212 includes the operating state of the heating and cooling system of a test vehicle and data collected from the vehicle's sensors during operation of the heating and cooling system, wherein the heating and cooling system is operating normally. A training engine 216 receives training data 212 and a model table 214. Model table 214 represents an untrained base model. Model table 214 can have preset weights and biases that can be adjusted during training. It should be understood that model table 214 can be selected from many different model tables depending on the task to be performed. Training 202 can be supervised learning, semi-supervised learning, unsupervised learning, reinforcement learning, etc., including combinations and / or multiples thereof. For example, supervised learning can be used to train a machine learning model to classify objects of interest in images. To this end, training data 212 includes labeled images, images that include objects of interest with associated labels (ground truth), and other images that do not include objects of interest with associated labels. In this example, the training engine 216 takes as input a training image from the training data 212, makes a prediction for classifying the image, and compares the prediction with the known label. The training engine 216 then adjusts the weights and / or biases of the model based on the results of the comparison, for example, by using backpropagation. Training 202 can be performed multiple times (referred to as "epochs") until a suitable model is trained (e.g., trained model 218).

[0043] Once trained, the trained model 218 can be used to perform inference 204 to perform a task, such as identifying improperly installed equipment in a vehicle's heating and cooling system. The inference engine 220 applies the trained model 218 to new data 222 (e.g., real-world, non-training data). For example, if the trained model 218 was trained to classify images of a particular object (e.g., a chair), the new data 222 can be images of a chair that was not part of the training data 212. In this way, the new data 222 represents data that the model 218 has not yet been exposed to. The inference engine 220 makes a prediction 224 (e.g., a classification of an object in the image of the new data 222) and passes the prediction 224 to the system 226. The system 226 can take an action, perform an operation, perform an analysis, etc. based on the prediction 224, including combinations and / or multiples thereof. In some embodiments, the system 226 can add and / or modify the new data 222 based on the prediction 224.

[0044] According to one or more embodiments, predictions 224 generated by inference engine 220 are periodically monitored and validated to ensure that inference engine 220 is functioning as expected. Based on this validation, additional training 202 can be performed using trained model 218 as a starting point. Additional training 202 can include all or a subset of original training data 212 and / or new training data 212. According to one or more embodiments, training 202 includes updating trained model 218 to account for changes in expected input data.

[0045] Now refer to Figure 3 , a flow chart of a method 300 for identifying mismatched sensors in a vehicle is shown according to an exemplary embodiment. In an exemplary embodiment, the method 300 is executed by the processing system 102 (e.g., Figure 1 ). Method 300 begins at block 302 by obtaining initial readings from each of a plurality of sensors disposed in a vehicle. In an exemplary embodiment, the plurality of sensors includes one or more temperature sensors and / or pressure sensors. Next, at block 304, method 300 includes activating one or more of a heating system and a cooling system disposed in the vehicle. In one embodiment, one or more of the heating system and the cooling system are configured to control the temperature of a battery pack of the vehicle.

[0046] After the heating system and / or cooling system is activated, method 300 includes obtaining a series of readings from each of the plurality of sensors at block 306. In an exemplary embodiment, the series of readings from each of the plurality of sensors is obtained during a predetermined time period after one or more of the heating system and the cooling system are activated. At block 308, method 300 includes identifying a delta value from the series of readings for each of the plurality of sensors. In one embodiment, the delta value for the series of readings is the average change from the initial reading to the series of readings during the predetermined time period. In another embodiment, the delta value for the series of readings is the maximum observed change from the initial reading to the series of readings during the predetermined time period.

[0047] At block 310, method 300 includes comparing the delta value to an expected delta value for each of the plurality of sensors. In an exemplary embodiment, the expected delta value for each of the plurality of sensors is an expected change in sensor measurements based on an operating mode of the heating and cooling system. In one embodiment, the expected delta value for each of the plurality of sensors is a constant value set by the vehicle manufacturer. At block 312, method 300 includes identifying two or more sensors from the plurality of sensors that are mismatched based on the difference between the delta value and the expected delta value.

[0048] In one embodiment, identifying two or more sensors from a plurality of sensors that have mismatched based on a difference between a delta value and an expected delta value includes identifying a first sensor from the plurality of sensors having a first delta value that is not within a first threshold range of a first expected delta value, and identifying a second sensor from the plurality of sensors having a second delta value that is not within a second threshold range of a second expected delta value. Identifying the mismatched sensors also includes determining that the first delta value is within the second threshold range of the second expected delta value, and that the second delta value is within the first threshold range of the first expected delta value. In an exemplary embodiment, the first threshold range and the second threshold range are different.

[0049] At block 314 , method 300 includes updating a mapping of two or more sensors in the vehicle software. In one embodiment, the mapping in the vehicle software is a logical mapping between each of the plurality of sensors and a variable in the software.

[0050] Now refer to Figure 4, a block diagram illustrating a mapping 400 of physical sensor identifications to logical sensor identifications according to an exemplary embodiment is shown. As shown, mapping 400 includes a correspondence between a physical sensor identification 402 and a logical sensor identification 404. In one embodiment, physical sensor identification 402 is associated with a physical port used to connect the sensor to a vehicle. In one embodiment, logical sensor identification 404 is a software variable associated with the sensor connected to the vehicle. In an exemplary embodiment, updating mapping 400 may include swapping 406 the logical sensor identification 404 associated with the physical sensor identification 402.

[0051] Now refer to Figure 5 , a flow chart illustrating a method 500 for identifying an improperly installed check valve in a vehicle according to an exemplary embodiment is shown. In an exemplary embodiment, the method 500 is executed by the processing system 102 (e.g., Figure 1 ) is executed by the processing system shown. In box 502, the method 500 includes starting one or more of a heating system and a cooling system provided in the vehicle. Next, in box 504, the method 500 includes obtaining a series of readings from one or more sensors provided in the vehicle. In an exemplary embodiment, a series of readings from each of the one or more sensors are obtained during a predetermined time period after starting one or more of the heating system and the cooling system. In one embodiment, one or more of the heating system and the cooling system are configured to control the temperature of the vehicle battery pack. In one embodiment, the one or more sensors include one or more of a pressure sensor, a temperature sensor, and a flow sensor. In one embodiment, the one or more sensors are provided in a fluid path of one or more of the heating system and the cooling system.

[0052] Next, at block 506, method 500 includes identifying a delta value from the series of readings for each of the one or more sensors. In one embodiment, the delta value for a series of readings for a sensor is the total change in the sensor reading in the series of readings (i.e., the maximum reading for the sensor in the series of readings minus the minimum reading for the sensor in the series of readings). In another embodiment, the delta value for the series of readings is the maximum observed change from the initial reading of the sensor to the series of readings. At block 508, method 500 includes comparing the delta value to an expected delta value for each of the one or more sensors. In an exemplary embodiment, the expected delta value for each of the plurality of sensors is an expected change in the sensor's measured value based on the operating mode of the heating and cooling system.

[0053] Next, at block 510, method 500 includes identifying a misinstalled check valve in the vehicle based on a difference between the delta value and an expected delta value. In one example, the check valve is identified as being misinstalled based on determining that the difference between delta values ​​of one or more sensors deviates from their corresponding expected delta values ​​by more than a threshold. In an exemplary embodiment, the check valve is configured to control the direction of flow in a fluid path. Once a check valve in the vehicle is identified as being misinstalled, method 500 proceeds to block 512 and includes generating an alert identifying the check valve.

[0054] Now refer to Figure 6A 、 6B , 6C and 6D show flow charts illustrating a method 600 for identifying mismatched sensors in a vehicle according to an exemplary embodiment. In an exemplary embodiment, the method 600 is executed by the processing system 102 (e.g., Figure 1 ) is performed by the processing system shown. At box 602, method 600 includes receiving a command to initiate a diagnostic process to identify mismatched sensors. Next, at decision box 604, method 600 determines whether the vehicle is in maintenance mode. Based on determining that the vehicle is not in maintenance mode, method 600 proceeds to box 620 and does not perform a diagnostic process. At decision box 608, method 600 includes determining whether a battery cooling process is being performed. If the battery cooling process is not being performed, method 600 proceeds to box 620 and does not perform a diagnostic process. If the battery cooling process is being performed, method 600 proceeds to box 612 and initiates a diagnostic process for detecting mismatched sensors in the battery cooling system.

[0055] At decision block 606, method 600 includes determining whether the vehicle's ambient temperature is within a threshold range. If the vehicle's ambient temperature is within the threshold, method 600 proceeds to block 614 and initiates a diagnostic process to detect mismatched sensors by heating the vehicle cabin. If the vehicle's ambient temperature is not within the threshold range, method 600 proceeds to block 616 and initiates a diagnostic process to detect mismatched sensors by cooling the vehicle cabin.

[0056] At block 622, method 600 includes opening the cooler expansion valve and the evaporator expansion valve, and closing the external condenser flow valve and the condenser heater flow valve. Next, at decision block 624, method 600 includes determining whether the condenser heater temperature sensor reading is increasing, whether the front evaporator and external condenser temperature sensors remain approximately constant, and whether the refrigerant cooler outlet temperature sensor is within a threshold range for the coolant cooler outlet temperature sensor. If so, the temperature sensors are operating as expected, and method 600 proceeds to block 628. Otherwise, the temperature sensors are not operating as expected, and method 600 proceeds to decision block 630.

[0057] At decision block 630, method 600 includes determining whether the condenser heater temperature sensor is within a threshold range of the refrigerant cooler outlet temperature sensor and whether the reading from the refrigerant cooler outlet temperature sensor is increasing. If so, method 600 proceeds to block 632 and identifies a mismatch between the condenser heater temperature sensor and the refrigerant cooler outlet temperature sensor. Otherwise, method 600 proceeds to decision block 634 and determines whether the reading from the condenser heater temperature sensor remains approximately constant and whether the front evaporator temperature reading is increasing. If so, method 600 proceeds to block 636 and identifies a mismatch between the condenser heater temperature sensor and the front evaporator temperature sensor.

[0058] At block 638, method 600 includes closing the condenser heater flow valve and the cooler expansion valve, and opening the evaporator expansion valve and the external condenser flow valve. Next, at decision block 640, method 600 includes determining whether the reading of the front evaporator temperature sensor is decreasing and whether the readings of the cooler outlet temperature sensor and the heater condenser temperature sensor remain approximately constant. If so, the temperature sensors are operating as expected, and method 600 proceeds to block 644. Otherwise, the temperature sensors are not operating as expected, and method 600 proceeds to decision block 646.

[0059] At decision block 646, method 600 includes determining whether the reading from the cooler outlet temperature sensor is decreasing and the reading from the front evaporator temperature sensor remains approximately constant. If so, method 600 proceeds to block 648 and identifies a mismatch between the cooler outlet temperature sensor and the front evaporator temperature sensor. Otherwise, method 600 proceeds to decision block 650 and determines whether the reading from the heater condenser temperature sensor is decreasing and the reading from the front evaporator temperature sensor remains approximately constant. If so, method 600 proceeds to block 652 and identifies a mismatch between the heater condenser temperature sensor and the front evaporator temperature sensor.

[0060] At block 654, method 600 includes closing the evaporator expansion valve and the condenser heater flow valve, and opening the external condenser flow valve and the cooler expansion valve. Next, at decision block 656, method 600 includes determining whether the reading of the refrigerant cooler outlet temperature sensor is within a threshold range of the coolant temperature sensor reading, and whether the readings of the front evaporator temperature sensor and the heater condenser temperature sensor remain substantially constant. If so, the temperature sensors are operating as expected, and method 600 proceeds to block 658. Otherwise, the temperature sensors are not operating as expected, and method 600 proceeds to decision block 662.

[0061] At decision block 662, method 600 includes determining whether the reading of the front evaporator temperature sensor is within a threshold range of the reading of the coolant cooler outlet temperature sensor, and whether the refrigerant cooler outlet temperature sensor remains approximately constant. If so, method 600 proceeds to block 664 and identifies a mismatch between the refrigerant cooler outlet sensor and the front evaporator temperature sensor. Otherwise, method 600 proceeds to decision block 666 and determines whether the reading of the heater condenser sensor is within a threshold range of the reading of the coolant cooler outlet sensor, and whether the refrigerant cooler outlet temperature sensor remains approximately constant. If so, method 600 proceeds to block 668 and identifies a mismatch between the refrigerant cooler outlet sensor and the heater condenser temperature sensor.

[0062] Now refer to Figure 7A , a flow chart illustrating a method 700 for identifying an improperly installed check valve in a vehicle according to an exemplary embodiment is shown. In an exemplary embodiment, the method 700 is executed by the processing system 102 (e.g., Figure 1 ). At box 702, method 700 includes initiating a cabin cooling check valve diagnostic process. Next, at box 704, method 700 includes setting the evaporator expansion valve and the condenser flow valve to greater than ten percent open, and setting the heater flow valve and the cooler expansion valve to less than ten percent open. Next, at decision box 706, method 700 includes determining whether the difference between the readings of the compressor outlet temperature sensor and the expansion valve inlet temperature sensor is greater than a threshold, whether the difference between the readings of the compressor outlet temperature sensor and the external condenser outlet temperature sensor is greater than a threshold, and whether the difference between the readings of the compressor mass flow sensor and the external condenser sensor is greater than a threshold. If all of these conditions are true, method 700 proceeds to box 708, and incorrect orientation of the external condenser check valve is detected. Otherwise, the method proceeds to box 710, and incorrect orientation of the external condenser check valve is not detected.

[0063] Now refer to Figure 7B , a flow chart illustrating a method 720 for identifying an improperly installed check valve in a vehicle according to an exemplary embodiment is shown. In an exemplary embodiment, the method 720 is performed by the processing system 102 (e.g., Figure 1) is executed by the processing system shown. At box 722, method 720 includes initiating a cabin heating check valve diagnostic process. Next, at box 724, method 720 includes setting the evaporator expansion valve and the condenser flow valve to less than ten percent open, and setting the heater flow valve and the cooler expansion valve to greater than ten percent open. Next, as shown in decision box 726, method 720 includes determining whether the difference between the readings of the compressor outlet temperature sensor and the expansion valve inlet temperature sensor is greater than a threshold, whether the difference between the readings of the compressor outlet temperature sensor and the cabin heating condenser outlet temperature sensor is greater than a threshold, and whether the difference between the readings of the compressor mass flow sensor and the cabin heating condenser sensor is greater than a threshold. If all of these conditions are true, method 720 proceeds to box 728, and incorrect orientation of the cabin heating condenser check valve is detected. Otherwise, the method proceeds to box 730, and incorrect orientation of the cabin heating condenser check valve is not detected.

[0064] The terms "a" and "an" do not indicate a limitation of quantity, but rather indicate the presence of at least one of the referenced item. The term "or" means "and / or" unless the context clearly indicates otherwise. References to "an aspect" throughout this specification mean that a particular element (e.g., feature, structure, step, or characteristic) described in conjunction with that aspect is included in at least one aspect described herein and may or may not be present in other aspects. Furthermore, it should be understood that the described elements may be combined in any suitable manner in various aspects.

[0065] When an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. In contrast, when an element is referred to as being "directly on" another element, there are no intervening elements present.

[0066] Unless otherwise indicated herein, all test standards are the most recent standards in effect as of the filing date of this application or, if priority is claimed, the filing date of the earliest priority application in which the test standards appear.

[0067] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0068] Although the above disclosure has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope thereof. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from the essential scope of the present disclosure. Therefore, it is intended that the present disclosure is not limited to the particular embodiments disclosed, but is intended to include all embodiments falling within its scope.

Claims

1. A method for identifying mismatched sensors in a vehicle, the method comprising: obtaining an initial reading from each of a plurality of sensors disposed in the vehicle; activating one or more of a heating system and a cooling system provided in the vehicle; obtaining a series of readings from each of the plurality of sensors; for each of the plurality of sensors, identifying a delta value from the series of readings; For each of the plurality of sensors, comparing the delta value to an expected delta value; identifying two or more sensors from the plurality of sensors that have been mismatched based on a difference between the delta value and an expected delta value; and Mappings for two or more sensors are updated in the vehicle's software.

2. The method according to claim 1, wherein The plurality of sensors are temperature sensors.

3. The method according to claim 1, wherein Identifying two or more sensors from the plurality of sensors that have mismatched based on a difference between the delta value and the expected delta value includes: identifying a first sensor of the plurality of sensors having a first delta value that is outside a first threshold range of first expected delta values; identifying a second sensor of the plurality of sensors having a second delta value that is not within a second threshold range of second expected delta values; and It is determined that the first incremental value is within a second threshold range of the second expected incremental value and that the second incremental value is within a first threshold range of the first expected incremental value.

4. The method according to claim 3, wherein: The first threshold range and the second threshold range are different.

5. The method according to claim 1, wherein The series of readings from each of the plurality of sensors are obtained during a predetermined time period after starting one or more of the heating system and the cooling system.

6. The method according to claim 1, wherein The mapping in the vehicle's software is a logical mapping between each of the plurality of sensors and a variable in the software.

7. The method according to claim 1, wherein One or more of the heating system and the cooling system is configured to control a temperature of a battery pack of the vehicle.

8. A method for identifying mismatched sensors in a vehicle, the method comprising: activating one or more of a heating system and a cooling system provided in the vehicle; obtaining a series of readings from each of a plurality of sensors disposed in the vehicle; inputting a series of readings from each of a plurality of sensors and an operating state of one or more of a heating system and a cooling system into a trained machine learning model; receiving an indication of an identification of two or more of the plurality of sensors that do not match from the trained machine learning model; and Mappings for two or more sensors are updated in the vehicle's software.

9. The method according to claim 8, wherein The plurality of sensors are temperature sensors.

10. A method for identifying an improperly installed check valve in a vehicle, the method comprising: activating one or more of a heating system and a cooling system provided in the vehicle; obtaining a series of readings from one or more sensors disposed in the vehicle; for each of the one or more sensors, identifying a delta value from the series of readings; for each of the one or more sensors, comparing the delta value to an expected delta value; identifying a check valve disposed in the vehicle that has been improperly installed based on a difference between the incremental value and an expected incremental value; as well as Generates an alarm identifying the check valve.

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