Enhanced fault isolation and mitigation of parasitic loads using smart energy hubs
By monitoring and analyzing the parameters of the electronic control unit, identifying parasitic load types and executing corresponding actions, the problem of battery leakage caused by parasitic load faults in the vehicle's electrical system was solved, thereby improving battery life and system reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GM GLOBAL TECHNOLOGY OPERATIONS LLC
- Filing Date
- 2022-10-20
- Publication Date
- 2026-06-12
Smart Images
Figure CN116795073B_ABST
Abstract
Description
Technical Field
[0001] This subject matter discloses information on diagnosing and mitigating the effects of parasitic loads in the electrical systems of vehicles, and in particular, on classifying the types of faults associated with parasitic loads and implementing remedial action plans based on the types of faults. Background Technology
[0002] Vehicles have multiple electrical units used to perform various operations within the vehicle. For example, these electrical units can operate the starter motor, power windows, control electronic communications, entertainment systems, etc. Parasitic loads or electrical faults at any of these electrical units can cause leakage current at the vehicle's battery. While the charge and current on the battery can be monitored and reported for vehicle maintenance and repair, this monitoring is pervasive and does not provide guidance on how to improve the electrical units or prevent further failures. Therefore, it is desirable to provide a system and method for diagnosing the types of faults occurring at the electrical system, in order to improve electrical unit design and mitigate leakage current at the battery. Summary of the Invention
[0003] In one exemplary embodiment, a method for detecting faults occurring in the electrical system of a vehicle is disclosed. Measurements of parameters of an electronic control unit (ECU) of the electrical system are obtained. Parasitic loads at the ECU are determined based on the measured parameter values. The type of fault occurring at the ECU due to the parasitic load is identified based on the measured values. An action is performed at the electrical system based on the type of fault.
[0004] In addition to one or more features described herein, the type of fault is at least one of the following: hardware fault, software fault, and customer-generated fault. The method also includes predicting the state of charge (SOC) level of the battery in the electrical system and estimating the battery leakage time based on the SOC level and the type of fault. Estimating the leakage time also includes at least one of the following: predicting the leakage time of permanent leakage, predicting the leakage time of periodic leakage, and predicting the leakage time due to random leakage. Performing actions also includes at least one of the following: changing the hardware design of the electronic control unit, changing the software design of the electronic control unit, mitigating leakage on the battery due to parasitic loads, and generating a warning signal. The method also includes generating a warning signal when the estimated discharge time exceeds a time threshold. The method also includes controlling the operation of the electronic control unit to mitigate the effects of leakage on the battery.
[0005] In another exemplary embodiment, an electrical system for a vehicle is disclosed. The electrical system includes an electronic control unit, a sensor configured to obtain measured values of parameters of the electronic control unit, and a processor. The processor is configured to determine parasitic loads at the electronic control unit based on the measured values of the parameters, identify the type of fault occurring at the electronic control unit due to the parasitic loads based on the measured values, and perform actions at the electrical system based on the type of fault.
[0006] In addition to one or more features described herein, the type of fault is at least one of the following: hardware fault, software fault, and customer-generated fault. The processor is also configured to predict the state of charge (SOC) level of the battery in the electrical system and estimate the battery leakage time based on the SOC level and the type of fault. The processor is also configured to estimate the leakage time by performing at least one of the following: predicting the leakage time of permanent leakage, predicting the leakage time of periodic leakage, and predicting leakage due to random leakage. Actions also include at least one of the following: changing the hardware design of the electronic control unit, changing the software design of the electronic control unit, mitigating leakage on the battery due to parasitic loads, and generating a warning signal. The processor is also configured to generate a warning signal when the estimated discharge time exceeds a time threshold. The processor is also configured to control the operation of the electronic control unit to mitigate the effects of leakage on the battery.
[0007] In yet another exemplary embodiment, a vehicle is disclosed. The vehicle includes an electronic control unit, sensors configured to obtain measured values of parameters of the electronic control unit, and a processor. The processor is configured to determine parasitic loads at the electronic control unit based on the measured values of the parameters, identify the type of fault occurring at the electronic control unit due to the parasitic load based on the measured values, and perform actions at the vehicle based on the type of fault.
[0008] In addition to one or more features described herein, the fault type is at least one of the following: hardware fault, software fault, and customer-generated fault. The processor is also configured to predict the state of charge (SOC) level of the vehicle's battery and estimate the battery leakage time based on the SOC level and the fault type. The processor is also configured to estimate the leakage time by performing at least one of the following: predicting the leakage time of permanent leakage, predicting the leakage time of periodic leakage, and predicting the leakage time due to random leakage. Actions also include at least one of the following: changing the hardware design of the electronic control unit, changing the software design of the electronic control unit, mitigating leakage on the battery due to parasitic loads, and generating a warning signal. The processor is also configured to perform at least one of the following: generating a warning signal when the estimated discharge time exceeds a time threshold, and controlling the operation of the electronic control unit to mitigate the effects of leakage on the battery.
[0009] The above-described features and advantages, as well as other features and advantages, of this disclosure will become apparent when taken in conjunction with the accompanying drawings and the following detailed description. Attached Figure Description
[0010] Other features, advantages, and details appear only by way of example in the following detailed description, which refers to the accompanying drawings, wherein:
[0011] Figure 1 A vehicle in an exemplary embodiment is shown.
[0012] Figure 2 A schematic diagram of the vehicle's electrical system is shown;
[0013] Figure 3 A flowchart is shown in an illustrative embodiment of a method for performing fault detection, isolation, and mitigation in a battery system;
[0014] Figure 4 An illustrative table is shown that is suitable for classifying parasitic loads based on measurements in a first snapshot;
[0015] Figure 5 A flowchart is shown indicating the process for data collection based on the type of fault;
[0016] Figure 6 A flowchart illustrating a method for mitigating leakage current based on leakage current duration is shown; and
[0017] Figure 7 A flowchart for mitigation processes used in electrical systems is shown. Detailed Implementation
[0018] The following description is exemplary in nature only and is not intended to limit this disclosure, its application, or use. It should be understood that throughout the drawings, corresponding reference numerals indicate the same or corresponding parts and features. As used herein, the term module refers to processing circuitry, which may include application-specific integrated circuits (ASICs), electronic circuitry, processors (shared, dedicated, or grouped) and memories executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functionality.
[0019] According to an exemplary embodiment, Figure 1A vehicle 100 is shown. Vehicle 100 typically includes an electrical system 102 and a communication system 104. The electrical system 102 includes components for controlling various electrical operations of the vehicle, such as a starter motor, generator, power windows, electrical communications, entertainment systems, etc. The electrical system 102 can monitor these components and obtain electrical measurements and diagnose any faults occurring in these components. Measurements, diagnostics, and other data can be transmitted to the communication system 104. The communication system 104 relays the measurements, diagnostics, and other data to a remote server 110. In various embodiments, the remote server may be a manufacturer's server or a server at a maintenance and repair shop.
[0020] Figure 2 A schematic diagram 200 of the electrical system 102 of the vehicle 100 is shown. The electrical system 102 includes a power source or battery 202, an electronic control center 204, a smart energy center (SEC) 206, and a central gateway module (CGM) 208.
[0021] The electronic control center 204 includes multiple electronic control units (ECUs) 210a-210n. Each ECU 210a-210n is dedicated to controlling the electrical loads of the vehicle 100, such as power windows, electronic communications, speedometer, air conditioning unit, entertainment system, starter, generator, etc.
[0022] The intelligent energy center 206 is a control circuit comprising multiple electronic fuses 212a-212n, each electronic fuse controlling the electrical connection between the battery 202 and one of the ECUs 210a-210n. Each of the electronic fuses 212a-212n is electrically coupled to a corresponding ECU 210a-210n in the electronic control center 204 via a wire or branch. In various embodiments, the electronic fuses 212a-212n may be MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors). Each electronic fuse has an associated set of sensors, such as a current sensor 216, a temperature sensor 218, and a voltage sensor 220 (shown relative to electronic fuse 212d for illustrative purposes), which respectively provide measurements of the current, voltage, and temperature of their associated electronic fuse.
[0023] The intelligent power center 206 includes a processor 230 that monitors electronic fuses 212a-212n based on measurements from sensors and other measurements. Based on these measurements, the processor 230 can identify the location of parasitic loads at any of the ECUs 210a-210n. A parasitic load is a load that consumes power even when the ECU is off. The processor 230 can determine which branch or which part of the ECUs 210a-210n is experiencing a parasitic load. By monitoring the current, the SEC 206 can determine the rate at which the parasitic load is draining the battery and / or the time it is expected to drain the battery or reduce the charge on the battery to a selected charge value.
[0024] CGM 208 is a monitoring unit or module that monitors the operation of the electronic control center 204. CGM 208 includes monitoring units 214a-214n, which monitor bus activity at corresponding ECUs 210a-210n. CGM 208 can also monitor for activity at a selected ECU (i.e., ECU 210a) when the selected ECU is in sleep mode. On the other hand, ECU 210a can monitor itself and report any malfunctions at the ECU to CGM 208.
[0025] Therefore, processor 230 receives current, voltage, and temperature measurements from sensors, as well as bus activity, ECU activity during sleep, and ECU malfunction information from CGM 208. Processor 230 can record these measurements and calculations based on the measurements in the snapshot to memory storage device 232.
[0026] Figure 3 A flowchart 300 is shown as a method for performing fault isolation and mitigation in an electrical system 200 in an illustrative embodiment. The method includes three phases: a detection phase 302, a classification phase 304, and a mitigation phase 306. The detection phase 302 includes determining which branch or ECU is experiencing a fault or parasitic load. In the classification phase 304, the type of fault corresponding to the parasitic load is isolated and classified. In the mitigation phase 306, steps are taken to mitigate any leakage current on the battery caused by the parasitic load.
[0027] Detection phase 302 begins at block 310. In block 312, current is measured at each electronic fuse 212a-212n of the intelligent energy center 206. Appropriate filters (e.g., moving average filters, Kalman filters, etc.) can be used to adjust the measured values. The measured values may also include various signals received from CGM 208, such as bus activity, ECU activity during sleep, ECU malfunction, etc. In block 314, the current signals and other measured values are stored as a first snapshot in memory storage device 232, representing the state of one or more of the ECUs 210a-210n at a given time.
[0028] Classification phase 304 begins at box 316. In box 316, a first snapshot is retrieved and analyzed to determine the type or category of the parasitic load fault. In box 318, the fault type is stored as a second snapshot at memory storage device 232.
[0029] Mitigation phase 306 begins at block 320. At block 320, processor 230 determines the state of charge (SOC) on battery 202 and the leakage rate on battery 202. Processor 230 determines these parameters based on the type of fault determined in block 316. Processor 230 generates an alarm to the customer indicating the predicted leakage time. In block 322, processor 230 sets a diagnostic fault code (DTC) and / or performs mitigation steps based on the type of fault. Mitigation steps may include hibernation reinitialization, soft or hard disconnection of the fuse, etc. In block 324, the data determined during mitigation phase 306 (e.g., diagnostic fault codes (SOC), mitigation steps) is stored as a third snapshot at memory storage device 232.
[0030] Figure 4 An illustrative table 400 is shown, suitable for classifying parasitic loads based on measurements in a first snapshot. The illustrative table 400 includes columns 402-412 indicating parameters of a signature 414 characterizing the parasitic load and rows 420, 422, 424 indicating the type of fault 428. The parameter in column 402 is the fuse current parameter. The magnitude of the current at the fuse is compared to a current threshold. In the illustrative embodiment, the current threshold is 20 mA. The fuse current parameter indicates whether the current exceeds the current threshold. The parameter in column 404 is bus activity, which is measured at CGM 208 and can be either on or off. The parameter in column 406 is the ECU status, which is measured at CGM 208 and can be either on or off.
[0031] The parameter in column 408 indicates an ECU malfunction. The ECU can monitor itself and provide values to the CGM when an error or malfunction occurs. The parameter in column 410 is the leakage duration, which can be measured at SEC 206. Leakage duration can be categorized as permanent leakage time (leakage time based on permanent parasitic load), periodic leakage time (leakage time based on periodic parasitic load), or random leakage duration (leakage time based on randomly generated parasitic load).
[0032] The parameter in column 412 indicates the result of comparing the current value of the load at the ECU with the current value obtained at the ECU of another vehicle. The other vehicles are typically vehicles of the same or equivalent type or category, such as passenger cars or trucks.
[0033] Processor 230 can determine the type of fault based on the status of the parameters in columns 402-412. Row 420 indicates a hardware fault, such as a hardware fault that may occur in the event of a short circuit in a wire or load. Row 422 indicates a software fault. Software faults may occur, for example, by an unexpected wake-up of a module or electronic control unit (sub-row 422A) or by a module or electronic control unit not being disconnected or entering sleep mode (sub-row 422b).
[0034] When the current at the electronic fuse is greater than 20mA, bus activity is turned off, ECU activity is turned off during sleep mode, the ECU is not malfunctioning, the leakage duration is a permanent leakage, and the value of the leakage duration is higher than the expected value in other vehicles of the same vehicle category, the processor determines that a hardware fault has occurred (line 420).
[0035] When the current at the electronic fuse is greater than 20mA, bus activity is enabled, ECU activity is enabled during sleep mode, the ECU is not malfunctioning, the leakage duration is periodic leakage, and the leakage duration is the same as expected in other vehicles of the same vehicle category, the processor determines the occurrence of a software fault based on the unexpected wake-up of the module (line 422a).
[0036] When the current at the electronic fuse is greater than 20mA, bus activity is turned off, ECU activity is turned off during sleep mode, there is a malfunction at the ECU, the leakage duration is a permanent leakage, and the value of the leakage duration is the same as the expected value in other vehicles of the same vehicle category, the processor determines the occurrence of a software fault based on the module's inability to shut down (line 422b).
[0037] When the current at the electronic fuse is greater than 20mA, bus activity is enabled, ECU activity is enabled during sleep mode, the ECU is not malfunctioning, the leakage duration is random leakage, and the leakage duration value is higher than the expected value in other vehicles of the same vehicle category, the processor determines that a customer-related leakage has occurred on the module (line 424), which is usually due to a customer-generated fault.
[0038] Figure 5 A flowchart 500 instructs a process for data collection based on fault type. In block 502, a hardware fault is identified at the ECU or ECU branch. In block 504, voltage and current measurements are represented by the corresponding fuse for the ECU. In block 514, the voltage and current measurements are stored at a second snapshot. In block 506, a software fault is identified at the ECU or ECU branch. In block 508, the processor creates a histogram of ECU wake-up frequency and current amplitude. In block 514, this data is stored at a second snapshot. In block 510, customer-related leakage on the ECU is identified. In block 512, the processor 230 searches a lookup table for features with the highest design fault mode severity and customer dissatisfaction. In block 513, this data can be transmitted to the vehicle via communication system 104. In block 514, this data is stored at a second snapshot.
[0039] Figure 6 A flowchart 600 illustrating a leakage mitigation method based on leakage duration is shown. In block 602, a fault is detected. In block 604, processor 230 determines whether the fault is a hardware fault. If the leakage is a result of a hardware fault, the method proceeds to block 606, where the processor calculates the predicted leakage time expected due to the hardware fault. The calculation of permanent or hardware leakage is shown in equations (1)-(3):
[0040]
[0041]
[0042]
[0043] Where SOC0 is the current state of charge of the battery, SOC end It is the threshold state of charge value, which can be a pre-selected value, t pm The leakage time is the permanent leakage current on the battery, I is the current at the ECU, and C is the charge at the ECU. SOC0 is a known quantity from sensor measurements or an SOC estimation algorithm that operates on sensor measurements. The method proceeds from box 606 to box 614. In box 614, the predicted leakage time is compared with a duration threshold, and an action is taken based on this comparison.
[0044] Returning to box 604, if processor 230 determines that the fault is not a hardware fault, the method proceeds to box 608. In box 608, processor 230 determines whether the fault is a software fault. If the fault is a software fault, the method proceeds to box 610. In box 610, processor 230 calculates the predicted leakage time due to the software fault. The calculation of the periodic or software leakage time is shown in equations (4)-(6):
[0045]
[0046] SOC0 = SOC end +t pr *dSCOC Equation (5)
[0047]
[0048] Where P is the period of the periodic leakage current, and T pr This refers to the leakage time of periodic leakage on the battery due to a software malfunction. The method can proceed from box 610 to box 614.
[0049] Returning to box 608, if the fault is not a software fault, the method proceeds to box 612. In box 612, processor 230 estimates the leakage time based on random leakage events. A historical record of the State of Charge (SOC) over time can be obtained, and curve fitting can be used to determine the equation for the SOC over time. The leakage time can then be expressed as a function of the State of Charge (SOC), as shown in equation (7):
[0050] t r = f(SOC) Equation (7)
[0051] Where t r This is the predicted leakage time of a random leakage event. At any time n, t r =t n -t0, where t0 is the current time. This method proceeds from box 612 to box 614.
[0052] In box 614, the predicted leakage time is compared with a predefined leakage time threshold T1. If the leakage time (i.e., t...) pm t pr or t r If the leakage time (i.e., t) is greater than the time threshold T1, the method proceeds to box 616. In box 616, an alarm signal or warning signal is generated. In various embodiments, the alarm signal can be sent to the customer or driver, the manufacturer, or a repair shop. Returning to box 614, if the leakage time (i.e., t) is greater than the time threshold T1, the method proceeds to box 616. pm t pr or t rIf the value is less than or equal to the threshold T1, the method proceeds to box 618. In box 618, mitigation steps are taken to reduce or minimize the impact of parasitic load when battery 202 is depleted.
[0053] Figure 7 A flowchart 700 for a mitigation process for an electrical system is shown. In block 702, the mitigation process is initialized. In block 704, the decay rate in the State of Charge (SOC) and / or the predicted leakage time are compared with relevant thresholds. If the predicted decay time is greater than the threshold decay time (or alternatively, if the SOC decay rate is greater than the decay rate threshold), the method proceeds to block 706. On the other hand, if the predicted decay time is less than or equal to the threshold decay time (or alternatively, if the SOC decay rate is less than or equal to the decay rate threshold), the method proceeds to block 718. In block 718, a hard reset of the ECU is performed by turning the ECU off and on.
[0054] In box 706, the fault type and predicted leakage time (or leakage rate) are stored. In box 708, the fault type is examined to determine whether the fault type results in permanent leakage. If the fault type results in permanent leakage, the method proceeds to box 710. If the fault type does not result in permanent leakage (i.e., results in periodic or random leakage), the method proceeds to box 712.
[0055] In box 710, a soft reset is performed, which can be a software restart or reinitialization. In box 712, a hibernation reinitialization is performed. The method proceeds from box 710 or box 712 to box 714. In box 714, a counter is incremented. The counter tracks the number of soft resets or hibernation reinitializations performed. In box 716, if the counter is greater than a counting threshold, the method proceeds to box 718 and performs a hard reset. Conversely, if the counter is less than or equal to the counting threshold at box 716, the method can return to box 706 and perform another round of soft resets or hibernation reinitializations.
[0056] In other embodiments, when leakage is caused by software design, the software for the electronic control unit can be redesigned.
[0057] While the above disclosure has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes can be made and elements can be substituted with equivalents without departing from its scope. Furthermore, many modifications can be made to adapt particular situations or materials to the teachings of this disclosure without departing from the basic scope of this disclosure. Therefore, this disclosure is not intended to be limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.
Claims
1. A method for detecting faults occurring in the electrical system of a vehicle, comprising: Obtain the measured values of the parameters of the electronic control unit of the electrical system; The parasitic load at the electronic control unit is determined based on the measured values of the parameters. The type of fault occurring at the electronic control unit due to the parasitic load is identified based on the measured values. as well as The action is performed on the electrical system based on the type of fault. Predict the state of charge (SOC) level of the battery in the electrical system, and estimate the leakage time of the battery based on the SOC level and the type of fault.
2. The method according to claim 1, wherein, The type of fault is at least one of the following: (i) hardware fault; (ii) software fault; and (iii) customer-generated fault.
3. The method according to claim 1, wherein, Performing the action further includes at least one of the following: (i) changing the hardware design of the electronic control unit; (ii) changing the software design of the electronic control unit; (iii) mitigating leakage current on the battery due to the parasitic load; and (iv) generating a warning signal.
4. The method according to claim 3 further includes generating the warning signal when the estimated discharge time is greater than a time threshold.
5. An electrical system for a vehicle, comprising: Electronic control unit; A sensor configured to obtain measured values of parameters of the electronic control unit; Processor, the processor being configured to: The parasitic load at the electronic control unit is determined based on the measured values of the parameters. The type of fault occurring at the electronic control unit due to the parasitic load is identified based on the measured values. as well as The action is performed on the electrical system based on the type of fault. The processor is further configured to predict the state of charge (SOC) level of the battery in the electrical system and to estimate the leakage time of the battery based on the SOC level and the type of the fault.
6. The electrical system according to claim 5, wherein, The type of fault is at least one of the following: (i) hardware fault; (ii) software fault; and (iii) customer-generated fault.
7. The electrical system according to claim 5, wherein, The action also includes at least one of the following: (i) changing the hardware design of the electronic control unit; (ii) changing the software design of the electronic control unit; (iii) mitigating leakage current on the battery due to parasitic load; and (iv) generating a warning signal.
8. The electrical system according to claim 7, wherein, The processor is also configured to generate the warning signal when the estimated discharge time exceeds a time threshold.