Whole vehicle electrical system management method, device and equipment

Abstract

This disclosure relates to a management method, apparatus, and device for a vehicle electrical system. The method includes: acquiring multiple target state information of the vehicle electrical system; determining multi-dimensional target state features based on the target state information; matching the target state features with preset sets of abnormal features; wherein different sets of abnormal features are associated with different charging faults; determining a fault diagnosis result based on the charging faults associated with the matched sets of abnormal features; and triggering a charging warning event matching the fault diagnosis result. This disclosure can more comprehensively and realistically reflect the charging status and health of the vehicle electrical system, improving the timeliness and accuracy of fault warnings.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and in particular to a method, apparatus and equipment for managing a vehicle electrical system. Background Technology

[0002] In the automotive industry, the 12V lead-acid battery serves as the core power supply unit of the vehicle's electrical system. Its status monitoring primarily relies on a single measurement of the battery's terminal voltage. When the voltage is detected to be below a set, fixed threshold, a malfunction indicator light illuminates on the dashboard as an alarm. This monitoring method based on a single voltage threshold is inherently lagging, typically triggering only when the battery is severely depleted, thus failing to achieve early identification and prevention. Furthermore, the false alarm rate for a single voltage signal is high, failing to accurately reflect the battery's true charging status and the health of the charging system.

[0003] Therefore, the existing monitoring methods for batteries are insufficient to meet the high reliability requirements of power supply systems driven by the current development of automotive electronics and intelligence. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a method, apparatus, and equipment for managing a vehicle's electrical system.

[0005] According to one aspect of this disclosure, a method for managing a vehicle electrical system is provided, the method comprising: Acquire various target status information of the vehicle's electrical system; Determine multi-dimensional target state features based on the target state information; The target state features are matched with each preset set of abnormal features; wherein different sets of abnormal features are associated with different charging faults. Based on the charging faults associated with the matched abnormal feature set, the fault diagnosis result is determined. Trigger a charging warning event that matches the fault diagnosis result.

[0006] According to another aspect of this disclosure, a management device for a vehicle electrical system is also provided, the device comprising: The status acquisition module is used to acquire various target status information of the vehicle's electrical system; The feature determination module is used to determine multi-dimensional target state features based on the target state information. The feature matching module is used to match the target state features with various preset abnormal feature sets; wherein different abnormal feature sets are associated with different charging faults. The fault diagnosis module is used to determine the fault diagnosis result based on the charging fault associated with the set of abnormal features matched. The early warning triggering module is used to trigger a charging early warning event that matches the fault diagnosis result.

[0007] According to another aspect of this disclosure, an electronic device is also provided, the electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the above method.

[0008] According to another aspect of this disclosure, a computer-readable storage medium is also provided, the storage medium storing a computer program for performing the above-described method.

[0009] According to another aspect of this disclosure, a vehicle is also provided, the vehicle comprising: Memory; Processor; and Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in the first aspect.

[0010] The technical solution provided in this disclosure has the following advantages compared with the prior art: The technical solution provided in this disclosure includes: acquiring multiple target state information of the vehicle electrical system; determining multi-dimensional target state features based on the target state information; matching the target state features with each preset abnormal feature set; wherein different abnormal feature sets are associated with different charging faults; determining the fault diagnosis result based on the charging fault associated with the matched abnormal feature set; and triggering a charging warning event that matches the fault diagnosis result.

[0011] This technical solution overcomes the limitations of relying solely on a single battery voltage signal. Instead, it acquires multiple target state information from the entire vehicle's electrical system and quantifies these target state information into multi-dimensional target state characteristics. These target state characteristics are quantified indicators resulting from processing multi-source heterogeneous target state information. They can capture early, subtle anomalies in the charging system (such as efficiency fluctuations and voltage difference changes), thus serving as a reliable data foundation for subsequent fault diagnosis. In this case, matching the target state characteristics with a set of associated abnormal features related to charging faults allows for rapid and accurate identification of the corresponding charging fault and generation of fault diagnosis results. The target state characteristics and abnormal feature sets used in this matching process are multi-feature logically interlocked, effectively filtering out false alarms from single features, thereby providing a more comprehensive and accurate reflection of the charging status and health of the entire vehicle's electrical system. Finally, a charging warning event matching the fault diagnosis result is triggered, thereby improving the timeliness and accuracy of fault warnings, shifting from passive response to proactive prevention, and effectively ensuring the operational safety of the entire vehicle's electrical system. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0013] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the vehicle electrical system management method described in the embodiments of this disclosure; Figure 2 This is a schematic diagram illustrating the management process of the vehicle electrical system as described in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of the vehicle electrical system management device according to an embodiment of this disclosure; Figure 4 This is a schematic diagram of the structure of the electronic device described in an embodiment of this disclosure. Detailed Implementation

[0015] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0016] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0017] With the development of automotive electronics technology, the level of vehicle electrification and intelligence is increasing, and the number of on-board electronic devices (such as electronic control units, sensors, infotainment systems, etc.) is surging, placing increasingly higher demands on the reliance on and stability of the 12V low-voltage power supply system. As the core of the vehicle's electrical system, the 12V lead-acid battery is not only responsible for starting the engine, but also for supplying power to all electrical equipment in the vehicle while it is running. Its health status directly affects the normal operation and safety of the vehicle.

[0018] Currently, battery monitoring methods based on a single voltage threshold have many problems.

[0019] First, this method is a reactive alarm, which is delayed. It is often only triggered when the battery is severely depleted, the vehicle may not be able to start, or a malfunction has occurred. It lacks early identification and prevention of potential faults in the charging system.

[0020] Secondly, the battery terminal voltage is easily affected by multiple factors such as instantaneous load fluctuations, ambient temperature changes, and measurement point impedance, resulting in a low signal-to-noise ratio and a high false alarm rate for a single voltage signal, making it difficult to accurately reflect the battery's true charging state (such as state of charge) and the health status of the charging system (such as generators and voltage regulators).

[0021] The main reasons for the above problems are as follows: First, the hardware architecture is simple, typically only connected to the positive terminal of the battery to obtain voltage signals, lacking synchronous perception of multi-dimensional information such as charging current and generator output voltage. Second, the diagnostic logic is simplistic, only setting fixed upper and lower voltage thresholds, without establishing an intelligent diagnostic model based on multi-parameter fusion and time-series analysis. Third, the system integration is low, lacking deep integration with vehicle network such as CAN (Controller Area Network) bus, and unable to obtain relevant operating condition information such as engine speed and electrical load to assist in diagnosis. Fourth, there is a lack of an effective human-machine interaction early warning mechanism, failing to provide users with clear and tiered warning prompts in the early stages of anomalies, resulting in users being unable to take timely countermeasures.

[0022] To address at least one of the above-mentioned problems, embodiments of this disclosure provide a management method, system, and device for a vehicle's electrical system. This solution collects various target state information of the vehicle's electrical system in real time, such as battery state information and generator state information, and extracts quantifiable target state features from this information. Then, by associating abnormal feature sets with charging faults, online analysis and fault determination of the target state features are performed, enabling accurate identification and early warning of early battery faults, thereby preventing problems before they occur and improving the reliability of the vehicle's electrical system.

[0023] Figure 1 This flowchart illustrates a method for managing a vehicle's electrical system, applicable to situations where a vehicle monitors the charging of its electrical systems, including the battery, and provides fault warnings. This method can be executed by a vehicle electrical system management device configured in the vehicle. This device can be implemented using software and / or hardware, specifically, for example, an electronic device or a server. The electronic device may include communication-enabled devices such as an in-vehicle host, tablet computer, desktop computer, laptop computer, and smartphone. The server may be a cloud server or server cluster, or a device with storage and computing capabilities.

[0024] like Figure 1 As shown, a method for managing a vehicle's electrical system may include the following steps.

[0025] S102, acquire various target status information of the vehicle's electrical system.

[0026] This embodiment does not require the addition of extra hardware sensors. Instead, it reuses existing resources such as the vehicle's battery smart sensors and CAN bus to achieve comprehensive status perception of various components related to the charging of the vehicle's electrical system, such as the battery, generator, engine, and load.

[0027] Combination Figure 2 This embodiment may include: First, collecting various raw state information of the vehicle's electrical system; the raw state information includes, but is not limited to: battery state information, generator state information, engine state information, and load information.

[0028] In practical implementation, a battery sensor can be connected via an analog signal input channel or a digital communication interface to collect battery status information in real time. This battery status information includes, for example, battery terminal voltage, charging / discharging current, battery temperature, and state of charge (SOC).

[0029] The MCU (Microcontroller Unit) monitors the CAN bus to obtain real-time generator status information from the Electronic Control Unit (ECU). This generator status information includes, for example, generator voltage, generator excitation status signal, and load response factor. The excitation status signal indicates whether the generator has been commanded by the ECU to enter an operating state; for example, an excitation status signal of 0 indicates off, and an excitation status signal of 1 indicates on. The load response factor is the percentage of the generator's current output current relative to its rated maximum current, reflecting the generator's load pressure.

[0030] The engine status information is obtained by subscribing to periodic messages from the engine control unit in real time via the CAN bus. This engine status information includes, for example, engine speed and engine ignition switch status; the engine ignition switch status is used to identify the status of the ignition key or push-button start.

[0031] The system obtains load information by polling or subscribing to status messages from multiple loads, such as the body control module, the air conditioning control unit, and the lighting control module, via the CAN bus. This load information includes, for example, the on / off status of the air conditioning compressor, the on / off status of the lights, and the on / off status of the seat ventilation and heating systems.

[0032] After acquiring the aforementioned multi-source heterogeneous raw state information, direct use for diagnosis can lead to misjudgment due to differences in sampling frequency, transmission delay, and noise characteristics among different sensors. Therefore, this embodiment performs data preprocessing on the raw state information to obtain target state information; the data preprocessing includes at least: validity verification, time synchronization, and data fusion.

[0033] In one specific example, the original state information can be validated first to remove abnormally dirty data caused by sensor malfunctions, line interference, or communication loss. Validation may include, for example: Check whether the original status information of each frame (such as battery terminal voltage) is within a preset reasonable range. If not, the original status information of that frame is determined to be invalid and discarded.

[0034] Calculate the data change between the original state information of two adjacent sampling periods; taking the battery terminal voltage as an example, if the voltage change exceeds the preset maximum response speed (e.g., the voltage jumps by 5V within 1ms), then the original state information of the frame is determined to be signal noise or transient interference and is filtered out.

[0035] For raw status information (such as engine speed) acquired via the CAN bus, determine whether the raw status information is received within a preset time window (such as 100ms). If not, determine that the raw status information frame indicates that the signal source has failed, and enable the default safety value or maintain the valid value of the previous cycle.

[0036] Then, the original state information that has passed the validity check is synchronized in time to improve the problem of inconsistent sampling times of different information, and to ensure that the original state information such as voltage, current and speed at the same time are aligned in time, so as to perform accurate fault analysis.

[0037] After time synchronization is completed, the original state information is fused to integrate the scattered and independent original state information into contextual features with clear physical meaning, thereby eliminating the ambiguity of single signals.

[0038] For example, based on the fused engine speed and ignition switch status, the original status information corresponding to the same moment is labeled with operating condition tags. For instance, if the engine speed is less than 50 rpm and the ignition switch is off, it is labeled as a pre-start resting condition; if the engine speed is greater than 800 rpm and the ignition switch is on, it is labeled as a normal charging condition.

[0039] After the above data preprocessing, standardized target status information is output, which includes, but is not limited to: battery status information, generator status information, engine status information, and load information.

[0040] This embodiment not only collects the status information of the battery, but also uses the CAN bus to obtain the status information of other components in the vehicle related to charging, such as generator status information, engine status information, and load information. It can overcome the limitations of single voltage monitoring. By integrating multi-dimensional information such as battery, generator, engine, and load, it can effectively distinguish whether the problem is with the battery or the charging system, making the diagnostic conclusion more consistent with the actual operating conditions, improving robustness, and reducing the false alarm rate.

[0041] S104, Determine multi-dimensional target state features based on target state information.

[0042] It is understood that the target state information obtained in the above steps is mainly the underlying basic physical quantity that reflects the instantaneous performance of the vehicle's electrical system, which is difficult to directly characterize the deep health state or fault mechanism. Therefore, this embodiment transforms these discrete target state information into high-order features with clear physical meaning that can quantitatively characterize the dynamic characteristics and potential fault modes of the system.

[0043] Some embodiments may include: determining a first state feature representing the dynamic state of the battery based on battery state information within a first time period; wherein the first state feature is a derived parameter extracted from the battery state information, including parameters such as voltage and current, that reflects the battery's instantaneous charging capacity, polarization state, and charging system response speed. The first state feature includes at least: charging current carrying dwell time, voltage recovery slope, and dynamic internal resistance.

[0044] Specifically, based on the charging current in the battery status information, the charging current that remains within the effective charging threshold and continues for a certain duration during the first time period is determined as the charging current carrying the dwell time; this dwell time is the continuous duration of the charging current within the effective charging threshold. If the dwell time is significantly lower than the theoretically expected value, for example, if the battery charging current is frequently intermittent when the generator is working normally, it may indicate severe internal polarization of the battery, resulting in the inability to continuously charge with a large current, or that there is an intermittent open circuit due to poor contact in the charging circuit.

[0045] Based on the charging current and voltage from the battery status information, the ratio of voltage change to current change is determined within the first time period to obtain the battery's dynamic internal resistance. The dynamic internal resistance reflects the battery's instantaneous impedance characteristics under current operating conditions and can be used to determine whether the battery is aging or sulfated.

[0046] Based on the voltage in the battery status information, if a preset trigger event such as sudden load disconnection occurs within the first time period, the voltage recovery slope is determined according to the rate of change of the battery terminal voltage as it returns to its steady-state value from the instantaneous drop or rise value. The voltage recovery slope can be used to assess battery health and electrolyte activity.

[0047] Some embodiments may further include: determining a second state feature representing the dynamic state of the charging circuit based on generator state information and battery state information during a second time period; wherein the second state feature includes at least: generator voltage carrying dwell time, voltage difference between generator and battery, and dynamic charging efficiency.

[0048] Specifically, based on the generator voltage in the generator status information, the generator voltage that remains within a preset regulating voltage range for a certain duration during the second time period is defined as the generator voltage with dwell time; this dwell time is the continuous duration during which the generator voltage is maintained within the regulating voltage range. The generator voltage with dwell time is used to characterize the generator's ability to maintain a stable output voltage, reflecting the response stability of the generator regulator and the reliability of the belt drive system. If the dwell time is short, it indicates frequent fluctuations in the generator voltage, which may suggest a voltage regulator malfunction, poor carbon brush contact, or belt slippage leading to unstable speed. Therefore, the generator voltage with dwell time can be used as a quality threshold for subsequent calculations of other parameters. If the generator voltage is unstable, the calculation of the loop resistance is suspended to avoid misjudgment.

[0049] Based on the generator voltage from the generator status information and the voltage and charging current from the battery status information, during the second time period, when the generator is operating, the voltage difference between the generator output voltage and the battery terminal voltage is determined. This voltage difference is the basis for calculating the circuit impedance and directly reflects the pressure loss along the power transmission path.

[0050] Based on the voltage difference between the generator and the battery, the equivalent resistance of the charging circuit can be obtained by dividing the voltage difference by the charging current. The equivalent resistance of the charging circuit includes the sum of the generator's internal resistance, line impedance, connector contact resistance, and battery internal resistance. An abnormally high equivalent resistance of the charging circuit usually indicates line corrosion, loose connectors, or poor contact.

[0051] Based on the generator voltage and charging current in the generator status information, and the voltage and charging current in the battery status information, the generator output power is calculated during the second time period based on the corresponding generator voltage and charging current; the battery power is calculated based on the corresponding battery voltage and charging current. The ratio of the actual power stored in the battery to the generator output power is calculated to obtain the energy transfer efficiency. A low energy transfer efficiency usually indicates that a large amount of energy is lost due to line heating or abnormal generator regulation.

[0052] Based on the above embodiments, the target state information, the first state feature, and the second state feature can be jointly determined as the target state feature.

[0053] S106, Match the target state characteristics with each preset set of abnormal characteristics; wherein, different sets of abnormal characteristics are associated with different charging faults.

[0054] It is understandable that the vehicle's electrical system may experience various types of charging faults during actual operation, such as generator failure, generator voltage faults (specifically, unstable or low voltage), poor charging line contact, and abnormal battery internal resistance. The physical mechanisms of these different charging faults vary, thus manifesting as a set of significantly different abnormal state characteristics in the data space. Therefore, this embodiment can mine and configure a set of abnormal state characteristics that have a causal or statistical correlation with each type of charging fault.

[0055] To facilitate data management and achieve data structuring and logical standardization, this embodiment logically binds charging faults with their corresponding abnormal state feature sets, encapsulating them into independent diagnostic rules. All generated diagnostic rules are added to a fault rule library for centralized storage and maintenance. This fault rule library facilitates the addition, deletion, and modification of diagnostic rules. By constructing this fault rule library, this embodiment significantly improves the scalability and maintenance convenience of diagnostic rules, enabling them to flexibly adapt to different vehicle platforms and the software iteration needs throughout the entire lifecycle.

[0056] The configuration of diagnostic rules in the fault rule base can include the following examples.

[0057] Example 1. First, a first anomalous feature carrying a core identifier and a second anomalous feature carrying an auxiliary identifier are configured for the generator not generating power fault. The first anomalous feature includes: an abnormal battery charging current that maintains at least a first dwell time, and a first abnormal generator voltage that maintains at least a second dwell time.

[0058] Among these, the abnormal charging current of the battery is, for example, 0A or a negative value; the first abnormal voltage of the generator is, for example, 0V or less than 11V. Accordingly, the aforementioned first abnormal characteristic specifically includes, for example, the battery charging current being 0A or a negative value for at least 10 seconds, and the generator voltage being 0V or less than a preset voltage value (such as 11V) for at least 10 seconds.

[0059] In this specific example, the first abnormal voltage is 0V, indicating a complete open circuit or complete damage to the generator's internal rectifier bridge and excitation circuit. The first abnormal voltage is less than 11V; for a 12V vehicle electrical system, 11V is typically lower than the battery's nominal voltage. If the generator voltage is below 11V, it means the generator has not established effective output, and the voltage of the vehicle electrical system has been pulled down to the battery's discharge range by the load.

[0060] For the reasons mentioned above, when the generator's first abnormal voltage (indicating extremely low voltage and inability to establish an electric field) and the battery's abnormal charging current (not only not charging but discharging) occur simultaneously, it can be physically confirmed that the generator is not providing energy to the vehicle's electrical system, and that the battery is bearing the load alone or is in an open-circuit state. This is the most essential electrical characteristic of a generator not generating power.

[0061] The second abnormal characteristic includes: engine speed exceeding a preset idle speed threshold, and / or the generator start signal being active. Specifically, for example: engine speed exceeding the 800 rpm idle speed threshold, and the generator start signal being active, i.e., the generator signal being set to 1.

[0062] The preset idle speed threshold is typically the lower limit of the engine's stable idle speed. When the engine speed exceeds the preset idle speed threshold, the generator theoretically has the ability to generate electricity. If there is still no output under this condition, it can help to more accurately determine that the generator is faulty.

[0063] Regarding the generator start signal: Under certain operating conditions, such as rapid acceleration for overtaking, extreme high-temperature protection, or during start-stop system shutdown, the ECU may actively send a command to shut down the generator excitation. In this case, the generator not generating electricity is a controlled behavior. Only when the ECU has issued a command to start the generator (signal set to 1), but the electrical feedback shows no power generation, can it be proven that the generator itself is faulty. This can help to more accurately determine the generator malfunction.

[0064] The first anomaly characteristic mentioned above confirms the physical fact of no power output; the second anomaly characteristic eliminates logical misjudgments related to non-operating conditions and controlled shutdown, helping to more accurately pinpoint the generator's failure to generate power. In other words, the results of the first and second anomaly characteristics ensure that the generator failure to generate power is only triggered in the unique scenario where the vehicle's electrical system explicitly requires power generation and has the conditions for power generation, but no power output is detected for an extended period. This gives the fault diagnosis extremely high specificity and confidence.

[0065] Then, the first and second abnormal features are added to the first abnormal feature set; and the association between the generator non-generating fault and the first abnormal feature set is established.

[0066] Establish a set of related generator non-generating faults and first abnormal features, encapsulate them into a first rule, and add the first rule to the fault rule base.

[0067] Example 2. First, configure a second set of abnormal features for generator voltage faults. The second set of abnormal features includes: a second abnormal generator voltage that maintains a dwell time of at least a third duration, and / or a generator voltage time-domain standard deviation that is greater than a standard deviation threshold.

[0068] Specifically, a generator second abnormal voltage that remains for at least a third dwell time is, for example, a generator voltage that is continuously lower than the normal lower limit (e.g., 13V) and remains for at least 15 seconds, indicates that the generator voltage is too low and is a generator voltage fault.

[0069] If the standard deviation of the generator voltage in the time domain is greater than the standard deviation threshold, for example, if the standard deviation of the generator voltage in the time domain is greater than the standard deviation threshold (e.g., ±0.5V) within a preset time window (e.g., 30 seconds), it indicates that the generator voltage is unstable and belongs to the generator voltage fault.

[0070] The combination of two features—maintaining a generator second abnormal voltage for at least a third dwell time and the generator voltage time domain standard deviation being greater than the standard deviation threshold—can accurately identify generator voltage faults, effectively filter out normal voltage fluctuations caused by load changes, and avoid false alarms.

[0071] Then, the association between generator voltage faults and the second set of abnormal features is established. The associated generator voltage faults and the second set of abnormal features are encapsulated into a second rule and added to the fault rule base.

[0072] Example 3. First, configure a third set of abnormal features for charging line faults. The third set of abnormal features includes: the voltage difference between the generator and the battery is greater than a preset voltage difference threshold (e.g., ΔV > 0.5V), and the dynamic charging efficiency is lower than a preset efficiency threshold (e.g., η < 70%) and remains for at least a fourth dwell time. Alternatively, the third set of abnormal features includes: the rate of change of the voltage difference between the generator and the battery is greater than a preset rate of change threshold, and the standard deviation of the dynamic charging efficiency is greater than a preset standard deviation threshold.

[0073] Specifically, if the voltage difference between the generator and the battery exceeds a preset voltage difference threshold (e.g., ΔV > 0.5V), and the dynamic charging efficiency is lower than a preset efficiency threshold (e.g., η < 70%) and remains below this threshold for at least four dwell times, this indicates that more than 30% of the electrical energy is consumed by the contact resistance of the connection lines. This typically signifies persistent degradation in the main charging circuit, such as severe physical connection oxidation, loose bolts, or cable corrosion. These characteristics align with the most fundamental electrical features of high-loss contact failures (i.e., steady-state high resistance) in charging circuits and can be used to prevent wiring harness burnout due to prolonged overheating.

[0074] The rate of change of the voltage difference between the generator and the battery exceeds a preset threshold, and the standard deviation of the dynamic charging efficiency exceeds a preset threshold. For example, the dynamic charging efficiency drops sharply from 95% to 60% within one second and then recovers rapidly. Such high-frequency, large-amplitude fluctuations cannot be explained by constant line resistance and are typical characteristics of unstable mechanical connections (such as loose connections), which can easily lead to arcing or system reset. These characteristics match the most essential electrical features of charging line faults such as intermittent poor contact and can be used to provide early warning of potential catastrophic connection failures.

[0075] In this embodiment, the two complementary sets of third abnormal features can simultaneously monitor both high-loss contact failures and intermittent contact failures in charging circuits, comprehensively covering the entire spectrum of charging circuit faults from chronic contact failures to acute intermittent contact jitter, significantly improving the coverage and timeliness of diagnosis.

[0076] Then, the association between charging line faults and the third set of abnormal features is established. The established association between the charging line faults and the third set of abnormal features is encapsulated into a third rule, and the third rule is added to the fault rule base.

[0077] Based on the various diagnostic rules provided in the above embodiments, this embodiment uses one or more target state features to match the abnormal feature sets in the fault rule base with those in each diagnostic rule. The charging fault associated with the matched abnormal feature set is then identified as the target charging fault, and a fault diagnosis result is generated for the target charging fault.

[0078] S108, Determine the fault diagnosis result based on the charging fault associated with the set of abnormal features matched.

[0079] To better understand the solution, please refer to the following: Figure 2 The description is further elaborated by combining steps S106 and S108.

[0080] In one embodiment, taking a first rule as an example, the target state features are matched with a first set of abnormal features in the first rule. This embodiment includes: The target state feature is matched with the first abnormal feature in the first abnormal feature set; if the first abnormal feature is matched, the target state feature is matched with the second abnormal feature.

[0081] If the first abnormal feature set also matches the second abnormal feature set, it is determined that the first abnormal feature set matches the target state feature set successfully. Thus, the generator non-generating fault associated with the first abnormal feature set is identified as the target charging fault, and a fault diagnosis result is determined for the generator non-generating fault.

[0082] Specifically, in this embodiment, it is first determined whether the target state feature matches the first abnormal feature, which is the core feature; if they match, that is, the charging current of the battery is an abnormal charging current and remains for at least a first dwell time, and the generator voltage is a first abnormal voltage and remains for at least a second dwell time.

[0083] If the target state feature matches the first abnormal feature, determine whether the target state feature matches the second abnormal feature; if they match, that is, at least one of the following is satisfied: the engine speed is greater than the preset idle speed threshold and the generator start signal is active.

[0084] If the first set of abnormal features also matches the second abnormal feature, the generator failure to generate electricity associated with the first set of abnormal features is identified as the current target charging fault of the vehicle charging system, and a fault diagnosis result is determined for the generator failure to generate electricity.

[0085] In the above embodiments, by first matching with a first anomalous feature carrying a core identifier, and only proceeding to match a second anomalous feature carrying an auxiliary identifier after a successful match, the judgment can be terminated in a timely manner if the first anomalous feature is not matched, avoiding unnecessary subsequent matching of the second anomalous feature, reducing unnecessary verification steps, thereby lowering computational costs and improving execution efficiency. Furthermore, this matching process considers not only components directly related to the charging state, such as the battery and generator, but also components directly related to the charging state, such as the engine, for auxiliary diagnosis, which can improve the accuracy of fault diagnosis and effectively reduce false alarms.

[0086] In another embodiment, taking the second rule as an example, the target state features are matched with the second set of abnormal features in the second rule. This embodiment includes: Determine whether the generator voltage in the target state characteristics is the second abnormal voltage and remains for at least the third dwell time, and / or determine whether the time domain standard deviation of the generator voltage in the target state characteristics is greater than the standard deviation threshold.

[0087] If the generator voltage is at the second abnormal voltage and remains there for at least a third dwell time, it indicates a low generator voltage process. This may be due to insufficient generator speed caused by belt slack during vehicle operation, resulting in a persistently low second abnormal voltage. At this point, it is determined that the second abnormal feature set successfully matches the target state feature, thereby identifying the generator voltage fault associated with the second abnormal feature set as the current target charging fault of the vehicle charging system, and determining the fault diagnosis result for the generator not generating electricity.

[0088] When the standard deviation of the generator voltage in the time domain exceeds the standard deviation threshold, it indicates that the generator voltage is unstable, possibly due to poor contact of the generator carbon brushes, resulting in irregular and violent voltage fluctuations. In this case, it is determined that the second set of abnormal features successfully matches the target state features, thus identifying the generator voltage fault associated with the second set of abnormal features as the current target charging fault of the vehicle charging system, and determining the fault diagnosis result for the generator not generating electricity.

[0089] Through this dual-dimensional feature configuration, this embodiment achieves full-coverage diagnosis of generator voltage faults, which not only prevents battery depletion caused by long-term low voltage, but also avoids potential damage to precision electronic components caused by high-frequency voltage fluctuations, significantly improving the sensitivity and robustness of the diagnostic system.

[0090] In another embodiment, taking a third rule as an example, the target state features are matched with the third set of anomaly features in the third rule. One embodiment may include: Determine whether the voltage difference between the generator and the battery in the target state characteristics is greater than a preset voltage difference threshold, and whether the dynamic charging efficiency in the target state characteristics is lower than a preset efficiency threshold and remains at least for a fourth dwell time.

[0091] When the voltage difference between the generator and the battery exceeds a preset voltage difference threshold, and the dynamic charging efficiency is lower than a preset efficiency threshold and remains below the threshold for at least the fourth dwell time, the condition is considered a high-loss contact failure. In this case, it is determined that the third set of abnormal features successfully matches the target state features, thus identifying the charging line fault associated with the third set of abnormal features as the current target charging fault of the vehicle charging system. Specifically, this target charging fault is a high-loss contact failure. Additionally, a fault diagnosis result is determined for the generator not generating power.

[0092] Another embodiment may include: Determine whether the rate of change of the voltage difference between the generator and the battery in the target state characteristics is greater than a preset rate of change threshold, and whether the standard deviation of the dynamic charging efficiency in the target state characteristics is greater than a preset standard deviation threshold.

[0093] When the rate of change of the voltage difference between the generator and the battery exceeds a preset threshold, and the standard deviation of the dynamic charging efficiency exceeds a preset standard deviation threshold, the condition is considered intermittent contact failure. In this case, it is determined that the third set of abnormal features successfully matches the target state features, thereby identifying the charging line fault associated with the third set of abnormal features as the current target charging fault of the vehicle charging system. Specifically, this target charging fault is an intermittent contact failure. Additionally, a fault diagnosis result is determined for the generator not generating electricity.

[0094] Based on the above embodiments, and based on the charging fault associated with the set of abnormal features matched, this embodiment may determine the fault diagnosis result, including: (1) Obtain the target charging fault associated with the set of matched abnormal features.

[0095] (2) Determine the target confidence level of the target charging fault based on the initial confidence level of each abnormal feature identifier in the abnormal feature set.

[0096] In this embodiment, each abnormal feature, such as abnormal battery charging current maintaining at least a first dwell time or engine speed exceeding a preset idle speed threshold, has its own preset initial confidence level. Based on this, the final target confidence level of the target charging fault can be determined according to the initial confidence levels of each abnormal feature matched with the target state feature.

[0097] As an example, if the target state feature matches the first set of abnormal features, specifically including the following abnormal features: the engine speed is greater than the preset idle speed threshold in the first abnormal feature with an initial confidence of 0.7 and the second abnormal feature with an initial confidence of 0.15, then the final confidence of the target charging fault is determined to be 0.85, and the confidence level is medium.

[0098] (3) Determine the target warning level based on the preset fault level and target confidence level of the target charging fault.

[0099] In this embodiment, each type of charging fault is identified by a severity level. Based on this, the warning level of the target charging fault is determined according to the preset severity level and confidence level of the target charging fault. For example, refer to Table 1 below.

[0100] In Table 1, the warning levels are listed from low to high as: Level 1 (Alert), Level 2 (Warning), and Level 3 (Danger), each corresponding to a different response strategy.

[0101] Table 1

[0102] (4) Generate a fault diagnosis result based on the target charging fault, the target confidence level and the target warning level.

[0103] Specifically, the target charging fault, target confidence level, and target warning level can be logically bound and structurally encapsulated to form the final fault diagnosis result.

[0104] The above embodiments match target state features with a set of abnormal features, and then determine the fault diagnosis result based on the charging fault associated with the matched set of abnormal features. This enables the identification of early, intermittent anomalies (such as a slow decline in charging efficiency) that are undetectable by traditional methods, achieving true preventative early warning. It overcomes the limitations of single voltage monitoring by fusing multi-dimensional information from the battery, generator, and charger, effectively distinguishing between battery and charging system problems, significantly reducing false alarms. Utilizing the CAN bus to acquire vehicle status information makes the diagnostic conclusions more consistent with actual operating conditions, improving robustness and reducing the false alarm rate.

[0105] S110 triggers a charging warning event that matches the fault diagnosis result.

[0106] This embodiment may include: determining whether the number of occurrences of the target charging fault is greater than a preset number threshold and / or whether the duration is greater than a preset duration threshold; if so, determining a warning method that matches the target warning level, and generating fault prompt content based on the target charging fault and the target confidence level; and triggering a charging warning event based on the warning method and the fault prompt content.

[0107] As examples, a Level 1 charging warning event corresponds to a confirmed and persistent target charging fault. Matching warning methods include: a solid red icon; and displaying text-based fault messages in the instrument cluster, such as: "Battery charging abnormal, low confidence level, inspection recommended."

[0108] A Level 2 charging warning event corresponds to a more serious target charging fault or an emergency situation that may prevent the vehicle from starting. Matching warning methods include: a flashing red icon accompanied by an audible alert; and displaying a strong warning message in the instrument cluster, such as: "Charging system fault, high confidence level, please stop safely immediately."

[0109] It is understandable that the warning notification method is not limited to the dashboard and buzzer, but can be extended to link with the vehicle T-BOX (remote information processor) to push warning information through a mobile APP, so as to realize remote and real-time reminders.

[0110] To balance the timeliness of alarms with the ability to prevent false alarms, this embodiment provides graded and differentiated charging warning events. The graded prompts and more user-friendly interaction can both attract the driver's attention and avoid the interference caused by frequent serious alarms.

[0111] In this embodiment, the method further includes: recording and tracing data on charging warning events.

[0112] Specifically, all charging warning events and key data snapshots at the trigger time, such as voltage, current, and engine status information, are timestamped and stored in the battery sensors to form a fault log, providing accurate information for subsequent maintenance. Furthermore, the data storage module may not be integrated into the warning device's MCU; instead, it can transmit warning events and key data via the CAN bus to the vehicle gateway or other controllers with storage capabilities (such as the body control module, BCM) for unified storage.

[0113] The stored fault logs can provide maintenance personnel with clear fault directions and historical data support, shortening troubleshooting time.

[0114] In summary, the vehicle electrical system management method provided in this disclosure includes: acquiring multiple target state information of the vehicle electrical system; determining multi-dimensional target state features based on the target state information; matching the target state features with each preset abnormal feature set; wherein different abnormal feature sets are associated with different charging faults; determining fault diagnosis results based on the charging faults associated with the matched abnormal feature sets; and triggering a charging warning event that matches the fault diagnosis results.

[0115] This technical solution overcomes the limitations of relying solely on a single battery voltage signal. Instead, it acquires multiple target state information from the entire vehicle's electrical system and quantifies these target state information into multi-dimensional target state characteristics. These target state characteristics are quantified indicators resulting from processing multi-source heterogeneous target state information. They can capture early, subtle anomalies in the charging system (such as efficiency fluctuations and voltage difference changes), thus serving as a reliable data foundation for subsequent fault diagnosis. In this case, matching the target state characteristics with a set of associated abnormal features related to charging faults allows for rapid and accurate identification of the corresponding charging fault and generation of fault diagnosis results. The target state characteristics and abnormal feature sets used in this matching process are multi-feature logically interlocked, effectively filtering out false alarms from single features, thereby providing a more comprehensive and accurate reflection of the charging status and health of the entire vehicle's electrical system. Finally, a charging warning event matching the fault diagnosis result is triggered, thereby improving the timeliness and accuracy of fault warnings, shifting from passive response to proactive prevention, and effectively ensuring the operational safety of the entire vehicle's electrical system.

[0116] More specifically, the embodiments disclosed herein can achieve the following technical effects: Early warnings provide users with ample time for repairs, significantly reducing the rate of vehicle breakdowns due to battery issues and preventing the embarrassing situation of vehicles being unable to start.

[0117] Timely alerts to charging abnormalities enable users to address potential faults early, preventing the battery from being undercharged or overcharged for extended periods and thus extending its lifespan.

[0118] It can reduce user complaints and rescue costs caused by vehicle breakdowns, enhance the vehicle's intelligent image and user sense of security, and help improve user experience and brand reputation.

[0119] Precise fault location and historical data recording make the repair process more efficient and accurate, helping to reduce the difficulty and cost of after-sales repair.

[0120] Furthermore, the hardware modifications required by this disclosed solution are minimal, as it is primarily implemented based on software algorithms. It is easy to upgrade and apply to existing vehicle platforms, resulting in high cost-effectiveness.

[0121] Figure 3 This is a schematic diagram of the structure of a vehicle electrical system management device provided in an embodiment of this disclosure. The vehicle electrical system management device can be a computing device as described in the above embodiments, or it can be a component or assembly within that computing device. The vehicle electrical system management device provided in this application embodiment can execute the processing flow provided in the vehicle electrical system management method embodiment, such as... Figure 3 As shown: The device includes: The status acquisition module 310 is used to acquire various target status information of the vehicle's electrical system; Feature determination module 320 is used to determine multi-dimensional target state features based on the target state information; The feature matching module 330 is used to match the target state features with each preset abnormal feature set; wherein, different abnormal feature sets are associated with different charging faults. The fault diagnosis module 340 is used to determine the fault diagnosis result based on the charging fault associated with the set of abnormal features matched. The early warning triggering module 350 is used to trigger a charging early warning event that matches the fault diagnosis result.

[0122] The device provided in this embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0123] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Figure 4 As shown, the electronic device 400 includes one or more processors 401 and memory 402.

[0124] The processor 401 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0125] The memory 402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 401 may execute the program instructions to implement the vehicle electrical system management method and / or other desired functions described in the embodiments of this disclosure above. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0126] In one example, the electronic device 400 may also include an input device 403 and an output device 404, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0127] In addition, the input device 403 may also include, for example, a keyboard, a mouse, etc.

[0128] The output device 404 can output various information to the outside, including determined distance information, direction information, etc. The output device 404 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0129] Of course, for the sake of simplicity, Figure 4 Only some of the components of the electronic device 400 relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 400 may include any other suitable components depending on the specific application.

[0130] Furthermore, this embodiment also provides a computer-readable storage medium storing a computer program for executing the above-described management method for the vehicle electrical system.

[0131] The present disclosure provides a computer program product for a vehicle electrical system management method, device, electronic device, and medium, including a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0132] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0133] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A management method for a vehicle electrical system, characterized in that, The method includes: Acquire various target status information of the vehicle's electrical system; Determine multi-dimensional target state features based on the target state information; The target state features are matched with each preset set of abnormal features; wherein different sets of abnormal features are associated with different charging faults. Based on the charging faults associated with the matched abnormal feature set, the fault diagnosis result is determined. Trigger a charging warning event that matches the fault diagnosis result.

2. The method according to claim 1, characterized in that, The target state information includes: battery state information and generator state information; the step of determining multi-dimensional target state features based on the target state information includes: Based on the battery status information within the first time period, a first state feature representing the dynamic state of the battery is determined; wherein, the first state feature includes at least: a charging current carrying the dwell time. Based on the generator status information and the battery status information during the second time period, a second state feature representing the dynamic state of the charging circuit is determined; wherein, the second state feature includes at least: the generator voltage carrying the dwell time, the voltage difference between the generator and the battery, and the dynamic charging efficiency; The target state information, the first state feature, and the second state feature are determined as the target state feature.

3. The method according to claim 1, characterized in that, The method further includes: For a generator not generating power failure, a first abnormal feature carrying a core identifier and a second abnormal feature carrying an auxiliary identifier are configured; wherein, the first abnormal feature includes: an abnormal battery charging current that maintains at least a first dwell time, and a first abnormal generator voltage that maintains at least a second dwell time; the second abnormal feature includes: an engine speed greater than a preset idle speed threshold, and / or a generator start signal that is in an active state; Add the first abnormal feature and the second abnormal feature to the first abnormal feature set; Establish the association between the generator failure and the first set of abnormal features.

4. The method according to claim 3, characterized in that, The step of matching the target state features with each preset set of abnormal features includes: The target state feature is matched with the first abnormal feature in the first abnormal feature set; If the first abnormal feature is matched, the target state feature is matched with the second abnormal feature.

5. The method according to claim 1, characterized in that, The method further includes: A second set of abnormal features is configured for generator voltage faults, the second set of abnormal features including: a second abnormal generator voltage that maintains a dwell time of at least a third duration, and / or a generator voltage time-domain standard deviation that is greater than a standard deviation threshold; Establish the correlation between the generator voltage fault and the second set of abnormal features.

6. The method according to claim 1, characterized in that, The method further includes: A third set of abnormal features is configured for charging line faults. The third set of abnormal features includes: the voltage difference between the generator and the battery is greater than a preset voltage difference threshold, and the dynamic charging efficiency is lower than a preset efficiency threshold and remains for at least a fourth dwell time; or, the third set of abnormal features includes: the rate of change of the voltage difference between the generator and the battery is greater than a preset rate of change threshold, and the standard deviation of the dynamic charging efficiency is greater than a preset standard deviation threshold. Establish the correlation between the charging line fault and the third set of abnormal features.

7. The method according to claim 1, characterized in that, The fault diagnosis result is determined based on the correlation between the abnormal feature set and the charging fault, including: Obtain the target charging fault associated with the matched set of abnormal features; The target confidence level of the target charging fault is determined based on the initial confidence level of each abnormal feature identifier in the abnormal feature set. The target warning level is determined based on the preset fault level of the target charging fault and the target confidence level; Based on the target charging fault, the target confidence level, and the target warning level, a fault diagnosis result is generated.

8. The method according to claim 7, characterized in that, The triggering of the charging warning event that matches the fault diagnosis result includes: Determine whether the number of occurrences of the target charging fault is greater than a preset number threshold and / or whether the duration is greater than a preset duration threshold; In the case of yes, determine the warning method that matches the warning level of the target, and generate a fault prompt content based on the target charging fault and the target confidence level; A charging warning event is triggered based on the aforementioned warning method and the fault message content.

9. A management device for a vehicle electrical system, characterized in that, The device includes: The status acquisition module is used to acquire various target status information of the vehicle's electrical system; The feature determination module is used to determine multi-dimensional target state features based on the target state information. The feature matching module is used to match the target state features with various preset abnormal feature sets; wherein different abnormal feature sets are associated with different charging faults. The fault diagnosis module is used to determine the fault diagnosis result based on the charging fault associated with the set of abnormal features matched. The early warning triggering module is used to trigger a charging early warning event that matches the fault diagnosis result.

10. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-8.

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