A battery monitoring method, vehicle, and system

Abstract

The embodiment of the present application relates to the technical field of battery monitoring, and discloses a battery monitoring method, a vehicle and a system, aiming to realize accurate and efficient monitoring of the battery during the static period of the vehicle. The method comprises: determining a target monitoring time period in which the vehicle is located based on monitoring strategy information of the static period; the static period comprises a plurality of monitoring time periods after the vehicle is powered off; the monitoring strategy information comprises time period frequency information and battery cell frequency information; the time period frequency information is used to represent a first monitoring frequency corresponding to each monitoring time period in the plurality of monitoring time periods, and the battery cell frequency information is used to represent a second monitoring frequency corresponding to each battery cell category in each monitoring time period; determining the first monitoring frequency corresponding to the target monitoring time period and the second monitoring frequency corresponding to each battery cell category based on the monitoring strategy information; and sampling and monitoring the battery of the vehicle based on the first monitoring frequency corresponding to the target monitoring time period and the second monitoring frequency corresponding to each battery cell category.

Description

Technical Field

[0001] This invention relates to the field of battery monitoring technology, and specifically to a battery monitoring method, vehicle, and system. Background Technology

[0002] With the rapid development of new energy vehicles, batteries, as the core energy source of electric vehicles, directly affect the performance of the entire vehicle and the safety of users due to their safety and health status. In actual use, after a battery completes charging and discharging, it usually enters a static state. To reduce static power consumption, vehicles typically enter a low-power mode or hibernation mode. In related technologies, some solutions cease status monitoring after the battery enters the static stage, indicating insufficient emphasis on battery safety monitoring and status management during this period. Other solutions still employ traditional, fixed detection and judgment methods, which are difficult to adapt to the changes in the battery's electrical characteristics under static conditions and cannot meet the demand for high-precision battery monitoring during this period. Summary of the Invention

[0003] In view of the shortcomings of the above-mentioned related technologies, the purpose of this application is to provide a battery monitoring method, vehicle and system, which aims to achieve accurate and efficient monitoring of vehicle batteries during the resting period.

[0004] In a first aspect, embodiments of this application provide a battery monitoring method, comprising: determining a target monitoring time period of a vehicle based on monitoring strategy information of a resting period; the resting period includes multiple monitoring time periods after the vehicle is powered off; the monitoring strategy information includes time period frequency information and cell frequency information; the time period frequency information is used to represent a first monitoring frequency corresponding to each monitoring time period in the multiple monitoring time periods, and the cell frequency information is used to represent a second monitoring frequency corresponding to each cell type in each monitoring time period; the vehicle's battery includes multiple cells of different types; determining the first monitoring frequency corresponding to the target monitoring time period and the second monitoring frequency corresponding to each cell type based on the monitoring strategy information; and sampling and monitoring the vehicle's battery based on the first monitoring frequency corresponding to the target monitoring time period and the second monitoring frequency corresponding to each cell type.

[0005] Based on the aforementioned technical means, this application clarifies multiple monitoring time periods included in the resting period by utilizing the time period frequency information in the monitoring strategy information, and determines the first monitoring frequency corresponding to the current target time period, thus achieving efficient time period monitoring that matches the dynamic characteristics of the resting period. Simultaneously, relying on the cell frequency information in the monitoring strategy information, differentiated second monitoring frequencies are matched for different types of cells within the battery, thereby enabling precise sampling of the operating status of various cell types. In summary, this method improves the sampling targeting of battery monitoring while ensuring the integrity of monitoring data for key time periods and key cells, achieving accurate and efficient monitoring of the battery status during the vehicle's resting period.

[0006] In one possible embodiment, the battery cells of the vehicle include a first cell category and a second cell category; wherein the risk level of the first cell category is greater than that of the second cell category; and in the cell frequency information, within each monitoring time period, the second monitoring frequency corresponding to the first cell category is greater than the second monitoring frequency corresponding to the second cell category.

[0007] Based on the aforementioned technical means, this application achieves focused and precise monitoring of high-risk cells by classifying them into different risk categories and configuring a higher second monitoring frequency for high-risk cells (i.e., the first cell category, which will be referred to as such hereinafter). This method, while ensuring the overall monitoring needs of the battery, can prioritize capturing early abnormal characteristics of high-risk cells, effectively avoiding missed risk detections due to insufficient monitoring frequency, and improving the targeting and effectiveness of battery resting period monitoring.

[0008] In one embodiment, the vehicle's battery is sampled and monitored based on a first monitoring frequency corresponding to a target monitoring time period and a second monitoring frequency corresponding to each cell category. This includes: sampling the battery based on the first monitoring frequency corresponding to the target monitoring time period; sampling and monitoring cells in the battery belonging to the first cell category using the second monitoring frequency corresponding to the first cell category; and sampling and monitoring cells in the battery belonging to the second cell category using the second monitoring frequency corresponding to the second cell category.

[0009] Based on the aforementioned technical means, this application achieves an organic combination of overall monitoring and focused monitoring by performing overall battery sampling at a first monitoring frequency within the target monitoring period, and then using differentiated second monitoring frequencies for specific sampling of cell categories with different risk levels. This method ensures the acquisition of basic data on the overall state of the battery pack while enabling high-frequency tracking of high-risk cells, thus optimizing the allocation of system sampling resources while ensuring the integrity of monitoring data.

[0010] In some embodiments, the multiple monitoring time periods after the vehicle is powered off include a first monitoring time period, a second monitoring time period, and a third monitoring time period arranged in chronological order; in the time period frequency information, the first monitoring frequency corresponding to the multiple monitoring time periods decreases sequentially in chronological order.

[0011] Based on the aforementioned technical means, this application achieves a phased monitoring strategy that matches the dynamic characteristics of the battery's resting period by dividing the resting period into multiple monitoring time periods and sequentially decreasing the first monitoring frequency in chronological order. High-frequency monitoring is used in the initial resting stage when the battery state is not yet stable, enabling timely capture of early risk signals such as voltage rebound and gradual temperature rise. As the battery state stabilizes, the monitoring frequency is gradually reduced, effectively balancing monitoring needs with system power consumption, thus achieving accurate and efficient monitoring throughout the entire resting period.

[0012] In an exemplary embodiment, the monitoring strategy information further includes a set of weighting coefficients and alarm level determination rules. The set of weighting coefficients represents the importance of each parameter among multiple parameters related to the degree of battery anomaly. The alarm level determination rules define the correspondence between the anomaly level value and the alarm level. The method further includes: determining historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators based on battery operation data obtained from sampling and detection, historical battery charge and discharge data of the vehicle, and real-time environmental data. The historical fluctuation characteristic indicators characterize the degree of battery parameter fluctuation determined based on historical battery charge and discharge data, the environmental risk characteristic indicators characterize the impact of environmental factors determined based on real-time environmental data on battery safety, and the health risk characteristic indicators characterize the degree of battery aging of the vehicle determined based on battery operation data. Based on the set of weighting coefficients, the historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators are weighted and calculated to determine the anomaly level value. Based on the alarm level determination rules and the anomaly level value, a target alarm level is determined.

[0013] Based on the aforementioned technical means, this application introduces historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators, and calculates the anomaly degree value based on a weighted coefficient set, thereby achieving a multi-dimensional comprehensive assessment of battery anomaly risks. This method overcomes the limitations of traditional single-parameter judgment, effectively identifying the potential risks of multiple minor anomalies superimposed, and improving the comprehensiveness and accuracy of battery anomaly detection.

[0014] In the exemplary embodiment, based on a set of weighted coefficients, a weighted calculation is performed on historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators to determine the degree of anomaly. This includes: comparing historical fluctuation characteristic indicators with preset fluctuation thresholds, comparing environmental risk characteristic indicators with preset environmental risk thresholds, and comparing health risk characteristic indicators with preset health risk thresholds; selecting a corresponding set of weighted coefficients from the set of weighted coefficients based on the comparison results; and performing a weighted calculation on the historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators based on the selected set of weighted coefficients to determine the degree of anomaly.

[0015] Based on the aforementioned technical means, this application achieves adaptive adjustment of the anomaly degree value calculation by comparing each feature indicator with a preset threshold and dynamically selecting the corresponding weight coefficients for weighted calculation based on the comparison results. When a certain risk factor is prominent, this method can automatically increase the weight corresponding to that factor, making the anomaly degree value more reflective of the dominant risk in the current scenario, thereby improving the scenario adaptability and accuracy of risk assessment.

[0016] In an exemplary embodiment, the method further includes: when the anomaly level value is greater than or equal to a first alarm threshold, storing the data from the sampling and monitoring of the vehicle's battery locally, and keeping the vehicle controller and / or the vehicle's in-vehicle communication terminal in a sleep state; when the anomaly level value is less than the first alarm threshold but greater than or equal to a second alarm threshold, marking the data from the sampling and monitoring of the vehicle's battery as abnormal data; and when the anomaly level value is less than the second alarm threshold, waking up the vehicle controller and / or the in-vehicle communication terminal, and reporting the data from the sampling and monitoring of the vehicle's battery as abnormal data to the cloud monitoring platform.

[0017] Based on the aforementioned technical means, this application achieves graded processing of risks of different degrees by setting multi-level alarm thresholds and adopting differentiated response strategies. Minor anomalies are stored locally to maintain minimum power consumption; potential risks are marked as abnormal and monitored more closely, but without waking up external controllers; serious risks immediately wake up the vehicle controller and onboard communication terminal and report to the cloud. This method ensures timely response to serious risks while effectively avoiding power waste caused by frequent wake-ups, achieving an optimal balance between safety and power consumption.

[0018] In an exemplary embodiment, the monitoring strategy information further includes: anomaly judgment criteria and threshold adjustment rules; the anomaly judgment criteria include anomaly judgment thresholds corresponding to multiple operating parameters, used to determine whether each operating parameter in the sampled and detected battery operating data is abnormal; the multiple operating parameters include at least two of the following: battery temperature, battery insulation resistance, battery current, battery voltage, and battery health status; the threshold adjustment rules are used to adjust the anomaly judgment thresholds according to the environmental information of the vehicle or the battery health status; the environmental information includes at least one of the following: ambient temperature, ambient humidity, and altitude; the method further includes: adjusting the multiple anomaly judgment thresholds in the anomaly judgment criteria based on the threshold adjustment rules; and performing anomaly detection on the sampled and detected battery operating data based on the adjusted anomaly judgment thresholds.

[0019] Based on the aforementioned technical means, this application introduces a threshold adjustment rule to dynamically adjust the anomaly detection threshold according to environmental information and battery health status, enabling the anomaly detection standard to adapt to the actual operating conditions of the battery. This method effectively solves the problems of false alarms easily occurring under special environments such as high temperature, high humidity, and high altitude, and false alarms easily occurring after battery aging, thus improving the environmental adaptability and life-cycle adaptability of anomaly detection.

[0020] In an exemplary embodiment, the threshold adjustment rule includes at least one of the following: responding to an ambient temperature greater than a first temperature threshold, lowering the anomaly judgment threshold corresponding to the battery temperature; responding to an ambient temperature less than a second temperature threshold, raising the anomaly judgment threshold corresponding to the battery temperature; wherein the first temperature threshold is greater than the second temperature threshold; responding to an ambient humidity greater than a humidity threshold, lowering the anomaly judgment threshold corresponding to the battery insulation resistance; responding to an altitude greater than an altitude threshold, lowering the anomaly judgment threshold corresponding to the battery voltage, and / or lowering the anomaly judgment threshold corresponding to the battery current; responding to a battery health state greater than a first health threshold, adjusting the anomaly judgment thresholds corresponding to multiple operating parameters to a first threshold set; responding to a battery health state less than or equal to the first health threshold and greater than the second health threshold, adjusting the anomaly judgment thresholds corresponding to multiple operating parameters to a second threshold set; responding to a battery health state less than or equal to the second health threshold, adjusting the anomaly judgment thresholds corresponding to multiple operating parameters to a third threshold set; wherein the threshold levels in the first threshold set are higher than those in the second threshold set, and the threshold levels in the second threshold set are higher than those in the third threshold set.

[0021] Based on the aforementioned technical means, this application employs a multi-dimensional threshold dynamic adjustment mechanism to adaptively adjust the anomaly judgment threshold according to changes in ambient temperature, humidity, altitude, and battery health status. This method effectively distinguishes between parameter drift during normal battery aging and actual faults, avoiding misjudging non-fault factors such as normal cell consistency degradation and calendar aging as faults, significantly reducing the false alarm rate, and improving the availability of the monitoring system and user experience.

[0022] In a second aspect, a vehicle includes a processor and a memory, the processor being connected to the memory, the memory storing computer instructions that, when executed on the vehicle, cause the vehicle to perform the method described in the first aspect.

[0023] Thirdly, a battery monitoring system includes: a vehicle as described in the second aspect, and a cloud-based monitoring platform. The cloud-based monitoring platform is configured to: generate monitoring strategy information based on historical vehicle operating data and send the monitoring strategy information to the vehicle; receive monitoring execution logs reported by the vehicle, update the monitoring strategy information based on the monitoring execution logs, and send the updated information to the vehicle via over-the-air (OTA) download technology.

[0024] In an exemplary embodiment, the vehicle includes: a battery management system, a vehicle controller, and an on-board communication terminal; the battery management system is configured to: remain awake and enter a rest period monitoring mode after the vehicle's high voltage is de-energized, and sample and monitor the vehicle's battery based on monitoring strategy information in the rest period monitoring mode; in response to the detected anomaly level meeting a preset reporting condition, sequentially wake up the vehicle controller and the on-board communication terminal; the battery management system is further configured to: automatically wake up and sequentially wake up the vehicle controller and the on-board communication terminal in response to the internal real-time clock timer reaching a preset wake-up period; the vehicle controller is configured to: enter a sleep state after the vehicle's high voltage is de-energized, and after being woken up by the battery management system, establish a communication link with the battery management system and wake up the on-board communication terminal; the on-board communication terminal is configured to: obtain monitoring strategy information from a cloud monitoring platform before the vehicle's high voltage is de-energized, and enter a sleep state after the vehicle's high voltage is de-energized; and, after being woken up by the vehicle controller, report the monitoring data to the cloud monitoring platform, and after the reporting is completed, instruct the vehicle controller and the battery management system to sequentially re-enter a sleep state.

[0025] Fourthly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions. When the processor is configured to execute the instructions, the electronic device causes the method described in the first aspect to be implemented.

[0026] Fifthly, this application provides a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the method described in the first aspect.

[0027] In a sixth aspect, this application provides a computer program product including computer program instructions that, when executed by a processor, implement the method described in the first aspect.

[0028] It should be noted that the technical effects of any of the implementation methods in aspects two through six can be found in the technical effects of the corresponding implementation method in aspect one, and will not be repeated here.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application will be described below.

[0031] Figure 1 This is a schematic diagram of the architecture of a battery monitoring system provided in an embodiment of this application; Figure 2This application provides a complete closed-loop flowchart of monitoring strategy information from generation to update and optimization. Figure 3 An interaction timing diagram between various modules during the settling period monitoring process provided in this application embodiment; Figure 4 A schematic flowchart of a battery monitoring method provided in an embodiment of this application; Figure 5 A flowchart illustrating a battery anomaly risk assessment method provided in this application embodiment; Figure 6 A schematic diagram illustrating a battery alarm diagnostic strategy provided in an embodiment of this application; Figure 7 A schematic flowchart illustrating a battery anomaly detection method based on dynamic threshold adjustment provided in this application embodiment; Figure 8 This is a schematic diagram illustrating parameter updates for monitoring strategy information provided in an embodiment of this application. Figure 9 This is a schematic diagram of the composition of an electronic device provided in an embodiment of this application. Detailed Implementation

[0032] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0033] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0034] In this application, the terms "exemplarily" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplarily" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0035] In the embodiments of this application, "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0036] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0037] In related technologies, some solutions stop status detection after the battery enters a static state, which is obviously lacking in battery safety monitoring and status management during the static process; other solutions still use traditional and fixed detection and judgment methods, which are difficult to adapt to the changes in the battery's own electrical characteristics under static conditions, and it is difficult to achieve the high-precision monitoring requirements of the battery during static periods.

[0038] Based on this, this application clarifies multiple monitoring time periods included in the resting period by using the time period frequency information in the monitoring strategy information, and determines the first monitoring frequency corresponding to the current target time period accordingly, thus achieving efficient time period monitoring that matches the dynamic characteristics of the resting period. Simultaneously, relying on the cell frequency information in the monitoring strategy information, differentiated second monitoring frequencies are matched for different types of cells within the battery, thereby enabling accurate sampling of the operating status of various cell types. In summary, this method improves the sampling efficiency of battery monitoring while ensuring the integrity of monitoring data for key time periods and key cells, achieving accurate and efficient monitoring of the battery status during the vehicle's resting period.

[0039] The batteries mentioned in this application include, but are not limited to, rechargeable power batteries, lithium-ion batteries, lithium iron phosphate batteries, ternary lithium batteries, and other automotive energy storage batteries. This application does not impose specific limitations on them.

[0040] The embodiments of this application are described below with reference to the accompanying drawings.

[0041] Please see Figure 1 , Figure 1 This is a schematic diagram of the architecture of a battery monitoring system provided in an embodiment of this application. The battery monitoring system includes a vehicle 101 and a cloud monitoring platform 102. The vehicle 101 and the cloud monitoring platform 102 are communicatively connected.

[0042] In some embodiments, the cloud monitoring platform 102 is configured to: generate monitoring strategy information based on historical vehicle 101 operating data and send the monitoring strategy information to the vehicle 101; receive monitoring execution logs reported by the vehicle 101, update the monitoring strategy information based on the monitoring execution logs, and send it to the vehicle 101 via over-the-air (OTA) technology. OTA technology is used to remotely send, update, and configure monitoring strategy information between the cloud monitoring platform 102 and the vehicle 101 via wireless communication.

[0043] It should be noted that the monitoring strategy information in this application refers to the information set used to define battery monitoring information during the vehicle's resting period. This information set can be obtained as a whole or in parts, and this application does not make a specific limitation in this regard. The specific carrier form of this information set includes, but is not limited to: configuration files, JSON data, XML documents, database records, code constant definitions, strategy data packages distributed from the cloud, etc., and this application also does not make a specific limitation in this regard.

[0044] As one possible implementation, the historical vehicle 101 operating data refers to data related to vehicle 101 battery monitoring, including the historical operating data of vehicle 101 itself and / or the historical operating data of at least one other vehicle 101. The historical vehicle 101 operating data includes one or more of the following: battery voltage data, battery temperature data, battery current data, battery health status data, and insulation resistance data.

[0045] As one possible implementation, statistical analysis is performed on the acquired historical vehicle 101 operating data to extract characteristic parameters related to battery safety; based on the characteristic parameters, a first monitoring frequency for different monitoring time periods and a second monitoring frequency for different cell types are determined; based on the characteristic parameters, a set of weighting coefficients and alarm level determination rules are determined; and based on the characteristic parameters, anomaly judgment thresholds and threshold adjustment rules are determined; the first monitoring frequency, second monitoring frequency, set of weighting coefficients, alarm level determination rules, anomaly judgment thresholds, and threshold adjustment rules are encapsulated into monitoring strategy information.

[0046] It should be understood that this application, through personalized strategies for encrypted monitoring of high-risk cells and dynamic threshold adjustment, can reduce the false alarm rate of risks such as early internal short circuits by more than 60%, and advance the average warning time by 2-4 hours.

[0047] As one possible implementation, vehicle 101 can be, but is not limited to, a pure electric vehicle (PEV / BEV), a hybrid electric vehicle (HEV), a range-extended electric vehicle (REEV), a plug-in hybrid electric vehicle (PHEV), or a new energy vehicle.

[0048] In some embodiments, vehicle 101 includes: a battery management system (BMS), a vehicle control unit (VCU), and a telematics box (T-Box).

[0049] As one possible implementation, the BMS is configured to: remain awake and enter a rest period monitoring mode after the high voltage of vehicle 101 is powered off; in the rest period monitoring mode, sample and monitor the battery of vehicle 101 based on monitoring strategy information; and wake up VCU and T-Box in sequence in response to the detected abnormality meeting the preset reporting conditions.

[0050] For example, the BMS can perform tiered alarm responses based on the level of anomaly: for the first alarm level, the BMS only records fault codes and data snapshots in its internal non-volatile memory, without changing the monitoring frequency or waking up other controllers; for the second alarm level, the BMS records fault information locally while temporarily increasing the monitoring frequency to a higher frequency to closely track the battery status, but still does not wake up the VCU and T-Box; for the third or fourth alarm level, the BMS immediately triggers a coordinated wake-up emergency procedure.

[0051] As one possible implementation, the BMS is also configured to: wake up automatically and sequentially wake up the VCU and T-Box in response to the internal real-time clock timer reaching a preset wake-up period.

[0052] For example, the BMS can wake up the VCU by sending an emergency wake-up frame via the CAN bus or by pulling a hard wire. After the VCU is woken up, it can further wake up the T-Box via a dedicated communication interface or hard wire. After the wake-up is completed, the BMS will forward key fault data such as fault type, occurrence time, cell number, voltage, and temperature to the T-Box via the VCU, and the T-Box will upload it to the cloud monitoring platform 102.

[0053] As one possible implementation, the VCU is configured to enter a sleep state after the vehicle 101 is powered off by high voltage, and after being woken up by the BMS, establish a communication link with the BMS and wake up the T-Box.

[0054] As one possible implementation, the T-Box is configured to: obtain monitoring strategy information from the cloud monitoring platform 102 before the high voltage of the vehicle 101 is de-energized, and enter a sleep state after the high voltage of the vehicle 101 is de-energized; and, after being woken up by the VCU, report the monitoring data to the cloud monitoring platform 102, and instruct the VCU and BMS to re-enter the sleep state in sequence after the reporting is completed.

[0055] For example, during the rest period, the BMS independently performs core monitoring tasks while the VCU and T-Box remain in sleep mode. When the internal RTC timer of the BMS reaches the preset period, the BMS wakes up on its own and wakes up the VCU and T-Box. The VCU obtains the battery status summary, and the T-Box uploads heartbeat data and monitoring logs to the cloud. After completion, the VCU and T-Box immediately return to sleep mode, achieving guaranteed low-power interaction.

[0056] In this embodiment, during the resting period, only the BMS of vehicle 101 operates in a low-power state, while the VCU and remote communication module enter a deep sleep mode. Simultaneously, the cloud monitoring platform 102 continuously optimizes personalized monitoring algorithms based on the transmitted data and updates the vehicle 101 monitoring strategy via OTA technology, achieving system adaptive evolution capabilities and thus balancing battery safety and energy consumption levels in the long term.

[0057] It should be understood that the end-to-end latency from BMS detecting a serious fault to T-Box issuing alarm data can be stably controlled within 500ms, representing a three-order-of-magnitude improvement in response speed compared to traditional solutions. This creates a crucial time window for early intervention in emergencies such as thermal runaway. During periods of inactivity without faults, the system's average static current can be reduced to below 200μA, with BMS power consumption below 100μA and the combined power consumption of the VCU and T-Box in deep sleep mode below 50μA. The timed wake-up and heartbeat reporting processes last only a few seconds, contributing minimally to long-term average power consumption. Through this low-power design, it is ensured that even after the vehicle 101 has been parked for weeks or even months, the low-voltage battery still retains sufficient charge for starting, effectively balancing monitoring needs during periods of inactivity with overall vehicle power consumption control.

[0058] For example, a complete closed-loop flowchart of monitoring strategy information from generation to update and optimization is provided in an embodiment of this application. Please refer to [link / reference]. Figure 2 As shown, it includes: The cloud-based monitoring platform determines monitoring strategies for idle periods based on historical vehicle operation data. This strategy information is then sent to the BMS before the vehicle's high-voltage power is cut off. Once in idle monitoring mode, the BMS performs tiered monitoring of vehicle-side battery data based on the strategy information. When an alarm occurs, intelligent diagnostics driven by abnormal events are initiated. Upon the occurrence of a third or fourth alarm level, or upon reaching a scheduled wake-up period, the BMS sequentially wakes up the VCU and T-Box. Simultaneously, the vehicle-side monitoring data is transmitted sequentially through the VCU and then to the T-Box before being reported to the cloud-based monitoring platform. The cloud-based platform analyzes the vehicle-side battery data, initiates emergency procedures, and then optimizes the monitoring algorithm based on the data. This generates updated idle period monitoring strategies, which are then sent back to the vehicle via OTA (Over-The-Air) technology, forming a complete data loop from monitoring to learning, optimization, and execution.

[0059] For example, the vehicle can send back monitoring execution logs, including the collection time sequence, diagnostic events, and response actions, to the cloud monitoring platform each time it is powered on after a rest period ends, or during the timed heartbeat reporting process. The cloud monitoring platform evaluates the effectiveness of the strategy based on multi-vehicle log data, iteratively optimizes the monitoring frequency, anomaly judgment threshold, and alarm rules, and realizes data closure and continuous strategy iteration.

[0060] Through the aforementioned closed-loop mechanism, the embodiments of this application achieve continuous evolution of monitoring strategies. For example, for a fleet of 100,000 vehicles, the cloud monitoring platform can complete the mining of new-generation risk characteristics within 24 hours and push the optimized personalized strategies to more than 90% of the vehicles via differential OTA within 72 hours, achieving "herd immunity" and rapid evolution of security strategies, enabling the entire fleet's safety protection capabilities during idle periods to be continuously and synchronously improved.

[0061] In some embodiments, the vehicle further includes a user application. The user application is used to receive battery alarm information, fault prompts, and emergency handling suggestions from the cloud monitoring platform, and to display and remind the user of these information.

[0062] For example, a timing diagram of the interactions between modules during a settling period monitoring process provided in an embodiment of this application is given. Please refer to [link / reference]. Figure 3 As shown, it includes: In the idle monitoring mode, the BMS remains awake, sampling and monitoring the battery. When a third or fourth alarm level (i.e., level 3 or 4 alarms) occurs, the BMS sends a wake-up signal to wake up the VCU and simultaneously sends fault data to the VCU. After being woken up, the VCU sends a wake-up signal to wake up the T-Box and simultaneously sends fault data to the T-Box. After being woken up, the T-Box reports the fault data to the cloud monitoring platform. Upon receiving the fault data, the cloud monitoring platform pushes the fault information to the user application (User APP). Upon receiving the push, the user APP initiates the emergency handling procedure. After the fault reporting is completed, the T-Box and VCU sequentially return to sleep mode, and the BMS continues to operate in the idle monitoring mode.

[0063] The above architecture does not constitute a limitation on the battery monitoring system. In some embodiments of this application, the battery monitoring system may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. The battery monitoring method provided in the embodiments of this application can be executed independently by the battery management system in the vehicle, or in collaboration between the vehicle and a cloud monitoring platform, or by other electronic devices with data processing capabilities; the specific executing entity is not limited in this application.

[0064] Please see Figure 4 , Figure 4 This is a flowchart illustrating a battery monitoring method provided in an embodiment of this application. The battery monitoring method includes: S401. Based on the monitoring strategy information of the resting period, determine the target monitoring time period in which the vehicle is located.

[0065] The resting period includes multiple monitoring time periods after the vehicle is powered off. Monitoring strategy information includes time period frequency information and cell frequency information. The time period frequency information represents the first monitoring frequency corresponding to each monitoring time period, and the cell frequency information represents the second monitoring frequency corresponding to each cell type within each monitoring time period.

[0066] As one possible implementation, a pre-defined communication window is set up before the vehicle's high voltage is turned off, during which monitoring strategy information generated for the vehicle is downloaded from a cloud monitoring platform as the basis for monitoring during the subsequent resting period.

[0067] As a possible implementation, determining the target monitoring time period in which the vehicle is located based on the monitoring strategy information during the static period includes: in response to the vehicle's high-voltage power-off, the BMS starts a timer to record the power-off duration; obtaining the current power-off duration recorded by the timer; matching the current power-off duration with the time ranges of each monitoring time period defined in the monitoring strategy information; and determining the monitoring time period with a successful match as the target monitoring time period in which the vehicle is currently located.

[0068] Exemplarily, it is assumed that the monitoring strategy information defines that the first monitoring time period corresponds to 0 to T1 hours after power-off, the second monitoring time period corresponds to T1 to T2 hours after power-off, and the third monitoring time period corresponds to more than T2 hours after power-off. If the current power-off duration is t and 0 ≤ t < T1, the target monitoring time period is determined as the first monitoring time period; if T1 ≤ t < T2, the target monitoring time period is determined as the second monitoring time period; if t ≥ T2, the target monitoring time period is determined as the third monitoring time period.

[0069] In a possible implementation, the multiple monitoring time periods after the vehicle's power-off include, in chronological order: the first monitoring time period, the second monitoring time period, and the third monitoring time period; in the period frequency information, the first monitoring frequencies corresponding to the multiple monitoring time periods decrease in chronological order.

[0070] Exemplarily, the first monitoring time period is a critical monitoring period, corresponding to the initial stage after the high voltage is disconnected, with a duration of T1 hours. In this stage, the internal electrochemical reaction of the battery has not been fully stabilized, and there is a potential risk of thermoelectric parameter fluctuations. Therefore, the first monitoring frequency corresponding to the first monitoring time period is set to once every A1 minutes, for high-frequency sampling monitoring of the battery's voltage, temperature, and insulation resistance, so as to detect early fault characteristics in a timely manner.

[0071] The second monitoring time period is a stable observation period, corresponding to the time interval from T1 hours to T2 hours after the high voltage is disconnected. In this stage, the battery gradually tends to be stable, and the parameter fluctuation range decreases. Therefore, the first monitoring frequency corresponding to the second monitoring time period is set to once every A2 minutes, for continuously tracking the change trend of the battery's core parameters to ensure that the battery is in a safe state.

[0072] The third monitoring time period is an ultra-low-frequency monitoring period, corresponding to the time interval of more than T2 hours after the high voltage is disconnected. In this stage, the battery state has been basically stable. Therefore, the first monitoring frequency corresponding to the third monitoring time period is set to once every A3 minutes, for maintaining basic monitoring while reducing system power consumption.

[0073] Among them, the frequency of once every A1 minutes is greater than once every A2 minutes, and once every A2 minutes is greater than once every A3 minutes.

[0074] As one possible implementation, the battery monitoring method also includes an event triggering mechanism: when an abnormal event is detected during monitoring, the monitoring frequency for the current monitoring period is increased in response to the abnormal event; and after the abnormal event is handled, the monitoring frequency is restored to the current monitoring period. The abnormal event includes at least one of the following: sudden change in battery temperature or abnormal battery voltage.

[0075] It should be understood that the vehicle enters a dormant state after power is off, resulting in a monitoring blind spot during the initial 0.5 to 2 hours of inactivity after power-off.

[0076] This stage is precisely the critical observation window where the internal electrochemical reactions of the battery are not yet fully stable, and potential risk signals such as voltage rebound, slow temperature rise, and deterioration of insulation performance are prone to appear. Related technologies cannot capture these abnormal signals in time, potentially missing the best opportunity for early intervention. This application's embodiment, by setting a phased, dynamically adjustable monitoring frequency, employs high-frequency sampling monitoring in the initial static stage, effectively filling the monitoring blind spots of related technologies. It can promptly capture abnormal characteristics in the early stages of a fault, providing a data foundation for subsequent risk identification and early warning.

[0077] In this embodiment, the vehicle's battery includes multiple cells of different types. A cell is the smallest energy storage unit of a battery. Multiple cells are connected in series and / or in parallel to form a battery module, and multiple modules are further connected to form a battery to provide power to the vehicle.

[0078] In some embodiments, in practical applications, due to differences in manufacturing processes, uneven operating temperatures, and varying aging rates, different cells within the same battery may exhibit differences in performance and health status. Some cells may become "high-risk cells" due to factors such as increased internal resistance, accelerated capacity decay, and high self-discharge rates, with a higher probability of failure than other cells. Serious battery failures such as thermal runaway often originate from an abnormality in a single cell. Therefore, focusing on monitoring high-risk cells allows for timely detection and intervention at the nascent stage of a failure, preventing the failure from spreading to the entire battery.

[0079] In one possible implementation, the battery cells of the vehicle include a first cell category and a second cell category; wherein the risk level of the first cell category is greater than that of the second cell category; and in the cell frequency information, within each monitoring time period, the second monitoring frequency corresponding to the first cell category is greater than the second monitoring frequency corresponding to the second cell category.

[0080] One implementation method classifies battery cells by at least one of the following: statistically analyzing the voltage fluctuation amplitude and temperature change rate of each cell during historical charging and discharging processes, and classifying cells with fluctuation amplitudes greater than preset fluctuation amplitude thresholds and temperature change rates greater than preset temperature change rate thresholds into a high-risk category (i.e., the first cell category); classifying cells with battery health status less than preset thresholds (i.e., higher aging degree) based on parameters such as internal resistance, capacity, and self-discharge rate into a high-risk category (i.e., the first cell category); classifying cells with consistency less than preset consistency thresholds and initial parameter deviations greater than preset initial parameter thresholds in a manufacturing batch into a high-risk category (i.e., the first cell category); and classifying cells at the edge or center of the battery with poorer heat dissipation conditions than preset heat dissipation conditions and higher operating temperatures than preset operating temperatures into a high-risk category (i.e., the first cell category).

[0081] As one possible implementation, the monitoring strategy information includes cell frequency information, which can record the correspondence between each cell type and the second monitoring frequency in different monitoring time periods in the form of a multi-dimensional mapping.

[0082] For example, suppose the cell frequency information defined in the monitoring strategy information is as follows: during the first monitoring time period, the second monitoring frequency corresponding to the first cell category is once every 1 second, and the second monitoring frequency corresponding to the second cell category is once every 10 seconds; during the second monitoring time period, the second monitoring frequency corresponding to the first cell category is once every 10 seconds, and the second monitoring frequency corresponding to the second cell category is once every 30 seconds; during the third monitoring time period, the second monitoring frequency corresponding to the first cell category is once every 30 seconds, and the second monitoring frequency corresponding to the second cell category is once every 60 seconds.

[0083] S402. Based on the monitoring strategy information, determine the first monitoring frequency corresponding to the target monitoring time period and the second monitoring frequency corresponding to each cell category.

[0084] As one possible implementation, the monitoring strategy information includes time period frequency information, which can record the correspondence between multiple monitoring time periods and a first monitoring frequency in the form of a mapping table or key-value pairs. After determining the target monitoring time period, the first monitoring frequency corresponding to the target monitoring time period is queried and read from the time period frequency information based on the identifier or index of the target monitoring time period.

[0085] For example, suppose the time period frequency information defined in the monitoring strategy information is as follows: the first monitoring time period corresponds to a first monitoring frequency of once every A1 minutes, the second monitoring time period corresponds to a first monitoring frequency of once every A2 minutes, and the third monitoring time period corresponds to a first monitoring frequency of once every A3 minutes. If the current target monitoring time period is determined to be the first monitoring time period in S401, then the corresponding first monitoring frequency read from the time period frequency information is "once every A1 minutes".

[0086] As one possible implementation, after determining the target monitoring time period, the second monitoring frequency corresponding to each cell category is queried and read from the cell frequency information based on the identifier of the target monitoring time period and the identifier of each cell category.

[0087] For example, if the current target monitoring time period is determined to be the first monitoring time period in S401, then the second monitoring frequency corresponding to the first battery cell category is read from the battery cell frequency information as once every 1 second, and the second monitoring frequency corresponding to the second battery cell category is read as once every 10 seconds.

[0088] As one possible implementation method, the cell category and its risk level are not fixed, but dynamically adjusted based on real-time battery monitoring data. During the monitoring process, the operating data of each cell are continuously updated. When the parameters of a cell show abnormal fluctuations or the health status deteriorates, its risk level is increased and it is classified into a higher priority cell category, and a higher secondary monitoring frequency is adopted for focused tracking.

[0089] It should be understood that related technologies treat all components in a battery equally using a fixed monitoring frequency, ignoring the differences in performance and health status between cells. This "one-size-fits-all" monitoring method has two problems: firstly, the monitoring frequency for high-risk cells is insufficient, making it difficult to detect early abnormalities in a timely manner and potentially missing the optimal time for fault intervention; secondly, using excessively high monitoring frequencies for low-risk cells results in unnecessary waste of system resources and power consumption. The embodiments of this application, by classifying cells and configuring differentiated second monitoring frequencies for cells of different risk levels, achieve focused attention on high-risk cells and power consumption optimization for low-risk cells, improving monitoring efficiency while ensuring safety.

[0090] S403. Based on the first monitoring frequency corresponding to the target monitoring time period and the second monitoring frequency corresponding to each cell type, the vehicle's battery is sampled and monitored.

[0091] In this embodiment, battery sampling employs a two-layer frequency monitoring system: the first monitoring frequency operates on the time dimension, determining the basic sampling rhythm of the battery within a specific monitoring period; the second monitoring frequency operates on the cell dimension, determining the differentiated sampling rhythm for cells with different risk levels. The two work together to ensure both basic monitoring of the entire battery and focused attention on high-risk cells.

[0092] In one possible implementation, the vehicle's battery is sampled and monitored based on a first monitoring frequency corresponding to the target monitoring time period and a second monitoring frequency corresponding to each cell category. This includes: sampling the battery based on the first monitoring frequency corresponding to the target monitoring time period; sampling and monitoring cells in the battery belonging to the first cell category using the second monitoring frequency corresponding to the first cell category; and sampling and monitoring cells in the battery belonging to the second cell category using the second monitoring frequency corresponding to the second cell category.

[0093] In one implementation, the battery is sampled based on a first monitoring frequency corresponding to the target monitoring time period, including: the BMS periodically triggers sampling operations on the battery according to the time interval defined by the first monitoring frequency corresponding to the target monitoring time period; in each sampling operation, the battery's core parameters such as voltage, temperature, current, and insulation resistance are acquired as basic data for overall battery status assessment.

[0094] For example, if the target monitoring time period is the first monitoring time period and the corresponding first monitoring frequency is once every A1 minutes, then the BMS performs a complete battery sampling once every A1 minutes, sampling parameters such as battery voltage, battery temperature, battery current, and insulation resistance at that moment.

[0095] In one implementation, the cells in the battery belonging to the first cell category are sampled and monitored at a second monitoring frequency corresponding to the first cell category. This includes: triggering overall sampling of the battery according to the first monitoring frequency corresponding to the target monitoring time period, and obtaining the overall operating parameters of the battery in each overall sampling; within the sampling interval of the overall sampling, triggering specific sampling of the cells in the battery belonging to the first cell category according to the second monitoring frequency corresponding to the first cell category, and obtaining the individual operating parameters of each cell in the first cell category; and triggering specific sampling of the cells in the battery belonging to the second cell category according to the second monitoring frequency corresponding to the second cell category, and obtaining the individual operating parameters of each cell in the second cell category.

[0096] This approach achieves an organic combination of a first monitoring frequency and a second monitoring frequency by setting up a sampling mechanism in which a main timer and multiple sub-timers work together. The first monitoring frequency defines the basic rhythm of overall sampling, used to acquire overall battery operating parameters; the second monitoring frequency defines a differentiated rhythm for specific sampling of different cell types within the overall sampling interval. When the second monitoring frequency for a certain cell type is higher than the first monitoring frequency, that type of cell will be sampled multiple times between two overall sampling sessions, achieving high-frequency, focused monitoring of high-risk cells. When the second monitoring frequency is lower than or equal to the first monitoring frequency, that type of cell is only sampled during overall sampling, avoiding unnecessary sampling overhead. Through this approach, this application achieves differentiated and accurate sampling of cells with different risk levels while ensuring overall battery status monitoring. This ensures that early abnormal characteristics of high-risk cells can be captured in a timely manner, optimizes the allocation of system sampling resources, and effectively balances monitoring accuracy and system power consumption.

[0097] As one possible implementation, the vehicle's battery is sampled and monitored based on a first monitoring frequency corresponding to the target monitoring time period and a second monitoring frequency corresponding to each cell category. This includes: the BMS maintaining a master timer and multiple sub-timers. The master timer triggers overall sampling according to the first monitoring frequency, recording battery parameters. Simultaneously, a sub-timer is maintained for each cell category, triggering sampling of each cell within that category according to the second monitoring frequency of that category. When the second monitoring frequency of a certain cell category is higher than the first monitoring frequency, the cells in that category will be sampled multiple times between two overall sampling sessions, achieving high-frequency monitoring of high-risk cells; when the second monitoring frequency of a certain cell category is lower than the first monitoring frequency, the cells in that category are only sampled during overall sampling, without the need for additional dedicated sampling, thus reducing system overhead.

[0098] For example, assuming the target monitoring period is the first monitoring period, the first monitoring frequency is once every 10 minutes, the second monitoring frequency corresponding to the first cell category (high risk) is once every 1 minute, and the second monitoring frequency corresponding to the second cell category (medium risk) is once every 5 minutes. At t=0 minutes, the main timer triggers overall sampling, recording parameters such as the battery's total voltage, average temperature, total current, and insulation resistance, and simultaneously acquiring the individual cell voltage and temperature of all cells; at t=1 minutes, the sub-timer for the first cell category triggers specific sampling, sampling only the cells under the first cell category to acquire their individual cell voltage and temperature; at t=5 minutes, the sub-timer for the second cell category triggers specific sampling, sampling only the cells under the second cell category to acquire their individual cell voltage and temperature; at t=10 minutes, the main timer triggers overall sampling again, recording macroscopic parameters and acquiring the individual parameters of all cells. This cycle repeats, enabling differentiated monitoring of battery cells with different risk levels. High-risk cells are sampled every minute, while medium-risk cells are sampled every five minutes, optimizing the use of system resources while ensuring safety.

[0099] In some embodiments, after determining the degree of abnormality, it is necessary to map it to a specific alarm level according to a preset alarm level determination rule in order to execute the corresponding response strategy.

[0100] In one possible implementation, the monitoring strategy information also includes a set of weighting coefficients and alarm level determination rules; the set of weighting coefficients is used to represent the importance of each parameter among multiple parameters related to the degree of battery abnormality; the alarm level determination rules are used to define the correspondence between the degree of abnormality value and the alarm level.

[0101] Please see Figure 5 , Figure 5 A flowchart illustrating a battery anomaly risk assessment method provided in this application embodiment includes: S501. Based on the battery operation data obtained from sampling and detection, as well as the vehicle's historical battery charge and discharge data and real-time environmental data, determine historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators.

[0102] Among them, the historical fluctuation characteristic index is used to characterize the degree of fluctuation of battery parameters determined based on historical battery charge and discharge data, the environmental risk characteristic index is used to characterize the degree of impact of environmental factors determined based on real-time environmental data on battery safety, and the health risk characteristic index is used to characterize the degree of battery aging of the vehicle determined based on battery operation data.

[0103] As one possible implementation method, the battery operation data obtained by sampling and detection includes: the individual cell voltage value, the individual cell temperature value, the total battery voltage value, the battery charging and discharging current value, and the battery insulation resistance value.

[0104] Among these parameters, the individual cell voltage is the core parameter for judging cell consistency and identifying internal short-circuit risks; abnormal fluctuations in individual cell voltage often indicate precursors to internal cell failures. The individual cell temperature is a key parameter for monitoring the battery's thermal state; abnormally high temperatures may indicate increased internal resistance or thermal runaway risks. The total battery voltage reflects the overall health of the battery; a sudden drop in total voltage may indicate a serious malfunction. The battery's charge and discharge current is used to monitor the battery's operating intensity; high current conditions will accelerate battery aging. The battery's insulation resistance is an important indicator for measuring the battery's high-voltage safety; a decrease in insulation resistance may lead to leakage risks.

[0105] As one possible implementation, the vehicle's battery historical charge and discharge data includes: voltage change curves, temperature change curves, current change curves, charge and discharge rate records, and battery state of charge (SOC) change rate records for each historical charge and discharge cycle.

[0106] Among them, the fluctuation amplitude and frequency in the historical voltage change curve are used to evaluate the stability of the battery cell. Cells with large fluctuation amplitude and high frequency are more prone to failure. The heating rate in the historical temperature change curve is used to evaluate the thermal management performance and internal resistance state of the battery. Abnormal heating rate may indicate the risk of internal short circuit. Historical charge and discharge rate records are used to evaluate the battery's usage intensity. Long-term high-rate charge and discharge will accelerate battery aging. Historical SOC change rate is used to evaluate the battery's capacity retention capability and self-discharge rate. Abnormal SOC change may indicate micro-short circuit in the battery cell.

[0107] As one possible implementation method, real-time environmental data includes: ambient temperature, ambient humidity, and altitude.

[0108] Among them, ambient temperature directly affects the chemical reaction rate and heat dissipation conditions of the battery. The risk of thermal runaway of the battery increases significantly under high temperature conditions, while the usable capacity of the battery decreases and the internal resistance increases under low temperature conditions. Ambient humidity affects the insulation performance of the battery. The insulation resistance decreases under high humidity conditions, and the risk of leakage increases. Altitude affects air density and heat dissipation efficiency. The battery has difficulty dissipating heat under high altitude conditions, and the thermal management pressure increases. At the same time, changes in air pressure may affect the mechanical stability of the battery.

[0109] As one possible implementation method, the historical fluctuation characteristic index is obtained by statistical analysis of the voltage change rate and temperature change rate in the historical charge and discharge data of the battery. Specifically, it includes: calculating the voltage standard deviation, voltage range, and temperature change rate of each cell in the historical charge and discharge process, and normalizing the statistical results to a preset range (such as 0-100) as the historical fluctuation characteristic index.

[0110] As one possible implementation method, the environmental risk characteristic index is obtained by normalizing and fusing the environmental temperature, environmental humidity, and altitude in real-time environmental data. Specifically, this includes mapping the environmental temperature, environmental humidity, and altitude to risk coefficients in a preset range (such as 0-100), and then summing or averaging them according to preset weights to obtain the environmental risk characteristic index.

[0111] As one possible implementation method, the health risk characteristic index is determined based on the battery's current state of health (SOH) value, internal resistance value, and capacity decay rate. Specifically, it includes mapping the SOH value, internal resistance value, and capacity decay rate to risk coefficients in a preset range (such as 0-100), and then summing or averaging them according to preset weights to obtain the health risk characteristic index.

[0112] S502. Based on the set of weighted coefficients, perform weighted calculations on historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators to determine the degree of abnormality.

[0113] As one possible implementation, the weighting coefficient set includes multiple sets of weighting coefficients. Each set includes a first weight corresponding to historical volatility characteristic indicators, a second weight corresponding to environmental risk characteristic indicators, and a third weight corresponding to health risk characteristic indicators. Different combinations of weighting coefficients correspond to different risk-dominant scenarios, used to highlight the contribution of dominant risk factors to the anomaly severity value under different operating conditions.

[0114] As one possible implementation method, based on a set of weighted coefficients, historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators are weighted and calculated to determine the degree of anomaly. This includes: comparing historical fluctuation characteristic indicators with preset fluctuation thresholds, comparing environmental risk characteristic indicators with preset environmental risk thresholds, and comparing health risk characteristic indicators with preset health risk thresholds; selecting a corresponding set of weighted coefficients from the set of weighted coefficients based on the comparison results; and performing a weighted calculation on the historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators based on the selected set of weighted coefficients to determine the degree of anomaly.

[0115] One implementation includes a preset fluctuation threshold comprising a first fluctuation threshold and a second fluctuation threshold, wherein the first fluctuation threshold is less than the second fluctuation threshold; a preset environmental risk threshold comprising a first environmental threshold and a second environmental threshold, wherein the first environmental threshold is less than the second environmental threshold; and a preset health risk threshold comprising a first health threshold and a second health threshold, wherein the first health threshold is less than the second health threshold.

[0116] When the historical fluctuation characteristic index is less than or equal to the first fluctuation threshold, it indicates that the battery has been operating stably in the past, and a coefficient combination with a lower weight is selected. When the historical fluctuation characteristic index is greater than the first fluctuation threshold and less than or equal to the second fluctuation threshold, it indicates that the battery has experienced some fluctuations in the past, and a coefficient combination with a medium weight is selected. When the historical fluctuation characteristic index is greater than the second fluctuation threshold, it indicates that the battery has experienced severe fluctuations in the past, and a coefficient combination with a higher weight is selected.

[0117] The environmental risk characteristic indicators and health risk characteristic indicators can also be classified and judged according to the same logic. Based on the threshold range of each of the three indicators, a set of weight coefficients corresponding to the combined state is matched from the set of weight coefficients.

[0118] It should be noted that the above implementation method is only an example, and this application does not make any specific limitation on the specific division method of each threshold in this application.

[0119] S503. Determine the target alarm level based on the alarm level determination rules and the anomaly degree value.

[0120] As one possible implementation, the alarm level determination rule defines multiple alarm threshold intervals, each interval corresponding to an alarm level. The calculated anomaly level value is compared with multiple alarm thresholds to determine the threshold interval to which it belongs, and thus the corresponding target alarm level is determined.

[0121] In some embodiments, after determining the target alarm level, an alarm strategy corresponding to that alarm level can be adopted to achieve differentiated risk response.

[0122] In one possible implementation, if the anomaly level is greater than or equal to a first alarm threshold, the data from the sampling and monitoring of the vehicle's battery is stored locally, and the vehicle's VCU and / or T-Box is kept in a dormant state. In another implementation, the battery state is normal or only slightly abnormal, requiring no external response. Therefore, the data obtained from sampling and monitoring is stored locally for subsequent data analysis and strategy optimization, while the vehicle's VCU and / or T-Box are kept in a dormant state to maintain minimum system power consumption.

[0123] For example, when the anomaly level is greater than or equal to the first alarm threshold, the target alarm level is the first alarm level. The first alarm level corresponds to minor faults that do not affect immediate safety, such as fluctuations in the SOC estimate or communication timeouts. At this level, the BMS only records fault information, does not change the existing monitoring frequency, and does not wake up any other controllers.

[0124] In one possible implementation, if the anomaly level is less than a first alarm threshold but greater than or equal to a second alarm threshold, the data from the vehicle's battery sampling and monitoring is marked as anomalous data. In another implementation, the battery presents a potential risk requiring close monitoring but not yet reaching a severity level necessitating immediate reporting. Therefore, the BMS marks the sampling and monitoring data as anomalous data for subsequent tracking and analysis, and can increase the current monitoring frequency as needed to closely track status changes, while keeping the VCU and T-Box in a dormant state and not triggering wake-up reporting.

[0125] For example, if the anomaly level is less than the first alarm threshold but greater than or equal to the second alarm threshold, the target alarm level is the second alarm level, which corresponds to the abnormal state that needs attention, such as slight overvoltage or undervoltage of the battery total voltage, or high or low average temperature. At this level, in addition to local recording, the BMS can temporarily increase the monitoring frequency for close tracking, but will still not wake up the VCU and T-Box.

[0126] In one possible implementation, if the anomaly level is less than a second alarm threshold, the VCU and / or T-Box will be activated, and the data from the vehicle's battery sampling and monitoring will be marked as abnormal and reported to the cloud monitoring platform. In another implementation, where the battery is at serious risk and immediate intervention is required, the BMS will activate the VCU and / or T-Box and report the sampling and monitoring results marked as abnormal to the cloud monitoring platform, so that the cloud can promptly notify the user or take remote emergency measures.

[0127] For example, if the anomaly level is less than the second alarm threshold, the target alarm level is either the third or fourth alarm level. The third alarm level corresponds to faults that may indicate serious risks, such as severely excessive single-unit voltage or insulation failure; the fourth alarm level corresponds to confirmed serious faults, such as thermal runaway alarms. At this level, the BMS immediately triggers a collaborative wake-up process, waking up the T-Box via the VCU and reporting the fault details to the cloud monitoring platform in real time.

[0128] In summary, a schematic diagram of a battery alarm diagnostic strategy is provided as an example. Please refer to [link / reference]. Figure 6The timeline represents the vehicle's resting period after the high-voltage power is cut off, comprising three phases: Phase 1, Phase 2, and Phase 3, corresponding to the first, second, and third monitoring periods, respectively. During the resting period, the BMS continuously operates, determining the fault alarm level at different stages based on monitoring data and taking differentiated responses: upon detecting the first alarm level, it records fault codes and takes a data snapshot; upon detecting the second alarm level, it records fault codes and takes a data snapshot, and switches to high-frequency monitoring mode for close tracking; upon detecting the third or fourth alarm level, it immediately triggers the "coordinated wake-up" emergency procedure.

[0129] Through the above-mentioned hierarchical alarm strategy, this application embodiment realizes differentiated responses to different risk levels: minor anomalies are only recorded locally, potential risks are monitored more closely, and serious risks are reported immediately, thus optimizing system power consumption to the maximum extent while ensuring safety.

[0130] In some embodiments, the method described above for calculating the degree of anomaly based on the fusion of multi-dimensional feature indicators is mainly used to comprehensively assess the overall risk level of the battery and identify the potential risks of multiple minor anomalies superimposed. However, the various battery operating parameters obtained from sampling and detection still need to be directly compared with the corresponding individual anomaly judgment thresholds to identify whether any individual parameter is seriously exceeding the standard.

[0131] Because the operating environment and health status of batteries are dynamically changing, using fixed anomaly detection thresholds is insufficient to meet the safety requirements under different operating conditions. For example, the thermal runaway threshold of a battery decreases under high temperature conditions, the safe lower limit of insulation resistance decreases under high humidity conditions, and the tolerance to voltage fluctuations weakens as the battery ages. Therefore, it is necessary to dynamically adjust the anomaly detection thresholds in the monitoring strategy information so that they can adaptively adjust the judgment criteria according to real-time environmental conditions and battery health status, thereby maintaining accurate and reliable anomaly detection capabilities in different scenarios.

[0132] As one possible implementation, it also includes: anomaly judgment criteria and threshold adjustment rules; the anomaly judgment criteria include anomaly judgment thresholds corresponding to multiple operating parameters, used to judge whether each operating parameter in the battery operating data obtained by sampling and detection is abnormal; the multiple operating parameters include at least two of the following: battery temperature, battery insulation resistance, battery current, battery voltage, and battery health status.

[0133] Among them, the abnormal judgment threshold corresponding to battery temperature is used to determine whether the temperature of a single battery cell or the battery exceeds the safe range; the abnormal judgment threshold corresponding to battery insulation resistance is used to determine whether the insulation resistance between the high-voltage circuit of the battery and the vehicle body is lower than the safety lower limit; the abnormal judgment threshold corresponding to battery current is used to determine whether the charging and discharging current of the battery exceeds the allowable safety limit; the abnormal judgment threshold corresponding to battery voltage is used to determine whether the voltage of a single battery cell or the total voltage of the battery exceeds the normal operating range; and the abnormal judgment threshold corresponding to battery health status is used to determine whether the health status of the battery is lower than the minimum standard required for safe operation.

[0134] The threshold adjustment rule is used to adjust the abnormal judgment threshold based on the vehicle's environmental information or battery health status; the environmental information includes at least one of the following: ambient temperature, ambient humidity, and altitude.

[0135] Among them, ambient temperature is used to characterize the atmospheric temperature at the vehicle's location, which directly affects the battery's chemical reaction rate and thermal management requirements; ambient humidity is used to characterize the air humidity at the vehicle's location, which directly affects the battery's insulation performance and leakage risk; and altitude is used to characterize the vehicle's altitude, which indirectly affects the battery's thermal and electrochemical characteristics by influencing air density and heat dissipation efficiency.

[0136] Please see Figure 7 , Figure 7 A flowchart illustrating a battery anomaly detection method based on dynamic threshold adjustment provided in this application embodiment includes: S701. Adjust multiple anomaly judgment thresholds in the anomaly judgment criteria based on threshold adjustment rules.

[0137] As one possible implementation, the threshold adjustment rule includes at least one of the following: (1) In response to the ambient temperature being greater than the first temperature threshold, the abnormal judgment threshold corresponding to the battery temperature is lowered; in response to the ambient temperature being less than the second temperature threshold, the abnormal judgment threshold corresponding to the battery temperature is raised; wherein, the first temperature threshold is greater than the second temperature threshold.

[0138] In this embodiment, when the ambient temperature is too high, the risk of battery thermal runaway increases and the tolerance decreases. By lowering the abnormal judgment threshold corresponding to the battery temperature, the temperature alarm can be made more sensitive and thermal anomalies can be detected in time. When the ambient temperature is too low, the battery activity decreases but the risk of thermal runaway is low. By raising the abnormal judgment threshold corresponding to the battery temperature, false alarms caused by normal temperature fluctuations in low-temperature environments can be avoided, and the threshold can be adapted to the safety requirements under different ambient temperatures.

[0139] (2) In response to the ambient humidity being greater than the humidity threshold, the abnormal judgment threshold corresponding to the battery insulation resistance is reduced.

[0140] In this embodiment, when the ambient humidity is too high, the battery insulation performance naturally decreases and the risk of leakage increases. By reducing the abnormal judgment threshold corresponding to the battery insulation resistance, the insulation monitoring can adapt to the changes in insulation characteristics under high humidity environment, avoid false alarms caused by the normal decrease in insulation resistance due to humidity, and still effectively identify the real insulation fault.

[0141] (3) In response to an altitude greater than the altitude threshold, reduce the abnormal judgment threshold corresponding to the battery voltage, and / or reduce the abnormal judgment threshold corresponding to the battery current.

[0142] In this embodiment, when the altitude is too high, the thin air leads to a deterioration in battery heat dissipation conditions, and changes in air pressure may affect the stability of the internal chemical reaction of the battery. By lowering the abnormal judgment threshold corresponding to the battery voltage and / or battery current, the voltage and current monitoring can be made more sensitive, and the abnormalities caused by the decrease in battery tolerance under high altitude environment can be detected in time, so as to realize the adaptive adjustment of the threshold to altitude conditions.

[0143] (4) In response to the battery health status being greater than the first health threshold, the abnormal judgment thresholds corresponding to multiple operating parameters are adjusted to the first threshold set; in response to the battery health status being less than or equal to the first health threshold and greater than the second health threshold, the abnormal judgment thresholds corresponding to multiple operating parameters are adjusted to the second threshold set; in response to the battery health status being less than or equal to the second health threshold, the abnormal judgment thresholds corresponding to multiple operating parameters are adjusted to the third threshold set; wherein, the threshold level in the first threshold set is higher than the second threshold set, and the threshold level in the second threshold set is higher than the third threshold set.

[0144] In this implementation, when the battery is in good health, its tolerance is strong, and a higher threshold level can reduce false alarms for healthy batteries. When the battery shows some signs of aging, a moderate threshold level is used to balance false alarms and missed alarms. When the battery is severely aged, the risk of failure increases significantly, and a lower threshold level is used to improve the sensitivity of anomaly detection for aging batteries. Through this tiered adjustment method, the anomaly judgment threshold can adapt to the changes in the battery's health status throughout its entire life cycle, effectively distinguishing between parameter drift during normal battery aging and actual faults. This reduces the number of false alarms triggered by non-fault factors such as normal cell inconsistency degradation and calendar aging by more than 50%, significantly improving the availability of the monitoring system and the user experience.

[0145] In one possible implementation, adjustment based on battery charge / discharge history is one of at least one of the threshold adjustment rules. In response to the battery's historical charge / discharge records showing frequent high-power charge / discharge, the anomaly judgment threshold corresponding to battery voltage and / or battery temperature is lowered; in response to the battery's historical charge / discharge records showing stable charge / discharge, the anomaly judgment threshold corresponding to battery voltage and / or battery temperature is maintained or increased; in response to the battery's historical charge / discharge records showing prolonged inactivity, the anomaly judgment threshold corresponding to battery internal resistance and / or battery self-discharge rate is adjusted.

[0146] In this embodiment, when a battery undergoes frequent high-power charging and discharging, its internal structure may suffer cumulative damage, increasing the risk of internal short circuits and thermal runaway. Lowering the voltage and temperature thresholds can improve the detection sensitivity for these risks. When a battery is in a stable charging and discharging state for a long period, its state is relatively stable, and using a higher threshold can reduce false alarms in daily monitoring. When a battery is not used for a long time, its internal chemical reactions tend to be static, and self-discharge and changes in internal resistance may become the main risk sources. Adjusting the thresholds for internal resistance and self-discharge-related parameters can better adapt to the monitoring needs of batteries in long-term static states. Through this adjustment method based on charging and discharging history, the anomaly judgment threshold can adapt to different battery usage modes and static durations.

[0147] S702. Based on the adjusted anomaly detection threshold, perform anomaly detection on the battery operation data obtained from sampling and detection.

[0148] As one possible implementation, after dynamically adjusting the anomaly judgment threshold in the monitoring strategy file, the BMS acquires the battery operation data obtained from real-time sampling and detection, and compares the battery operation data with the corresponding adjusted anomaly judgment threshold to determine whether each battery operation parameter is in an abnormal state.

[0149] As one possible implementation, when any battery operating parameter is determined to be abnormal, the BMS triggers a corresponding abnormality log or alarm response. The abnormality log includes storing information such as the time of the abnormal event, parameter type, measured value, and adjusted threshold in local memory; the alarm response includes determining whether to wake up the VCU and T-Box based on the abnormality level, and reporting the abnormal information to the cloud monitoring platform.

[0150] In the above manner, this application embodiment uses a dynamically adjusted anomaly judgment threshold to detect anomalies in battery operating data, making the detection results more consistent with the current environmental conditions and health status of the battery, effectively reducing the false alarm rate of anomaly detection, and improving the accuracy and reliability of anomaly detection.

[0151] In summary, an exemplary diagram illustrating the parameter update of monitoring strategy information in a monitoring strategy file is provided. Please refer to [link / reference]. Figure 8 As shown, it includes: The cloud-based monitoring platform is used to iteratively optimize monitoring strategy information based on the monitoring execution logs reported by vehicles, and to send the updated monitoring strategy information to vehicles through OTA technology, thereby realizing the adaptive evolution of monitoring strategy information.

[0152] The core monitoring parameters involved in this application embodiment are updated for vehicles that receive monitoring strategy information. Specifically, this includes: updating dynamic monitoring thresholds: corresponding to the anomaly judgment thresholds in this application, such as overvoltage alarm thresholds and temperature rise alarm rate thresholds, used to dynamically adjust the anomaly judgment criteria of individual parameters based on environmental information and battery health status. Updating monitoring time for each stage: corresponding to the time range of multiple monitoring time periods in this application, divided by time points T1 and T2, including stage 1 corresponding to the first monitoring time period, stage 2 corresponding to the second monitoring time period, and stage 3 corresponding to the third monitoring time period, used to divide different monitoring stages within the resting period. Updating monitoring frequencies of different modules in each stage: corresponding to the cell frequency information in this application, including the second monitoring frequencies f1, f2, and f3 corresponding to module 1 (such as the first cell type) in stages 1, 2, and 3 respectively, and the second monitoring frequencies f4, f5, and f6 corresponding to module 2 (such as the second cell type) in stages 1, 2, and 3 respectively, used to achieve differentiated monitoring of cell modules with different risk levels in different time periods.

[0153] Specifically, Figure 8 The dashed box represents the entire resting period monitoring process; the horizontal axis is the time axis, indicating the stage divisions within the resting period (based on time points T1 and T2); the vertical axis represents different monitoring modules, indicating the monitoring frequency of each module at different stages. Through continuous optimization and updating of the above parameters, the time period frequency information, cell frequency information, anomaly judgment threshold, and time range of each monitoring time period in the monitoring strategy information can adapt to the actual operating status of the vehicle and environmental changes, continuously improving the accuracy and effectiveness of battery monitoring during the resting period.

[0154] Please see Figure 9 , Figure 9 This is a schematic diagram illustrating the composition of an electronic device according to an embodiment of this application. The electronic device 900 may include a processor 901 and a memory 902. The processor 901 and the memory 902 are communicatively connected. The memory 902 stores programs, and the processor 901 executes the programs, specifically performing the relevant steps described in the battery monitoring method embodiment.

[0155] Specifically, the program may include program code, which includes computer-executable instructions. Memory 902 may include high-speed RAM memory 902, and may also include non-volatile memory 902, such as at least one disk storage device 902. Processor 901 may be a central processing unit (CPU), a microcontroller unit (MCU), or an application-specific integrated circuit (ASIC).

[0156] This application also provides a computer-readable storage medium storing at least one executable instruction that, when executed on an electronic device, causes the electronic device to perform the method in any of the above method embodiments.

[0157] This application provides a computer program product that can be executed by the processor 901 of the electronic device 900 to perform the methods described in the above embodiments.

[0158] This application also provides a vehicle, including a processor and a memory, the processor being connected to the memory, the memory storing computer instructions, which, when executed on the vehicle, cause the vehicle to perform the above-described method.

[0159] It should be understood that the application of this application is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Those skilled in the art can understand that implementing all or part of the processes of the above embodiments and making equivalent changes according to the claims of this application still fall within the scope of this application.

Claims

1. A battery monitoring method, characterized in that, Applied to vehicles, the method includes: The target monitoring time period of the vehicle is determined based on the monitoring strategy information of the resting period; the resting period includes multiple monitoring time periods after the vehicle is powered off; the monitoring strategy information includes time period frequency information and cell frequency information; the time period frequency information is used to represent the first monitoring frequency corresponding to each monitoring time period in the multiple monitoring time periods, and the cell frequency information is used to represent the second monitoring frequency corresponding to each cell type in each monitoring time period; the vehicle's battery includes multiple different types of cells; Based on the monitoring strategy information, a first monitoring frequency corresponding to the target monitoring time period and a second monitoring frequency corresponding to each cell category are determined. The vehicle's battery is sampled and monitored based on the first monitoring frequency corresponding to the target monitoring time period and the second monitoring frequency corresponding to each cell type.

2. The battery monitoring method according to claim 1, characterized in that, The battery cells of the vehicle include a first cell category and a second cell category; wherein the risk level of the first cell category is greater than that of the second cell category. In the cell frequency information, within each monitoring time period, the second monitoring frequency corresponding to the first cell category is greater than the second monitoring frequency corresponding to the second cell category.

3. The battery monitoring method according to claim 2, characterized in that, The sampling and monitoring of the vehicle's battery based on a first monitoring frequency corresponding to the target monitoring time period and a second monitoring frequency corresponding to each cell type includes: During the target monitoring time period, the battery is sampled based on a first monitoring frequency corresponding to the target monitoring time period; The cells in the battery that belong to the first cell category are sampled and monitored at the second monitoring frequency corresponding to the first cell category. The cells in the battery that belong to the second cell category are sampled and monitored at the second monitoring frequency corresponding to the second cell category.

4. The battery monitoring method according to claim 1, characterized in that, The multiple monitoring time periods after the vehicle is powered off include, in chronological order: a first monitoring time period, a second monitoring time period, and a third monitoring time period; in the time period frequency information, the first monitoring frequency corresponding to the multiple monitoring time periods decreases sequentially in chronological order.

5. The battery monitoring method according to claim 1, characterized in that, The monitoring strategy information also includes a set of weighting coefficients and alarm level determination rules; the set of weighting coefficients is used to represent the importance of each parameter among multiple parameters related to the degree of battery abnormality; the alarm level determination rules are used to define the correspondence between the degree of abnormality value and the alarm level. The method further includes: Based on the battery operation data obtained from sampling and detection, as well as the vehicle's historical battery charge and discharge data and real-time environmental data, historical fluctuation characteristic indicators, environmental risk characteristic indicators, and health risk characteristic indicators are determined. Among them, the historical fluctuation characteristic indicators are used to characterize the degree of fluctuation of battery parameters determined based on the battery's historical charge and discharge data; the environmental risk characteristic indicators are used to characterize the degree of impact of environmental factors determined based on the real-time environmental data on battery safety; and the health risk characteristic indicators are used to characterize the degree of battery aging of the vehicle determined based on the battery operation data. Based on the set of weighting coefficients, the historical fluctuation characteristic indicators, the environmental risk characteristic indicators, and the health risk characteristic indicators are weighted and calculated to determine the degree of abnormality. The target alarm level is determined based on the alarm level determination rules and the anomaly severity value.

6. The battery monitoring method according to claim 5, characterized in that, The step of determining the degree of anomaly by weighting the historical fluctuation characteristic indicators, the environmental risk characteristic indicators, and the health risk characteristic indicators based on the set of weighting coefficients includes: The historical fluctuation characteristic indicators are compared with preset fluctuation thresholds, the environmental risk characteristic indicators are compared with preset environmental risk thresholds, and the health risk characteristic indicators are compared with preset health risk thresholds. Based on the comparison results, a set of corresponding weight coefficients is selected from the set of weight coefficients. Based on the selected set of weighting coefficients, the historical fluctuation characteristic indicators, the environmental risk characteristic indicators, and the health risk characteristic indicators are weighted and calculated to determine the degree of abnormality.

7. The battery monitoring method according to claim 5, characterized in that, The method further includes: If the abnormality level value is greater than or equal to the first alarm threshold, the data of the battery sampling and monitoring of the vehicle will be stored locally, and the vehicle controller and / or the vehicle's on-board communication terminal will be kept in a dormant state. If the abnormality level is less than the first alarm threshold and greater than or equal to the second alarm threshold, the data sampled and monitored for the vehicle's battery will be marked as abnormal data. If the abnormality level is less than the second alarm threshold, the vehicle controller and / or the vehicle communication terminal will be activated, and the data of the battery sampling and monitoring of the vehicle will be marked as abnormal data and reported to the cloud monitoring platform.

8. The battery monitoring method according to claim 1, characterized in that, The monitoring strategy information also includes: anomaly judgment criteria and threshold adjustment rules; the anomaly judgment criteria include anomaly judgment thresholds corresponding to multiple operating parameters, used to judge whether each operating parameter in the battery operating data obtained by sampling and detection is abnormal; the multiple operating parameters include at least two of the following: battery temperature, battery insulation resistance, battery current, battery voltage, and battery health status; The threshold adjustment rule is used to adjust the anomaly judgment threshold based on the environmental information of the vehicle or the battery health status; the environmental information includes at least one of the following: ambient temperature, ambient humidity, and altitude; The method further includes: Based on the threshold adjustment rules, adjust multiple anomaly judgment thresholds in the anomaly judgment criteria; Based on the adjusted anomaly detection threshold, anomaly detection is performed on the battery operation data obtained from sampling and detection.

9. The battery monitoring method according to claim 8, characterized in that, The threshold adjustment rule includes at least one of the following: In response to the ambient temperature being greater than a first temperature threshold, the abnormality judgment threshold corresponding to the battery temperature is lowered; in response to the ambient temperature being less than a second temperature threshold, the abnormality judgment threshold corresponding to the battery temperature is raised; wherein, the first temperature threshold is greater than the second temperature threshold; In response to the ambient humidity being greater than a humidity threshold, the abnormal judgment threshold corresponding to the battery insulation resistance is lowered. In response to the altitude being greater than an altitude threshold, the abnormal judgment threshold corresponding to the battery voltage is lowered, and / or the abnormal judgment threshold corresponding to the battery current is lowered. In response to the battery health status being greater than a first health threshold, the anomaly judgment thresholds corresponding to the plurality of operating parameters are adjusted to a first threshold set; in response to the battery health status being less than or equal to the first health threshold and greater than a second health threshold, the anomaly judgment thresholds corresponding to the plurality of operating parameters are adjusted to a second threshold set; in response to the battery health status being less than or equal to the second health threshold, the anomaly judgment thresholds corresponding to the plurality of operating parameters are adjusted to a third threshold set; wherein, the threshold levels in the first threshold set are higher than those in the second threshold set, and the threshold levels in the second threshold set are higher than those in the third threshold set.

10. A vehicle, characterized in that, The system includes a processor and a memory, the processor being connected to the memory, the memory storing computer instructions that, when executed on the vehicle, cause the vehicle to perform the method as described in any one of claims 1-9.

11. A battery monitoring system, characterized in that, The system includes: the vehicle and cloud monitoring platform as described in claim 10; The cloud-based monitoring platform is configured to: generate monitoring strategy information based on historical vehicle operation data and send the monitoring strategy information to the vehicle; receive monitoring execution logs reported by the vehicle, update the monitoring strategy information based on the monitoring execution logs, and send the updated information to the vehicle via over-the-air (OTA) technology.

12. The battery monitoring system according to claim 11, characterized in that, The vehicle includes: a battery management system, a vehicle controller, and an on-board communication terminal; The battery management system is configured to: remain awake and enter a rest period monitoring mode after the vehicle's high voltage is de-energized; sample and monitor the vehicle's battery based on the monitoring strategy information in the rest period monitoring mode; and wake up the vehicle controller and the vehicle communication terminal in sequence when the detected anomaly level meets the preset reporting conditions. The battery management system is also configured to: automatically wake up and sequentially wake up the vehicle controller and the vehicle communication terminal in response to the internal real-time clock timer reaching a preset wake-up period; The vehicle controller is configured to: enter a sleep state after the vehicle's high voltage is cut off, and after being woken up by the battery management system, establish a communication link with the battery management system and wake up the vehicle communication terminal. The vehicle-mounted communication terminal is configured to: obtain the monitoring strategy information from the cloud monitoring platform before the vehicle's high voltage is turned off, and enter a sleep state after the vehicle's high voltage is turned off; and, after being woken up by the vehicle controller, report the monitoring data to the cloud monitoring platform, and instruct the vehicle controller and the battery management system to re-enter the sleep state in sequence after the reporting is completed.

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