Battery falling detection method, battery and vehicle

Abstract

The invention discloses a battery falling detection method, a battery and a vehicle. According to the method, multiple pieces of collection data are obtained according to a preset time interval, the collection data are acceleration sensor signals, the acceleration value of a battery is determined according to the collection data, the falling time of the battery is obtained according to the acceleration value, and the battery falling detection result is determined according to the falling time. Therefore, the falling of the battery can be accurately detected, and a basis is provided for battery state detection.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a battery drop detection method, a battery, and a vehicle. Background Art

[0002] With rising environmental awareness and the increasing problem of traffic congestion, electric vehicles are attracting increasing attention. Battery status monitoring is a key technology for ensuring safe and efficient operation of electric vehicles. Battery status is not only related to the number of charge and discharge cycles and usage time, but also to battery drops. Therefore, accurate battery drop detection plays a crucial role in determining battery status. Currently, there is a lack of methods for detecting battery drops in electric vehicles. Summary of the Invention

[0003] In view of this, an object of the embodiments of the present invention is to provide a battery drop detection method, a battery, and a vehicle, which can accurately detect battery drops and provide a basis for battery status detection.

[0004] In a first aspect, an embodiment of the present invention provides a battery drop detection method, the method comprising:

[0005] Acquire a plurality of collected data at predetermined time intervals, wherein the collected data are acceleration sensor signals;

[0006] determining an acceleration value of the battery according to the collected data;

[0007] Obtaining the falling time of the battery according to the acceleration value; and

[0008] A battery drop detection result is determined according to the drop time.

[0009] In some embodiments, the step of acquiring a plurality of collected data at predetermined time intervals is as follows:

[0010] In response to detecting that the battery meets a predetermined trigger condition, acquiring a predetermined number of collected data at predetermined time intervals;

[0011] The predetermined trigger condition is that the power supply port or the charging port of the battery is disconnected.

[0012] In some embodiments, the acceleration sensor signal is a three-axis gravity acceleration sensor signal, including a first acceleration signal, a second acceleration signal, and a third acceleration signal;

[0013] The method of determining the acceleration value of the battery according to the acceleration sensor signal is as follows:

[0014] A vector sum of the first acceleration signal, the second acceleration signal, and the third acceleration signal is used as the acceleration value.

[0015] In some embodiments, obtaining the drop time of the battery according to the acceleration value is specifically:

[0016] Obtaining the falling time of the battery according to a comparison result of the acceleration value and a predetermined threshold;

[0017] The predetermined threshold value includes a first threshold value and a second threshold value, the first threshold value is less than the acceleration of gravity, and the second threshold value is greater than the acceleration of gravity.

[0018] In some embodiments, obtaining the drop time of the battery according to a comparison result of the acceleration value and a predetermined threshold value includes:

[0019] The collected data corresponding to the first acceleration value smaller than the first threshold is used as the starting data;

[0020] The collected data corresponding to the first acceleration value greater than the second threshold value after the start data is used as the end data;

[0021] Obtain the number of data between the start data and the end data;

[0022] The battery drop time is obtained according to the number of data and the predetermined time interval.

[0023] In some embodiments, determining the battery drop detection result according to the drop time includes:

[0024] In response to the drop time being greater than a predetermined time threshold, it is determined that the battery drop detection result is a battery drop.

[0025] In some embodiments, determining the battery drop detection result according to the drop time includes:

[0026] Obtaining a drop height of the battery according to the drop time; and

[0027] In response to the drop height being greater than a predetermined height threshold, the battery drop detection result is determined to be a battery drop.

[0028] In some embodiments, obtaining the drop height of the battery according to the drop time is specifically:

[0029] Calculating the drop height of the battery according to the drop acceleration and the drop time;

[0030] The falling acceleration is an average acceleration of acceleration values corresponding to the collected data between the start data and the end data, or the falling acceleration is a predetermined acceleration.

[0031] In some embodiments, the method further comprises:

[0032] In response to the battery drop detection result being a battery drop, updating a stored number of battery drops; and

[0033] The battery drop count is sent to a server.

[0034] In some embodiments, the method further comprises:

[0035] In response to the battery drop detection result being a battery drop, updating a stored number of battery drops;

[0036] In response to detecting a power supply port connection or a charging port connection of the battery, the battery drop count is sent to the connected vehicle or charging cabinet, so that the vehicle or charging cabinet sends the battery drop count to the server.

[0037] In a second aspect, an embodiment of the present invention provides a battery, comprising:

[0038] accelerometer; and

[0039] A memory and a processor, the memory being used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to the first aspect.

[0040] In a third aspect, an embodiment of the present invention provides a vehicle, comprising:

[0041] frame; and

[0042] A battery includes an acceleration sensor, a memory, and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present invention provides a battery drop detection device, the device comprising:

[0044] A data acquisition unit, configured to acquire a plurality of collected data at predetermined time intervals, wherein the collected data are acceleration sensor signals;

[0045] an acceleration value determining unit, configured to determine an acceleration value of the battery based on the collected data;

[0046] a falling time acquiring unit, configured to acquire the falling time of the battery according to the acceleration value; and

[0047] A detection result determining unit is configured to determine a battery drop detection result according to the drop time.

[0048] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a computer program. When the computer program runs on a computer, the computer executes the method described in the first aspect above.

[0049] In a sixth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer program instructions, which implement the method described in the first aspect when executed by a processor.

[0050] The technical solution of the embodiment of the present invention acquires multiple data points at predetermined time intervals. The data points are acceleration sensor signals. The battery acceleration value is determined based on the acquired data. The battery drop time is then determined based on the acceleration value. The battery drop detection result is then determined based on the drop time. This allows accurate battery drop detection and provides a basis for battery status detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0052] Figure 1 is a circuit diagram of a battery according to an embodiment of the present invention;

[0053] Figure 2 is a flow chart of battery drop detection according to an embodiment of the present invention;

[0054] Figure 3 This is a flow chart of obtaining the battery drop time according to an embodiment of the present invention;

[0055] Figure 4 is a schematic diagram of acceleration values according to an embodiment of the present invention;

[0056] Figure 5 FIG. 4 is a schematic diagram of a battery drop detection device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described below based on the following embodiments, but the present invention is not limited to these embodiments. In the detailed description of the present invention below, certain specific details are described in detail. Those skilled in the art can fully understand the present invention without these details. To avoid obscuring the essence of the present invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0058] Furthermore, persons of ordinary skill in the art will appreciate that the figures provided herein are for illustration purposes only and are not necessarily drawn to scale.

[0059] At the same time, it should be understood that in the following description, "circuit" refers to a conductive loop composed of at least one element or subcircuit connected electrically or electromagnetically. When an element or circuit is said to be "connected to" another element or an element / circuit is said to be "connected" between two nodes, it can be directly coupled or connected to the other element or there can be intermediate elements. The connection between the elements can be physical, logical, or a combination thereof. Conversely, when an element is said to be "directly coupled to" or "directly connected to" another element, it means that there are no intermediate elements between the two.

[0060] Unless the context clearly requires otherwise, words like "include," "comprising," and the like throughout this application should be construed as including, rather than exclusive or exhaustive; that is, as meaning "including but not limited to."

[0061] In the description of the present invention, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance. In addition, in the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0062] A wide variety of electric vehicles are currently used daily, including electric bicycles, electric motorcycles, electric unicycles, electric four-wheelers, electric tricycles, and electric scooters. These vehicles are all powered by batteries. Battery status monitoring is a key technology for ensuring safe and efficient operation. Battery status is not only influenced by the number of charge and discharge cycles and usage time, but can also be affected to some extent by battery drops.

[0063] For example, shared electric scooters are a new mode of transportation that has gained widespread popularity with advantages like dockless parking and low rental prices. They offer significant convenience and alleviate some of the pressure on urban public transportation. The batteries used in shared electric scooters are gradually shifting towards lithium-ion batteries. Due to their demanding operating conditions, lithium-ion batteries require a BMS (battery management system) protection board within the battery pack to control battery output, charge and discharge, estimate battery status, and record and upload battery information. Furthermore, in actual use, the lithium batteries used in shared electric scooters require maintenance personnel to regularly collect low-charge batteries, bring them to maintenance points for full charging, and then re-install them in shared electric scooters. During maintenance and transportation, lithium batteries can be damaged by falling to the ground due to improper handling. Repeated drops during a lithium battery's lifecycle can cause permanent damage to the internal cells, leading to internal short circuits and potentially resulting in safety risks such as thermal runaway, fire, and explosion. To mitigate lithium battery safety risks, a lithium battery BMS needs to be able to identify the height from which the battery has been dropped, record and upload the number of drops, and use this as an indicator for a comprehensive assessment of lithium battery safety risks. Currently, there is a lack of methods for detecting battery drops in electric vehicles. For electronic devices other than electric vehicles, existing technologies typically implement battery drop detection using angle change algorithms, light change algorithms, and machine learning algorithms.

[0064] The angle change algorithm analyzes data from the angle sensor to detect changes in the battery's angle when it is dropped. Filtering and thresholding can be used to identify dropped batteries. The light change algorithm uses a photoelectric sensor to detect changes in the light around the battery, determining whether the battery has been dropped when the light suddenly changes. The machine learning algorithm uses machine learning to analyze and train data from drop events to build a drop detection model. Algorithms such as support vector machines (SVMs) and random forests can be used to identify battery drop conditions.

[0065] However, the angle change detection algorithm cannot accurately identify the drop of the shared electric motorcycle lithium battery during operation and use. The battery may not change in angle when it falls, and during operation and maintenance, the lithium battery will be manually operated by the operation and maintenance personnel. During vehicle driving, operations such as turning the vehicle will also lead to misidentification of angle changes and thus false alarms of drops. At the same time, there is no space on the outer casing of the shared electric motorcycle lithium battery to install a photoelectric sensor, and it cannot be detected by the light change algorithm. The machine learning algorithm has high requirements on the performance of the BMS processor and requires a large amount of data for training. At present, the MCU (Micro Control Unit) of the BMS of the shared electric motorcycle lithium battery usually uses a single-chip microcomputer as a processor, and its performance cannot support the machine learning algorithm. Therefore, an embodiment of the present invention provides a method for detecting the drop of an electric vehicle battery and a corresponding battery and vehicle.

[0066] Figure 1 : is a circuit diagram of a battery according to an embodiment of the present invention. Figure 1 In the illustrated embodiment, the battery includes an acceleration sensor 11, a processor 12, and a memory 13. The acceleration sensor is configured to acquire acceleration sensor signals. The memory 13 is configured to store one or more computer program instructions, which are executed by the processor 12 to implement the battery drop detection method according to an embodiment of the present invention.

[0067] The acceleration sensor 11 can be implemented as a three-axis acceleration sensor. A three-axis acceleration sensor is an acceleration sensor that works based on the basic principle of acceleration and can measure acceleration on three coordinate axes. It has the advantages of being small in size and light in weight.

[0068] The processor 12 can be implemented by an MCU (Microcontroller Unit), a PLC (Programmable Logic Controller), an FPGA (Field-Programmable Gate Array), a DSP (Digital Signal Processor), or an ASIC (Application Specific Integrated Circuit).

[0069] Figure 2 This is a flow chart of battery drop detection according to an embodiment of the present invention. Figure 2 In the embodiment shown, battery drop detection includes the following steps:

[0070] Step S100: Acquire multiple collected data at predetermined time intervals.

[0071] In this embodiment, in response to detecting that the battery meets a predetermined trigger condition, the processor collects a plurality of acceleration sensor signals at predetermined time intervals, and the collected data are acceleration sensor signals.

[0072] Furthermore, the predetermined trigger condition is that the power supply port or the charging port of the battery is disconnected. Specifically, when the battery falls, there will generally be a period of free fall. When the battery is normally installed on the vehicle, the power supply port is connected to the vehicle, and when the battery is normally installed on the charging cabinet, the charging port is connected to the charging cabinet. When the battery falls off from the vehicle on its own, or the staff accidentally drops the battery when removing it from the vehicle, the power supply port of the battery will be disconnected from the vehicle. When the battery falls from the charging cabinet on its own, or the staff drops the battery when removing it from the charging cabinet, the charging port of the battery will be disconnected from the charging cabinet. Therefore, the embodiment of the present invention takes the disconnection of the power supply port or the charging port of the battery as a predetermined trigger condition. On the one hand, it can avoid false detection when the vehicle is driving normally, resulting in inaccurate battery drop detection results. On the other hand, it can start detection when it is detected that the battery meets the predetermined trigger condition, and not detect before it is detected that the battery meets the predetermined trigger condition, which can reduce the power consumption of the processor.

[0073] The predetermined time interval may be set according to actual conditions, for example, the time interval may be 10 milliseconds, 20 milliseconds, etc.

[0074] Furthermore, the processor collects a predetermined number of collected data at a predetermined time interval, wherein the predetermined number can be set according to actual conditions, such as 100, 200, 300, etc.

[0075] Each acceleration sensor signal is a three-axis acceleration sensor signal, including three components, namely a first acceleration signal, a second acceleration signal and a third acceleration signal.

[0076] Step S200: Determine the acceleration value of the battery according to the collected data.

[0077] In this embodiment, after the processor collects a predetermined number of collected data at a predetermined time interval, it calculates the acceleration value corresponding to each acceleration sensor signal in the order of the collection time of each collected data.

[0078] As mentioned above, each acceleration sensor signal includes three components: a first acceleration signal, a second acceleration signal, and a third acceleration signal. Therefore, it is necessary to determine the battery acceleration value based on the three components of the acceleration sensor signal. Specifically, the vector sum of the first acceleration signal, the second acceleration signal, and the third acceleration signal is used as the acceleration value. Specifically, the acceleration value is calculated using the following formula:

[0079]

[0080] Among them, a x is the first acceleration, a y is the second acceleration, a z is the third acceleration, and a is the acceleration value.

[0081] Step S300: Obtain the drop time of the battery according to the acceleration value.

[0082] In this embodiment, the corresponding acceleration values are different when the battery's motion state is different. When the battery falls, it is generally in free fall motion. Therefore, the time the battery is in free fall motion state can be judged as the battery's falling time based on the acceleration value.

[0083] Furthermore, the processor obtains the drop time of the battery based on a comparison result between the acceleration value and a predetermined threshold value, wherein the predetermined threshold value includes a first threshold value and a second threshold value, the first threshold value is less than the acceleration due to gravity, and the second threshold value is greater than the acceleration due to gravity.

[0084] Specifically, under ideal conditions, when the battery is at rest, the acceleration value is equal to the gravitational acceleration G. When the battery is in free fall, the acceleration value is equal to 0. At the moment the battery falls from a falling state to the ground, the acceleration value is generally greater than G. Therefore, in this embodiment of the present invention, a first threshold a1 and a second threshold a2 are set, wherein the first threshold a1 is less than the gravitational acceleration G, and the second threshold a2 is greater than the gravitational acceleration G. Furthermore, whether the battery is in a falling state can be determined based on the comparison results of the acceleration value with the first and second thresholds.

[0085] It should be understood that the embodiment of the present invention does not limit the specific values of the first threshold a1 and the second threshold a2, and can be set according to actual conditions, as long as the first threshold is less than the acceleration of gravity and the second threshold is greater than the acceleration of gravity. Specifically, the acceleration values of the battery in different scenarios can be tested and the optimal values can be determined based on the test results to reduce misjudgment. For example, the acceleration of gravity G is 9.8m / s 2 , the first threshold a1 can be set to 8, and the second threshold a2 can be set to 11.

[0086] Figure 3 FIG. 1 is a flow chart of obtaining the battery drop time according to an embodiment of the present invention. Figure 3 As shown, obtaining the drop time of the battery according to the comparison result of the acceleration value and the predetermined threshold value includes the following steps:

[0087] Step S310: Use the collected data corresponding to the first acceleration value smaller than the first threshold as the starting data.

[0088] In this embodiment, step S200 can obtain the acceleration value corresponding to each piece of collected data, where the collected data is the collected acceleration sensor signal. The acceleration value corresponding to each acceleration sensor signal is calculated in descending order based on the collection time of the acceleration sensor signal corresponding to each acceleration value, and the collected data corresponding to the first acceleration value less than a first threshold is used as the starting data.

[0089] Step S320: The collected data corresponding to the first acceleration value greater than the second threshold value after the start data is used as the end data.

[0090] In this embodiment, the acceleration value corresponding to each acceleration sensor signal is calculated according to the acquisition time of the velocity sensor signal corresponding to each acceleration value in descending order of acquisition time, and the acquisition data corresponding to the first acceleration value greater than the second threshold after the starting data is used as the termination data.

[0091] The above-mentioned starting data is the first data that meets the falling state, and the ending data is the first data that meets the landing state after the starting data.

[0092] Step S330: Obtain the number of data between the start data and the end data.

[0093] In this embodiment, the data between the start data and the end data is regarded as data meeting the drop state, and the number of data between the start data and the end data is obtained.

[0094] Step S340: Obtain the battery drop time according to the number of data and the predetermined time interval.

[0095] In this embodiment, the processor obtains the battery drop time according to the number of data and the predetermined time interval.

[0096] The number of data between the start data and the end data does not include the start data and the end data. Correspondingly, the calculation formula for the battery drop time is as follows:

[0097] T=(n+1)*ΔT

[0098] Wherein, n is the number of data between the start data and the end data, ΔT is the predetermined time interval, and T is the battery drop time.

[0099] Figure 4 Schematic diagram of acceleration values according to an embodiment of the present invention, wherein the vertical axis represents the acceleration value a, and the horizontal axis represents the sequence number m of the collected data, where the sequence numbers of the collected data are arranged in order of the collection time of the collected data. Figure 4 The acceleration values corresponding to 16 collected data are shown. Figure 4 As shown, the first acceleration value smaller than the first threshold a1 corresponds to the collected data m5, and after the start data, the first acceleration value larger than the second threshold a2 corresponds to the collected data m 14 , that is, the starting data is m5 and the ending data is m 14 , thus, the number of data between the start data and the end data is 8. Assuming that the predetermined time interval is 10ms (milliseconds), the battery drop time is 90ms.

[0100] Step S400: Determine a battery drop detection result according to the drop time.

[0101] In one optional implementation, the processor determines a battery drop detection result based on the drop time and a predetermined time threshold. In response to the drop time being greater than the predetermined time threshold, the battery drop detection result is determined to be a battery drop. The predetermined time threshold can be set based on actual circumstances. For example, when a battery is dropped in free fall, it will drop approximately 0.2 meters in 0.2 seconds. Therefore, the predetermined time threshold can be set to 0.2 seconds, or 200 milliseconds.

[0102] In another optional implementation, the processor obtains the drop height of the battery based on the drop time, and in response to the drop height being greater than a predetermined height threshold, determines that the battery drop detection result is a battery drop. The drop height of the battery is calculated based on the drop acceleration and the drop time. Specifically, the drop height calculation formula is as follows:

[0103]

[0104] Among them, a t is the drop acceleration, T is the drop time, and h is the drop height.

[0105] The drop acceleration is the average acceleration of the acceleration values corresponding to the collected data between the start data and the end data, or the drop acceleration is a predetermined acceleration. The predetermined acceleration may be the acceleration due to gravity. The predetermined height threshold may be set based on actual conditions. For example, the predetermined height threshold may be 0.2 meters.

[0106] The embodiments of the present invention acquire multiple data sets at predetermined time intervals. The data sets are acceleration sensor signals. The battery's acceleration value is determined based on the acquired data. The battery's drop time is then determined based on the acceleration value. The battery drop detection result is then determined based on the drop time. This allows accurate battery drop detection and provides a basis for battery status detection.

[0107] In some embodiments, the battery further includes a first communication module, the first communication module being configured to communicate with a server. Correspondingly, the battery drop detection method further includes:

[0108] Step S500: In response to the battery drop detection result being a battery drop, updating the stored battery drop count.

[0109] In this embodiment, the processor stores the number of battery drops, and in response to the battery drop detection result indicating that the battery has dropped, updates the stored number of battery drops, wherein updating the stored number of battery drops specifically includes adding 1 to the number of battery drops.

[0110] Step S600: Send the battery drop count to a server.

[0111] In this embodiment, the battery communicates with the server through the first communication module and sends the battery drop count to the server.

[0112] Among them, the first communication module can be a GSM (Global System for Mobile Communications) module, a GPRS (General packet radio service) module, an eMTC (LTE enhanced MTO, enhanced machine type communication) module, an NB-IoT (Narrow Band Internet of Things) module, etc.

[0113] The embodiments of the present invention acquire multiple data sets at predetermined time intervals. The data sets are acceleration sensor signals. The battery's acceleration value is determined based on the acquired data. The battery's drop time is then determined based on the acceleration value. The battery drop detection result is then determined based on the drop time. This allows accurate battery drop detection and provides a basis for battery status detection.

[0114] In some embodiments, the battery further includes a second communication module, the second communication module being configured to communicate with the vehicle or charging cabinet. Correspondingly, the battery drop detection method further includes:

[0115] Step S700: In response to the battery drop detection result being a battery drop, updating the stored battery drop count.

[0116] In this embodiment, the processor stores the number of battery drops, and in response to the battery drop detection result indicating that the battery has dropped, updates the stored number of battery drops, wherein updating the stored number of battery drops specifically includes adding 1 to the number of battery drops.

[0117] Step S800: In response to detecting that the power supply port or charging port of the battery is connected, the number of battery drops is sent to the connected vehicle or charging cabinet, so that the vehicle or charging cabinet sends the number of battery drops to the server.

[0118] In this embodiment, the battery communicates with the vehicle or charging cabinet through the second communication module, and in response to detecting that the power supply port or charging port of the battery is connected, the battery drop count is sent to the connected vehicle or charging cabinet, so that the vehicle or charging cabinet sends the battery drop count to the server.

[0119] The second communication module can communicate with the vehicle or charging cabinet through wired communication or wireless communication. Wired communication can be achieved through bus interfaces such as CAN (Controller Area Network), LIN (Local Interconnect Network), RS-485, and UART (Universal Asynchronous Receiver / Transmitter). CAN is a serial communication protocol of the ISO International Organization for Standardization. LIN (bus is a low-cost serial communication protocol based on UART / SCI (Universal Asynchronous Receiver / Serial Interface), mainly used for serial communication between sensors and controllers. The RS-485 bus standard is a bidirectional, balanced transmission standard interface widely used in industry (attendance, monitoring, and data acquisition systems) that supports multi-point connection. UART is a universal serial data bus used for asynchronous communication. The bus can communicate in both directions and can achieve full-duplex transmission and reception. Wireless communication can be achieved through wireless networks such as Bluetooth, NB-IoT (Narrow Band Internet of Things), LoRa, or ZigBee.

[0120] The embodiments of the present invention acquire multiple data sets at predetermined time intervals. The data sets are acceleration sensor signals. The battery's acceleration value is determined based on the acquired data. The battery's drop time is then determined based on the acceleration value. The battery drop detection result is then determined based on the drop time. This allows accurate battery drop detection and provides a basis for battery status detection.

[0121] Figure 5 FIG is a schematic diagram of a battery drop detection device according to an embodiment of the present invention. Figure 5 As shown, the battery drop detection device according to an embodiment of the present invention includes a data acquisition unit 51, an acceleration value determination unit 52, a drop time acquisition unit 53, and a detection result determination unit 54. The data acquisition unit 51 is configured to acquire multiple collected data at predetermined time intervals, wherein the collected data is an acceleration sensor signal. The acceleration value determination unit 52 is configured to determine the battery's acceleration value based on the collected data. The drop time acquisition unit 53 is configured to acquire the battery's drop time based on the acceleration value. The detection result determination unit 54 is configured to determine the battery drop detection result based on the drop time.

[0122] In some embodiments, the data acquisition unit is specifically configured to:

[0123] In response to detecting that the battery meets a predetermined trigger condition, acquiring a predetermined number of collected data at predetermined time intervals;

[0124] The predetermined trigger condition is that the power supply port or the charging port of the battery is disconnected.

[0125] In some embodiments, the acceleration sensor signal is a three-axis gravity acceleration sensor signal, including a first acceleration signal, a second acceleration signal, and a third acceleration signal;

[0126] The acceleration value determination unit is specifically configured to:

[0127] A vector sum of the first acceleration signal, the second acceleration signal, and the third acceleration signal is used as the acceleration value.

[0128] In some embodiments, the fall time acquisition unit is specifically configured to:

[0129] Obtaining the falling time of the battery according to a comparison result of the acceleration value and a predetermined threshold;

[0130] The predetermined threshold value includes a first threshold value and a second threshold value, the first threshold value is less than the acceleration of gravity, and the second threshold value is greater than the acceleration of gravity.

[0131] In some embodiments, the fall time acquisition unit includes:

[0132] A starting data acquisition subunit, configured to use the collected data corresponding to the first acceleration value smaller than a first threshold as the starting data;

[0133] The termination data acquisition subunit is configured to use the collected data corresponding to the first acceleration value greater than the second threshold value after the start data as the termination data;

[0134] A data number obtaining subunit, used to obtain the number of data between the start data and the end data;

[0135] The fall time acquisition subunit is used to acquire the fall time of the battery according to the number of data and the predetermined time interval.

[0136] In some embodiments, the detection result determination unit includes:

[0137] The first determining subunit is configured to determine, in response to the drop time being greater than a predetermined time threshold, that the battery drop detection result is a battery drop.

[0138] In some embodiments, the detection result determination unit includes:

[0139] a drop height acquisition subunit, configured to acquire the drop height of the battery according to the drop time; and

[0140] The second determining subunit is configured to determine, in response to the falling height being greater than a predetermined height threshold, that the battery falling detection result is a battery falling.

[0141] In some embodiments, the drop height acquisition subunit is specifically configured to:

[0142] Calculating the drop height of the battery according to the drop acceleration and the drop time;

[0143] The falling acceleration is an average acceleration of acceleration values corresponding to the collected data between the start data and the end data, or the falling acceleration is a predetermined acceleration.

[0144] In some embodiments, the apparatus further comprises:

[0145] a first updating unit, configured to update a stored number of battery drops in response to the battery drop detection result being a battery drop; and

[0146] The first sending unit is configured to send the battery drop count to a server.

[0147] In some embodiments, the method further comprises:

[0148] a second updating unit, configured to update a stored number of battery drops in response to the battery drop detection result being a battery drop;

[0149] The second sending unit is used to send the battery drop count to the connected vehicle or charging cabinet in response to detecting the connection of the battery power port or charging port, so that the vehicle or charging cabinet sends the battery drop count to the server.

[0150] The embodiments of the present invention acquire multiple data sets at predetermined time intervals. The data sets are acceleration sensor signals. The battery's acceleration value is determined based on the acquired data. The battery's drop time is then determined based on the acceleration value. The battery drop detection result is then determined based on the drop time. This allows accurate battery drop detection and provides a basis for battery status detection.

[0151] In some embodiments, an embodiment of the present invention further provides a vehicle comprising a frame and a battery, the battery comprising an acceleration sensor, a memory and a processor, the memory being used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the battery drop detection method of an embodiment of the present invention.

[0152] In some embodiments, the present invention further provides a computer program product, which includes a computer program. When the computer program runs on a computer, the computer executes the battery drop detection method according to the present invention.

[0153] In some embodiments, an embodiment of the present invention further provides a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed by a processor, the battery drop detection method of the embodiment of the present invention is implemented.

[0154] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatuses (devices), or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] The present invention is described with reference to flowcharts of methods, apparatuses (devices), and computer program products according to embodiments of the present application. It should be understood that each process in the flowcharts can be implemented by computer program instructions.

[0156] These computer program instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device that implements the process Figure 1 A function specified in a process or multiple processes.

[0157] These computer program instructions can also be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the instructions for implementing the process Figure 1 A device that specifies functions in a process or multiple processes.

[0158] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A battery drop detection method, characterized in that: The method comprises: Acquire a plurality of collected data at predetermined time intervals, wherein the collected data are acceleration sensor signals; determining an acceleration value of the battery according to the collected data; Obtaining the falling time of the battery according to the acceleration value; and A battery drop detection result is determined according to the drop time.

2. The method according to claim 1, characterized in that The method of obtaining a plurality of collected data at predetermined time intervals is specifically as follows: In response to detecting that the battery meets a predetermined trigger condition, acquiring a predetermined number of collected data at predetermined time intervals; The predetermined trigger condition is that the power supply port or the charging port of the battery is disconnected.

3. The method according to claim 1, characterized in that The acceleration sensor signal is a three-axis gravity acceleration sensor signal, including a first acceleration signal, a second acceleration signal and a third acceleration signal; The method of determining the acceleration value of the battery according to the acceleration sensor signal is as follows: A vector sum of the first acceleration signal, the second acceleration signal, and the third acceleration signal is used as the acceleration value.

4. The method according to claim 1, wherein The step of obtaining the battery drop time according to the acceleration value is specifically as follows: Obtaining the falling time of the battery according to a comparison result of the acceleration value and a predetermined threshold; The predetermined threshold value includes a first threshold value and a second threshold value, the first threshold value is less than the acceleration of gravity, and the second threshold value is greater than the acceleration of gravity.

5. The method according to claim 4, characterized in that The obtaining of the drop time of the battery according to the comparison result of the acceleration value and a predetermined threshold value includes: The collected data corresponding to the first acceleration value smaller than the first threshold is used as the starting data; The collected data corresponding to the first acceleration value greater than the second threshold value after the start data is used as the end data; Obtain the number of data between the start data and the end data; The battery drop time is obtained according to the number of data and the predetermined time interval.

6. The method according to claim 1, characterized in that Determining the battery drop detection result according to the drop time includes: In response to the drop time being greater than a predetermined time threshold, it is determined that the battery drop detection result is a battery drop.

7. The method according to claim 5, characterized in that Determining the battery drop detection result according to the drop time includes: Obtaining a drop height of the battery according to the drop time; and In response to the drop height being greater than a predetermined height threshold, the battery drop detection result is determined to be a battery drop.

8. The method according to claim 7, characterized in that The specific method of obtaining the drop height of the battery according to the drop time is: Calculating the drop height of the battery according to the drop acceleration and the drop time; The falling acceleration is an average acceleration of acceleration values corresponding to the collected data between the start data and the end data, or the falling acceleration is a predetermined acceleration.

9. The method according to claim 6 or 7, characterized in that The method further comprises: In response to the battery drop detection result being a battery drop, updating a stored number of battery drops; and The battery drop count is sent to a server.

10. The method according to claim 6 or 7, characterized in that The method further comprises: In response to the battery drop detection result being a battery drop, updating a stored number of battery drops; In response to detecting a power supply port connection or a charging port connection of the battery, the battery drop count is sent to the connected vehicle or charging cabinet, so that the vehicle or charging cabinet sends the battery drop count to the server.

11. A battery, characterized in that: The battery comprises: accelerometer; and A memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 10.

12. A vehicle, characterized in that: The vehicle comprises: frame; and A battery comprises an acceleration sensor, a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 10.

13. A battery drop detection device, characterized in that: The device comprises: A data acquisition unit, configured to acquire a plurality of collected data at predetermined time intervals, wherein the collected data are acceleration sensor signals; an acceleration value determining unit, configured to determine an acceleration value of the battery based on the collected data; a falling time acquiring unit, configured to acquire the falling time of the battery according to the acceleration value; and A detection result determining unit is configured to determine a battery drop detection result according to the drop time.

14. A computer program product, comprising a computer program, characterized in that When the computer program is run on a computer, the computer executes the method according to any one of claims 1 to 10.

15. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 10 when executed by a processor.

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