Method and device for determining collision energy of power battery, vehicle and electronic equipment

Abstract

The invention relates to a collision energy determination method and device of a power battery, a vehicle and electronic equipment, and relates to the technical field of vehicles. The method comprises the following steps: in response to collision of a power battery, acquiring collision wave signals respectively acquired by at least two sensors; wherein the at least two sensors are arranged at different positions of the power battery; determining energy characteristics and energy peak time corresponding to the at least two sensors based on the collision wave signals; and determining the collision energy of the power battery based on the energy characteristics and the energy peak time. Therefore, the attenuated energy is compensated and corrected by combining the energy characteristics of the sensors, so that the real collision energy of the initial collision point is accurately calculated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to the technical field of vehicle power batteries, and specifically to a collision energy determination method and device for a power battery, a vehicle, and an electronic device. BACKGROUND

[0002] With the continuous improvement of the market penetration rate of electric vehicles, the bottom protection performance of the power battery, as the core energy and safety-critical component of the vehicle, directly determines the driving safety of the vehicle. Since the power battery is generally integrated at a low position of the vehicle chassis, it is easily subjected to bottom impacts such as road bumps, flying stones, and speed bump impacts in complex road conditions. Such mechanical actions may cause plastic deformation of the battery shell, damage to the cell structure, and even internal short circuit-induced thermal runaway chain reactions. Therefore, real-time monitoring and energy evaluation of impact events are of great significance for safety warning of the battery system. However, the related art is difficult to accurately predict the collision energy. SUMMARY

[0003] The present application provides a collision energy determination method and device for a power battery, a vehicle, and an electronic device to at least solve the technical problem that the related art is difficult to accurately predict the collision energy. The technical solution of the present application is as follows: In a first aspect, the present application provides a collision energy determination method for a power battery, comprising: in response to a collision of the power battery, acquiring collision wave signals collected by at least two sensors respectively; wherein the at least two sensors are disposed at different positions of the power battery; determining energy features and energy peak time corresponding to the at least two sensors respectively based on the collision wave signals; and determining the collision energy of the power battery based on the energy features and the energy peak time.

[0004] According to the above technical means, the present application can acquire collision wave signals through two sensors disposed at different positions of the power battery. First, the energy peak time is obtained by using the spatial difference to determine the propagation path, and then the energy loss of the path is analyzed, and finally the real collision energy of the initial collision point is accurately calculated by combining the energy features of each sensor to compensate and correct the attenuated energy.

[0005] In one possible manner, the energy features include integral energy, and the determination process of the integral energy corresponding to each of the at least two sensors includes: for a target sensor in the at least two sensors, determining the power spectral density corresponding to the collision wave signal collected by the target sensor; and extracting the integral energy in the preset frequency band from the power spectral density.

[0006] According to the technical means, the vibration energy can be characterized in the frequency domain by converting the time domain signal into the power spectrum density, and then the integral energy of the preset frequency band can be extracted to effectively focus on the frequency band specific to the collision event and filter irrelevant noise, so that the energy feature can more essentially reflect the collision strength, and the accuracy of the collision energy is significantly improved.

[0007] In a possible manner, the energy feature further includes a wavelet packet energy entropy, and a determination process of the wavelet packet energy entropy corresponding to each of the at least two sensors includes: performing wavelet packet analysis on the collision wave signal collected by the target sensor to determine the wavelet packet energy entropy corresponding to the target sensor.

[0008] According to the technical means, the non-stationary collision signal can be finely decomposed by wavelet packet analysis, so that the transient impact characteristics that are difficult to characterize by traditional Fourier transform can be captured, and then the complexity and concentration of the time-frequency domain distribution of the collision energy can be quantified by calculating the wavelet packet energy entropy, so that the model is finally provided with a complementary feature dimension representing the characteristics of the impact object and the collision mode, the collision events with similar energy but different impact forms can be distinguished, the generalization ability of the subsequent energy prediction model under complex working conditions is significantly enhanced, and the collision energy can be accurately calculated.

[0009] In a possible manner, the collision energy of the power battery is determined based on the energy feature and the energy peak time, including: determining the collision energy based on the energy feature, the energy peak time, and target information; wherein the target information at least includes any one of the following: a battery temperature of the power battery, a vehicle speed of the vehicle where the power battery is located.

[0010] According to the technical means, the calculation dimension of the collision energy can be expanded by introducing the target information such as the battery temperature and the vehicle speed, that is, the influence of the change of the material characteristics of the battery shell on the vibration wave propagation is corrected by using the temperature information, and the additional kinetic energy caused by the motion state of the vehicle is distinguished by using the vehicle speed information, and finally the estimation result of the collision energy can dynamically adapt to more complex real road conditions and battery states by comprehensively considering these multi-dimensional environmental parameters.

[0011] In a possible manner, the collision energy is determined based on the energy feature, the energy peak time, and the target information, including: determining an energy peak time difference between the at least two sensors and the target sensor based on the energy peak time; wherein the target sensor is one of the at least two sensors; determining the collision energy based on the energy feature, the energy peak time difference, the deployment positions of the at least two sensors respectively, and the target information.

[0012] According to the technical means, the absolute energy peak time of each sensor can be converted into a relative time difference based on the target sensor, so as to construct a time sequence relationship representing a collision wave propagation path, and then, by combining known sensor deployment position information, the abstract time difference can be mapped to a specific physical space relationship to accurately lock the collision point, and finally, the measured energy feature is positionally compensated and environmentally corrected through the propagation path loss, so that the accuracy of the collision energy estimation is significantly improved.

[0013] In a possible manner, the collision energy is determined based on the energy feature, the energy peak time difference, the respective deployment positions of the at least two sensors, and the target information, and includes: inputting the energy feature, the energy peak time difference, the respective deployment positions of the at least two sensors, and the target information into an energy prediction model to determine the collision energy predicted by the energy prediction model.

[0014] According to the technical means, the energy feature, the time difference, the sensor position, and the environmental information are collectively used as inputs to construct a high-dimensional feature space to comprehensively describe the physical nature of the collision event, and then the powerful nonlinear mapping capability of the energy prediction model is used to automatically learn the deep correlation between the complex features and the collision energy, and finally the comprehensive compensation and correction of various influencing factors (such as position attenuation, material characteristics, and environmental noise) are realized, so that the accuracy and generalization ability of the collision energy prediction are significantly improved.

[0015] In a possible manner, the collision energy determination method of the power battery further includes: obtaining a training sample set; wherein a training sample of the training sample set includes a sample collision energy, and a sample energy feature, a sample energy peak time difference, a sample deployment position of each of at least two sample sensors, and sample target information corresponding to the sample collision energy; and training the energy prediction model based on the training sample set.

[0016] According to the technical means, the training sample set containing the real collision energy and the corresponding multi-dimensional features (such as the sample energy feature, the time difference, and the position information) is constructed, so as to provide a data basis for the model to learn the collision physical law, and then through supervised training, the energy prediction model can automatically fit a high-precision nonlinear mapping relationship from the complex features to the collision energy from the data, and can accurately estimate the energy of the real collision event.

[0017] In a second aspect, the present application provides a collision energy determination device of a power battery, comprising: an acquisition unit and a determination unit; the acquisition unit is configured to acquire collision wave signals collected by at least two sensors in response to a collision of the power battery; wherein the at least two sensors are arranged at different positions of the power battery; the determination unit is configured to determine energy features and energy peak time corresponding to each of the at least two sensors based on the collision wave signals; and the determination unit is further configured to determine the collision energy of the power battery based on the energy features and the energy peak time.

[0018] In a possible manner, the energy features comprise integral energy, and the determination unit is specifically configured to: determine a power spectral density corresponding to the collision wave signal collected by a target sensor of the at least two sensors; and extract integral energy in a preset frequency band from the power spectral density.

[0019] In a possible manner, the energy features further comprise wavelet packet energy entropy, and the determination unit is specifically configured to: perform wavelet packet analysis on the collision wave signal collected by the target sensor, and determine the wavelet packet energy entropy corresponding to the target sensor.

[0020] In a possible manner, the determination unit is specifically configured to: determine the collision energy based on the energy features, the energy peak time, and target information; wherein the target information at least comprises any one of the following: a battery temperature of the power battery, and a vehicle speed of a vehicle in which the power battery is arranged.

[0021] In a possible manner, the determination unit is specifically configured to: determine an energy peak time difference between the at least two sensors and a target sensor based on the energy peak time; wherein the target sensor is one of the at least two sensors; and determine the collision energy based on the energy features, the energy peak time difference, the arrangement positions of the at least two sensors, and the target information.

[0022] In a possible manner, the determination unit is specifically configured to: input the energy features, the energy peak time difference, the arrangement positions of the at least two sensors, and the target information into an energy prediction model, to determine a collision energy predicted by the energy prediction model.

[0023] In a possible manner, the device further comprises a training unit, and the training unit is specifically configured to: acquire a training sample set; wherein a training sample of the training sample set comprises a sample collision energy, and sample energy features, a sample energy peak time difference, sample arrangement positions of at least two sensors, and sample target information corresponding to the sample collision energy; and train the energy prediction model based on the training sample set.

[0024] Thirdly, this application provides a vehicle, including: a collision energy determination device for a power battery, a power battery, and at least two sensors; wherein the at least two sensors are deployed at different locations on the power battery; the sensors are used to collect collision wave signals in response to a collision of the power battery and send the collision wave signals to the collision energy determination device for the power battery; the collision energy determination device for the power battery is used to perform the method as described in any of the first aspects to determine the collision energy of the power battery.

[0025] Fourthly, this application provides an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method described in the first aspect and any of its possible embodiments.

[0026] Fifthly, this application provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any of their possible implementations.

[0027] In a sixth aspect, this application provides a computer program product including computer instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[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 methods 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] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0031] Figure 1 This is a schematic diagram of the hardware structure of a collision energy calculation and determination system for a power battery, according to an exemplary embodiment. Figure 2 This is a schematic diagram illustrating a sensor deployment according to an exemplary embodiment; Figure 3 This is a flowchart illustrating a method for determining the collision energy of a power battery according to an exemplary embodiment; Figure 4 This is a schematic diagram of the structure of an energy prediction model according to an exemplary embodiment; Figure 5 This is a block diagram illustrating a collision energy determination device for a power battery according to an exemplary embodiment; Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment. 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] 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.

[0035] The collision energy method for power batteries provided in this application can be applied to vehicles. Vehicles can also be referred to as vehicles, mobile carriers, electric vehicles (EVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), fuel cell vehicles (FCVs), autonomous vehicles, intelligent and connected vehicles (ICVs), driverless vehicles, etc.

[0036] In this application, the vehicle can be a sedan, a sport utility vehicle (SUV), a truck, an electric vehicle, a tricycle, a special vehicle (such as an ambulance, fire truck, police car, etc.), a driverless taxi, an intelligent connected bus, an autonomous logistics vehicle, an electric truck, etc. Furthermore, this method is also applicable to various special-purpose vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, and port vehicles. This application does not impose specific limitations in this regard.

[0037] like Figure 1 As shown, Figure 1 A schematic diagram of the hardware structure of a system for calculating and determining the collision energy of a power battery is shown. The system may include a collision energy determination device 101, a sensor 102, and a power battery 103.

[0038] Figure 1 A communication connection can be established between the collision energy determination device 101 of the power battery 103 and the sensor 102. The sensor 102 can be deployed with the power battery 103. There can be at least two sensors 102.

[0039] Optionally, the number of sensors 102 can be at least two. For example, the number of sensors 102 can be six or eight. This application does not impose a specific limitation in this regard.

[0040] For example, such as Figure 2 As shown, three sensors 102 (a total of six) are arranged at the bottom of the outer casing on both the left and right sides of the power battery, and are linearly distributed to collect vibration signals generated at the moment of collision.

[0041] In practical applications, the collision energy determination device 101 of the power battery can be communicatively connected with one or more sensors 102.

[0042] Optional, Figure 1 The collision energy determination device 101 and sensor 102 of the power battery can be functional modules integrated into the same device, or they can be independently set devices. This application does not impose any restrictions on this.

[0043] It is easy to understand that when the collision energy determination device 101 and the sensor 102 of the power battery are functional modules integrated within the same device, the communication method between the collision energy determination device 101 and the sensor 102 of the power battery is the same as the communication method between modules within the device. In this case, the communication process between the two is the same as the communication process when the collision energy determination device 101 and the sensor 102 of the power battery are set up independently.

[0044] For ease of understanding, this application mainly uses the example of the independent configuration of the collision energy determination device 101 and the sensor 102 of the power battery.

[0045] Figure 1 The sensor 102 in the middle can respond to the collision of the power battery, collect the collision wave signal generated by the collision, and send the collision wave signal to the collision energy determination device 101 of the power battery.

[0046] The collision energy determination device 101 for a power battery can determine the energy characteristics and peak energy time of at least two sensors based on the collision wave signal, and then determine the collision energy of the power battery based on the energy characteristics and peak energy time.

[0047] Optionally, Figure 1 The collision energy determination device 101 for the power battery can be deployed on a terminal, a server, or other types of electronic devices. Figure 1 The diagram shown is merely an example of the device configuration of the collision energy determination device 101 for the power battery and does not constitute a limitation thereof.

[0048] When the collision energy determination device 101 for the power battery is deployed in a terminal, the terminal can be a device providing voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The terminal can communicate with one or more core networks via a radio access network (RAN). The terminal can be a mobile terminal, such as a computer with a mobile terminal, or a mobile device built into the collision energy calculation and determination system for the power battery, exchanging voice and / or data with the radio access network, such as a mobile phone, tablet, laptop, netbook, or personal digital assistant (PDA). This application does not impose any limitations on this.

[0049] When the collision energy determination device 101 for the power battery is deployed on a server, the server can be a single server or a server cluster consisting of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. This application does not impose any limitations in this regard.

[0050] It should be noted that the structures illustrated in the embodiments of this application do not constitute a limitation on the determination of the collision energy of the power battery. It 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.

[0051] Figure 3 This is a flowchart illustrating a method for determining the collision energy of a power battery according to an exemplary embodiment. The entity executing this method may be... Figure 1 The collision energy determination device 101 of the power battery in the middle, such as Figure 3 As shown, the method for determining the collision energy of the power battery includes the following steps: S301-S303.

[0052] S301, In response to a collision with the power battery, acquire collision wave signals collected by at least two sensors.

[0053] At least two sensors are deployed at different locations on the power battery.

[0054] In one possible implementation, the battery may be impacted or collided with by an external object or scraped against the chassis during vehicle use. Sensors deployed on the battery can collect vibration signals from the battery in real time.

[0055] For example, when a vehicle is traveling at high speed, stones kicked up by the tires can hit the bottom or side of the power battery at high speed, generating an instantaneous shock wave. When a vehicle passes over potholes or speed bumps, the power battery collides with ground protrusions (such as stones or the edge of manhole covers), causing continuous low-frequency vibrations.

[0056] Furthermore, the aforementioned execution entity can, in response to a collision with the power battery, acquire collision wave signals collected by at least two sensors and preprocess the collision wave signals collected by at least two sensors.

[0057] Specifically, the aforementioned execution entity can perform detrending and bandpass filtering on each collision wave signal to filter out collision wave signals outside the preset frequency band, that is, to filter out noise caused by low-frequency vibration of the vehicle body and high-frequency noise.

[0058] For example, the preset frequency band can be from 50 Hz to 2000 Hz.

[0059] S302. Based on the collision wave signal, determine the energy characteristics and energy peak time corresponding to at least two sensors.

[0060] Among them, energy characteristics include integral energy.

[0061] In one possible implementation, the process of determining the integral energy corresponding to at least two sensors includes: The aforementioned execution entity can determine the power spectral density corresponding to the collision wave signal collected by the target sensor among at least two sensors, and then extract the integrated energy in the preset frequency band from the power spectral density.

[0062] For example, the aforementioned execution entity can perform Fourier transform or Welch method on the collision wave signal collected by the target sensor to determine the power spectral density corresponding to the collision wave signal collected by the target sensor, and extract the integrated energy within the preset frequency band to form an energy feature vector E=[E1,E2,…,E6].

[0063] In the example above, the energy feature vector E=[E1,E2,…,E6] is used to represent six sensors. E can be used to characterize the energy feature vector. E1,E2,…,E6 can each be used to characterize the energy feature corresponding to one sensor.

[0064] In another possible implementation, the energy characteristics may also include wavelet packet energy entropy. The aforementioned execution entity performs wavelet packet analysis on the collision wave signal collected by the target sensor to determine the wavelet packet energy entropy corresponding to the target sensor. Furthermore, the aforementioned execution entity can determine the energy characteristics based on the integrated energy and the wavelet packet energy entropy.

[0065] S303. Based on energy characteristics and energy peak time, determine the collision energy of the power battery.

[0066] In one possible implementation, the aforementioned actuator can determine the energy peak time difference between at least two sensors and the target sensor based on the energy peak time. Then, the actuator can input the energy characteristics and the energy peak time difference into an energy prediction model to determine the collision energy predicted by the energy prediction model.

[0067] The target sensor is one of at least two sensors.

[0068] For example, the aforementioned execution entity can calculate the time delay correlation of each pair of sensor signals in at least two sensors through cross-correlation analysis, select a target sensor (e.g., the sensor that detects the energy peak earliest as a reference, and determine the energy peak time of the target sensor as 0), and define the energy peak arrival time difference of other sensors relative to this reference as Δt. i Finally, an energy peak time difference vector T=[Δt1, Δt2, Δt3, Δt4, Δt5, Δt6] containing multi-dimensional relative time delay features (e.g., using six sensors) is constructed to accurately characterize the differences in the propagation path of the collision wave within the power battery structure. Then, the aforementioned execution entity can concatenate the energy peak time difference vector and the energy feature vector, and input the concatenated vector into the energy prediction model to determine the collision energy predicted by the energy prediction model.

[0069] In another possible implementation, the aforementioned executing entity can determine the collision energy based on energy characteristics, the time difference of energy peaks, and target information.

[0070] The target information includes at least one of the following: the battery temperature of the power battery and the vehicle speed of the vehicle where the power battery is located.

[0071] Specifically, the aforementioned implementing entity can input energy characteristics, energy peak time difference, deployment locations of at least two sensors, and target information into the energy prediction model to determine the collision energy predicted by the energy prediction model.

[0072] Increased battery temperature can alter the yield strength and elastic modulus of the metal casing, as well as the brittleness of the internal cell separator. For example, aluminum casings may be more prone to plastic deformation at high temperatures, leading to changes in the absorption / transfer efficiency of impact energy within the structure. Energy prediction models can dynamically correct energy characteristics (such as vibration wave velocity and damping coefficient) based on temperature input, avoiding prediction biases in conventional models under low-temperature brittleness / high-temperature ductility scenarios. Furthermore, temperature anomalies may induce internal short circuits or thermal runaway within the battery, resulting in an additional component of electrochemical energy release mixed into the impact energy. By incorporating temperature into the energy prediction model, the superposition effect of mechanical impact energy and potential thermal runaway energy can be distinguished, enabling more accurate energy source tracing.

[0073] Furthermore, the propagation characteristics of collision waves (such as high-frequency component attenuation and low-frequency vibration modes) differ at different vehicle speeds. For example, high-speed collisions may generate more high-frequency shock waves, while low-speed scrapes are dominated by low-frequency deformation. The model can dynamically adjust signal processing strategies (such as filter bandwidth and feature extraction window) based on vehicle speed to more accurately predict the magnitude of collision energy.

[0074] Furthermore, by considering the deployment locations of at least two sensors and combining the time difference of energy peaks, energy loss during energy propagation can be accurately predicted, thereby accurately predicting collision energy.

[0075] For example, such as Figure 4 As shown, the structure of the energy prediction model includes an input layer, hidden layer 1, hidden layer 2, and an output layer.

[0076] Optionally, the input layer may include 12 nodes. Hidden layer 1 may include 128 nodes and employ the Rectified Linear Unit (ReLU) activation function. Hidden layer 2 may include 64 nodes. The output layer may include 1 node.

[0077] In one possible implementation, the training process for the energy prediction model includes: The aforementioned implementing entity can obtain a training sample set, and then, based on the training sample set, train an energy prediction model.

[0078] The training samples in the training sample set include sample collision energy, sample energy characteristics corresponding to the sample collision energy, sample energy peak time difference, deployment locations of at least two sensors for each sample, and sample target information.

[0079] Specifically, the aforementioned execution entity can input the sample energy features, peak time differences of sample energy, sensor deployment locations, and target information from the training sample set into the hidden layer network (hidden layer 1 and hidden layer 2) through the input layer to predict collision energy. This execution entity can quantify the difference between the predicted value and the actual collision energy of the sample using the mean squared error loss function, and dynamically adjust the network weights using the Adam optimizer (with adaptive learning rate). It then iteratively optimizes the model parameters using the backpropagation algorithm until the loss converges or a preset number of training epochs are reached, ultimately generating a deep learning model with high-precision collision energy prediction capabilities.

[0080] Optionally, the content included in the training sample set can be set according to actual needs. For example, the training samples in the training sample set may also include sample collision energy, as well as the sample energy characteristics corresponding to the sample collision energy and the peak time difference of the sample energy. This application does not impose specific limitations in this regard.

[0081] Based on the above technical solution, this application can collect collision wave signals by using two sensors deployed at different locations of the power battery. First, the spatial difference is used to obtain the energy peak time to determine the propagation path. Then, the energy loss due to the path is analyzed. Finally, the energy characteristics of each sensor are combined to compensate and correct the attenuated energy, thereby accurately calculating the true collision energy at the initial collision point.

[0082] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the collision energy determination device or electronic device for the power battery includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] This application embodiment can, based on the above method, exemplarily divide the collision energy determination device or electronic device for a power battery into functional modules. For example, the collision energy determination device or electronic device for a power battery may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0084] Figure 5 This is a block diagram illustrating a collision energy determination device for a power battery according to an exemplary embodiment. (Refer to...) Figure 5 The collision energy determination device for the power battery includes: an acquisition unit 501, a determination unit 502, and a training unit 503.

[0085] In one possible approach, the acquisition unit 501 is used to acquire collision wave signals collected by at least two sensors in response to a collision with the power battery; wherein the at least two sensors are deployed at different locations on the power battery; the determination unit 502 is used to determine the energy characteristics and energy peak time corresponding to each of the at least two sensors based on the collision wave signals; the determination unit 502 is also used to determine the collision energy of the power battery based on the energy characteristics and energy peak time.

[0086] Based on the above technical solution, this application can collect collision wave signals by using two sensors deployed at different locations of the power battery. First, the spatial difference is used to obtain the energy peak time to determine the propagation path. Then, the energy loss due to the path is analyzed. Finally, the energy characteristics of each sensor are combined to compensate and correct the attenuated energy, thereby accurately calculating the true collision energy at the initial collision point.

[0087] In one possible approach, the energy characteristics include integrated energy. The determining unit 502 is specifically used to: determine the power spectral density corresponding to the collision wave signal collected by the target sensor among at least two sensors; and extract the integrated energy in a preset frequency band from the power spectral density.

[0088] In one possible approach, the energy feature also includes wavelet packet energy entropy. The determining unit 502 is specifically used to: perform wavelet packet analysis on the collision wave signal collected by the target sensor to determine the wavelet packet energy entropy corresponding to the target sensor.

[0089] In one possible approach, the determining unit 502 is specifically used to: determine the collision energy based on energy characteristics, energy peak time, and target information; wherein the target information includes at least one of the following: the battery temperature of the power battery and the vehicle speed of the vehicle where the power battery is located.

[0090] In one possible approach, the determining unit 502 is specifically used to: determine the energy peak time difference between at least two sensors and a target sensor based on the energy peak time; wherein the target sensor is one of the at least two sensors; and determine the collision energy based on energy characteristics, the energy peak time difference, the deployment positions of the at least two sensors, and target information.

[0091] In one possible approach, the determining unit 502 is specifically used to: input energy characteristics, energy peak time difference, deployment locations of at least two sensors, and target information into the energy prediction model to determine the collision energy predicted by the energy prediction model.

[0092] In one possible approach, the training unit 503 is specifically used to: acquire a training sample set; wherein the training samples in the training sample set include sample collision energy, sample energy characteristics corresponding to the sample collision energy, sample energy peak time difference, deployment locations of at least two sensors for the sample, and sample target information; and train an energy prediction model based on the training sample set.

[0093] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0094] Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Figure 6 As shown, the electronic device includes, but is not limited to, a processor 601 and a memory 602.

[0095] The aforementioned memory 602 is used to store the executable instructions of the aforementioned processor 601. It is understood that the aforementioned processor 701 is configured to execute instructions to implement the method for determining the collision energy of the power battery in the above embodiment.

[0096] It should be noted that those skilled in the art will understand that Figure 6 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 6 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0097] Processor 601 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 602, and by calling data stored in memory 602, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 601 may include one or more processing units. Optionally, processor 601 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 601.

[0098] The memory 602 can be used to store software programs and various data. The memory 602 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0099] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 702 including instructions, which can be executed by a processor 601 of an electronic device to implement the methods in the above embodiments.

[0100] In actual implementation, Figure 5 The functions of the acquisition unit 501, determination unit 502, and training unit 503 can all be provided by... Figure 6 The processor 601 calls the computer program stored in the memory 602 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0101] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a CD-ROM, magnetic tape, a floppy disk, and an optical data storage device.

[0102] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by a processor 601 of an electronic device to perform the methods described above.

[0103] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of the electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0104] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0106] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0108] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0109] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the method for determining the collision energy of a power battery as described in the above method embodiments.

[0110] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method for determining the collision energy of a power battery in the method flow shown in the above method embodiments.

[0111] The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a register, a hard disk, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC). In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0112] Since the collision energy determination device for the power battery, the computer-readable storage medium, and the computer program product in the embodiments of this application can be applied to the above method, the technical effects that can be obtained can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.

[0113] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining the collision energy of a power battery, characterized in that, The method for determining the collision energy of the power battery includes: In response to a collision with the power battery, collision wave signals collected by at least two sensors are acquired; wherein the at least two sensors are deployed at different locations on the power battery. Based on the collision wave signal, determine the energy characteristics and energy peak time corresponding to each of the at least two sensors; The collision energy of the power battery is determined based on the energy characteristics and the energy peak time.

2. The method for determining the collision energy of a power battery according to claim 1, characterized in that, The energy characteristic includes integrated energy, and the process of determining the integrated energy corresponding to each of the at least two sensors includes: For the target sensor among the at least two sensors, determine the power spectral density corresponding to the collision wave signal collected by the target sensor; The integral energy in the preset frequency band is extracted from the power spectral density.

3. The method for determining the collision energy of a power battery according to claim 2, characterized in that, The energy feature also includes wavelet packet energy entropy, and the process of determining the wavelet packet energy entropy corresponding to each of the at least two sensors includes: Wavelet packet analysis is performed on the collision wave signal collected by the target sensor to determine the wavelet packet energy entropy corresponding to the target sensor.

4. The method for determining the collision energy of a power battery according to any one of claims 1-3, characterized in that, Determining the collision energy of the power battery based on the energy characteristics and the energy peak time includes: The collision energy is determined based on the energy characteristics, the energy peak time, and the target information; wherein the target information includes at least one of the following: the battery temperature of the power battery and the vehicle speed of the vehicle where the power battery is located.

5. The method for determining the collision energy of a power battery according to claim 4, characterized in that, Determining the collision energy based on the energy characteristics, the energy peak time, and the target information includes: Based on the energy peak time, the energy peak time difference between the at least two sensors and the target sensor is determined; wherein, the target sensor is one of the at least two sensors; The collision energy is determined based on the energy characteristics, the energy peak time difference, the deployment locations of the at least two sensors, and the target information.

6. The method for determining the collision energy of a power battery according to claim 5, characterized in that, The determination of the collision energy based on the energy characteristics, the energy peak time difference, the deployment locations of the at least two sensors, and the target information includes: The energy characteristics, the energy peak time difference, the deployment locations of the at least two sensors, and the target information are input into the energy prediction model to determine the collision energy predicted by the energy prediction model.

7. The method for determining the collision energy of a power battery according to claim 6, characterized in that, The method for determining the collision energy of the power battery further includes: Obtain a training sample set; wherein, the training samples in the training sample set include sample collision energy, as well as the sample energy characteristics corresponding to the sample collision energy, the peak time difference of the sample energy, the deployment positions of at least two sensors of the sample, and the sample target information; The energy prediction model is trained based on the training sample set.

8. A device for determining the collision energy of a power battery, characterized in that, The collision energy determination device for the power battery includes: an acquisition unit and a determination unit; The acquisition unit is used to acquire collision wave signals collected by at least two sensors in response to a collision with the power battery; wherein the at least two sensors are deployed at different locations on the power battery. The determining unit is used to determine the energy characteristics and energy peak time corresponding to each of the at least two sensors based on the collision wave signal. The determining unit is further configured to determine the collision energy of the power battery based on the energy characteristics and the energy peak time.

9. A vehicle, characterized in that, The vehicle includes: a collision energy determination device for a power battery, a power battery, and at least two sensors; wherein the at least two sensors are deployed at different locations on the power battery; The sensor is used to collect collision wave signals in response to a collision with the power battery, and to send the collision wave signals to the collision energy determination device of the power battery. The collision energy determination device for the power battery is used to perform the method as described in any one of claims 1 to 7 to determine the collision energy of the power battery.

10. An electronic device, characterized in that, include: Collision energy determination device for power batteries processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 7.

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