CN115817283A - New energy automobile battery monitoring method and system - Google Patents
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Abstract
The embodiment of the specification provides a method and a system for monitoring a new energy automobile battery, wherein the method comprises the following steps: acquiring environmental temperature data and battery temperature data of a battery; and determining a monitoring early warning scheme and an auxiliary processing scheme based on the environmental temperature data and the battery temperature data.
Description
Technical Field
The specification relates to the field of new energy automobile batteries, in particular to a new energy automobile battery monitoring method and system.
Background
The new energy automobile has the unique advantages of environmental protection, energy conservation, emission reduction and the like, and is widely applied. But the new energy automobile battery is also susceptible to temperature. When the temperature is too high or too low, the battery is affected, and the service life of the battery is shortened, and safety accidents such as battery explosion may even be caused.
Therefore, it is desirable to provide a new energy automobile battery monitoring method and system, which can monitor and manage the temperature of the battery in real time, so as to improve the cruising ability of the new energy automobile and the service life of the battery, and meet the user requirements.
Disclosure of Invention
One or more embodiments of the present specification provide a new energy vehicle battery monitoring method. The new energy automobile battery monitoring method comprises the following steps: acquiring environmental temperature data and battery temperature data of a battery; and determining a monitoring early warning scheme and an auxiliary processing scheme based on the environment temperature data and the battery temperature data.
One or more embodiments of the present specification provide a new energy vehicle battery monitoring system, including: the device comprises an acquisition module and a determination module; the acquisition module is used for acquiring environmental temperature data of the battery and battery temperature data; the determining module is used for determining a monitoring early warning scheme and an auxiliary processing scheme based on the environment temperature data and the battery temperature data.
One or more embodiments of the present specification provide a new energy vehicle battery monitoring device, which includes at least one processor and at least one memory; the at least one memory is for storing computer instructions; and the at least one processor executes at least part of the computer instructions to realize the new energy automobile battery monitoring method.
One or more embodiments of the present specification provide a computer-readable storage medium, which stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the new energy vehicle battery monitoring method.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
fig. 1 is a schematic view of an application scenario of a new energy vehicle battery monitoring system according to some embodiments of the present disclosure;
FIG. 2 is an exemplary block diagram of a new energy vehicle battery monitoring system according to some embodiments herein;
fig. 3 is an exemplary flow diagram of a new energy vehicle battery monitoring method according to some embodiments herein;
FIG. 4 is an exemplary schematic diagram illustrating determining heating power of a heating device according to some embodiments herein;
FIG. 5 is an exemplary schematic diagram of a warming model according to some embodiments herein;
FIG. 6 is an exemplary schematic diagram of a cooling model, as shown in some embodiments herein.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or stated otherwise, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to or removed from these processes.
Fig. 1 is a schematic view of an application scenario of a new energy vehicle battery monitoring system according to some embodiments of the present disclosure.
As shown in fig. 1, an application scenario 100 of the new energy vehicle battery monitoring system may include a new energy vehicle 110, a processor 120, a storage device 130, a user terminal 140, and a network 150.
The new energy vehicle 110 may include various types of vehicles. For example, the new energy vehicle 110 may be a sedan, an SUV, a van, or the like. The new energy vehicle 110 may include a battery system 110-1 composed of various devices related to a battery of the vehicle, and a new energy vehicle body 110-2. The battery system 110-1 is a power source for starting and running of the new energy automobile. For example, the battery system 110-1 may be a lead-acid battery system, a nickel metal hydride battery system, a lithium ion battery system, or the like. The battery system 110-1 of the new energy vehicle 110 may be part of a new energy vehicle battery monitoring system. The processor 120 may acquire the ambient temperature data of the battery and the battery temperature data through the battery system 110-1 of the new energy vehicle.
The processor 120 may be configured to perform one or more of the functions disclosed in one or more embodiments of the present disclosure. For example, the processor 120 may be configured to determine a monitoring precaution scheme and an auxiliary treatment scheme based on the ambient temperature data and the battery temperature data. For another example, the processor 120 may be configured to determine a predicted operating voltage of the battery via a temperature-increasing model based on the ambient temperature data, the preset heating power, and the heat release data of the battery, and determine the heating power of the heating device based on the predicted operating voltage.
In some embodiments, processor 120 may include one or more processing engines (e.g., a single chip processing engine or a multi-chip processing engine). Merely by way of example, the processor 120 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), a Physical Processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.
The storage device 130 may be used to store data and/or instructions related to the application scenario 100 of the new energy vehicle monitoring system. In some embodiments, storage device 130 may store data and/or information obtained from new energy vehicle 110, processor 120, and/or the like. For example, the storage device 130 may store ambient temperature data, battery temperature data, heat release data for a battery, and the like.
The user terminal 140 may refer to one or more terminal devices or software used by the new energy vehicle driver. In some embodiments, the user terminal 140 may include a mobile device, a tablet, a laptop, a desktop, etc., or any combination thereof. In some embodiments, the processor 120 may interact with the new energy vehicle driver through the user terminal 140. The above examples are intended only to illustrate the broad scope of the user terminal and not to limit its scope.
The network 150 may connect the various components of the system and/or connect the system with external resource components. The network 150 enables communication between the various components and with other components outside the system to facilitate the exchange of data and/or information. For example, processor 120 may retrieve ambient temperature data and battery temperature data from storage device 130 via network 150.
In some embodiments, the network 150 may be any one or more of a wired network or a wireless network. For example, network 150 may include a cable network, a fiber optic network, a telecommunications network, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network (ZigBee), near Field Communication (NFC), an in-device bus, an in-device line, a cable connection, and the like, or any combination thereof. The network connection between the parts can be in one way or in multiple ways. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies.
It should be noted that the application scenario 100 of the new energy vehicle battery monitoring system is provided for illustrative purposes only and is not intended to limit the scope of the present application. It will be apparent to those skilled in the art that various modifications and variations can be made in light of the description herein. For example, the application scenario 100 of the new energy vehicle battery monitoring system may implement similar or different functions on other devices. However, such changes and modifications do not depart from the scope of the present application.
Fig. 2 is an exemplary block diagram of a new energy vehicle battery monitoring system according to some embodiments of the present disclosure. As shown in fig. 2, the new energy vehicle battery monitoring system 200 includes an obtaining module 210 and a determining module 220.
In some embodiments, the acquisition module 210 may be used to acquire ambient temperature data and battery temperature data of the battery.
In some embodiments, the determination module 220 may be configured to determine a monitoring and warning scheme and an auxiliary treatment scheme based on the ambient temperature data and the battery temperature data. The specific contents of the monitoring and early warning scheme and the auxiliary processing scheme can be detailed in the description of fig. 3.
In some embodiments, the determining module 220 may be further configured to determine whether the battery temperature data satisfies a first preset condition; and responding to the battery temperature data meeting a first preset condition, and determining an auxiliary treatment scheme, wherein the auxiliary treatment scheme is that the battery is subjected to heat preservation treatment through a heating device.
In some embodiments, the determining module 220 may be further configured to determine the predicted operating voltage of the battery through a temperature-rising model based on the battery temperature data, the preset heating power, and the heat release data of the battery, wherein the temperature-rising model is a machine learning model; based on the predicted operating voltage, a heating power of the heating device is determined.
In some embodiments, the warming model may further include a first embedding layer through which the ambient temperature feature is determined based on the ambient temperature data, wherein the ambient temperature feature is an input to the first determining layer of the warming model.
In some embodiments, the heating power of the heating device is related to the remaining charge of the battery and/or the charge usage rate of the battery.
In some embodiments, the determining module 220 may be further configured to determine whether the battery temperature data satisfies a second preset condition; and determining an auxiliary treatment scheme in response to the battery temperature data meeting a second preset condition, wherein the auxiliary treatment scheme is to perform cooling treatment on the battery through a cooling device.
In some embodiments, the determining module 220 may be further configured to determine predicted battery temperature data of the battery through a cooling model based on the battery temperature data, a preset cooling power, and heat release data of the battery, wherein the cooling model is a machine learning model; based on the predicted battery temperature data, a cooling power of the cooling device is determined.
In some embodiments, the cooling model may include a second input layer, wherein the second input layer shares parameters with the first input layer of the warming model.
In some embodiments, the cooling model may further include a second embedding layer that shares parameters with the first embedding layer of the warming model.
It should be understood that the system and its modules shown in FIG. 2 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware.
It should be noted that the above description of the new energy vehicle battery monitoring system and the module thereof is only for convenience of description, and the description is not limited to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the obtaining module and the determining module disclosed in fig. 2 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.
Fig. 3 is an exemplary flowchart of a new energy vehicle battery monitoring method according to some embodiments described herein. In some embodiments, the process 300 may be performed by the processor 120. The process 300 may include the following steps:
The ambient temperature data may be data relating to the ambient temperature around the battery. The ambient temperature data may be the ambient temperature at a plurality of different points in time. The plurality of time points may be a plurality of time points in succession. The ambient temperature data may include an ambient temperature, an ambient temperature ramp rate, and the like. For example, the ambient temperature data may include "the ambient temperature is 30 ℃ around the current time point of the in-vehicle position where the battery of the new energy automobile is located, the ambient temperature increase rate is 3 ℃/h, and the like".
The battery temperature data may be data relating to the temperature of the battery itself. The battery temperature data may be battery temperatures at a plurality of different points in time. The plurality of time points may be a plurality of time points in succession. The battery temperature data may include a battery temperature, a battery temperature ramp rate, and the like. For example, the battery temperature data may include "the battery temperature at the current time point of the new energy vehicle is 40 ℃ and the ambient temperature rise rate is 5 ℃/h".
In some embodiments, the obtaining module 210 may obtain the ambient temperature data, the battery temperature data, based on a variety of ways. For example, if the battery of the new energy automobile is arranged in the trunk, the environmental temperature data may be acquired by a temperature sensor installed in the trunk of the new energy automobile. The battery temperature data may be acquired by a temperature sensor mounted on an outer surface of the battery. The temperature sensor may be a thermocouple, an RTD (resistance temperature detector), a thermistor, a semiconductor-based Integrated Circuit (IC), and the like.
And step 320, determining a monitoring early warning scheme and an auxiliary processing scheme based on the environmental temperature data and the battery temperature data. In some embodiments, step 320 may be performed by determination module 220.
The monitoring and early warning scheme can be used for determining whether to send out early warning notification to a driver or not and how to send out early warning notification to the driver when the temperature of the battery is too high or too low. For example, the monitoring and warning scheme may include that when the battery temperature is too high, 50 ℃, normal use of the battery is affected, and it is determined that a warning notification is sent to the driver, the warning notification may send a voice alarm that the battery temperature is too high through a central control of the vehicle, and may also send an alarm message to a user terminal (e.g., a mobile phone) of the driver.
The auxiliary treatment scheme may refer to determining whether to treat the battery, and whether to perform a battery warm-keeping treatment or a cooling treatment when the temperature of the battery is too high or too low. For example, the auxiliary treatment scheme may include determining that the battery is to be treated when the temperature of the battery is 50 ℃ too high, which affects normal use of the battery, and performing a cooling treatment on the battery to cool the battery.
In some embodiments, the determining module 220 may determine whether the environmental temperature data and/or the battery temperature data satisfy a preset condition, where the preset condition may refer to whether the battery needs to be heated or thermally insulated in a low-temperature or high-temperature environment; and determining a monitoring early warning scheme and an auxiliary processing scheme in response to the environmental temperature data and/or the battery temperature data meeting preset conditions. For example, if the battery temperature is 50 ℃ and the preset condition is that the maximum temperature threshold is 40 ℃, and the battery temperature meets the preset condition, the monitoring and early warning scheme and the auxiliary processing scheme are determined to be used. In some embodiments, if the battery temperature is 30 ℃, the preset condition is that the maximum temperature threshold is 40 ℃, the minimum temperature threshold is 0 ℃, and the battery temperature data does not meet the preset condition, the monitoring and early warning scheme and the auxiliary processing scheme are not used.
In some embodiments of the present specification, by monitoring the environmental temperature data of the new energy vehicle battery and the change condition of the battery temperature data, when the battery temperature is too high or too low, it is determined whether to start the auxiliary processing scheme to perform heat preservation or cooling processing on the battery, and what heat preservation or cooling power is used, so as to improve the vehicle endurance to a certain extent and meet the user demand.
In some embodiments, the determination module 220 may determine whether the battery temperature data satisfies a first preset condition; and responding to the battery temperature data meeting a first preset condition, and determining an auxiliary treatment scheme, wherein the auxiliary treatment scheme is that the battery is subjected to heat preservation treatment through a heating device.
The first preset condition may refer to a condition that the battery needs to be subjected to a heat-insulating treatment in a low-temperature environment. The first preset condition may include any one of a minimum temperature threshold reached by the battery temperature, a cooling rate threshold reached by the battery cooling rate, and the like. The minimum temperature threshold may refer to a minimum battery temperature at which the battery can operate normally, for example, the minimum temperature threshold may be 5 ℃. The cooling rate threshold may refer to a maximum cooling rate of the battery temperature at which the battery can normally operate, for example, the cooling rate threshold is 5 ℃/h. For example, the first preset condition may be that the battery temperature cooling rate in the battery temperature data is greater than a cooling rate threshold (e.g., 5 ℃/h, etc.), or the battery temperature in the battery temperature data is less than a minimum temperature threshold (e.g., 5 ℃) or the like.
In some embodiments, the actual minimum temperature or maximum cool down rate at which the battery can operate properly may be determined empirically. And taking the actual lowest temperature or the maximum cooling rate as a first preset condition.
The heating device can be a device for carrying out heat preservation treatment on the new energy automobile battery. For example, the heating device may be an external air conditioner, and the temperature of the outer surface of the battery is raised by blowing hot air through the external air conditioner. For example, the heating device may be a PTC (Positive Temperature Coefficient) heater, a silicon heating film, a flexible electric heating film, or the like. This is not limited by the present description.
In some embodiments, the heat preservation processing means that when the temperature data of the new energy automobile battery meets a first preset condition, the heating device uses the new energy automobile battery as a power supply to heat and preserve heat of the outer surface of the battery so that the battery can work normally.
For example, the battery temperature data is 0 ℃, the first preset condition is that the minimum temperature threshold is 5 ℃, and then the battery temperature data 0 ℃ is less than the minimum temperature threshold 5 ℃, so that the first preset condition is met. The determination module 220 may determine to use an auxiliary treatment scheme for the battery, wherein the auxiliary treatment scheme is to perform a heat preservation treatment on the battery through a heating device.
In some embodiments, the determination module 220 may determine the heating power of the heating device by analyzing the ambient temperature data and the battery temperature data through modeling or various data analysis algorithms. The data analysis algorithm may include a plurality of algorithms such as regression analysis, discriminant analysis, vector matching, statistical analysis, and the like.
In some embodiments, the determination module 220 may determine a predicted operating voltage of the battery through a temperature rise model based on the battery temperature data, the preset heating power, and the heat release data of the battery, and then determine the heating power of the heating device based on the predicted operating voltage. For the above detailed description, reference may be made to the description of fig. 4 in the specification.
In some embodiments of the present description, when the temperature data of the battery meets the first preset condition, the temperature of the battery is timely raised, so that the battery failure and the automobile failure can be effectively avoided, and the service life of the battery is prolonged. According to a first preset condition preset by practical experience, whether the heating device is started to carry out heat preservation treatment on the battery or not is determined, whether the battery needs to be heated or not can be accurately determined, and the use requirement of a user is met.
In some embodiments, the determination module 220 may determine whether the battery temperature data satisfies a second preset condition; and determining an auxiliary treatment scheme in response to the battery temperature data meeting a second preset condition, wherein the auxiliary treatment scheme is to perform cooling treatment on the battery through a cooling device.
The second preset condition may refer to a condition under which the battery needs to be cooled under a high temperature environment. The second preset condition may include any one of a maximum temperature threshold reached by the battery temperature, a temperature increase rate threshold reached by the battery temperature increase rate, and the like. The maximum temperature threshold may refer to a maximum battery temperature at which the battery can operate normally, for example, the maximum temperature threshold may be 40 ℃. The temperature-rise rate threshold may refer to a maximum temperature-rise rate of the battery at which the battery can operate normally, for example, the temperature-rise rate threshold is 5 ℃/h. For example, the second preset condition may be that the temperature rise rate of the battery temperature in the battery temperature data is greater than a temperature rise rate threshold (e.g., 5 ℃/h, etc.), or the battery temperature in the battery temperature data is greater than a maximum temperature threshold (e.g., 40 ℃, etc.), and so on.
In some embodiments, the actual maximum temperature or maximum rate of temperature increase at which the battery can operate properly may be determined empirically. The actual maximum temperature or maximum rate of temperature rise is taken as a second preset condition.
The cooling device may refer to a device for cooling a new energy automobile battery. For example, the cooling device may be an air-conditioning cycle cooling type, a water cooling type, and an air cooling type. This is not limited by the present description.
In some embodiments, the cooling process means that when the temperature data of the new energy vehicle battery meets a second preset condition, the cooling device uses the new energy vehicle battery as a power supply to cool the outer surface of the battery so that the battery can work normally.
For example, if the battery temperature data is 50 ℃, the first preset condition is a maximum temperature threshold of 40 ℃, the battery temperature data is 50 ℃ higher than the maximum temperature threshold of 40 ℃, the second preset condition is met, and an auxiliary treatment scheme for cooling the battery by using a cooling device is determined.
In some embodiments, the determination module 220 may perform analysis processing on the ambient temperature data and the battery temperature data by modeling or using various data analysis algorithms to determine a reasonable cooling operation power of the cooling device. The data analysis algorithm may include a plurality of algorithms such as regression analysis, discriminant analysis, vector matching, statistical analysis, and the like.
In some embodiments, the determination module 220 may determine predicted battery temperature data of the battery through a cooling model based on the battery temperature data, a preset cooling power, and heat release data of the battery, and determine the cooling power of the cooling device based on the predicted battery temperature data. For the above detailed description, reference may be made to the description of fig. 6 in the specification.
In some embodiments of the present description, by cooling the battery in time, the occurrence of battery failure and vehicle failure can be effectively avoided, and the service life of the battery can be prolonged.
It should be noted that the above description of the process 300 is for illustration and description only and is not intended to limit the scope of the present disclosure. Various modifications and changes to flow 300 will be apparent to those skilled in the art in light of this description. However, such modifications and variations are intended to be within the scope of the present description. For example, battery temperature data is acquired, and a monitoring and early warning scheme and an auxiliary processing scheme are directly determined based on the battery temperature data.
FIG. 4 is an exemplary diagram illustrating determining heating power of a heating device according to some embodiments herein.
In some embodiments, the determination module 220 may determine the heating power of the heating device based on a warming model. The determination module 220 may determine the predicted operating voltage of the battery through a temperature-increasing model based on the battery temperature data, the preset heating power, the heat release data of the battery, and the like. The determination module 220 may determine the heating power of the heating device based on the predicted operating voltage. The relevant description of the battery temperature data can be found in relation to fig. 3.
The heating power is the working power of the heating device when the heating device actually carries out heat preservation treatment on the battery. For example, the heating power may be 50W or the like. For a description of the heating device, reference is made to fig. 3.
The preset heating power refers to the working power of the heating device preset in advance. For example, the preset heating power may be 50W, 60W, or the like.
The heat release data of the battery refers to heat release data generated by converting part of electric energy into heat energy when the battery works. For example, the exotherm data for the cell may be a kilojoules per hour.
In some embodiments, the preset heating power of the heating device may be preset artificially based on the actual driving time of the new energy vehicle, the ambient temperature data, and the like. For example, the lower the ambient temperature, the higher the preset heating power, etc. Also for example, the longer the actual travel time, the smaller the preset heating power, and the like.
In some embodiments, the determination module 220 may determine heat release data for the battery based on existing data. For example, the determination module 220 may determine the energy conversion efficiency of the battery based on existing experimental data, existing actual driving data, and the like, and further determine heat release data of the battery. For example, when other conditions such as the rated voltage are the same, the higher the energy conversion efficiency is, the more electric energy is generated, and the smaller the heat release data of the corresponding battery is.
The predicted operating voltage refers to a predicted possible operating voltage of the battery during driving of the vehicle. For example, the predicted operating voltage may be 400V or the like. In some embodiments, the operating voltage of the battery may also be correlated to ambient temperature data. For example, when the ambient temperature decreases, the battery output voltage under the same condition may decrease. For a description of the correlation between the ambient temperature data and the predicted operating voltage, reference may be made to fig. 5 and its related parts.
As shown in fig. 4, the heating power of the heating device may be determined based on the following steps:
and step 410, determining the predicted working voltage of the battery through a temperature rising model based on the temperature data of the battery, the preset heating power and the heat release data of the battery.
The temperature increase model 412 is a model for predicting the operating voltage of the battery during running of the new energy vehicle. In some embodiments, the warming model may be a machine learning model. For example, the warming model may include a Recurrent Neural Network (RNN), a Neural Network (NN), a Deep Neural Network (DNN), and the like, or any combination thereof.
In some embodiments, the warming model 412 may determine the predicted operating voltage 413 based on the battery temperature data 411-1, the preset heating power 411-2, the heat release data 411-3 of the battery, and so on.
In some embodiments, the inputs 411 of the warming model may include battery temperature data 411-1, preset heating power 411-2, and heat release data 411-3 of the battery, among others, and the outputs may include a predicted operating voltage 413.
In some embodiments, the warming model may be trained based on a large amount of historical data. The historical data may include a first set of training samples and a first training label. In some embodiments, the historical data may be acquired by manual collection based on historical driving data of the new energy vehicle. Each training sample in the first set of training samples comprises historical battery temperature data, historical preset heating power, historical heat release data of a battery and the like at the same historical time point. Each of the first training labels includes a historical operating voltage of the battery corresponding to each set of training samples. The training process comprises the following steps: inputting a first group of training samples with first training labels into a temperature-rising model without set parameters; and iteratively updating parameters of the heating model based on the loss function until the conditions that the loss function is smaller than a threshold value, convergence is achieved, or the training period reaches the threshold value and the like are met, and obtaining the trained heating model.
In other embodiments of the present description, there may be an elevated temperature model comprising a plurality of layers. For a related description of the temperature increase model including a plurality of layers, refer to fig. 5 and the related description thereof.
Based on the predicted operating voltage, a heating power of the heating device is determined, step 420.
In some embodiments, the determination module 220 may determine the heating power of the heating device from a plurality of preset heating powers based on a first preset rule. Wherein the first preset rule may be to select a minimum value among the preset heating powers, etc. For example, the determination module 220 may determine a minimum value of the plurality of preset powers as the heating power of the heating device.
In some embodiments, the determination module 220 may also determine the heating power of the heating device based on a plurality of predicted operating voltages corresponding to a plurality of different preset heating powers. In some embodiments, the determining module 220 may select a preset heating power corresponding to the predicted operating voltage closest to the first preset voltage as the heating power of the heating device. The first preset voltage can be a voltage at which the new energy automobile can normally and stably run. For example, the first preset voltage may be a middle value of a voltage range in which the battery normally operates, or the like. In some embodiments, the first preset voltage may be determined based on a voltage range in which the battery normally operates. For example, when the voltage of the battery is 320V to 380V during normal operation, the first preset voltage may be 350V, etc. The first preset voltage may also be determined based on various ways, which are not limited herein.
In some embodiments, the determining module 220 predicts the operating voltage of the battery through the temperature-rising model, and further determines the heating power of the heating device, so that the heating efficiency of the battery can be improved, and the tedious operation of manual repeated adjustment is avoided.
In some embodiments, the heating power of the heating device may also be related to the remaining charge of the battery and/or the charge usage rate of the battery.
The remaining capacity of the battery may be a percentage of the remaining capacity to the total capacity of the battery. For example, the remaining battery capacity may be 70%, etc. In some embodiments, the remaining battery power may be obtained through a dashboard or the like.
The charge usage rate of the battery may be a percentage of the total charge of the battery used per unit time (e.g., 1 hour, etc.). For example, the charge usage rate of the battery may be 10%/h, etc. In some embodiments, the charge usage rate of the battery may be obtained based on existing experimental data, existing actual driving data, and the like.
In some embodiments, the heating power of the heating device may also be related to the remaining charge of the battery and/or the charge usage rate of the battery. For example, the determining module 220 may determine the relationship between the heating device and the remaining capacity of the battery and/or the capacity usage rate of the battery through the second preset voltage. The second preset voltage is a battery working voltage capable of reducing the influence on the cruising ability of the new energy automobile.
In some embodiments, when the remaining capacity of the battery is low, the second preset voltage may be set to a lower value of the normal operating voltage range of the battery. For example, when the voltage range in which the battery normally operates is 320V to 380V, the second preset voltage may be 330V, etc.
In some embodiments, the second predetermined voltage may be set to a lower value of the normal operating voltage range of the battery when the charge usage rate of the battery is above a first predetermined threshold (e.g., 20%/h). The first preset threshold may refer to a maximum value of a power usage rate of the battery. For example, when the voltage range in which the battery normally operates is 320V to 380V, the second preset voltage may be 330V, etc.
In some embodiments, the determining module 220 may select a preset heating power corresponding to the predicted operating voltage closest to the second preset voltage as the heating power of the heating device.
In some embodiments, the heating power of the heating device is related to the remaining capacity of the battery and/or the capacity usage rate of the battery, so that the battery can be heated while the influence on the cruising ability of the automobile is reduced, and the requirement of a user can be better met.
FIG. 5 is an exemplary schematic diagram of a warming model according to some embodiments described herein. As shown in fig. 5, the warming model 500 may include a first input layer 510, a first determination layer 530, and the like.
The first input layer 510 may be used to extract battery temperature characteristics. In some embodiments, the first input layer may be a machine learning model, such as a Long Short-Term Memory network model (LSTM) or the like. In some embodiments, the input to the first input layer may include battery temperature data 411-1. The battery temperature data 411-1 may include temperature data at a plurality of time points. The output of the first input layer may include a battery temperature characteristic 510-1. For a related explanation of the battery temperature data, reference may be made to fig. 3.
The battery temperature profile 510-1 refers to a profile corresponding to battery temperature data. The battery temperature characteristic may include a temperature characteristic, a temperature change characteristic, a maximum temperature characteristic, etc. at a plurality of different time points. In some embodiments, the temperature at successive time points may be included in the temperature profile at the plurality of different time pointsA changing characteristic. The battery temperature characteristic may be represented by a temperature characteristic vector. E.g. temperature eigenvectors (T) 1 ，T 2 ，T 3 ，…，T n ) The battery temperature characteristics at different points in time may be indicated. Wherein, T 1 ，T 2 ，T 3 ，…，T n Respectively showing the temperature data of the batteries corresponding to the 1 st, the 2 nd, the 3 rd, \8230;, and the nth time points. The highest temperature feature may refer to a feature vector corresponding to the largest battery temperature data among the plurality of battery temperature data. For example, the maximum temperature characteristic may be T max . The temperature change characteristic may refer to a characteristic vector of a change in the battery temperature data at a plurality of time points. For example, the temperature variation characteristic may be (+ 5 ℃, -1 ℃, \ 8230;) or the like. Wherein different elements in the temperature change characteristic represent the change situation of the battery temperature data at different time points. For example, +5 ℃ means that the cell temperature increased by 5 ℃ at the 1 st time point, and-1 ℃ means that the cell temperature decreased by 1 ℃ at the 2 nd time point, etc.
The first determination layer 530 may be used to predict an operating voltage of the battery. In some embodiments, the first determination layer may be a machine learning model, such as a recurrent neural network model, a deep neural network model, or the like, or any combination thereof. In some embodiments, the inputs to the first determination layer may include a battery temperature signature 510-1, a preset heating power 411-2, heat release data 411-3 for the battery, etc., and the output may include a predicted operating voltage 413. For a detailed description of the preset heating power, the heat release data of the battery, and the predicted operating voltage, reference may be made to fig. 4.
In some embodiments, the warming model may further include a first embedding layer to determine an ambient temperature characteristic based on the ambient temperature data. Wherein the ambient temperature characteristic may be used as an input to a first determining layer of the warming model.
The first embedding layer 520 may be used to obtain ambient temperature characteristics. In some embodiments, the first embedding layer may be a long-short term memory network model or the like. In some embodiments, the input to the first embedding layer includes ambient temperature data 520-1 and the output includes ambient temperature signature 520-2. For a detailed description of the ambient temperature data, reference may be made to fig. 3.
The ambient temperature signature 520-2 refers to a signature corresponding to ambient temperature data. In some embodiments, the ambient temperature feature may be a feature vector comprising ambient temperature data for a plurality of different points in time. Different elements in the ambient temperature signature represent ambient temperature data corresponding to different points in time.
In some embodiments, the accuracy of the model output may be improved by taking into account the effect of ambient temperature data on the battery operating voltage.
In some embodiments, the warming model may be determined by a first input layer, a first determination layer, and a combination training.
In some embodiments, each of the second set of training samples of the warming model may include historical battery temperature data, historical heating power, historical heat release data for the battery, and the like, for historical same points in time. In some embodiments, each of the second training labels of the warming model may include a historical operating voltage of the battery corresponding to each set of training samples, and the like. In some embodiments, the second set of training samples and the second training labels may be obtained based on data associated with historical driving of the vehicle.
In some embodiments, the cell temperature characteristic output by the first input layer may be used as an input to the first determination layer. The process of joint training may include: taking historical battery temperature data in the second set of training samples as input to the first input layer; the battery temperature characteristics output by the first input layer and the historical heating power in the second group of training samples, the heat release data of the historical batteries and the like are used as the input of the first determination layer to determine the output of the temperature rising model; inputting the predicted working voltage output by the heating model and a second training label into a loss function; and iteratively updating the heating model based on the loss function until the conditions that the loss function is smaller than a threshold value, convergence is achieved, or the training period reaches the threshold value and the like are met, and obtaining the trained heating model.
In some embodiments, the training of the warming model further comprises training of the first embedding layer. In some embodiments, the first embedding layer may be determined based on the embedding layer sample and the embedding layer tag. In some embodiments, the embedded layer sample may include historical ambient temperature data and the embedded layer tag may include historical ambient temperature characteristics. The training process comprises the following steps: and inputting the embedded layer sample with the embedded layer label into the first embedded layer, and updating the parameters of the first embedded layer through training until the conditions that the loss function is smaller than the threshold value, the convergence is realized, or the training period reaches the threshold value and the like are met.
In some embodiments, the first embedding layer may also train the determination jointly with the first input layer, the first determination layer. The embedded layer samples and embedded layer labels may be in a one-to-one correspondence with the second set of training samples and second training labels. For example, each of the plurality of training samples and labels is historical data for the same historical point in time. In some embodiments, the ambient temperature characteristic of the first embedded layer output may be an input to the first determining layer. The process of joint training may include: taking historical battery temperature data in the second group of training samples as input of the first input layer; taking historical ambient temperature data of the same time point in the embedding layer sample as an input of a first embedding layer; the battery temperature characteristic output by the first input layer, the environment temperature characteristic output by the first embedded layer, historical heating power of the same time point in the second group of training samples, heat release data of historical batteries and the like are used as the input of the first determination layer to determine the output of the temperature rise model; inputting the predicted working voltage output by the heating model and a second training label into a loss function; and iteratively updating the heating model based on the loss function until the conditions that the loss function is smaller than a threshold value, convergence is achieved, or the training period reaches the threshold value and the like are met, and obtaining the trained heating model.
In some embodiments, the first input layer and the first determination layer are jointly trained to improve the accuracy of the obtained output of the warming model.
In some embodiments, the temperature-increasing model includes a plurality of layers, such as a first embedding layer, a first input layer, and a first determining layer, which may better predict the operating voltage of the battery.
FIG. 6 is an exemplary schematic diagram of a cooling model according to some embodiments herein.
In some embodiments, when the battery temperature data satisfies the second preset condition, the determining module 220 may control the cooling device to perform a cooling process on the battery. For a related description of the second preset condition, refer to fig. 3 and its related parts.
In some embodiments, the determination module 220 may determine the cooling power of the cooling device based on a cooling model. The determination module 220 may determine the predicted battery temperature data through a cooling model based on the battery temperature data, a preset cooling power, heat release data of the battery, and the like. The determination module 220 may determine the cooling power of the cooling device based on the predicted battery temperature data. For a related explanation of the battery temperature data, reference may be made to fig. 3. A related explanation of the heat release data for the cell can be seen in fig. 4.
The cooling power is an operation power when the cooling device actually cools the battery. For example, the cooling power may be 60W or the like. For a detailed description of the cooling device, reference may be made to fig. 3.
The preset cooling power refers to the working power of the cooling device preset in advance. For example, the preset cooling power may be 40W, 50W, or the like.
In some embodiments, the preset cooling power of the cooling device may be preset artificially based on the actual driving time of the new energy vehicle, the ambient temperature data, and the like. For example, the higher the ambient temperature data, the greater the preset cooling power. For another example, the longer the actual running time, the larger the preset cooling power, and the like.
The predicted battery temperature data refers to the possible working temperature of the battery of the new energy automobile in the running process. For example, the predicted battery temperature data may be 25 ℃ or the like. In some embodiments, the predicted battery temperature data may be used to determine a cooling power for the cooling device, as described in more detail below in the related section.
In some embodiments, the predicted battery temperature data may be determined manually based on historical travel data, ambient temperature data, or the like. For example, the higher the ambient temperature, the higher the predicted battery temperature data. For example, the longer the new energy vehicle travels, the higher the predicted battery temperature data is.
In some embodiments, the predicted battery temperature data may also be determined based on a cooling model.
The cooling model is a model for acquiring predicted battery temperature data of the new energy automobile during driving. In some embodiments, the cooling model may be a machine learning model. For example, the cooling model may include a recurrent neural network model, a deep neural network model, or the like, or any combination thereof.
As shown in fig. 6, the cooling model 600 may include a second input layer 610, a second determination layer 640, and the like.
The second input layer 610 may be used to extract battery temperature characteristics. In some embodiments, the second input layer may be a machine learning model, such as a long-short term memory network model or the like. In some embodiments, the input to the second input layer may include battery temperature data 411-1 and the output may include battery temperature profile 510-1. For a related explanation of the battery temperature data, reference may be made to fig. 3. For a description of the battery temperature characteristics, reference may be made to fig. 5.
The second determination layer 640 may be used to obtain predicted battery temperature data. In some embodiments, the second determination layer may be a machine learning model. For example, a recurrent neural network model, a deep neural network model, or the like, or any combination thereof. In some embodiments, the inputs to the second determination layer may include, among other things, battery temperature profile 510-1, preset cooling power 630, and battery heat release data 411-3, and the outputs may include, among other things, predicted battery temperature data 650. For a detailed description of the battery temperature characteristic, the preset cooling power, the heat release data of the battery, and the predicted battery temperature data, reference may be made to the relevant portions above.
In some embodiments, the cooling model may further include a second embedded layer for determining an ambient temperature characteristic based on the ambient temperature data. Wherein the ambient temperature characteristic may be used as an input to the second determined layer of the cooling model.
The second embedding layer 620 may be used to obtain ambient temperature characteristics. In some embodiments, the second embedding layer may be a long-short term memory network model or the like. In some embodiments, the input to the second embedding layer includes ambient temperature data 520-1 and the output includes ambient temperature signature 520-2. For a detailed description of the ambient temperature data, reference may be made to fig. 3. For a detailed description of the ambient temperature characteristics, reference may be made to fig. 5.
In some embodiments, the cooling model may be determined by a second input layer, a second determination layer, and joint training.
In some embodiments, each of the third set of training samples of the cooling model may include historical battery temperature data, historical cooling power, and historical heat release data for the battery, etc., for historical same points in time. In some embodiments, each of the third training labels of the cooling model may include a historical operating temperature of the battery corresponding to each set of training samples, and the like. In some embodiments, the third set of training samples and the third training labels may be obtained based on data associated with historical driving of the vehicle.
In some embodiments, the cell temperature characteristic output by the second input layer may be used as an input to the second determination layer. The process of joint training may include: taking historical battery temperature data in the third group of training samples as input of a second input layer; taking the battery temperature characteristics output by the second input layer and historical cooling power in the third group of training samples, heat release data of historical batteries and the like as the input of a second determination layer to determine the output of the cooling model; inputting the predicted battery temperature data output by the cooling model and a third training label into a loss function; and iteratively updating the cooling model based on the loss function until the conditions that the loss function is smaller than a threshold value, convergence is achieved, or the training period reaches the threshold value and the like are met, and obtaining the trained cooling model.
In some embodiments, the trained first embedded layer in the warming model can be used as the second embedded layer of the cooling model without separately training the second embedded layer.
In some embodiments, the second input layer and the second determination layer are jointly trained to improve the accuracy of the obtained cooling model output.
In some embodiments, the second input layer of the cooling model may share parameters with the first input layer of the warming model.
In some embodiments, the second embedding layer of the cooling model may share parameters with the first embedding layer of the warming model.
In some embodiments, the cooling model and the warming model embedded layer share parameters with the input layer, so that the time for model training can be reduced, the efficiency is improved, and the cost is saved.
In some embodiments, the determination module 220 may determine the cooling power of the cooling device from a plurality of preset cooling powers based on a second preset rule. The second preset rule may include a plurality of rules, for example, selecting a minimum value of a plurality of preset cooling powers, and the like.
In some embodiments, the determining module 220 may further determine the cooling power of the cooling device based on a plurality of predicted battery temperature data corresponding to a plurality of different preset cooling powers.
In some embodiments, the determination module 220 may select a preset cooling power corresponding to the predicted battery temperature data closest to the safe temperature as the cooling power of the cooling device. The safe temperature may be a temperature at which the battery can normally operate. For example, the safe temperature may be 35 ℃ or the like. In some embodiments, the safe temperature may be artificially preset based on historical travel data. In some embodiments, when the maximum temperature characteristic of the battery is above a second preset threshold (e.g., 40 ℃), the cooling power of the cooling device needs to be increased. The second preset threshold may refer to a maximum temperature of the battery. In some embodiments, when a certain element in the temperature variation characteristic is above a third preset threshold (e.g., +5 ℃), the cooling power of the cooling device needs to be increased. The third preset threshold may refer to a maximum value of the temperature change. For more description of the maximum temperature characteristic and the temperature change characteristic, reference may be made to the description of fig. 5.
In some embodiments of the present description, a more appropriate cooling power of the cooling device may be determined more accurately according to battery temperature data and the like through a cooling model to obtain a better cooling effect.
Some embodiments of the present specification provide a new energy automobile battery monitoring device, which includes at least one processor and at least one memory. At least one memory for storing computer instructions; at least one processor is used for executing at least part of the computer instructions to realize the new energy automobile battery monitoring method.
Some embodiments of the present disclosure provide a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the new energy vehicle battery monitoring method.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features are required than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (10)
1. A new energy automobile battery monitoring method is characterized by comprising the following steps:
acquiring environmental temperature data and battery temperature data of a battery;
and determining a monitoring early warning scheme and an auxiliary processing scheme based on the environment temperature data and the battery temperature data.
2. The method of claim 1, wherein determining a monitoring and warning scheme and an auxiliary processing scheme based on the ambient temperature data and the battery temperature data comprises:
judging whether the battery temperature data meet a first preset condition or not;
and responding to the battery temperature data meeting the first preset condition, and determining the auxiliary treatment scheme, wherein the auxiliary treatment scheme is that the battery is subjected to heat preservation treatment through a heating device.
3. The method of claim 2, wherein the heating power of the heating device is determined based on a warming model, comprising:
determining the predicted working voltage of the battery through the temperature-rising model based on the battery temperature data, preset heating power and heat release data of the battery, wherein the temperature-rising model is a machine learning model;
determining the heating power of the heating device based on the predicted operating voltage.
4. The method of claim 1, wherein determining a monitoring and warning scheme and an auxiliary processing scheme based on the ambient temperature data and the battery temperature data comprises:
judging whether the battery temperature data meet a second preset condition or not;
and determining the auxiliary treatment scheme in response to the battery temperature data meeting the second preset condition, wherein the auxiliary treatment scheme is to perform cooling treatment on the battery through a cooling device.
5. The utility model provides a new energy automobile battery monitoring system which characterized in that includes: the device comprises an acquisition module and a determination module;
the acquisition module is used for acquiring environmental temperature data of the battery and battery temperature data;
the determining module is used for determining a monitoring early warning scheme and an auxiliary processing scheme based on the environment temperature data and the battery temperature data.
6. The system of claim 5, wherein the determination module is further configured to:
judging whether the battery temperature data meet a first preset condition or not;
and responding to the battery temperature data meeting the first preset condition, and determining the auxiliary treatment scheme, wherein the auxiliary treatment scheme is that the battery is subjected to heat preservation treatment through a heating device.
7. The system of claim 6, wherein the heating power of the heating device is determined based on a warming model, the determination module further configured to:
determining a predicted working voltage of the battery through the temperature-rising model based on the battery temperature data, preset heating power and heat release data of the battery, wherein the temperature-rising model is a machine learning model;
determining the heating power of the heating device based on the predicted operating voltage.
8. The system of claim 5, wherein the determination module is further configured to:
judging whether the battery temperature data meet a second preset condition or not;
and determining the auxiliary treatment scheme in response to the battery temperature data meeting the second preset condition, wherein the auxiliary treatment scheme is to perform cooling treatment on the battery through a cooling device.
9. The new energy automobile battery monitoring device is characterized by comprising at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least some of the computer instructions to implement the method of any of claims 1 to 4.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the method of any one of claims 1 to 4.
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| CN115339354A (en) * | 2022-05-11 | 2022-11-15 | 中国第一汽车股份有限公司 | Power battery heating and heat preservation control method and device, terminal and storage medium |
| CN114919467A (en) * | 2022-06-02 | 2022-08-19 | 岚图汽车科技有限公司 | Method for increasing driving range of pure electric vehicle, battery management system and vehicle |
| CN115139859A (en) * | 2022-06-27 | 2022-10-04 | 中国第一汽车股份有限公司 | Temperature regulation and control method and device, electronic equipment and storage medium |
| CN115195532A (en) * | 2022-08-24 | 2022-10-18 | 安徽仓擎机器人有限公司 | Intelligent heating management system for transport vehicle battery |
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