Potential risk detection and service life prediction method and device for outdoor battery and medium

Abstract

The embodiment of the invention discloses a potential risk detection and service life prediction method and device for an outdoor battery and a medium. A specific embodiment of the method comprises the following steps: acquiring relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information and battery internal temperature information of a preset battery in an outdoor low-temperature environment; generating battery potential risk detection information; in response to determining that risk detection information representing the risk of ion conduction fracture caused by solidification of the preset battery exists in the battery potential risk detection information, carrying out preheating protection treatment on the preset battery; in response to determining that risk detection information representing the risk that lithium dendrites pierce the diaphragm in the preset battery exists in the battery potential risk detection information, carrying out load cutting protection treatment on the preset battery; generating battery service life prediction information; and carrying out battery replacement early warning processing. According to the embodiment, the use stability and the use safety of the battery are improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the field of computer technology, in particular to an outdoor battery potential risk detection and service life prediction method, device and medium. BACKGROUND

[0002] The equipment in the outdoor low-temperature environment is often long-term unattended inspection, and the battery cannot be replaced or maintained in time when it fails. The outdoor battery potential risk detection and service life prediction is a technology for detecting potential risks and predicting service life of the battery in the outdoor low-temperature environment. At present, when detecting potential risks and predicting service life of the battery, the commonly used way is to rely on the voltage or current parameters of the battery to monitor the state of the battery, and detect potential risks and predict service life of the battery.

[0003] However, when the above method is used to detect potential risks and predict service life of the battery, the following technical problems often exist:

[0004] In the outdoor low-temperature environment, the battery electrolyte may freeze due to too low temperature, causing ion conduction to be blocked or even broken, and low temperature can also accelerate the growth of lithium dendrites, causing the lithium dendrites to pierce the separator. However, these changes in physical state may not cause obvious changes in voltage and current at the initial stage. When relying on the voltage or current parameters of the battery to monitor the state of the battery and detect potential risks and predict service life of the battery, it is usually when the battery has obvious performance decline or failure that potential risks are detected (such as ion conduction being blocked or even broken or lithium dendrites piercing the separator), which has obvious hysteresis, resulting in poor stability (for example, sharp decline in performance of the battery) and poor safety (for example, the growth of lithium dendrites may pierce the separator inside the battery, causing the battery to overheat, catch fire, and even explode due to positive and negative short circuits) of the battery.

[0005] The above information disclosed in this BACKGROUND section is only for the purpose of enhancing the understanding of the background of the present inventive concepts, and therefore, it can include information that does not form the prior art that is already known to those of ordinary skill in the art. SUMMARY

[0006] The summary section of the present disclosure is used to introduce the concepts in a brief manner, which will be described in detail in the following detailed description section. The summary section of the present disclosure is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to be used to limit the scope of the claimed technical solutions.

[0007] Some embodiments of the present disclosure propose an outdoor battery potential risk detection and service life prediction method, device, electronic equipment and computer readable medium to solve one or more of the technical problems mentioned in the above BACKGROUND section.

[0008] In a first aspect, some embodiments of the present disclosure provide an outdoor battery potential risk detection and service life prediction method, which comprises: collecting relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and battery internal temperature information of a preset battery in an outdoor low-temperature environment; generating battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information; in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of ion conduction fracture caused by freezing, performing preheating protection processing on the preset battery; in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of lithium dendrite piercing the separator, performing load removal protection processing on the preset battery; in response to determining that the battery potential risk detection information indicates that the preset battery has no potential risk, generating battery service life prediction information based on the relaxation voltage curve information, the impedance spectrum information, and a pre-trained battery service life prediction model; and in response to determining that the battery service life prediction information meets a battery replacement warning condition, performing battery replacement warning processing.

[0009] In a second aspect, some embodiments of the present disclosure provide an outdoor battery potential risk detection and service life prediction device, which comprises: a collection unit configured to collect relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and battery internal temperature information of a preset battery in an outdoor low-temperature environment; a first generation unit configured to generate battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information; a preheating protection processing unit configured to perform preheating protection processing on the preset battery in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of ion conduction fracture caused by freezing; a load removal protection processing unit configured to perform load removal protection processing on the preset battery in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of lithium dendrite piercing the separator; a second generation unit configured to generate battery service life prediction information based on the relaxation voltage curve information, the impedance spectrum information, and a pre-trained battery service life prediction model in response to determining that the battery potential risk detection information indicates that the preset battery has no potential risk; and a warning unit configured to perform battery replacement warning processing in response to determining that the battery service life prediction information meets a battery replacement warning condition.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having stored thereon one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation manner of the first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0012] The above various embodiments of the present disclosure have the following beneficial effects: the outdoor battery potential risk detection and service life prediction method of some embodiments of the present disclosure improves the stability of battery use and reduces the safety hazards of battery use. Specifically, the reason for poor stability and safety of battery use is that in an outdoor low-temperature environment, the battery electrolyte may freeze due to too low temperature, causing ion conduction to be blocked or even broken, and low temperature can also accelerate the growth of lithium dendrites, causing lithium dendrites to pierce the separator. However, these changes in physical state may not cause obvious changes in voltage and current at the initial stage. When relying on the voltage or current parameters of the battery to monitor the battery state, the potential risk detection and service life prediction of the battery are usually detected when the battery has obvious performance decline or failure, and the potential risk detection (such as ion conduction being blocked or even broken or lithium dendrites piercing the separator) is detected, which has obvious hysteresis, so that the stability of battery use (for example, the performance of the battery decreases sharply) and the use safety (for example, the growth of lithium dendrites may pierce the separator inside the battery, causing the battery to overheat, catch fire, or even explode due to positive and negative short circuits) are poor. Based on this, the outdoor battery potential risk detection and service life prediction method of some embodiments of the present disclosure first collects the relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and internal battery temperature information of a preset battery in an outdoor low-temperature environment. Thus, the relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and internal battery temperature information of the preset battery can be collected in an outdoor low-temperature environment. Then, based on the above lithium dendrite growth thickness information and the above internal battery temperature information, battery potential risk detection information is generated. Thus, according to the thickness information of the lithium dendrite growth and the internal battery temperature information, the battery potential risk detection information for judging whether the battery has ion conduction breakage risk (i.e., electrolyte freezing) and lithium dendrite piercing separator risk caused by low temperature can be generated. Then, in response to determining that the above battery potential risk detection information has risk detection information representing that the preset battery has ion conduction breakage risk caused by freezing, the above preset battery is subjected to preheating protection processing. Thus, in the case of ion conduction breakage risk caused by freezing, the electrolyte freezing can be slowed down through preheating protection, thereby improving the stability of outdoor battery use. Next, in response to determining that the above battery potential risk detection information has risk detection information representing that the preset battery has lithium dendrite piercing separator risk, the above preset battery is subjected to load removal protection processing. Thus, in the case of lithium dendrite piercing separator risk, the load of the battery can be automatically removed to prevent the battery from overheating, catching fire, or exploding, thereby reducing the safety hazards of outdoor battery use. Then, in response to determining that the above battery potential risk detection information represents that the preset battery has no potential risk, based on the above relaxation voltage curve information, the above impedance spectrum information, and a pre-trained battery service life prediction model, battery service life prediction information is generated.Thus, the remaining service life of the battery can be continuously predicted for replacement warning without potential risks of the outdoor battery. Finally, in response to determining that the battery service life prediction information satisfies the battery replacement warning condition, a battery replacement warning process is performed. Also because, before the service life of the outdoor battery is predicted, the battery potential risk detection information for judging whether the battery has risks of ion conduction fracture (i.e. electrolyte freezing) caused by low temperature and the risk of lithium dendrite puncturing the separator is generated on the basis of the lithium dendrite growth thickness information of the base battery and the above-mentioned battery internal temperature information, and according to the detection information, the preheating protection process and / or the load shedding protection process are used when there is a risk of ion conduction fracture caused by freezing and / or a risk of lithium dendrite puncturing the separator, the stability and safety of the battery use are improved. BRIEF DESCRIPTION OF DRAWINGS

[0013] The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by describing in detail some embodiments thereof with reference to the attached drawings in which:

[0014] Figure 1 is a flowchart of some embodiments of the outdoor battery potential risk detection and service life prediction method according to the present disclosure;

[0015] Figure 2 is a structural schematic diagram of some embodiments of the outdoor battery potential risk detection and service life prediction device according to the present disclosure;

[0016] Figure 3 is a structural schematic diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. While several embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied by various forms and should not be interpreted as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present disclosure are only for illustrative purposes and are not intended to limit the scope of protection of the present disclosure.

[0018] It should also be noted that, for the sake of description, only the parts related to the invention are shown in the drawings. The embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0019] It should be noted that the terms "first", "second", and the like in the present disclosure are merely intended to distinguish different devices, modules or units, and do not imply the sequence of the functions performed by these devices, modules or units or the mutual dependency of these devices, modules or units.

[0020] It should be noted that the terms "one", "multiple" in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that "one" or "multiple" should be understood as "one or more" unless otherwise explicitly indicated in the context.

[0021] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0022] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] Figure 1 Flow 100 of some embodiments of the outdoor battery potential risk detection and service life prediction method according to the present disclosure is shown. The outdoor battery potential risk detection and service life prediction method includes the following steps:

[0024] Step 101, collecting relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and battery internal temperature information of a preset battery in an outdoor low-temperature environment.

[0025] In some embodiments, an execution subject (e.g., a computing device) of the outdoor battery potential risk detection and service life prediction method can collect relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and battery internal temperature information of a preset battery in an outdoor low-temperature environment. The execution subject can be a BMS battery system. The outdoor low-temperature environment can be a winter or a cold geographical region, and the outdoor low-temperature environment can be an outdoor environment with a temperature ranging from 5°C to -40°C. The relaxation voltage curve information represents the relaxation voltage curve of the preset battery within a preset collection time period (e.g., 3 hours) after a preset time period (e.g., 15 minutes) after a charge-discharge cycle of the preset battery. The relaxation voltage curve information includes a voltage data point information sequence, and each voltage data point in the voltage data point information sequence includes a voltage value and a time point. The voltage value can represent the voltage of the preset battery at the time point. The impedance spectrum information includes an impedance spectrum sequence, and each impedance spectrum in the impedance spectrum sequence corresponds to a modified voltage data point information in the modified voltage data point information sequence. The collection time of the impedance spectrum is the same as the time point included in the corresponding modified voltage data point information. The impedance spectrum is an electrochemical impedance spectrum. The lithium dendrite growth thickness information can represent the lithium dendrite thickness collected after a preset time period after the charge-discharge cycle of the preset battery. The battery internal temperature information can be the temperature collected by a temperature sensor embedded in the preset battery after a preset time period after the charge-discharge cycle of the preset battery.

[0026] In some optional implementations of some embodiments, the execution subject can collect the relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and battery internal temperature information of the preset battery in the outdoor low-temperature environment by the following steps:

[0027] First, after a preset time period after the charge-discharge cycle of the preset battery, the battery internal temperature information is collected by a temperature sensor embedded in the preset battery, and the following collection steps are performed in response to determining that the battery internal temperature information satisfies a preset temperature condition:

[0028] First sub-step, the relaxation voltage curve information of the preset battery within a preset collection time period is collected by a voltage collector connected to the preset battery. The temperature sensor can be an NTC temperature sensor. The preset temperature condition can be that the represented temperature is less than or equal to a preset temperature threshold (e.g., 5 degrees).

[0029] Second sub-step, the impedance spectrum information of the preset battery within the preset collection time period is collected by an impedance spectrum collector connected to the preset battery.

[0030] A third sub-step is to collect the lithium dendrite growth thickness information of the preset battery through the optical fiber sensor pre-implanted in the battery. The optical fiber sensor can be a fiber Bragg grating sensor.

[0031] A step 102 is to generate battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information.

[0032] In some embodiments, the execution subject can generate battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information.

[0033] In some optional implementations of some embodiments, the execution subject can generate battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information through the following steps:

[0034] A first step is to obtain temperature and electrolyte condensation mapping information of the preset battery. The temperature and electrolyte condensation mapping information includes each mapping information, and each mapping information includes preset temperature range information and mapping condensation information. The temperature and electrolyte condensation mapping information of the preset battery can be a mapping table that records a one-to-one correspondence between temperature and electrolyte condensation in advance. The mapping condensation information can be a condensation corresponding to the preset temperature information. For example, the mapping information can be "preset temperature range information: -10 degrees to -20 degrees, mapping condensation information: 0.95".

[0035] A second step is to query the mapping information corresponding to the battery internal temperature information from each mapping information in the temperature and electrolyte condensation mapping information as target mapping information. In practice, the execution subject can determine each preset temperature range information included in each mapping information. Then, the preset temperature range information in which the temperature represented by the battery internal temperature information is determined as the target preset temperature range information. After that, the execution subject can determine the mapping information in which the target preset temperature range information is as the target mapping information.

[0036] A third step is to determine the mapping condensation information included in the target mapping information as the target condensation information.

[0037] In the fourth step, in response to determining that the condensation degree represented by the target condensation degree information is greater than or equal to the preset electrolyte condensation degree threshold, information representing that the preset battery has a risk of ion conduction fracture caused by condensation is determined as the risk detection information. When the condensation degree is greater than or equal to the preset electrolyte condensation degree threshold (for example, 0.7), it means that the condensation or high viscosity state of the electrolyte can cause problems in ion conduction, thereby causing risks. The information representing that the preset battery has a risk of ion conduction fracture caused by condensation can be text information. For example, the information representing that the preset battery has a risk of ion conduction fracture caused by condensation can be “Warning: The electrolyte has been condensed, and the risk of ion conduction interruption is high”.

[0038] In the fifth step, in response to determining that the lithium dendrite growth thickness represented by the lithium dendrite growth thickness information is greater than or equal to the preset generation thickness threshold, information representing that the preset battery has a risk of lithium dendrite puncturing the separator is determined as the risk detection information. It should be noted that when the growth thickness of the lithium dendrite is greater than or equal to the preset generation thickness threshold, it means that the lithium dendrite can puncture the separator of the battery, which has a very high risk. The information representing that the preset battery has a risk of lithium dendrite puncturing the separator can be text information. For example, the information representing that the preset battery has a risk of lithium dendrite puncturing the separator can be “Warning: There is a risk of short circuit due to possible puncture of the separator”.

[0039] In the sixth step, the determined at least one risk detection information is determined as the battery potential risk detection information.

[0040] In the seventh step, in response to determining that the condensation degree represented by the target condensation degree information is less than the preset electrolyte condensation degree threshold and the lithium dendrite growth thickness represented by the lithium dendrite growth thickness information is less than the preset generation thickness threshold, information representing that the preset battery has no potential risk is determined as the battery potential risk detection information. The information representing that the preset battery has no potential risk can be text information. For example, the information representing that the preset battery has no potential risk can be “No potential risk exists”.

[0041] In step 103, in response to determining that the battery potential risk detection information includes the risk detection information representing that the preset battery has a risk of ion conduction fracture caused by condensation, the preset battery is subjected to preheating protection processing.

[0042] In some embodiments, the execution subject can perform preheating protection processing on the preset battery in response to determining that the battery potential risk detection information includes the risk detection information representing that the preset battery has a risk of ion conduction fracture caused by condensation.

[0043] In some optional implementations of some embodiments, the execution subject can perform the preheating protection processing on the preset battery in response to determining that the battery potential risk detection information contains risk detection information indicating that the preset battery has a risk of ion conduction fracture caused by solidification.

[0044] First, in response to determining that the battery potential risk detection information contains risk detection information indicating that the preset battery has a risk of ion conduction fracture caused by solidification, a preset preheating protection instruction information is sent to the preset battery to control the heating sheet built-in the preset battery to start. The preset preheating protection instruction information can be a control instruction for controlling the heating sheet built-in the preset battery to start. The heating sheet can be a heating element that heats by current to raise the temperature of the battery (the heating sheet is usually started in a low-temperature environment to prevent the battery from being solidified due to too low temperature, thereby affecting the performance of the battery).

[0045] Step 104, in response to determining that the battery potential risk detection information contains risk detection information indicating that the preset battery has a risk of lithium dendrite piercing the separator, performing load shedding protection processing on the preset battery.

[0046] In some embodiments, the execution subject can perform load shedding protection processing on the preset battery in response to determining that the battery potential risk detection information contains risk detection information indicating that the preset battery has a risk of lithium dendrite piercing the separator.

[0047] In some optional implementations of some embodiments, the execution subject can perform load shedding protection processing on the preset battery in response to determining that the battery potential risk detection information contains risk detection information indicating that the preset battery has a risk of lithium dendrite piercing the separator.

[0048] First, in response to determining that the battery potential risk detection information contains risk detection information indicating that the preset battery has a risk of lithium dendrite piercing the separator, a preset load shedding information is sent to the control module of the load device associated with the preset battery to control the load device to be disconnected from the preset battery. The load device can be a load device in an outdoor low-temperature environment (for example, a weather monitoring device in an outdoor low-temperature environment). The control module can be an electronic system or device that controls the flow of current between the battery and the load device. The preset load shedding information can be an electrical signal or a digital instruction for controlling the load device to be disconnected from the preset battery.

[0049] Step 105, in response to determining that the battery potential risk detection information indicates that the preset battery has no potential risk, generating battery service life prediction information based on the relaxation voltage curve information, the impedance spectrum information, and the pre-trained battery service life prediction model.

[0050] In some embodiments, the execution subject can generate the battery service life prediction information based on the relaxation voltage curve information, the impedance spectrum information and the pre-trained battery service life prediction model, in response to determining that the battery potential risk detection information characterizes that the preset battery does not have a potential risk.

[0051] In some optional implementations of some embodiments, the execution subject can generate the battery service life prediction information based on the relaxation voltage curve information, the impedance spectrum information and the pre-trained battery service life prediction model by the following steps:

[0052] Firstly, for each voltage data point information in the sequence of voltage data point information included in the relaxation voltage curve information, the following temperature compensation correction processing is performed:

[0053] Firstly, the ambient temperature of the environment in which the preset battery is located is collected. In practice, the execution subject can collect the ambient temperature on the load device as the ambient temperature of the environment in which the preset battery is located through a digital temperature sensor.

[0054] Secondly, the temperature correction factor information is generated based on the ambient temperature and a preset linear correction model. The preset linear correction model can be a mathematical model (i.e. a mathematical formula) for quantifying the effect of temperature change on battery voltage. As an example, the preset linear correction model can be:

[0055] f(T) = 1 + a(T-Tref)

[0056] Wherein, f(T) represents the temperature correction factor information when the ambient temperature is T. a can be a temperature coefficient, which is determined by experiment and represents the ratio of battery voltage change per 1℃ change in temperature. Tref represents a selected reference temperature (usually standard room temperature 25℃). In practice, the ambient temperature and the reference temperature can be input into the preset linear correction model to obtain the temperature correction factor information.

[0057] Thirdly, the corrected voltage value is generated based on the voltage value included in the voltage data point information and the temperature correction factor information. In practice, the execution subject can determine the product of the voltage value included in the voltage data point information and the value represented by the temperature correction factor information as the corrected voltage value.

[0058] Fourthly, the voltage value in the voltage data point information is deleted, and the corrected voltage value is added to the voltage data point information to update the voltage data point information.

[0059] Fifthly, the updated voltage data point information is determined as the corrected voltage data point information.

[0060] Secondly, the determined each modified voltage data point information is arranged to obtain a modified voltage data point information sequence. In practice, the above execution subject can sort each modified voltage data point information according to the order of each voltage data point information corresponding to each modified voltage data point information in the voltage data point information sequence to obtain the modified voltage data point information sequence.

[0061] Thirdly, based on the above ambient temperature, the above modified voltage data point information sequence, the above impedance spectrum information and the pre-trained battery service life prediction model, battery service life prediction information is generated.

[0062] In the process of adopting technical solutions to solve the problems mentioned in the background, the following problems often occur:

[0063] The entire impedance spectrum information, ambient temperature and modified voltage data point information sequence are directly input into the battery service life prediction model to obtain battery service life prediction information. Directly inputting the entire impedance spectrum information into the battery service life prediction model does not separate the impedance spectrum containing multiple components in the impedance spectrum, which cannot directly capture the characteristics of the electrolyte resistance and electrode polarization resistance that affect the battery life, thereby causing a large deviation between the prediction result and the actual remaining life. Moreover, if the impedance spectrum is not separated and corrected at low temperature, the model cannot accurately distinguish whether the resistance change is caused by temperature or battery aging, resulting in poor accuracy of the generated battery service life prediction information, and further causing the battery replacement warning to be prone to early or late conditions, and early warning will waste battery resources. In the face of the above technical problems, the inventors decided to adopt the following solutions:

[0064] In some optional implementations of some embodiments, the above execution subject can generate battery service life prediction information based on the above ambient temperature, the above modified voltage data point information sequence, the above impedance spectrum information and the pre-trained battery service life prediction model by the following steps:

[0065] Firstly, based on the impedance spectrum sequence included in the impedance spectrum information, the following separation steps are performed:

[0066] Firstly, the first impedance spectrum in the impedance spectrum sequence is subjected to resistance separation processing to obtain electrolyte resistance information and electrode polarization resistance information. In practice, the above execution subject can fit the above impedance spectrum by an impedance model (such as a Randle circuit model) to extract the electrolyte resistance information and the electrode polarization resistance information of the impedance spectrum. The above electrolyte resistance information can be an electrolyte resistance sequence. The above electrode polarization resistance information can be an electrode polarization resistance sequence.

[0067] a second sub-step of adding the electrolyte resistance information to the first preset queue to update the first preset queue.

[0068] a third sub-step of adding the electrode polarization resistance information to the second preset queue to update the second preset queue.

[0069] a fourth sub-step of deleting the first impedance spectrum from the impedance spectrum sequence to update the impedance spectrum sequence.

[0070] a second step of, in response to determining that the updated impedance spectrum sequence is not empty, performing again the separating step based on the updated impedance spectrum sequence.

[0071] a third step of, in response to determining that the updated impedance spectrum sequence is not empty, performing the following generating step:

[0072] a first sub-step of arranging the electrolyte resistance information in the updated first preset queue in order to obtain an electrolyte resistance information sequence. In practice, the executing subject can arrange the electrolyte resistance information in the first preset queue in order according to the order of the electrolyte resistance information in the first preset queue to obtain the electrolyte resistance information sequence.

[0073] a second sub-step of arranging the electrode polarization resistance information in the updated second preset queue in order to obtain an electrode polarization resistance information sequence.

[0074] a fourth step of performing low-temperature correction processing on the electrolyte resistance information sequence and the electrode polarization resistance information sequence to obtain a low-temperature corrected electrolyte resistance information sequence and a low-temperature corrected electrode polarization resistance information sequence. In practice, for each electrolyte resistance included in the electrolyte resistance information sequence, the executing subject can correct the electrolyte resistance by the Arrhenius formula to obtain a corrected electrolyte resistance, and replace the electrolyte resistance in the electrolyte resistance information sequence with the corrected electrolyte resistance to update the electrolyte resistance information sequence. Then, the executing subject can determine the updated electrolyte resistance information sequence as the low-temperature corrected electrolyte resistance information sequence. Similarly, the executing subject can correct each electrode polarization resistance included in the electrode polarization resistance information sequence by the Arrhenius formula to obtain a low-temperature corrected electrode polarization resistance information sequence.

[0075] In the fifth step, the modified voltage data point information sequence, the electrolyte resistance information sequence, and the electrode polarization resistance information sequence are input into a time sequence feature extraction layer of the battery service life prediction model to obtain a hidden state feature information sequence, wherein the battery service life prediction model comprises the time sequence feature extraction layer, an initial service life prediction layer, and an optimization output layer. The time sequence feature extraction layer can be a GRU layer with the modified voltage data point information sequence, the electrolyte resistance information sequence, and the electrode polarization resistance information sequence as inputs and the hidden state feature information sequence as outputs. The hidden state feature information sequence can be obtained after the modified voltage data point information sequence, the electrolyte resistance information sequence, and the electrode polarization resistance information sequence are input into the time sequence feature extraction layer (GRU layer) of the battery service life prediction model. The hidden state feature information sequence is an internal state representation generated by the GRU network at each time during the processing of the input sequence. Each hidden state feature information in the hidden state feature information sequence represents a dynamic understanding of the input sequence (such as battery voltage, electrolyte resistance, electrode polarization resistance, etc.), that is, an internal representation of the current input (such as modified voltage, electrolyte resistance, electrode polarization resistance, etc.), which integrates the current input and all previous input information. The hidden state feature information sequence can represent the hidden state feature of the battery degradation feature.

[0076] In the sixth step, the hidden state feature information sequence is input into the initial service life prediction layer to obtain initial service life prediction information. The initial service life prediction layer can be a fully connected layer with the hidden state feature information sequence as inputs and the initial service life prediction information as outputs. The initial service life prediction information can represent the predicted remaining service life (e.g., number of charge cycles or number of days of use) of the preset battery.

[0077] In the seventh step, the initial service life prediction information and the environmental temperature are input into the optimization output layer. The optimization output layer optimizes the initial service life prediction information according to the preset lithium dendrite growth constraint information to obtain battery service life prediction information. The battery service life prediction information can be the remaining service time of the battery. Since the growth of lithium dendrites slows down at low temperatures, the battery life becomes relatively longer. The optimization output layer can be a correction factor corresponding to the environmental temperature learned by the environmental temperature as input, and then the correction factor is used to correct the initial service life prediction information to obtain the battery service life prediction information. The correction factor can be a correction value corresponding to the environmental temperature. For example, the environmental temperature can be -20°C, the correction factor corresponding to it can be 1.2, the initial service life prediction information can be 10 days, and the battery service life prediction information can be 10 days x 1.2 = 12 days.

[0078] The above technical solution combines step 106 and its related content as one invention point of the embodiment of the present disclosure, which solves the technical problem of "waste of battery resources due to early warning". The factors that lead to the waste of battery resources due to early warning are often as follows: directly inputting the entire impedance spectrum information, the environmental temperature, and the sequence of the corrected voltage data points into the battery service life prediction model to obtain the battery service life prediction information. Directly inputting the entire impedance spectrum information into the battery service life prediction model does not separate the impedance spectrum containing multiple components in the impedance spectrum, and cannot directly capture the characteristics of the electrolyte resistance and the electrode polarization resistance that affect the battery life, thereby causing a large deviation between the prediction result and the actual remaining life. Moreover, if the impedance spectrum is not separated and corrected at low temperature, the model cannot accurately distinguish between the resistance change caused by temperature and the resistance change caused by battery aging, resulting in poor accuracy of the generated battery service life prediction information, which further causes the battery replacement warning to be prone to early or late conditions, and early warning will waste battery resources. If the above factors are solved, the effect of reducing the waste of battery resources due to early warning can be achieved. To achieve this effect, first, based on the impedance spectrum sequence included in the impedance spectrum information, the following separation steps are performed: first, performing resistance separation processing on the first impedance spectrum in the impedance spectrum sequence to obtain electrolyte resistance information and electrode polarization resistance information. Second, adding the electrolyte resistance information to the first preset queue to update the first preset queue. Third, adding the electrode polarization resistance information to the second preset queue to update the second preset queue. Fourth, deleting the first impedance spectrum from the impedance spectrum sequence to update the impedance spectrum sequence. Fifth, in response to determining that the updated impedance spectrum sequence is not empty, based on the updated impedance spectrum sequence, the separation steps are performed again. Then, in response to determining that the updated impedance spectrum sequence is empty, the following generation steps are performed: first, arranging each electrolyte resistance information in the updated first preset queue in order to obtain an electrolyte resistance information sequence. Second, arranging each electrode polarization resistance information in the updated second preset queue in order to obtain an electrode polarization resistance information sequence. In this way, the electrolyte resistance and electrode polarization resistance information (i.e., the electrolyte resistance information sequence and the electrode polarization resistance information sequence) can be obtained separately through the above separation steps and generation steps, so that the subsequent model can more directly capture the influence characteristics of the two resistances on the battery life. Then, the above electrolyte resistance information sequence and the above electrode polarization resistance information sequence are subjected to low-temperature correction processing to obtain a low-temperature corrected electrolyte resistance information sequence and a low-temperature corrected electrode polarization resistance information sequence. Temperature changes and battery aging have different effects on resistance. If low-temperature correction is not performed, the model may mistakenly confuse the resistance change caused by temperature with the change caused by battery aging, thereby causing prediction deviation.By low temperature correction on the electrolyte resistance information sequence and the electrode polarization resistance information sequence, the resistance change caused by temperature can be eliminated, and the low temperature corrected electrolyte resistance information sequence and the low temperature corrected electrode polarization resistance information sequence are obtained. Then, the above corrected voltage data point information sequence, the above electrolyte resistance information sequence and the above electrode polarization resistance information sequence are input into the time sequence feature extraction layer of the battery service life prediction model to obtain the hidden state feature information sequence, wherein the battery service life prediction model comprises the time sequence feature extraction layer, an initial service life prediction layer and an optimization output layer. In this way, the time sequence feature extraction layer can be used to extract the hidden state feature information sequence reflecting the hidden state feature of the battery degradation based on the corrected voltage data point information sequence, the electrolyte resistance information sequence and the electrode polarization resistance information sequence. Then, the hidden state feature information sequence is input into the initial service life prediction layer to obtain the initial service life prediction information. Then, the initial service life prediction information and the environmental temperature are input into the optimization output layer, so that the optimization output layer optimizes the initial service life prediction information according to the preset lithium dendrite growth constraint information to obtain the battery service life prediction information. The optimization output layer comprehensively considers the influence of the environmental temperature on the lithium dendrite growth, optimizes the initial prediction through the preset constraint information, so that the final battery service life prediction information is more consistent with the actual situation, further improves the prediction accuracy, and obtains the battery service life prediction information with higher accuracy. Moreover, through the above separation step and generation step, the electrolyte resistance and the electrode polarization resistance can be separated, and the electrolyte resistance information sequence and the electrode polarization resistance information sequence are obtained, so that the subsequent model can more directly capture the influence characteristics of the two resistances on the battery life. Moreover, by low temperature correction on the electrolyte resistance information sequence and the electrode polarization resistance information sequence, the resistance change caused by temperature can be eliminated, and the low temperature corrected electrolyte resistance information sequence and the low temperature corrected electrode polarization resistance information sequence are obtained. The corrected voltage data point information sequence, the low temperature corrected electrolyte resistance information sequence and the low temperature corrected electrode polarization resistance information sequence are input into the battery service life prediction model, the model can comprehensively consider the time sequence dependence relationship in these sequences and the characteristics of the electrolyte resistance and the electrode polarization resistance affecting the battery life, and optimize the initial service life prediction information according to the preset lithium dendrite growth constraint information to generate the battery service life prediction information with higher accuracy. Through step 106, the battery replacement warning can be performed based on the battery service life prediction information with higher accuracy, so as to reduce the situation of early or late battery replacement warning, and further reduce the waste of battery resources caused by early warning.

[0079] Step 106, in response to determining that the battery service life prediction information meets the battery replacement warning condition, performing battery replacement warning processing.

[0080] In some embodiments, the execution subject can perform the battery replacement warning processing in response to determining that the battery service life prediction information satisfies the battery replacement warning condition. The battery replacement warning condition can be that the remaining service life of the battery represented by the battery service life prediction information is less than or equal to a preset time length (e.g., 1 day).

[0081] In some optional implementations of some embodiments, the execution subject can perform the battery replacement warning processing in response to determining that the battery service life prediction information satisfies the battery replacement warning condition by the following steps:

[0082] First, in response to determining that the battery service life prediction information satisfies the battery replacement warning condition, the battery replacement warning information is sent to a preset monitoring terminal, and it is detected whether there is a usable backup battery in the battery compartment where the preset battery is located, to obtain detection information. The preset monitoring terminal can be a terminal used by an operator (e.g., a mobile terminal (such as a mobile phone or a tablet computer) or a computer terminal used by an operator). The battery replacement warning information can be information representing that the preset battery needs to be replaced. In practice, the voltage value of the backup battery can be detected by the battery management system (BMS) or the battery monitoring circuit in the battery compartment. If the battery voltage is within the normal range (e.g., the voltage is higher than a preset voltage threshold), it means that the backup battery is available, and information representing that there is a usable backup battery is determined as the detection information. If the backup battery is not within the normal range (e.g., the voltage is lower than or equal to the preset voltage threshold), information representing that there is no usable backup battery is determined as the detection information.

[0083] Second, in response to determining that the detection information represents that there is a usable backup battery in the battery compartment, the battery compartment where the preset battery is located is controlled to switch the backup battery to supply power to the load device.

[0084] Third, in response to determining that the detection information represents that there is no usable backup battery in the battery compartment, the location information of the load device is collected as the target point location information. In practice, the geographic location information of the load device can be collected by a GPS receiver built-in the load device as the target point location information. The load device has a corresponding battery replacement robot.

[0085] In the fourth step, the position information of the location where the battery-equipped robot corresponding to the load device is located is obtained as the start point position information. In practice, the geographic position information of the location where the battery-equipped robot corresponding to the load device is located can be obtained through GPS positioning technology. Then, the execution subject can convert the obtained geographic position information to the start node position in the outdoor two-dimensional grid map as the start point position information. The upper left grid of the outdoor two-dimensional grid map can be the start grid corresponding to the start point position information, and the position information of the start grid represents the start node position (for example, (0, 0)). The lower right grid of the outdoor two-dimensional grid map can be the end grid corresponding to the target point position information, and the position information of the end grid represents the position of the end node in the outdoor two-dimensional grid map (for example, (2, 2)). The battery-equipped robot can be an outdoor robot that replaces the battery of the load device.

[0086] In the fifth step, based on the target point position information and the start point position information, the following update scheduling process is performed:

[0087] In the first sub-step, an outdoor two-dimensional grid map is obtained. Each node in the outdoor two-dimensional grid map has a feasible mark or an obstacle mark. The node represents a grid in the outdoor two-dimensional grid map, and the node has corresponding geographic position information. The outdoor two-dimensional grid map can be a two-dimensional grid map of the area where the load device and the battery-equipped robot corresponding to the load device are located. The outdoor two-dimensional grid map can reflect the distribution of obstacles in the environment.

[0088] In the second sub-step, a node corresponding to the start point position information in the outdoor two-dimensional grid map is determined as the start node.

[0089] A third sub-step is to determine each target neighbor node meeting a preset condition from each neighbor node of the start node in the outdoor two-dimensional grid map, and generate the current node position information based on the target point position information and the outdoor two-dimensional grid map. The preset condition can be a feasible mark. In practice, for each target neighbor node, the execution subject can determine a path cost value of the target neighbor node. The path cost value can be denoted as h, h=g value+h value. The g value represents the actual cost from the node corresponding to the start point position information to the target neighbor node, which is usually the actual distance along the grid path (it should be noted that the distance of each upward, downward, left, right or diagonal movement can be set to 1). For example, if the target neighbor node is the first step from the node corresponding to the start point position information, the g value is 1. If the target neighbor node is the second step from the node corresponding to the start point position information, the g value is 2. The h value can be the estimated cost from the target neighbor node to the target node represented by the target point position information, which can be represented by the Manhattan distance, and the length of a cell in the outdoor two-dimensional grid map is represented by 1. Then, the execution subject can determine the path cost value with the smallest value from the determined path cost values as the target path cost value. After that, the execution subject can determine the position of the target neighbor node corresponding to the target path cost value in the outdoor two-dimensional grid map as the current node position information (for example, (2, 2)).

[0090] A fourth sub-step is to control the battery-equipped robot to move to the position represented by the current node position information in response to determining that the position information of the end grid corresponding to the target point position information in the outdoor two-dimensional grid map is different from the current node position information. In practice, the execution subject can determine the geographic position information corresponding to the node corresponding to the current node position information as the movement position information. After that, the execution subject can control the battery-equipped robot to move to the position represented by the movement position information.

[0091] A fifth sub-step is to update the current node position information as the start point position information to update the start point position information.

[0092] A sixth sub-step is to execute the updating scheduling step again based on the updated start point position information and the target point position information.

[0093] A sixth step is to schedule the battery-equipped robot to the position of the load device represented by the target point position information in response to determining that the position information of the end grid corresponding to the target point position information in the outdoor two-dimensional grid map is the same as the current node position information.

[0094] A seventh step is to control the battery-equipped robot to take the spare battery from the robot spare battery compartment and replace the taken spare battery to the load device through the mechanical arm.

[0095] The above technical solution and related content are an invention point of an embodiment of the present disclosure, which solves the technical problem of "causing an increase in scheduling resources". Factors that cause an increase in scheduling resources are often as follows: a fixed path from a starting position of a battery-equipped robot to a position of a load device is planned in advance, and the robot moves along the fixed path. Obstacles in an outdoor environment are constantly changing. If new obstacles (such as fallen trees or temporarily stacked goods) appear on the way, the robot cannot continue to move along the original path and needs to be re-planned. If a closer passable road appears during scheduling (obstacles are removed, and a closer road appears), because the battery-equipped robot can only move along the planned fixed path, scheduling resources increase. If the above factors are solved, the effect of saving scheduling resources can be achieved. To achieve this effect, first, in response to determining that the battery life prediction information meets the battery replacement warning condition, battery replacement warning information is sent to a preset monitoring end, and it is detected whether there is a usable spare battery in the battery compartment where the preset battery is located, to obtain detection information. In this way, it can be detected whether there is a usable spare battery in the battery compartment. Then, in response to determining that the detection information indicates that there is a usable spare battery in the battery compartment, the battery compartment where the preset battery is located is controlled to switch the spare battery to power the load device. In response to determining that the detection information indicates that there is no usable spare battery in the battery compartment, position information of the load device is collected as target point position information. In this way, in the case where there is no usable spare battery in the battery compartment, the target point position information of the position of the load device can be collected. Then, the position information of the position of the battery-equipped robot corresponding to the load device is obtained as starting position information. In this way, the position information of the position of the battery-equipped robot can be obtained. Then, based on the target point position information and the starting position information, the following update scheduling processing is performed: first, an outdoor two-dimensional grid map is obtained, wherein each node in the outdoor two-dimensional grid map corresponds to a feasible mark or an obstacle mark. In this way, the outdoor two-dimensional grid map of the obstacle distribution in the current base environment can be obtained. Second, a node corresponding to the starting position information in the outdoor two-dimensional grid map is determined as a starting node. Third, each target neighbor node that meets a preset condition among the neighbor nodes of the starting node is determined in the outdoor two-dimensional grid map, and current node position information is generated based on the target point position information and the outdoor two-dimensional grid map. In this way, the nodes corresponding to the starting position and the target position of the robot can be determined in the outdoor two-dimensional grid map, and the feasible neighbor nodes of the starting node are screened out as the target neighbor nodes. Fourth, in response to determining that the end grid position information corresponding to the current node position information and the target point position information is different, the battery-equipped robot is controlled to move to the position represented by the current node position information. Fifth, the current node position information is updated as the starting position information to update the starting position information.In the sixth step, based on the updated start point position information and the target point position information, the updating scheduling step is executed again. Thus, through the fourth step to the sixth step, the current node position information is updated as the start point position information, and the path planning is executed again based on the updated information. In the seventh step, in response to determining that the current node position information is the same as the end point grid position information corresponding to the target point position information, the battery-equipped robot is scheduled to the position where the load device represented by the target point position information is located. Thus, through the above updating scheduling process, the robot can update the current node position information as the start point position information every time it moves to a new position, and execute the path planning again based on the updated information, so that the robot can adapt to the environmental changes in real time. If a closer passable road appears during the scheduling process (such as the removal of obstacles, the appearance of a closer road), the robot can timely adjust the path and select a more optimal route. It can also effectively avoid the situation that when moving along a fixed path, a new obstacle appears in the middle of the path (such as a fallen tree, temporarily stacked goods), the robot cannot continue to move along the original path, and thus the scheduling resources during the movement of the battery-equipped robot can be saved. Finally, the battery-equipped robot takes out the spare battery from the robot spare battery compartment, and replaces the taken-out spare battery to the load device through the mechanical arm. Thus, when the battery-equipped robot is scheduled to the position where the load device is located, the battery of the load device is replaced.

[0096] The above various embodiments of the present disclosure have the following beneficial effects: the outdoor battery potential risk detection and service life prediction method of some embodiments of the present disclosure improves the stability of battery use and reduces the safety hazards of battery use. Specifically, the reason for poor stability and safety of battery use is that in an outdoor low-temperature environment, the battery electrolyte may freeze due to too low temperature, causing ion conduction to be blocked or even broken, and low temperature can also accelerate the growth of lithium dendrites, causing lithium dendrites to pierce the separator. However, these changes in physical state may not cause obvious changes in voltage and current at the initial stage. When relying on the voltage or current parameters of the battery to monitor the battery state, the potential risk detection and service life prediction of the battery are usually detected when the battery has obvious performance decline or failure, and the potential risk detection (such as ion conduction being blocked or even broken or lithium dendrites piercing the separator) is detected, which has obvious hysteresis, so that the stability of battery use (for example, the performance of the battery decreases sharply) and the use safety (for example, the growth of lithium dendrites may pierce the separator inside the battery, causing the battery to overheat, catch fire, or even explode due to positive and negative short circuits) are poor. Based on this, the outdoor battery potential risk detection and service life prediction method of some embodiments of the present disclosure first collects the relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and internal battery temperature information of a preset battery in an outdoor low-temperature environment. Thus, the relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and internal battery temperature information of the preset battery can be collected in an outdoor low-temperature environment. Then, based on the above lithium dendrite growth thickness information and the above internal battery temperature information, battery potential risk detection information is generated. Thus, according to the thickness information of the lithium dendrite growth and the internal battery temperature information, the battery potential risk detection information for judging whether the battery has ion conduction breakage risk (i.e., electrolyte freezing) and lithium dendrite piercing separator risk caused by low temperature can be generated. Then, in response to determining that the above battery potential risk detection information has risk detection information representing that the preset battery has ion conduction breakage risk caused by freezing, the above preset battery is subjected to preheating protection processing. Thus, in the case of ion conduction breakage risk caused by freezing, the electrolyte freezing can be slowed down through preheating protection, thereby improving the stability of outdoor battery use. Next, in response to determining that the above battery potential risk detection information has risk detection information representing that the preset battery has lithium dendrite piercing separator risk, the above preset battery is subjected to load removal protection processing. Thus, in the case of lithium dendrite piercing separator risk, the load of the battery can be automatically removed to prevent the battery from overheating, catching fire, or exploding, thereby reducing the safety hazards of outdoor battery use. Then, in response to determining that the above battery potential risk detection information represents that the preset battery has no potential risk, based on the above relaxation voltage curve information, the above impedance spectrum information, and a pre-trained battery service life prediction model, battery service life prediction information is generated.Thus, the remaining service life of the battery can be predicted to continue to be warned of replacement without potential risks of the outdoor battery, so as to be warned of replacement. Finally, in response to determining that the battery service life prediction information meets the battery replacement warning condition, a battery replacement warning process is performed. Also, before predicting the service life of the outdoor battery, the battery potential risk detection information for judging whether the battery has the ion conduction fracture (i.e., electrolyte freezing) risk caused by low temperature and the lithium dendrite puncture diaphragm risk is generated based on the lithium dendrite growth thickness information of the base battery and the above-mentioned battery internal temperature information, and according to the detection information, the preheating protection process and / or the load shedding protection process are used when there is the ion conduction fracture risk caused by freezing and / or the lithium dendrite puncture diaphragm risk, thereby improving the stability and safety of the battery use.

[0097] Further reference Figure 2 , as an implementation of the method shown in each figure, the present disclosure provides some embodiments of an outdoor battery potential risk detection and service life prediction device, which device embodiments correspond to the method embodiments shown in Figure 1 , which device can be applied in various electronic devices.

[0098] As shown in Figure 2 , the outdoor battery potential risk detection and service life prediction device 200 of some embodiments includes an acquisition unit 201, a first generation unit 202, a preheating protection processing unit 203, a load shedding protection processing unit 204, a second generation unit 205, and a warning unit 206. Wherein the acquisition unit 201 is configured to acquire the relaxation voltage curve information, the impedance spectrum information, the lithium dendrite growth thickness information, and the battery internal temperature information of a preset battery under an outdoor low temperature environment; the first generation unit 202 is configured to generate battery potential risk detection information based on the above-mentioned lithium dendrite growth thickness information and the above-mentioned battery internal temperature information; the preheating protection processing unit 203 is configured to perform preheating protection processing on the above-mentioned preset battery in response to determining that there is risk detection information in the above-mentioned battery potential risk detection information indicating that the preset battery has the ion conduction fracture risk caused by freezing; the load shedding protection processing unit 204 is configured to perform load shedding protection processing on the above-mentioned preset battery in response to determining that there is risk detection information in the above-mentioned battery potential risk detection information indicating that the preset battery has the lithium dendrite puncture diaphragm risk; the second generation unit 205 is configured to generate battery service life prediction information based on the above-mentioned relaxation voltage curve information, the above-mentioned impedance spectrum information, and a pre-trained battery service life prediction model in response to determining that the above-mentioned battery potential risk detection information indicates that the preset battery has no potential risk; and the warning unit 206 is configured to perform battery replacement warning processing in response to determining that the above-mentioned battery service life prediction information meets the battery replacement warning condition.

[0099] It is appreciated that the units described in this apparatus 200 correspond to the respective steps in the method described above. Thus, the operations, features, and advantages described above for the method apply equally to the apparatus 200 and the units contained therein, which will not be repeated here. Figure 1

[0100] Reference is made below to Figure 3 which shows a structural schematic diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the function and use range of the embodiments of the present disclosure.

[0101] As shown, the electronic device 300 can include a processing device (such as a central processor, a graphics processor, etc.) 301, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or loaded into a random access memory (RAM) 303 from a storage device 308. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304. Figure 3 Generally, the following devices can be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 can allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although

[0102] The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or have all of the devices shown. More or less devices can alternatively be implemented or included. Figure 3 Each block shown in the figure can represent a device or, as needed, multiple devices. Figure 3

[0103] ​​In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to some embodiments of the present disclosure. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the methods of some embodiments of the present disclosure are performed.

[0104] It should be noted that the computer readable medium in some embodiments of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus or device. In some embodiments of the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as part of a carrier wave, in which the computer readable program code is carried. Such a propagated data signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. Program code contained on a computer readable medium can be transmitted by any suitable medium, including but not limited to wire, cable, RF (radio frequency), etc., or any suitable combination thereof.

[0105] In some embodiments, the client, server, or both can communicate using any known or later developed form of computer-readable media, including wireless media, wire-based media, and the like. In some embodiments, the client, server, or both can communicate using any current or later developed network protocol, such as the HyperText Transfer Protocol (HTTP), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current or later developed networks.

[0106] The computer readable medium can be contained in the electronic device or can exist separately from the electronic device. The computer readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: collect relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and battery internal temperature information of a preset battery in an outdoor low-temperature environment; generate battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information; in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of ion conduction fracture due to freezing, perform preheating protection processing on the preset battery; in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of lithium dendrite piercing the separator, perform load removal protection processing on the preset battery; in response to determining that the battery potential risk detection information indicates that the preset battery has no potential risk, generate battery service life prediction information based on the relaxation voltage curve information, the impedance spectrum information, and a pre-trained battery service life prediction model; and in response to determining that the battery service life prediction information meets a battery replacement warning condition, perform battery replacement warning processing.

[0107] Computer program code for carrying out operations of some embodiments of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network ("LAN") or a wide area network ("WAN"), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0108] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0109] The units described in some embodiments of the present disclosure can be implemented by means of software, or can be implemented by hardware. The described units can also be arranged in a processor, for example, can be described as: a processor comprising an acquisition unit, a first generation unit, a preheating protection processing unit, a load shedding protection processing unit, a second generation unit and a pre-warning unit. Among them, the name of these units does not constitute a limitation to the unit itself in some cases, for example, the first generation unit can also be described as: a unit for generating battery potential risk detection information based on the above lithium dendrite growth thickness information and the above battery internal temperature information.

[0110] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, example types of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), etc.

[0111] The above description is merely some of the preferred embodiments of the present disclosure and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combinations of technical features, and should also cover other technical solutions formed by any combination of technical features or their equivalent features without departing from the inventive concept. For example, the features are replaced with the technical features disclosed in the embodiments of the present disclosure (but not limited to) having similar functions to form technical solutions.

Claims

1. An outdoor battery potential risk detection and service life prediction method, comprising: collecting relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and battery internal temperature information of a preset battery in an outdoor low-temperature environment; generating battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information; in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of ion conduction fracture caused by solidification, performing preheating protection processing on the preset battery; in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of lithium dendrite puncturing the separator, performing load removal protection processing on the preset battery; in response to determining that the battery potential risk detection information indicates that the preset battery has no potential risk, generating battery service life prediction information based on the relaxation voltage curve information, the impedance spectrum information, and a pre-trained battery service life prediction model; in response to determining that the battery service life prediction information meets a battery replacement warning condition, performing battery replacement warning processing.

2. The method of claim 1, wherein, The preheating protection processing on the preset battery in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of ion conduction fracture caused by solidification, comprises: in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of ion conduction fracture caused by solidification, sending a preset preheating protection instruction information to the preset battery to control the heating sheet built-in the preset battery to start.

3. The method of claim 1, wherein, The load removal protection processing on the preset battery in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of lithium dendrite puncturing the separator, comprises: in response to determining that there is risk detection information in the battery potential risk detection information indicating that the preset battery has a risk of lithium dendrite puncturing the separator, sending a preset load removal information to a control module of a load device associated with the preset battery to control the load device to disconnect from the preset battery.

4. The method of claim 1, wherein, The collection of the relaxation voltage curve information, the impedance spectrum information, the lithium dendrite growth thickness information, and the battery internal temperature information of the preset battery in the outdoor low-temperature environment, comprises: after a preset time period after the preset battery is charged and discharged, collecting the battery internal temperature information by a temperature sensor embedded in the preset battery, and in response to determining that the battery internal temperature information meets a preset temperature condition, performing the following collection steps: collecting the relaxation voltage curve information of the preset battery in a preset collection time period by a voltage collection instrument connected to the preset battery; collecting the impedance spectrum information of the preset battery in the preset collection time period by an impedance spectrum instrument connected to the preset battery; collecting the lithium dendrite growth thickness information of the preset battery by an optical fiber sensor pre-implanted in the battery.

5. The method of claim 1, wherein, The generation of the battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information, comprises: obtain temperature and electrolyte concentration mapping information of the preset battery, wherein the temperature and electrolyte concentration mapping information comprises respective mapping information, each of the respective mapping information comprises preset temperature information and mapping concentration information; query mapping information corresponding to the battery internal temperature information from the respective mapping information in the temperature and electrolyte concentration mapping information as target mapping information; determine the mapping concentration information included in the target mapping information as target concentration information; determine information representing that the preset battery has a risk of ion conduction rupture caused by freezing as risk detection information in response to determining that the concentration represented by the target concentration information is greater than or equal to a preset electrolyte concentration threshold; determine information representing that the preset battery has a risk of lithium dendrite puncturing the separator as risk detection information in response to determining that the lithium dendrite growth thickness represented by the lithium dendrite growth thickness information is greater than or equal to a preset generation thickness threshold; determine at least one of the determined risk detection information as battery potential risk detection information; determine information representing that the preset battery has no potential risk as battery potential risk detection information in response to determining that the concentration represented by the target concentration information is less than the preset electrolyte concentration threshold and the lithium dendrite growth thickness represented by the lithium dendrite growth thickness information is less than the preset generation thickness threshold.

6. The method of claim 1, wherein, The relaxation voltage curve information comprises a voltage data point information sequence, each voltage data point in the voltage data point information sequence comprises a voltage value and a time point, and the battery service life prediction information is generated based on the relaxation voltage curve information, the impedance spectrum information and a pre-trained battery service life prediction model, comprising: For each voltage data point information in the voltage data point information sequence included in the relaxation voltage curve information, the following temperature compensation correction processing is performed: acquire the ambient temperature of the environment in which the preset battery is located; generate temperature correction factor information based on the ambient temperature and a preset linear correction model; generate a corrected voltage value based on the voltage value included in the voltage data point information and the temperature correction factor information; delete the voltage value in the voltage data point information, and add the corrected voltage value to the voltage data point information to update the voltage data point information; determine the updated voltage data point information as corrected voltage data point information; arrange the determined respective corrected voltage data point information to obtain a corrected voltage data point information sequence; generate battery service life prediction information based on the ambient temperature, the corrected voltage data point information sequence, the impedance spectrum information and a pre-trained battery service life prediction model.

7. An outdoor battery potential risk detection and service life prediction device, comprising: an acquisition unit configured to acquire relaxation voltage curve information, impedance spectrum information, lithium dendrite growth thickness information, and battery internal temperature information of a preset battery in an outdoor low-temperature environment; a first generation unit configured to generate battery potential risk detection information based on the lithium dendrite growth thickness information and the battery internal temperature information; The preheating protection processing unit is configured to perform preheating protection processing on the preset battery in response to determining that there is risk detection information representing that the preset battery has a risk of ion conduction fracture due to solidification in the battery potential risk detection information; The load shedding protection processing unit is configured to perform load shedding protection processing on the preset battery in response to determining that there is risk detection information representing that the preset battery has a risk of lithium dendrite puncturing the separator in the battery potential risk detection information; The second generation unit is configured to generate battery service life prediction information based on the relaxation voltage curve information, the impedance spectrum information and a pre-trained battery service life prediction model in response to determining that the battery potential risk detection information represents that the preset battery does not have a potential risk. The early warning unit is configured to perform battery replacement early warning processing in response to determining that the battery service life prediction information satisfies a battery replacement early warning condition.

8. An electronic device, comprising: one or more processors; a memory device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-6.

9. A computer readable medium having stored thereon a computer program, wherein, The program is executed by the processor to implement the method of any one of claims 1-6. The program is executed by the processor to implement the method of any one of claims 1-6.

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