Thermal runaway prediction method, battery management system and storage medium

Abstract

The present invention provides a thermal runaway prediction method, a battery management system and a storage medium, the method comprising: determining actual status information of a battery at a first moment; when it is determined according to threshold status information that the actual status information is abnormal, determining predicted status information of the battery at a future set time based on a thermal runaway model according to the actual status information; determining a status information change rate at a second moment according to the predicted status information, and determining whether the battery has a risk of thermal runaway according to the status information change rate at the second moment; the second moment is any moment in the future set time, by first judging whether the actual status information is abnormal based on the threshold status information, and when it is abnormal, determining the predicted status information at the future set time based on the thermal runaway model, thereby improving the accuracy of predicting whether there is thermal runaway.

Description

Technical Field

[0001] The present invention relates to the field of energy storage, and in particular to a thermal runaway prediction method, a battery management system and a storage medium. Background Art

[0002] Energy storage battery system is a device that can store electrical energy. It is used to provide backup power and stabilize the voltage of the power grid, such as energy storage battery cabinet. With the development of renewable energy, lithium battery energy storage battery cabinet plays an increasingly important role in the field of new energy. Its safety is directly related to the stability and reliability of the entire system. Therefore, it is very important to ensure the safety of energy storage battery system.

[0003] At present, energy storage battery systems are equipped with safety protection measures, which can monitor the operating status of the battery in real time through temperature sensors and smoke sensors. When thermal runaway is detected, the alarm system is immediately activated, and the pressure relief device and automatic fire extinguishing device are activated to prevent explosion and fire. When safety measures are taken after thermal runaway is detected, the energy released when thermal runaway occurs is very large, which can easily cause major accidents such as fire or explosion of batteries and energy storage battery cabinets.

[0004] There are still some methods to predict thermal runaway, but the prediction accuracy is low. Summary of the invention

[0005] The present invention provides a thermal runaway prediction method, a battery management system and a storage medium, which are used to solve the problem of low accuracy in predicting whether thermal runaway occurs in the prior art.

[0006] In a first aspect, the present invention provides a thermal runaway prediction method, the method comprising:

[0007] Determine actual status information of the battery at a first moment;

[0008] When it is determined according to the threshold state information that the actual state information is abnormal, then based on the thermal runaway model, the predicted state information of the battery at a future set time is determined according to the actual state information;

[0009] The rate of change of the state information at a second moment is determined according to the predicted state information, and the risk of thermal runaway of the battery is determined according to the rate of change of the state information at the second moment; the second moment is any moment in the future set time.

[0010] Optionally, the predicted state information includes a predicted voltage and a predicted temperature; the state information change rate includes a temperature rise rate and a voltage drop rate; and determining that the battery has a risk of thermal runaway according to the state information change rate at the second moment includes:

[0011] When it is determined that the temperature rise rate at the second moment is greater than the target temperature rise rate, or the voltage drop rate is greater than the target drop rate, then determining the actual state information at the third moment;

[0012] If the actual state information at the third moment matches the predicted state information at the moment, it is determined that the battery has a risk of thermal runaway at the second moment; the third moment is any moment between the first moment and the second moment.

[0013] Optionally, the method further includes:

[0014] Determine the historical state information according to the target time; the target time is the time when the time difference from the last correction time is greater than or equal to the first preset time interval; the last correction time is the time when the parameters in the thermal runaway model were last corrected; the historical state information is the actual state information corresponding to the multiple charging and discharging processes before the target time;

[0015] The parameters in the thermal runaway model are modified according to the historical state information.

[0016] Optionally, the method further includes:

[0017] After the parameters in the thermal runaway model are corrected according to the historical state information, the threshold state information is determined based on the thermal runaway model according to the operating condition information, voltage and temperature of the battery in a standard state.

[0018] Optionally, a plurality of batteries constitute a module, and the actual state information includes: actual temperature and actual voltage; the threshold state information includes: at least one of a voltage threshold, a temperature threshold, a maximum voltage difference threshold within the module, and a maximum temperature difference threshold within the module;

[0019] The method further includes: determining that the actual status information is abnormal when at least one of the following conditions is met:

[0020] The actual voltage of any battery is greater than the voltage threshold;

[0021] The actual temperature of any battery is greater than the temperature threshold;

[0022] The actual maximum voltage difference of any module is greater than the maximum voltage difference threshold within the module;

[0023] The actual maximum temperature difference of any module is greater than the maximum temperature difference threshold within the module.

[0024] Optionally, when it is determined that the battery has a risk of thermal runaway at the second moment, the method further includes:

[0025] Determine the time interval between the second moment and the current moment;

[0026] When the time interval is greater than a second preset time interval, an active safety measure of increasing the refrigeration capacity of the refrigeration device is executed;

[0027] When the time interval is less than or equal to the second preset time interval, an active safety measure is taken to set the cooling capacity of the refrigeration device to a maximum value and to stop charging and discharging the battery.

[0028] Optionally, the method further includes:

[0029] After a third preset time interval has elapsed since the moment when the active safety measure is executed, determining the actual state information of the battery again;

[0030] When it is predicted that the battery will experience thermal runaway according to the actual state information determined again, at least one of a pressure relief operation, a fire extinguishing operation and an alarm operation is triggered.

[0031] Optionally, the method is applied to a battery system, the battery system includes a computing node; the actual state information includes an actual voltage, an actual temperature, and an actual current; the computing node includes other nodes and N battery nodes that divide each battery; N is a positive integer greater than or equal to 3; the other nodes are nodes set for the locations of components other than batteries in the module; and the predicted state information of the battery at a future set time is determined based on the thermal runaway model according to the actual state information, including:

[0032] The following steps are performed through the thermal runaway model to determine the predicted temperature of each computing node:

[0033] For each computing node, a target node is determined according to a target direction, and a temperature gradient is determined according to a temperature value of the target node; the thermophysical property parameters of a material corresponding to the computing node are corrected according to the temperature value of the computing node at a previous moment; when a side reaction is determined to be triggered according to the temperature value of the computing node at a previous moment, a first amount of heat is determined; when the computing node is a battery node, a second amount of heat is determined according to a voltage value of the computing node and the actual current; the temperature value of the computing node at a previous moment is related to the actual state information at the first moment;

[0034] The predicted temperature of the computing node is determined according to the temperature value of the computing node at the last moment, the heat dissipation condition in the operating condition information, the temperature gradient, the first heat amount, and the second heat amount.

[0035] Optionally, determining predicted state information of the battery at a set time in the future based on a thermal runaway model according to the actual state information includes:

[0036] The following steps are performed by the thermal runaway model to determine the predicted voltage of the battery:

[0037] Correcting the parameters in the thermal runaway model according to the temperature value of the battery node at the last moment, and determining the state of charge at different moments in the future according to the actual current; the temperature value of the battery node at the last moment is related to the actual state information at the first moment;

[0038] For each battery node, determine the positive and negative potential difference of the battery node according to the parameters in the modified thermal runaway model, the actual current, the volume of the battery node and the state of charge;

[0039] For each battery, the negative potential of each battery node in the battery is calculated according to the potential diffusion equation and the negative potential at the negative electrode tab of the battery, and the positive potential of the battery node is determined according to the positive and negative potential difference of the battery node and the corresponding negative potential;

[0040] A predicted voltage of the battery is determined based on the positive potential and the negative potential of each battery node in the battery.

[0041] Optionally, the thermal runaway model is a low-dimensional thermal runaway model; the low-dimensional thermal runaway model is determined based on the target direction in the battery; the target direction includes a direction perpendicular to the pole piece.

[0042] In a second aspect, the present invention provides a thermal runaway prediction device, comprising:

[0043] A first determining module, used to determine actual state information of the battery at a first moment;

[0044] A prediction module, configured to determine predicted state information of the battery at a set time in the future based on a thermal runaway model according to the actual state information when it is determined that the actual state information is abnormal according to the threshold state information;

[0045] The second determination module is used to determine the rate of change of the state information at a second moment according to the predicted state information, and determine the risk of thermal runaway of the battery according to the rate of change of the state information at the second moment; the second moment is any moment in the future set time.

[0046] In a third aspect, the present invention provides a battery management system, comprising: at least one processor and a memory;

[0047] The memory stores computer-executable instructions;

[0048] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method as described in any one of the first aspects.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method described in any one of the first aspects is implemented.

[0050] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0051] The present invention provides a thermal runaway prediction method, a battery management system and a storage medium, the method comprising: determining actual status information of a battery at a first moment; when it is determined according to threshold status information that the actual status information is abnormal, determining predicted status information of the battery at a future set time based on a thermal runaway model according to the actual status information; determining a status information change rate at a second moment according to the predicted status information, and determining whether the battery has a risk of thermal runaway according to the status information change rate at the second moment; the second moment is any moment in the future set time, by first judging whether the actual status information is abnormal based on the threshold status information, and when it is abnormal, determining the predicted status information at the future set time based on the thermal runaway model, thereby improving the accuracy of predicting whether there is thermal runaway. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0053] Figure 1 An application scenario diagram provided for an embodiment of the present invention;

[0054] Figure 2 A schematic flow chart of a thermal runaway prediction method provided by an embodiment of the present invention;

[0055] Figure 3 A schematic flow chart of another thermal runaway prediction method provided by an embodiment of the present invention;

[0056] Figure 4 An equivalent circuit used to calculate the battery potential in a thermal runaway model provided in an embodiment of the present invention;

[0057] Figure 5 A schematic diagram of computing node division provided by an embodiment of the present invention;

[0058] Figure 6 A schematic diagram of the structure of a battery balancing device provided by an embodiment of the present invention;

[0059] Figure 7A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention.

[0060] The above drawings have shown clear embodiments of the present invention, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present invention in any way, but to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0061] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention.

[0062] In this article, it should be understood that the terms involved are only used to facilitate understanding and do not represent any limitation on the meaning. In addition, any number of elements in the drawings is used for example rather than limitation, and any naming is only used to distinguish and does not have any limiting meaning.

[0063] The data involved in the present invention may be data authorized by the user or fully authorized by all parties, and the collection, dissemination, and use of the data shall comply with the requirements of relevant national laws and regulations.

[0064] At present, the following methods can be used in energy storage battery systems to improve safety to a certain extent. First, improve battery materials and structures. For example, by developing flame-retardant and non-flammable electrolytes, improving battery consistency, and using high-efficiency thermal insulation materials, thermal runaway of batteries in energy storage battery systems can be prevented to improve battery safety. Second, safety protection facilities are installed in the energy storage battery system to monitor the operating status of the battery module in real time through temperature sensors and smoke sensors. Once an abnormal situation occurs, the alarm system is immediately activated, and the pressure relief device and automatic fire extinguishing device are activated to prevent explosion and fire.

[0065] In the first method, the risk of thermal runaway still exists even if the material and structure of the battery are improved. In the second method, the system will only take safety measures when thermal runaway occurs, which cannot prevent the risk in advance. Therefore, the present invention considers providing a method for predicting thermal runaway.

[0066] Figure 1 An application scenario diagram provided by an embodiment of the present invention, such as Figure 1As shown, when predicting thermal runaway, in order to improve the accuracy of the prediction result, the present invention judges whether there is an abnormality in the actual state information collected at the first moment based on the threshold state information. When there is an abnormality, a thermal runaway model can be used to perform a calculation. Specifically, the predicted state information is determined based on the thermal runaway model, and whether there is a risk of thermal runaway is determined based on the predicted state information. By predicting thermal runaway based on two judgment processes, the accuracy of the prediction result can be improved.

[0067] Figure 2 A flow chart of a thermal runaway prediction method provided in an embodiment of the present invention is provided. The method is applied in a BMS (Battery Management System). The method includes:

[0068] Step S201: Determine actual status information of the battery at a first moment.

[0069] Optionally, the method of the present application can predict whether a single cell battery will experience thermal runaway, and can also predict whether a battery system including multiple batteries will experience thermal runaway. The battery system can be an energy storage battery system, such as an energy storage battery cabinet. The battery system can include multiple modules, each module including multiple batteries. For any battery, it can be predicted whether the battery will experience thermal runaway.

[0070] In order to reduce the amount of calculation, it is not necessary to monitor the battery at every moment, and a sampling interval can be set to monitor the battery based on the sampling interval. Specifically, it can be determined whether the first moment meets the sampling interval, that is, whether the difference between the first moment and the last sampling time is equal to or greater than the sampling interval. If it is equal to or greater, the actual status information of the battery at the first moment is obtained.

[0071] At the first moment, when predicting whether the battery will have thermal runaway, the actual state information of the battery can be collected, and the actual state information can be information such as voltage and temperature at the first moment. Optionally, the actual state information of the battery at the first moment can also be the actual state information of the battery within a period of time including the first moment.

[0072] The setting of the sampling interval may vary according to the actual situation. For example, it may be a value between 1 and 30 minutes. When the sampling interval is smaller, the computing power requirement of the battery management system is higher. The present application does not limit the specific value of the sampling interval.

[0073] Optionally, when the total battery usage time is short, a longer sampling interval can be set; when the total battery usage time is long, a shorter sampling interval can be set. The total usage time refers to the cumulative usage time of the battery from the time it leaves the factory to the first moment. Alternatively, different sampling intervals can be set according to the different working steps and charge and discharge rates of the battery.

[0074] Optionally, since the heat and electric potential of the battery will diffuse, the temperature or voltage change trend of any battery in the battery system is related to the temperature or voltage of its surrounding batteries. Therefore, when predicting whether any battery in the battery system will have thermal runaway, the actual status information of each battery can be obtained to achieve accurate prediction of the predicted status information of the battery.

[0075] Step S202: when it is determined according to the threshold state information that the actual state information is abnormal, the predicted state information of the battery at a set time in the future is determined based on the thermal runaway model according to the actual state information.

[0076] After obtaining the actual state information, it can be determined whether the actual state information is abnormal based on the predetermined threshold information. The threshold information can be predetermined. When the actual state information is greater than the threshold information, it indicates that the actual state information is abnormal, that is, there is a risk of thermal runaway, and further judgment is required.

[0077] The threshold information is related to the battery usage time. As the battery usage time increases, the threshold information will decrease. By regularly updating the threshold information, the accuracy of determining whether the actual status information is abnormal can be improved.

[0078] When it is determined that the actual state information is abnormal, the predicted state information of the battery can be determined based on the thermal runaway model. The thermal runaway model can simulate the operation of the battery to obtain the predicted voltage or predicted temperature of the battery at different times under the working state.

[0079] Optionally, if the battery generates more heat during the charging or discharging process, the actual state information at the first moment of the charging or discharging process can be collected, and the predicted state information at a future set time after that moment can be predicted. In order to reduce the amount of calculation, the future set time here can be part of the time period from that moment to the end of charging or discharging. Exemplarily, the predicted state information of the battery can be calculated at a frequency of every 2 minutes, such as calculating the predicted state information corresponding to the moment 2 minutes after the first moment, the moment 4 minutes after the first moment, etc.

[0080] The thermal runaway model may include a temperature calculation module and a voltage calculation module. The temperature calculation module is used to calculate the temperature diffusion of the battery; the voltage calculation module is used to calculate the voltage diffusion of the battery, thereby determining the predicted state information of any battery at a set time in the future.

[0081] Optionally, when the actual state information collected is abnormal, whether the battery is at risk of thermal runaway is predicted based on the thermal runaway model; when the actual state information collected is not abnormal, the energy storage battery system is controlled to operate according to the working steps.

[0082] Step S203, determining a rate of change of the state information at a second moment according to the predicted state information, and determining that the battery has a risk of thermal runaway according to the rate of change of the state information at the second moment; the second moment is any moment in the future set time.

[0083] The thermal runaway model can output predicted state information at a set time in the future, and based on the output predicted state information, it can be determined whether the battery will experience thermal runaway.

[0084] The predicted state information may be predicted values ​​at multiple moments in the future. When the predicted state information at different moments is determined, the change rate of the state information at different moments may be determined, for example, the voltage change rate or the temperature change rate. Based on the change rate of the state information, it may be determined whether the battery has a risk of thermal runaway at that moment.

[0085] Optionally, the rate of change of the state information at any moment may be calculated based on the predicted state information at that moment and the predicted state information at the previous moment.

[0086] The present invention provides a thermal runaway prediction method, which includes: determining actual state information of a battery at a first moment; when it is determined according to threshold state information that the actual state information is abnormal, determining predicted state information of the battery at a future set time based on a thermal runaway model according to the actual state information; determining a state information change rate at a second moment according to the predicted state information, and determining whether the battery has a risk of thermal runaway according to the state information change rate at the second moment; the second moment is any moment in the future set time, by first judging whether the actual state information is abnormal based on the threshold state information, and when it is abnormal, determining the predicted state information at the future set time based on the thermal runaway model, thereby improving the accuracy of predicting whether there is thermal runaway.

[0087] Figure 3 A schematic flow chart of another thermal runaway prediction method provided by an embodiment of the present invention is shown in FIG. Figure 3As shown, when making a thermal runaway judgment, historical message data (including historical status information) can be collected regularly to correct the parameters in the thermal runaway model, and real-time message information (including actual status information) can be collected at set time intervals to determine whether the real-time message information is abnormal. When there is an abnormality, the thermal runaway model is calculated, that is, the prediction result of the thermal runaway model is determined based on the actual status information to determine whether thermal runaway will occur. If thermal runaway is predicted to occur, active safety measures are initiated, and real-time message information is collected after the specified interval of starting the active safety measures, and the thermal runaway model is calculated to determine whether thermal runaway will occur. If thermal runaway is predicted to occur again, firefighting measures are initiated. Among them, when there is no abnormality in the real-time message information, or when the prediction result of the thermal runaway model is that thermal runaway will not occur, the battery continues to operate according to the process step. The detailed steps of the above-mentioned whole process are described below.

[0088] Optionally, the predicted state information includes a predicted voltage and a predicted temperature; the state information change rate includes a temperature rise rate and a voltage drop rate; and determining that the battery has a risk of thermal runaway according to the state information change rate at the second moment includes:

[0089] When it is determined that the temperature rise rate at the second moment is greater than the target temperature rise rate, or the voltage drop rate is greater than the target drop rate, then determining the actual state information at the third moment;

[0090] If the actual state information at the third moment matches the predicted state information at the moment, it is determined that the battery has a risk of thermal runaway at the second moment; the third moment is any moment between the first moment and the second moment.

[0091] For any battery, the predicted state information may be a predicted voltage or a predicted temperature. Based on the predicted voltage or the predicted temperature at a set time in the future, the temperature rise rate and the voltage drop rate at a certain moment can be determined. Based on the temperature rise rate and the voltage drop rate, it can be determined whether thermal runaway may occur at that moment. For example, for battery 1, when it is determined according to the predicted state information that the temperature rise rate is greater than 1 degree per second after 30 minutes, or the voltage drop rate is greater than a certain value, it means that thermal runaway may occur in battery 1 at 30 minutes.

[0092] Optionally, the temperature rise rate (if the temperature decreases relative to the previous moment, the temperature rise rate is a negative value) and the voltage drop rate (if the voltage increases relative to the previous moment, the voltage drop rate is a negative value) at multiple moments in the future set time can be calculated. When there is a second moment when the temperature rise rate is greater than the target temperature rise rate, or the voltage drop rate is greater than the target drop rate, it indicates that there may be a risk of thermal runaway at the second moment.

[0093] Optionally, the target temperature rise rate and the target temperature drop rate can be set according to user needs and are not specifically limited here.

[0094] Since the system is disturbed by the environment, there may be a problem of inaccurate collection of actual state information, resulting in errors in the predicted state information, and then an erroneous prediction of the occurrence of thermal runaway. Based on the above problem, when determining whether there is really a risk of thermal runaway at the second moment, the actual state information at the third moment and the predicted state information at the third moment can be compared, and the actual state information and the predicted state information can be matched to verify whether the actual state information is consistent with the trend of the predicted state information, thereby improving the accuracy of the result of determining whether the battery will have thermal runaway. For example, the second moment is 30 minutes after the current moment, and the third moment is 10 minutes after the current moment. When there is no second moment in the prediction result where the temperature rise rate is greater than the target temperature rise rate, or the voltage drop rate is greater than the target drop rate, it means that the battery does not have a risk of thermal runaway.

[0095] Optionally, when there is a match, it indicates that there is a risk of thermal runaway at the second moment. When there is no match, one or more third moments can be selected to determine whether there is a match. If there is no match, it indicates that there is no risk of thermal runaway at the second moment.

[0096] Optionally, the match here means that the actual state information at the third moment is greater than the predicted state information at the third moment, or the actual state information is less than the predicted state information and the difference between the actual state information and the predicted state information is less than a preset value. If matched, it means that the actual state information is consistent with the trend of the predicted state information.

[0097] Optionally, according to actual needs, the third moment can be set to one or more, that is, when verifying whether the actual state information is consistent with the trend of the predicted state information, one third moment can be selected for verification, or multiple third moments can be selected for verification.

[0098] By first determining the second moment when thermal runaway may occur based on changes in status information, and then determining whether there is a risk of thermal runaway at the second moment based on whether the actual status information at the third moment matches the predicted status information at that moment, the problem of prediction errors caused by environmental interference can be eliminated, and the accuracy of thermal runaway prediction can be improved.

[0099] Optionally, the method further includes:

[0100] Determine the historical state information according to the target time; the target time is the time when the time difference from the last correction time is greater than or equal to the first preset time interval; the last correction time is the time when the parameters in the thermal runaway model were last corrected; the historical state information is the actual state information corresponding to the multiple charging and discharging processes before the target time;

[0101] The parameters in the thermal runaway model are modified according to the historical state information.

[0102] Batteries will decay and age during use. As the batteries decay and age, the parameters in the thermal runaway model will also change. Therefore, it is necessary to regularly revise the parameters in the thermal runaway model to maintain the accuracy of the thermal runaway model prediction results.

[0103] Optionally, historical message data may be collected periodically, the historical message data includes historical status information, and the parameters in the thermal runaway model may be corrected based on the historical status information. Specifically, the time of each correction of the parameters in the thermal runaway model may be recorded, and a target time may be determined, the target time being the time when the time difference from the time when the parameters in the thermal runaway model were last corrected is greater than or equal to a first preset time interval, the historical status information corresponding to the target time may be obtained, and the parameters in the thermal runaway model may be corrected.

[0104] When the battery system is always in operation, the time interval between each target moment is the first preset time interval. When the battery system is not always in operation, the time interval between two adjacent target moments may be greater than the first preset time interval.

[0105] Optionally, the first preset time interval may be set according to the performance of the battery, and illustratively, may be set to half a month to one month. The present application does not impose any specific limitation on the value of the first preset time interval.

[0106] In order to improve the accuracy of parameter correction in the thermal runaway model, the actual state information corresponding to multiple charging and discharging processes before the target moment can be collected, and the parameters in the thermal runaway model can be corrected based on the actual state information of multiple charging and discharging processes to avoid the problem of inaccurate corrected parameters due to the randomness of the data collected at a certain time.

[0107] Optionally, the actual status information corresponding to multiple batteries in multiple charging and discharging processes can be collected, and the data can be filtered first, and then the filtered data can be averaged, and then the parameters of the thermal runaway model can be corrected according to the obtained results. Among them, the filtering process can be to process the data through a filter function to eliminate noise in the data. By collecting the historical status information of multiple batteries, the problem of inaccurate corrected parameters due to the randomness of collecting the historical status information of a certain battery can be avoided.

[0108] Optionally, when modifying the parameters in the model, the historical state information used may include voltage, current and state of charge. The parameters in the thermal runaway model that can be modified include: equivalent circuit model parameters, battery capacity, etc.

[0109] Figure 4 An equivalent circuit used to calculate the battery potential in a thermal runaway model provided in an embodiment of the present invention, such as Figure 4 As shown, a battery or a battery node among multiple battery nodes divided by the battery can be equivalent to the circuit. There are parameters such as open circuit voltage, internal resistance and internal short circuit resistance in the equivalent circuit. The parameters such as open circuit voltage, internal resistance and internal short circuit resistance can be determined from historical status information through an algorithm.

[0110] When correcting the battery capacity parameter, it can be determined based on the active material loss coefficient, and the corrected battery capacity can be determined based on the product of the active material loss coefficient and the battery capacity before correction. The active material loss coefficient can be determined based on the normalized active material concentration and the change rate of the active material loss coefficient after the last correction. The change rate of the active material loss coefficient can be modeled based on historical state information and calculated using the Arrhenius formula.

[0111] Among them, the battery releases a lot of heat during the charging or discharging process, which means that there is a greater possibility of thermal runaway. Therefore, the battery charging process or discharging process can be monitored to predict whether the battery will experience thermal runaway. Therefore, when predicting thermal runaway, a charging time or a discharging time can be determined first. The thermal runaway model can determine whether to stop predicting whether the battery will experience thermal runaway based on the charging time or the discharging time. Optionally, the battery capacity can be used to determine the charging time or the discharging time. Specifically, the charging time or the discharging time is determined based on the battery capacity and the charge and discharge rate.

[0112] Since battery degradation will affect the parameters in the thermal runaway model, the accuracy of the parameters in the thermal runaway model can be improved by regularly correcting the parameters in the thermal runaway model, thereby improving the accuracy of the output results of the thermal runaway model.

[0113] Optionally, the method further includes:

[0114] After the parameters in the thermal runaway model are corrected according to the historical state information, the threshold state information is determined based on the thermal runaway model according to the operating condition information, voltage and temperature of the battery in a standard state.

[0115] The determination of the threshold state information is related to the parameters of the thermal runaway model. In addition, it is also related to the operating condition information, voltage and temperature under the standard state.

[0116] Since the parameters in the thermal runaway model need to be corrected regularly, after the parameters in the thermal runaway model are corrected, the threshold state information can be determined based on the parameters in the corrected thermal runaway model. Specifically, the standard state refers to the standard operating condition of the battery system, and also includes the voltage and temperature when charging starts in this state.

[0117] Optionally, the operating condition information may include charge and discharge rates, and heat dissipation conditions under standard conditions, which will affect the voltage and temperature predicted by the thermal runaway model.

[0118] After the voltage, temperature and operating condition information under the standard state is input into the thermal runaway model, when the battery starts to charge from a SOC value of 0%, the model output results corresponding to different SOCs (State of Charge) during charging can be determined, and the threshold information corresponding to the SOC can be determined based on the model output results.

[0119] Optionally, the thermal runaway model may output the voltage and temperature of each battery, and may determine the minimum voltage value and the minimum temperature value of all batteries, thereby determining a voltage threshold value according to the minimum voltage value, and determining a temperature threshold value according to the minimum temperature value.

[0120] Optionally, the maximum voltage difference threshold in the module can be determined according to the difference between the maximum voltage value and the minimum voltage value in each battery; the maximum temperature difference threshold in the module can be determined according to the difference between the maximum temperature value and the minimum temperature value in each battery.

[0121] For example, when the SOC is 20%, if the minimum voltage value output by the thermal runaway model is U1, U1*target ratio (for example, 125%) is used as the threshold voltage when the SOC is 20%. The target ratio can be set according to user needs and is not limited here.

[0122] By determining whether the actual state information is abnormal based on the threshold state information, and then determining whether to start the thermal runaway model to obtain the predicted state information based on the judgment result, there is no need to perform prediction through the thermal runaway model every time after obtaining the actual state information, which can reduce the amount of calculation.

[0123] Optionally, a plurality of batteries constitute a module, and the actual state information includes: actual temperature and actual voltage; the threshold state information includes: at least one of a voltage threshold, a temperature threshold, a maximum voltage difference threshold within the module, and a maximum temperature difference threshold within the module;

[0124] The method further includes: determining that the actual status information is abnormal when at least one of the following conditions is met:

[0125] The actual voltage of any battery is greater than the voltage threshold;

[0126] The actual temperature of any battery is greater than the temperature threshold;

[0127] The actual maximum voltage difference of any module is greater than the maximum voltage difference threshold within the module;

[0128] The actual maximum temperature difference of any module is greater than the maximum temperature difference threshold within the module.

[0129] In a battery system (for example, a storage battery cabinet), there may be multiple modules, each module contains multiple batteries, and the actual status information obtained includes the voltage and temperature of each battery. In order to determine whether the actual status information obtained is abnormal, the threshold information can be determined. Optionally, the threshold information may include voltage thresholds and temperature thresholds corresponding to different SOCs, as well as the maximum voltage threshold in the module and the maximum temperature difference threshold in the module. SOC is used to measure different moments in the charging or discharging process.

[0130] When determining whether the collected actual status information has an abnormality, the actual status information of each battery is actually collected, and the actual voltage difference and actual temperature difference of each module can be calculated based on the actual status information of each battery.

[0131] For each battery, the actual state information obtained contains actual SOC information, and different threshold state information is set for different SOCs. Therefore, the threshold state information corresponding to the actual SOC information can be determined from the threshold state information according to the actual SOC information, and then it is determined whether the actual state information of the battery is abnormal based on the determined threshold state information.

[0132] For example, when the SOC is 20% and 40%, the corresponding voltage thresholds are voltage 1 and voltage 2 respectively. The actual status information obtained includes the actual SOC information (such as 20%) and the actual voltage. Therefore, the voltage threshold can be determined to be voltage 1 based on the SOC being 20%, and then whether there is an abnormality can be determined based on voltage 1 and the actual voltage.

[0133] Optionally, when the actual voltage of the battery is greater than the voltage threshold, it indicates that the actual status information of the battery is abnormal; or, when the actual temperature of the battery is greater than the temperature threshold, it indicates that the actual status information of the battery is abnormal. For each module, when the actual maximum voltage difference of the module is greater than the maximum voltage difference threshold within the module, it indicates that the actual status information of some batteries in the module is abnormal; or, when the actual maximum temperature difference of the module is greater than the maximum temperature difference threshold within the module, it indicates that the actual status information of some batteries in the module is abnormal.

[0134] By setting a plurality of different threshold state information and threshold information under different SOCs as the determination condition of whether the actual state information is abnormal, the accuracy of detecting the abnormality of the actual state information is improved.

[0135] Optionally, when it is determined that the battery has a risk of thermal runaway at the second moment, the method further includes:

[0136] Determine the time interval between the second moment and the current moment;

[0137] When the time interval is greater than a second preset time interval, an active safety measure of increasing the refrigeration capacity of the refrigeration device is executed;

[0138] When the time interval is less than or equal to the second preset time interval, an active safety measure is taken to set the cooling capacity of the refrigeration device to a maximum value and to stop charging and discharging the battery.

[0139] Here, the moment when it is determined that the battery has a risk of thermal runaway at the second moment is defined as the current moment. Therefore, after predicting that thermal runaway will occur at the second moment in the future at the current moment, active safety measures can be initiated at the current moment to reduce the risk of thermal runaway.

[0140] When it is determined that thermal runaway will occur at the second moment, the time interval between the second moment and the current moment can be determined, and different active safety measures can be executed according to the length of the time interval.

[0141] When the time interval is greater than the second preset time interval, it means that the current moment is far away from the second moment when thermal runaway occurs. Only the cooling capacity of the refrigeration device can be controlled to increase to reduce the possibility of thermal runaway at the second moment, while not affecting the operation of the energy storage battery system, for example, the battery is charged or discharged normally.

[0142] When the time interval is less than or equal to the second preset time interval, it means that the current moment is close to the second moment when thermal runaway occurs. If only the cooling capacity of the refrigeration device is controlled to increase, thermal runaway may not be avoided. Therefore, the cooling capacity of the refrigeration device can be controlled to be at the maximum value, and the battery can be controlled to stop charging and discharging to reduce heat dissipation, thereby reducing the possibility of thermal runaway at the second moment. Optionally, when the number of batteries is large, at least some of the batteries can be controlled to stop charging and discharging.

[0143] Optionally, when it is determined that thermal runaway will occur at the second moment, early warning information can also be uploaded to remind relevant personnel to pay attention to the operation of the energy storage battery system.

[0144] Optionally, the second preset time interval can be set according to actual conditions, and this application does not limit the specific value of the second preset time interval.

[0145] By executing different measures according to the length of the time interval between the second moment and the current moment, it is possible to avoid thermal runaway without affecting the normal operation of the battery system.

[0146] Optionally, the method further includes:

[0147] After a third preset time interval has elapsed since the moment when the active safety measure is executed, determining the actual state information of the battery again;

[0148] When it is predicted that the battery will experience thermal runaway according to the actual state information determined again, at least one of a pressure relief operation, a fire extinguishing operation and an alarm operation is triggered.

[0149] In order to further reduce the risk of thermal runaway, if the time interval from the moment of initiating active safety measures reaches a third preset time interval, the actual status information of the battery can be obtained again, and the thermal runaway model can be used to predict again whether the battery will experience thermal runaway.

[0150] Optionally, the third preset time interval can be set according to actual conditions. Exemplarily, it can be any value from half an hour to 2 hours. The specific value of the third preset time interval is not limited here.

[0151] If thermal runaway still occurs after prediction, active firefighting measures can be initiated, such as triggering pressure relief operations, fire extinguishing operations, or alarm operations, to prevent thermal runaway from causing casualties.

[0152] By predicting whether thermal runaway will occur again a certain period of time after the active safety measures are initiated, and taking active fire-fighting measures when there is still a risk of thermal runaway, casualties can be avoided, further improving the safety of the energy storage battery system operation.

[0153] Optionally, the method is applied to a battery system, the battery system includes a computing node; the actual state information includes an actual voltage, an actual temperature, and an actual current; the computing node includes other nodes and N battery nodes that divide each battery; N is a positive integer greater than or equal to 3; the other nodes are nodes set for the locations of components other than batteries in the module; and the predicted state information of the battery at a future set time is determined based on the thermal runaway model according to the actual state information, including:

[0154] The following steps are performed through the thermal runaway model to determine the predicted temperature of each computing node:

[0155] For each computing node, a target node is determined according to a target direction, and a temperature gradient is determined according to a temperature value of the target node; the thermophysical property parameters of a material corresponding to the computing node are corrected according to the temperature value of the computing node at a previous moment; when a side reaction is determined to be triggered according to the temperature value of the computing node at a previous moment, a first amount of heat is determined; when the computing node is a battery node, a second amount of heat is determined according to a voltage value of the computing node and the actual current; the temperature value of the computing node at a previous moment is related to the actual state information at the first moment;

[0156] The predicted temperature of the computing node is determined according to the temperature value of the computing node at a previous moment, the acquired heat dissipation condition, the temperature gradient, the first heat amount, and the second heat amount.

[0157] The battery system includes computing nodes. Usually, the temperature of different parts of a battery is different. If the battery is used as a computing node to calculate the temperature, the accuracy is not high. This is because there is a diaphragm in the battery, and the thermal conductivity of the diaphragm is low, so the temperature of different parts of the battery varies greatly. Therefore, the present invention decomposes each battery into a calculation method of small batteries in parallel. Exemplarily, a battery is divided into multiple battery nodes (for example, 3 battery nodes), that is, three predicted temperature values ​​are obtained for a battery, thereby accurately predicting the temperature of different parts of a battery and improving the accuracy of temperature prediction.

[0158] Figure 5 A schematic diagram of computing node division provided by an embodiment of the present invention is as follows: Figure 5 As shown, each battery is divided into three battery nodes. Each battery node represents an equivalent small battery, and the total capacity of the equivalent small batteries is equal to the capacity of the original battery.

[0159] In addition to normal chemical reactions, side reactions may also occur during the operation of the battery. Side reactions refer to additional unnecessary reactions in the battery, which can lead to reduced charging efficiency, capacity loss, life loss, or performance degradation. Usually, side reactions are triggered when the battery temperature is high, and the occurrence of side reactions releases a lot of heat, which affects the calculation of battery temperature and, in turn, the calculation of battery voltage. Therefore, by dividing the battery into multiple battery nodes, the accuracy of the prediction results output by the thermal runaway model can be improved, thereby improving the active safety performance of the battery system.

[0160] In addition to the battery node, other nodes can be set for components other than the battery. Optionally, a heat insulating material (e.g., aerogel) between two batteries and a fixture can each be set with an other node. The other nodes and the battery node together constitute the calculation node. For all the calculation nodes, the predicted temperature value can be calculated separately.

[0161] Optionally, actual status information of the battery is obtained, that is, actual status information of each computing node is obtained.

[0162] The heat dissipation conditions corresponding to the different positions of the batteries in the battery system are different. For batteries at the edge, the heat dissipation conditions are better, and for batteries at non-edge positions, the heat dissipation conditions are worse. Therefore, the heat dissipation conditions at different positions in the battery system can also be obtained.

[0163] Since the temperature of each computing node is related to the temperature of its adjacent computing nodes (heat diffusion principle), when calculating the temperature of the current computing node, the target direction can be determined first.

[0164] Optionally, the thermal runaway model is a low-dimensional thermal runaway model; the low-dimensional thermal runaway model is determined based on the target direction in the battery; the target direction includes a direction perpendicular to the pole piece.

[0165] In order to reduce the amount of calculation of the thermal runaway model, the thermal runaway model is set to a one-dimensional thermal runaway model or a two-dimensional thermal runaway model. These thermal runaway models are low-dimensional thermal runaway models that only calculate the diffusion of heat in the target direction in space. The target direction is the main direction of heat diffusion, and here the target direction is the direction perpendicular to the pole piece. Figure 5 As shown in the figure, the direction indicated by the arrow is perpendicular to the pole piece. If the temperature diffusion in three or more directions in the calculation space is calculated, the amount of calculation is large and the modeling is more complicated.

[0166] By setting the target direction and determining the computing nodes that affect the thermal runaway model when calculating the voltage or temperature of a certain computing node based on the target direction, the calculation accuracy of the thermal runaway model can be guaranteed while reducing the calculation amount and required computing resources of the thermal runaway model. At the same time, the difficulty of modeling is reduced, the local computing resources of the battery system can be used to complete the calculation, and the occurrence of thermal runaway can be predicted as early as possible, so that the battery system can have a faster response speed.

[0167] Optionally, a target node that affects the temperature of the current computing node can be determined based on the target direction. A temperature gradient can be determined based on the temperature value of the target node, and the temperature gradient represents the difference between the temperature of the target node and the temperature of the current computing node. The predicted temperature of the current node to be calculated is related to the temperature gradient. Figure 5 As shown, when the current node is node 2, the target nodes are node 1 and node 3.

[0168] When the temperature value of the calculation node is high, the thermophysical parameters of the corresponding material will change. Optionally, a database can be pre-set to store the correspondence between different temperatures and corresponding thermophysical parameters. When the temperature of a certain calculation node is calculated through the thermal runaway model, the thermophysical parameters in the thermal runaway model can be corrected according to the temperature value of the calculation node at the previous moment, such as density, specific heat capacity, thermal conductivity and other parameters.

[0169] Optionally, when the temperature value of the computing node is high, a side reaction may be triggered. When the side reaction is determined to be triggered according to the temperature value of the computing node at the previous moment, the first heat, that is, the heat generated by the side reaction, is determined. Based on the first heat, the impact on the computing node temperature can be determined.

[0170] Optionally, when the computing node is a battery node, the second heat can be determined according to the voltage value and actual current of the computing node. The second heat is Joule heat, that is, the heat generated by the normal charging or discharging process of the battery. Here, the actual current is used to calculate the second heat instead of the charge and discharge rate to calculate the second heat, which can improve the accuracy of the calculated second heat.

[0171] Optionally, when predicting the temperature value of the computing node, the temperature value at the next moment is determined based on the voltage value and temperature collected at the current moment, and the temperature value at the next moment is predicted based on the temperature value at the next moment. Therefore, the temperature value at the previous moment that appears here is related to the actual state information collected at the first moment.

[0172] After determining the temperature value of the computing node at the previous moment, the obtained heat dissipation condition (the heat dissipation condition at the first moment), the temperature gradient, the first heat quantity and the second heat quantity, the predicted temperature of the computing node can be determined according to the heat diffusion equation. Optionally, the heat diffusion equation is as follows, where: is the density, is the specific heat capacity, represents the thermal conductivity, Indicates the impact of the temperature value at the previous moment on the temperature at the current moment. is the temperature value to be calculated, is the impact of the target node on the temperature of the current computing node; It represents the sum of the first heat, the second heat and the heat dissipation corresponding to the heat dissipation condition, where the heat dissipation is a negative value. The heat dissipation for computing nodes at different locations is different. Optionally, the heat dissipation condition may include information such as the return air temperature of the air conditioner, the cooling point of the air conditioner, and the ambient temperature.

[0173]

[0174] The predicted temperature of any computing node can be calculated using the above formula.

[0175] By dividing the battery into multiple computing nodes, the accuracy of the thermal runaway model in predicting the battery temperature can be improved, thereby improving the accuracy of whether the side reactions are triggered, thereby improving the output results of the thermal runaway model; at the same time, by considering the triggering of side reactions by temperature, the correction of thermal physical parameters by temperature, and the influence of heat dissipation conditions at different locations, the accuracy of the output predicted temperature can be improved.

[0176] Optionally, determining predicted state information of the battery at a set time in the future based on a thermal runaway model according to the actual state information includes:

[0177] The following steps are performed by the thermal runaway model to determine the predicted voltage of the battery:

[0178] Correcting the parameters in the thermal runaway model according to the temperature value of the battery node at the last moment, and determining the state of charge at different moments in the future according to the actual current; the temperature value of the battery node at the last moment is related to the actual state information at the first moment;

[0179] For each battery node, determine the positive and negative potential difference of the battery node according to the parameters in the modified thermal runaway model, the actual current, the volume of the battery node and the state of charge;

[0180] For each battery, the negative potential of each battery node in the battery is calculated according to the potential diffusion equation and the negative potential at the negative electrode tab of the battery, and the positive potential of the battery node is determined according to the positive and negative potential difference of the battery node and the corresponding negative potential;

[0181] A predicted voltage of the battery is determined based on the positive potential and the negative potential of each battery node in the battery.

[0182] The battery is divided into multiple battery nodes. When determining the voltage of the battery, the voltage of the battery can be determined according to the potentials of the multiple divided battery nodes. Therefore, the positive potential and negative potential of each battery node can be determined first. For other nodes in the calculation node, since there is no voltage and current, the potential of these other nodes is not calculated.

[0183] The temperature value of the battery node can affect the parameters in the thermal runaway model, such as open circuit voltage, internal resistance, internal short circuit resistance, conductivity, etc. Therefore, the parameters in the thermal runaway model can be corrected based on the temperature value of the battery node at the previous moment, so as to determine the predicted voltage value based on the corrected parameters.

[0184] The actual state information collected includes the actual current. Based on the actual current, the charge state at different times of the charging process can be determined, and the charge state can affect the open circuit voltage parameter in the thermal runaway model.

[0185] For a battery node, there are two potentials, positive potential and negative potential , the positive and negative potential difference between positive potential and negative potential can be calculated according to the following formula. Represents volume current density, which is the ratio of actual current to the volume of the battery node ratio. represents the open circuit voltage, is the parameter of internal resistance in thermal runaway model.

[0186]

[0187] For a battery, including multiple battery nodes, the negative potential at the negative electrode ear of the battery (the negative potential of a battery node) is a known boundary condition, usually 0. Then, the negative potential of other battery nodes in the battery node can be calculated according to the potential diffusion equation, where: is the conductivity, is the influence of the adjacent battery node on the potential of the current battery node, Indicates the negative potential of the battery node to be calculated.

[0188]

[0189] For a battery, after the negative potential of each battery node is calculated, the positive potential of the battery node can be calculated according to the positive and negative potential difference of the battery node. Therefore, the positive potential and negative potential of each battery node in a battery can be calculated.

[0190] For a battery, after determining the positive and negative potentials of each battery node in the battery, the predicted voltage of the battery at different times in the future can be calculated. Because different charge states correspond to different times in the future, the current volume density will be affected, and thus the battery voltage will change.

[0191] By dividing the battery into multiple computing nodes, the calculation accuracy of the potential diffusion process can be improved, the accuracy of the thermal runaway model in predicting the battery voltage can be improved, and the parameters in the thermal runaway model can be corrected by the temperature value of the battery node at the previous moment, thereby improving the accuracy of the output results of the thermal runaway model.

[0192] Figure 6 A schematic diagram of the structure of a thermal runaway prediction device 60 provided in an embodiment of the present invention, the device comprising:

[0193] A first determining module 603, used to determine actual state information of the battery at a first moment;

[0194] A prediction module 602 is used to determine predicted state information of the battery at a future set time based on a thermal runaway model according to the actual state information when it is determined that the actual state information is abnormal according to the threshold state information;

[0195] The second determination module 603 is used to determine the rate of change of the state information at a second moment according to the predicted state information, and determine the risk of thermal runaway of the battery according to the rate of change of the state information at the second moment; the second moment is any moment in the future set time.

[0196] Optionally, the predicted state information includes a predicted voltage and a predicted temperature; the state information change rate includes a temperature rise rate and a voltage drop rate; when the second determination module 603 determines that the battery has a risk of thermal runaway according to the state information change rate at the second moment, it is specifically used to:

[0197] When it is determined that the temperature rise rate at the second moment is greater than the target temperature rise rate, or the voltage drop rate is greater than the target drop rate, then determining the actual state information at the third moment;

[0198] If the actual state information at the third moment matches the predicted state information at the moment, it is determined that the battery has a risk of thermal runaway at the second moment; the third moment is any moment between the first moment and the second moment.

[0199] Optionally, the device further comprises: a correction module, configured to:

[0200] Determine the historical state information according to the target time; the target time is the time when the time difference from the last correction time is greater than or equal to the first preset time interval; the last correction time is the time when the parameters in the thermal runaway model were last corrected; the historical state information is the actual state information corresponding to the multiple charging and discharging processes before the target time;

[0201] The parameters in the thermal runaway model are modified according to the historical state information.

[0202] Optionally, the device further includes: a third determining module, configured to:

[0203] After the parameters in the thermal runaway model are corrected according to the historical state information, the threshold state information is determined based on the thermal runaway model according to the operating condition information, voltage and temperature of the battery in a standard state.

[0204] Optionally, a plurality of batteries constitute a module, and the actual state information includes: actual temperature and actual voltage; the threshold state information includes: at least one of a voltage threshold, a temperature threshold, a maximum voltage difference threshold within the module, and a maximum temperature difference threshold within the module;

[0205] The device further includes: a fourth determining module, configured to determine that the actual status information is abnormal when at least one of the following conditions is met:

[0206] The actual voltage of any battery is greater than the voltage threshold;

[0207] The actual temperature of any battery is greater than the temperature threshold;

[0208] The actual maximum voltage difference of any module is greater than the maximum voltage difference threshold within the module;

[0209] The actual maximum temperature difference of any module is greater than the maximum temperature difference threshold within the module.

[0210] Optionally, when it is determined that the battery has a risk of thermal runaway at the second moment, the device further includes: a first processing module, configured to:

[0211] Determine the time interval between the second moment and the current moment;

[0212] When the time interval is greater than a second preset time interval, an active safety measure of increasing the refrigeration capacity of the refrigeration device is executed;

[0213] When the time interval is less than or equal to the second preset time interval, an active safety measure is taken to set the cooling capacity of the refrigeration device to a maximum value and to stop charging and discharging the battery.

[0214] Optionally, the device further includes: a second processing module, configured to:

[0215] After a third preset time interval has elapsed since the moment when the active safety measure is executed, determining the actual state information of the battery again;

[0216] When it is predicted that the battery will experience thermal runaway according to the actual state information determined again, at least one of a pressure relief operation, a fire extinguishing operation and an alarm operation is triggered.

[0217] Optionally, the method is applied to a battery system, the battery system includes a computing node; the actual state information includes an actual voltage, an actual temperature, and an actual current; the computing node includes other nodes and N battery nodes that divide each battery; N is a positive integer greater than or equal to 3; the other nodes are nodes set for the locations of components other than batteries in the module; the prediction module 602 is specifically used for:

[0218] The following steps are performed through the thermal runaway model to determine the predicted temperature of each computing node:

[0219] For each computing node, a target node is determined according to a target direction, and a temperature gradient is determined according to a temperature value of the target node; the thermophysical property parameters of a material corresponding to the computing node are corrected according to the temperature value of the computing node at a previous moment; when a side reaction is determined to be triggered according to the temperature value of the computing node at a previous moment, a first amount of heat is determined; when the computing node is a battery node, a second amount of heat is determined according to a voltage value of the computing node and the actual current; the temperature value of the computing node at a previous moment is related to the actual state information at the first moment;

[0220] The predicted temperature of the computing node is determined according to the temperature value of the computing node at a previous moment, the acquired heat dissipation condition, the temperature gradient, the first heat amount, and the second heat amount.

[0221] Optionally, the prediction module 602 is specifically used for:

[0222] The following steps are performed by the thermal runaway model to determine the predicted voltage of the battery:

[0223] Correcting the parameters in the thermal runaway model according to the temperature value of the battery node at the last moment, and determining the state of charge at different moments in the future according to the actual current; the temperature value of the battery node at the last moment is related to the actual state information at the first moment;

[0224] For each battery node, determine the positive and negative potential difference of the battery node according to the parameters in the modified thermal runaway model, the actual current, the volume of the battery node and the state of charge;

[0225] For each battery, the negative potential of each battery node in the battery is calculated according to the potential diffusion equation and the negative potential at the negative electrode tab of the battery, and the positive potential of the battery node is determined according to the positive and negative potential difference of the battery node and the corresponding negative potential;

[0226] A predicted voltage of the battery is determined based on the positive potential and the negative potential of each battery node in the battery.

[0227] Optionally, the thermal runaway model is a low-dimensional thermal runaway model; the low-dimensional thermal runaway model is determined based on the target direction in the battery; the target direction includes a direction perpendicular to the pole piece.

[0228] The thermal runaway prediction device provided in the embodiment of the present invention can realize the above-mentioned Figure 2 The thermal runaway prediction method of the illustrated embodiment has similar implementation principles and technical effects, which will not be described in detail here.

[0229] Figure 7 The hardware structure diagram of a battery management system provided by an embodiment of the present invention is shown in FIG. Figure 7 As shown, the battery management system provided in this embodiment includes: at least one processor 701 and a memory 702. The processor 701 and the memory 702 are connected via a bus 703.

[0230] In a specific implementation process, at least one processor 701 executes the computer execution instructions stored in the memory 702, so that at least one processor 701 executes the method in the above method embodiment.

[0231] The specific implementation process of the processor 701 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0232] In the above Figure 7 In the illustrated embodiment, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0233] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage.

[0234] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0235] An embodiment of the present invention further provides an energy storage battery system, comprising: a battery and a battery management system, wherein the battery management system is used to execute the method of the above method embodiment.

[0236] An embodiment of the present invention further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method of the above method embodiment is implemented.

[0237] An embodiment of the present application also provides a computer program product, including a computer program, which implements the method of the above method embodiment when executed by a processor.

[0238] The computer-readable storage medium mentioned above may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium may be any available medium that can be accessed by a general or special-purpose computer.

[0239] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0240] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0241] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0242] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0243] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A thermal runaway prediction method, characterized in that: include: Determine actual status information of the battery at a first moment; When it is determined according to the threshold state information that the actual state information is abnormal, then based on the thermal runaway model, the predicted state information of the battery at a future set time is determined according to the actual state information; Determine a state information change rate at a second moment according to the predicted state information, and determine that the battery has a risk of thermal runaway according to the state information change rate at the second moment; the second moment is any moment in the future set time; The method is applied to a battery system, the battery system includes a computing node; the computing node includes other nodes and N battery nodes that divide each battery; the other nodes are nodes set for the locations of components other than batteries in the module; the actual state information includes actual voltage, actual temperature and actual current; N is a positive integer greater than or equal to 3; and the predicted temperature of each computing node is determined by the thermal runaway model according to the actual state information, including: For each computing node, a target node is determined according to a target direction, and a temperature gradient is determined according to a temperature value of the target node; the thermophysical property parameters of a material corresponding to the computing node are corrected according to the temperature value of the computing node at a previous moment; when a side reaction is determined to be triggered according to the temperature value of the computing node at a previous moment, a first amount of heat is determined; when the computing node is a battery node, a second amount of heat is determined according to a voltage value of the computing node and the actual current; the temperature value of the computing node at a previous moment is related to the actual state information at the first moment; The predicted temperature of the computing node is determined according to the temperature value of the computing node at a previous moment, the acquired heat dissipation condition, the temperature gradient, the first heat amount, and the second heat amount.

2. The method according to claim 1, characterized in that The predicted state information includes a predicted voltage and a predicted temperature; the state information change rate includes a temperature rise rate and a voltage drop rate; and determining that the battery has a risk of thermal runaway according to the state information change rate at the second moment includes: When it is determined that the temperature rise rate at the second moment is greater than the target temperature rise rate, or the voltage drop rate is greater than the target drop rate, then determining the actual state information at the third moment; If the actual state information at the third moment matches the predicted state information at the moment, it is determined that the battery has a risk of thermal runaway at the second moment; the third moment is any moment between the first moment and the second moment.

3. The method according to claim 1, characterized in that The method further comprises: Determine the historical state information according to the target time; the target time is the time when the time difference from the last correction time is greater than or equal to the first preset time interval; the last correction time is the time when the parameters in the thermal runaway model were last corrected; the historical state information is the actual state information corresponding to the multiple charging and discharging processes before the target time; The parameters in the thermal runaway model are modified according to the historical state information.

4. The method according to claim 3, characterized in that The method further comprises: After the parameters in the thermal runaway model are corrected according to the historical state information, the threshold state information is determined based on the thermal runaway model according to the operating condition information, voltage and temperature of the battery in a standard state.

5. The method according to claim 1, characterized in that A plurality of batteries constitute a module, the actual state information includes: an actual temperature and an actual voltage; the threshold state information includes: at least one of a voltage threshold, a temperature threshold, a maximum voltage difference threshold within the module, and a maximum temperature difference threshold within the module; The method further includes: determining that the actual status information is abnormal when at least one of the following conditions is met: The actual voltage of any battery is greater than the voltage threshold; The actual temperature of any battery is greater than the temperature threshold; The actual maximum voltage difference of any module is greater than the maximum voltage difference threshold within the module; The actual maximum temperature difference of any module is greater than the maximum temperature difference threshold within the module.

6. The method according to claim 2, characterized in that When it is determined that the battery has a risk of thermal runaway at the second moment, the method further includes: Determine the time interval between the second moment and the current moment; When the time interval is greater than a second preset time interval, an active safety measure of increasing the refrigeration capacity of the refrigeration device is executed; When the time interval is less than or equal to the second preset time interval, an active safety measure is taken to set the cooling capacity of the refrigeration device to a maximum value and to stop charging and discharging the battery.

7. The method according to claim 6, characterized in that The method further comprises: After a third preset time interval has elapsed since the moment when the active safety measure is executed, determining the actual state information of the battery again; When it is predicted that the battery will experience thermal runaway according to the actual state information determined again, at least one of a pressure relief operation, a fire extinguishing operation and an alarm operation is triggered.

8. The method according to claim 1, characterized in that Determining predicted state information of the battery at a future set time based on a thermal runaway model according to the actual state information, further comprising: The following steps are performed by the thermal runaway model to determine the predicted voltage of the battery: Correcting the parameters in the thermal runaway model according to the temperature value of the battery node at the last moment, and determining the state of charge at different moments in the future according to the actual current; the temperature value of the battery node at the last moment is related to the actual state information at the first moment; For each battery node, determine the positive and negative potential difference of the battery node according to the parameters in the modified thermal runaway model, the actual current, the volume of the battery node and the state of charge; For each battery, the negative potential of each battery node in the battery is calculated according to the potential diffusion equation and the negative potential at the negative electrode tab of the battery, and the positive potential of the battery node is determined according to the positive and negative potential difference of the battery node and the corresponding negative potential; A predicted voltage of the battery is determined based on the positive potential and the negative potential of each battery node in the battery.

9. The method according to claim 1, characterized in that: The thermal runaway model is a low-dimensional thermal runaway model; the low-dimensional thermal runaway model is determined based on the target direction in the battery; the target direction includes a direction perpendicular to the pole piece.

10. A battery management system, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method according to any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the method according to any one of claims 1 to 9 is implemented.

12. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 9.

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