Energy storage battery heating control method, system, device and storage medium

Abstract

The present application relates to a method, system, device, and storage medium for heating and regulating an energy storage battery, which belongs to the field of battery management technology. The method includes real-time monitoring of battery temperature data, analyzing whether the battery temperature data meets preset temperature regulation conditions; if so, formulating a heating plan based on the analysis results of the current battery temperature data, and the heating plan satisfies: enabling the battery temperature to reach a preset ideal operating temperature; wherein the heating plan at least includes: utilizing a preset heat source to heat the energy storage battery, and / or regulating the charge and discharge process of the energy storage battery to enable the energy storage battery to self-heat; executing the heating plan until the battery temperature reaches the preset ideal operating temperature. The present application has the effect of achieving efficient temperature control of energy storage batteries under low temperature conditions, so that the energy storage battery can stably maintain its optimal working state.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a method, system, device and storage medium for regulating heating of an energy storage battery. Background Art

[0002] In recent years, with the development and application of energy storage systems, batteries with various electrolyte compositions have entered large-scale use. Lithium batteries are currently the most widely used type of battery. Due to the characteristics of lithium batteries (optimal operating temperature: around 25°C), their chemical reaction rate is slow in low-temperature environments, making them prone to lithium deposition. This can lead to reduced battery capacity, low charge and discharge efficiency, and even inoperability. However, given the complexity and diversity of application scenarios for energy storage products, to ensure the practical lifespan and charge and discharge performance of lithium batteries, lithium batteries used in low-temperature environments generally use heating methods such as external heat sources (such as heating wires attached to the battery surface or foam insulation wrapped around the battery to form an insulation layer) and internal heat generation. While these methods can slow the cooling rate to a certain extent and provide a certain heating effect, in actual use, they are not easy to accurately and stably maintain the energy storage battery (such as lithium batteries) at the corresponding optimal operating state (i.e., at the optimal operating temperature). Therefore, current heating methods for energy storage batteries have certain limitations and need to be improved. Summary of the Invention

[0003] In order to achieve efficient temperature control of energy storage batteries under low temperature conditions so that the energy storage batteries can stably maintain their optimal working state, the present application provides an energy storage battery heating control method, system, device and storage medium.

[0004] In a first aspect, the present application provides a method for regulating heating of an energy storage battery, comprising:

[0005] Real-time monitoring of battery temperature data, and analysis of whether the battery temperature data meets the preset temperature adjustment conditions;

[0006] If the conditions are met, a heating plan is formulated based on the analysis results of the current battery temperature data, and the heating plan satisfies: enabling the battery temperature to reach a preset ideal operating temperature; wherein the heating plan at least includes: heating the energy storage battery using a preset heat source, and / or regulating the charge and discharge process of the energy storage battery to cause the energy storage battery to self-heat;

[0007] The heating scheme is executed until the battery temperature reaches a preset ideal operating temperature.

[0008] By adopting the above technical solution, the present application proposes to monitor the battery temperature in real time, analyze the monitored battery temperature in real time and formulate a corresponding heating plan. The present application also proposes two independent and complementary heating methods, namely: external heat source heating and self-heating of the energy storage battery. The present application proposes that the above two heating methods can be used as heating plans separately, or the above heating methods can be combined as a heating plan, so that the battery temperature can be maintained at a preset ideal operating temperature; finally, the above heating control logic is used to ensure that the energy storage battery can efficiently switch to the optimal working state in the environment (especially in a low-temperature environment).

[0009] Optionally, the battery temperature data includes battery temperature data at each preset detection point of the energy storage battery;

[0010] If the above conditions are met, a heating plan is formulated based on the analysis result of the current battery temperature data, and the heating plan is executed until the battery temperature reaches a preset ideal operating temperature, including:

[0011] Determine whether the lowest battery temperature data and the temperature difference data among the battery temperature data monitored corresponding to all preset points meet a preset first determination condition;

[0012] If the first determination condition is met, a first heating mode is executed, wherein the first heating mode heats the battery module through an external heat source; and during the execution of the first heating mode, it is determined whether a first heating stop condition is met. If so, the first heating mode is stopped; if not, the second heating mode is executed until the first heating stop condition is met; wherein the second heating mode adjusts the charging current of the energy storage battery to achieve self-heating of the energy storage battery;

[0013] If the first judgment condition is not met, the third heating mode is executed until the preset second heating stop condition is met, at which point the third heating mode is stopped; wherein, the third heating mode refers to using the remaining battery power to achieve self-heating of the energy storage battery when the remaining power of the energy storage battery is sufficient.

[0014] By adopting the above-mentioned technical solution, the above-mentioned solution specifically discloses the specific control logic for determining and executing the heating scheme. During this period, the battery temperature data is limited to include the temperature data at multiple preset detection points on the surface of the energy storage battery. In the subsequent judgment process, the lowest temperature data and temperature difference are selected as the basis for judgment, thereby improving the comprehensiveness of battery temperature monitoring. In addition, multiple heating modes (i.e., heating schemes) are proposed, including: heating the energy storage battery using an external heat source, charging the energy storage battery with external electricity to achieve self-heating of the energy storage battery, and utilizing the battery's own residual power to achieve self-heating. By using these multiple heating modes independently or in combination, the battery temperature is ultimately controlled to the optimal operating temperature, thereby extending the service life of the energy storage battery.

[0015] Optionally, the method further includes:

[0016] Whenever the second heating mode is required, the data value corresponding to each preset influencing factor at the current moment is obtained, the data value is input into a pre-built analysis model, and the temperature rise rate is analyzed by the analysis model to obtain the temperature rise rate; wherein the influencing factor refers to a parameter that affects the temperature rise rate of the energy storage battery during the process of achieving self-heating of the energy storage battery by increasing the charging current, and the influencing factor includes at least the battery internal resistance and heat capacity;

[0017] Based on the analyzed temperature rise rate and the current battery temperature data, a heating strategy is generated, and the second heating mode is executed according to the heating strategy, wherein the heating strategy at least includes the adjusted charging current and the execution time of the second heating mode.

[0018] By adopting the above technical solution, since the second heating mode is based on regulating the charging current of the energy storage battery to achieve self-heating of the energy storage battery, it is necessary to determine the charging current increment and the execution duration of the second heating mode beforehand. These factors are affected by the battery temperature rise rate and are not fixed. This is because the battery temperature rise rate is susceptible to change with the operating environment and operating state of the energy storage battery. The influencing factors are specific parameters used to characterize the operating environment and operating state. For example, as the battery ages, its internal resistance and heat capacity will change. These specific changes will cause the battery temperature rise rate to change, which in turn will affect the heating efficiency of the energy storage battery when it is heated to the ideal operating temperature. Therefore, the present application proposes that whenever the second heating mode is required, the operating state and operating environment of the energy storage battery will be analyzed. Based on the analysis results, a corresponding temperature rise rate and heating strategy will be derived. In this way, the specific heating strategy of the second heating mode can be adaptively adjusted in combination with the operating state of the energy storage battery to ensure that the temperature of the energy storage battery is efficiently adjusted to the optimal operating temperature.

[0019] Optionally, generating a heating strategy based on the analyzed temperature rise rate and current battery temperature data includes:

[0020] Determine the execution time required to reach the ideal operating temperature based on the analyzed temperature rise rate and current battery temperature data;

[0021] Based on the data value corresponding to each of the current influencing factors, a pre-built prediction model is used to predict the change trend of the influencing factor within the execution time and the state stage it is in; wherein the same influencing factor has different degrees of influence on the battery temperature rise rate in different state stages;

[0022] If, within the execution duration, among all the influencing factors, there is a target influencing factor that satisfies: the state stage of the target influencing factor changes within the execution duration, then the change moment is used as a change node, and the execution duration is divided into a number of sub-periods using the change node; based on the predicted state stage corresponding to the influencing factor in each sub-period, a pre-built analysis model is used to analyze and obtain a segmented temperature rise rate for each sub-period, and based on the segmented temperature rise rate corresponding to each sub-period, a segmented charging current is determined for each sub-period, and a heating strategy is generated with all sub-periods, the segmented temperature rise rate corresponding to each sub-period, and the segmented charging current.

[0023] If there is no target influencing factor within the execution time, the charging current is determined based on the temperature rise rate, the current battery temperature data, and the execution time, and a heating strategy with the execution time, temperature rise rate, and charging current is generated.

[0024] By adopting the above technical solution, the data value corresponding to the influencing factor is also prone to change with the change of time, and as the corresponding data value changes, the degree of its influence on the temperature rise rate is also prone to be different. Therefore, the present application proposes to divide the influencing factors into different state stages according to the different degrees of influence of the influencing factors on the temperature rise rate, and predict the state stage of each influencing factor within the execution time through the prediction model. If the state stage of the influencing factor changes within the execution time, the execution time is divided into several sub-periods based on the change time, and then the analysis model is reused based on the state stage of the influencing factor within the sub-period to re-analyze the impact of the changes in the state stage of the influencing factor on the battery temperature rise rate, and finally obtain a more refined and accurate heating strategy.

[0025] Optionally, the charging current included in the heating strategy is a current curve that changes with time during the execution duration;

[0026] The executing the second heating mode according to the heating strategy includes:

[0027] The second heating mode is executed according to the heating strategy, and during the execution time, the charging current is synchronously adjusted in real time according to the current curve.

[0028] By adopting the above technical solution, when executing the second heating mode according to the heating strategy, the present application proposes to use a gradual method to adjust the charging current, that is, the growth process of the charging current is a curve that changes with time, so as to avoid a surge in charging current and aggravate the aging of the energy storage battery.

[0029] Optionally, determining the execution duration of the second mode based on the analyzed temperature rise rate and current battery temperature data includes:

[0030] Determine the current increment based on the analyzed temperature rise rate and current battery temperature data, and generate multiple current increment curves according to different current change rates; wherein the current increment curve is a current change curve in which the charging current increases over time, and the increment is consistent with the current increment;

[0031] executing the second heating mode based on the heating strategy corresponding to each current increasing curve using a pre-built simulation model, and outputting simulation results, wherein the simulation results are used to characterize performance indicators of the energy storage battery after being heated by the second heating mode, and the simulation results at least include temperature data and energy consumption of the energy storage battery after heating;

[0032] Based on the simulation results corresponding to each of the current increasing curves, an optimal increasing curve is determined, and the execution time is determined according to the optimal increasing curve.

[0033] By adopting the above technical solution, a simulation model is used to simulate the generated multiple current increase curves to obtain the heating results of the energy storage battery according to the second heating mode executed according to the heating strategy containing the corresponding current increase curve. The most suitable current increase curve is selected by analyzing the heating results. It can be considered here that the performance index corresponding to the simulation result obtained by executing according to the optimal increase curve is optimal; among them, the difference in the current increase curves only lies in the different rate of change of the charging current over time, that is, when controlling the gradual growth of the charging current, the simulation model is further used to simulate and determine the optimal current growth rate.

[0034] Optionally, determining the optimal increasing curve based on the simulation results corresponding to each current increasing curve includes:

[0035] According to the current working scenario of the energy storage battery, the weight value of each performance indicator is determined, and based on the weight value of the performance indicator, the simulation results corresponding to each change curve are matched and analyzed, and the optimal incremental curve is selected using an optimization algorithm.

[0036] By adopting the above-mentioned technical solution, the emphasis on the final performance indicators in different work scenarios is different. Therefore, this application proposes to adaptively adjust the emphasis on performance indicators in combination with specific work scenarios, that is, analyze and adjust the importance of each performance indicator in the current work scenario in the form of a weight value, and finally select the change curve corresponding to the simulation result that matches the corresponding demand importance as the optimal change curve.

[0037] In a second aspect, the present application provides an energy storage battery heating control system, comprising:

[0038] A battery temperature monitoring module is used to monitor battery temperature data in real time and analyze whether the battery temperature data meets the preset temperature adjustment conditions;

[0039] a heating plan formulation module, configured to formulate a heating plan based on the analysis results of the current battery temperature data, if the conditions are met, and the heating plan satisfies: enabling the battery temperature to reach a preset ideal operating temperature; wherein the heating plan at least includes: heating the energy storage battery using a preset heat source, and / or regulating the charge and discharge process of the energy storage battery to cause the energy storage battery to self-heat;

[0040] The battery heating execution module is used to execute the heating plan until the battery temperature reaches a preset ideal operating temperature.

[0041] In a third aspect, the present application provides an energy storage battery heating control device, comprising a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method described in the first aspect.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the method described in the first aspect.

[0043] In summary, this application has the following beneficial technical effects:

[0044] This application proposes real-time monitoring of battery temperature, real-time analysis of the monitored battery temperature, and formulation of a corresponding heating scheme. This application also proposes two independent and complementary heating methods, namely: external heat source heating and energy storage battery self-heating. This application proposes that the above two heating methods can be used as heating schemes separately, or the above heating methods can be combined as a heating scheme to maintain the battery temperature at a preset ideal operating temperature. Ultimately, the above heating control logic is used to ensure that the energy storage battery can efficiently switch to the optimal working state in the environment (especially in low-temperature environments). BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 It is a flow chart of a method for heating and controlling an energy storage battery disclosed in an embodiment of the present application.

[0047] Figure 2 It is a flowchart used to reflect the logic of formulating and executing the heating plan in the embodiment of this application.

[0048] Figure 3 This is a structural block diagram of an energy storage battery heating control system disclosed in an embodiment of the present application.

[0049] Description of reference numerals: 201, battery temperature monitoring module; 202, heating plan formulation module; 203, battery heating execution module. DETAILED DESCRIPTION

[0050] The following is combined with Figure 1-3 This application is described in further detail.

[0051] The present application discloses a method for heating and controlling an energy storage battery (hereinafter referred to as the control method), which is used to monitor the operating temperature of the energy storage battery in real time in combination with the operating environment (especially the low temperature environment) in which the energy storage battery is located, and to efficiently formulate and execute a heating plan based on the monitoring results, so that the temperature of the energy storage battery can be maintained at an ideal operating temperature, thereby ensuring the charge and discharge performance of the energy storage battery. The execution subject of the heating and controlling method is an energy storage battery heating and controlling system (hereinafter referred to as the control system), which will be described below in conjunction with the attached drawings. Figure 1-2 Specifically explain the specific process steps of the control system to execute the control method.

[0052] S101, real-time monitoring of battery temperature data, and analysis of whether the battery temperature data meets a preset temperature adjustment condition.

[0053] S102: If the conditions are met, a heating plan is formulated based on the analysis results of the current battery temperature data, and the heating plan satisfies: enabling the battery temperature to reach a preset ideal operating temperature; wherein the heating plan at least includes: heating the energy storage battery using a preset heat source, and / or regulating the charge and discharge process of the energy storage battery to cause the energy storage battery to self-heat.

[0054] S103, execute the heating plan until the battery temperature reaches the preset ideal operating temperature

[0055] Wherein, S102 and S103 specifically include the following sub-steps:

[0056] It is determined whether the lowest battery temperature data and the temperature difference data among the battery temperature data corresponding to all preset points monitored meet a preset first determination condition.

[0057] If the first determination condition is met, the first heating mode is executed, and the first heating mode is to heat the battery module through an external heat source; and during the execution of the first heating mode, it is determined whether the first heating stop condition is met. If so, the first heating mode is stopped; if not, the second heating mode is executed until the first heating stop condition is met; wherein, the second heating mode is to adjust the charging current of the energy storage battery to achieve self-heating of the energy storage battery.

[0058] If the first judgment condition is not met, the third heating mode is executed until the preset second heating stop condition is met, at which point the third heating mode is stopped; wherein the third heating mode refers to using the remaining battery power to achieve self-heating of the energy storage battery when the remaining power of the energy storage battery is sufficient.

[0059] In practice, the energy storage battery disclosed in the embodiments of the present application can specifically be a lithium-ion battery. The energy storage battery's power comes from two sources: one is the inverter converting solar energy into electrical energy through photovoltaics and storing it in the energy storage battery; the other is the mains electricity supply to replenish the energy storage battery. In addition, the energy storage battery disclosed in the embodiments of the present application can be specifically applied to home energy storage scenarios, that is, powering home loads through the energy storage battery. The following will exemplify how to achieve heating of the energy storage battery when operating in a low-temperature environment, that is, how to achieve temperature control of the energy storage battery.

[0060] Specifically, the surface of the energy storage battery includes several preset points. The battery temperature data at each preset point is monitored in real time through a preset battery cell temperature monitoring module (such as NTC). The battery temperature data at all preset points (i.e., the battery temperature data mentioned above) is ultimately transmitted by the battery cell temperature monitoring module to the control system. The control system specifically includes a BMS, which is pre-electrically connected to the battery cell temperature monitoring module to obtain battery temperature data and formulate and execute a heating plan when the battery temperature data meets the preset temperature control conditions.

[0061] Specifically, refer to Figure 2The specific content of the preset temperature control condition can be whether the connection between the energy storage battery and the inverter is normal. If so, it is determined whether the battery temperature data corresponding to all preset points, the minimum battery temperature data Tmin, and the temperature difference data meet the preset first judgment condition. The first judgment condition can be specifically 2°C < Tmin < 12°C and the temperature difference data < 15°C; if not, the third heating mode is executed. If the first judgment condition is met, it is further determined whether the inverter is connected to the mains or photovoltaic power, that is, it is determined whether the charging source when charging the energy storage battery through the inverter is the mains charging or the photovoltaic power conversion of electrical energy into electrical energy charging; if so, the first heating mode is executed, if not, the third heating mode is executed.

[0062] The process steps for executing the first heating mode specifically include: closing the heating MOS tube, allowing the current of the charging current to the energy storage battery to increase, and closing the heating film temperature control switch. The heating film can specifically be a PI heating film (i.e., the external heat source mentioned above), which is used to heat the energy storage battery by physically contacting the surface of the energy storage battery after power is applied. The PI heating film mainly has two power sources: one is electrically connected to the energy storage battery to form a heating circuit, and the BMS is used to control the on and off of the heating circuit by controlling the heating MOS tube provided in the heating circuit (for example, when the heating MOS tube is closed, the heating circuit is connected), so as to power the heating film with the energy storage battery's own power, thereby enabling the heating film to conduct heat back to the energy storage battery; the other is to power the heating film by converting the AC power or photovoltaic power connected by the inverter into DC power. In addition, the heating film is equipped with a heating film temperature control switch, which can be specifically a normally closed temperature control switch PTC; the PTC is used to monitor the real-time surface temperature of the heating film. When the surface temperature of the heating film reaches the maximum temperature threshold (such as 55°C), the normally closed temperature control switch opens, thereby disconnecting the heating film from the energy storage battery, that is, disconnecting the heating circuit, until the heating film temperature drops to a preset temperature threshold (such as 45°C), the normally closed temperature control switch PTC closes again to reconnect the heating film to the heating circuit; that is, the heating MOS and the normally closed temperature control switch PTC jointly control the on-off of the heating circuit. Only when the heating MOS and the normally closed temperature control switch PTC are both closed, the heating circuit is in a connected state, that is, the heating scheme of the energy storage battery can be realized by energizing the heating film for heating.

[0063] In addition, in the process steps of the first heating mode, the scheme of "allowing an increase in the charging current of the energy storage battery" is also mentioned. This means that in the process of heating the energy storage battery using the heating film, the energy storage battery itself can also be in a charging state, that is, charging is achieved with the help of the AC power or photovoltaic power connected by the inverter, and during the charging process, the BMS can adjust the size of the charging current, such as further increasing the charging current on the basis of the current charging current, so that the energy storage battery generates heat by itself based on the increase in internal current, thereby achieving temperature rise, and thus realizing the execution operation of the first heating mode.

[0064] During the execution of the first heating mode, the control system is also used to determine in real time whether the heating process meets the first heating stop condition. The specific content of the first heating stop condition can be: Tmin>15°C or the execution time of the first heating mode>60 minutes. If so, the heating MOS transistor is disconnected, that is, the first heating mode is terminated and heating is completed. If the first heating stop condition is not met during the execution of the first heating mode, the second heating mode is further executed until the first heating stop condition is met.

[0065] The specific process steps for executing the second heating mode are: determine whether Tmin is greater than 5°C. If so, execute: allow the charging current of the energy storage battery to be increased, and close the heating film temperature control switch before re-determining whether the first heating stop condition is met; if not, allow the charging current to be increased to a specified current value (such as 3A), and then re-determine whether the first heating stop condition is met.

[0066] The specific process steps for the third heating mode executed when the first determination condition is not met are as follows: first, it is determined whether -20°C ≤ Tmin ≤ 2°C and the temperature difference data is less than 15°C; if not, then if it is, the charging MOS transistor is disconnected (the charging MOS transistor is used to control the on / off of the charging circuit connecting the energy storage battery and the inverter. When the charging MOS transistor is disconnected, the inverter and the energy storage battery are disconnected, and charging is stopped). It is further determined whether the remaining power SOC of the energy storage battery is greater than 30%. If so, the heating MOS transistor is closed, that is, the remaining power of the energy storage battery is used to power the heating film, so that the heating film is powered on and heated to heat the energy storage battery. During this power supply and heating process, it is determined whether the preset second heating stop condition is met.

[0067] Among them, the preset second heating stop condition specifically includes: Condition 1: determining whether the SOC is less than 20%, and Condition 2: determining whether Tmin>15°C or the execution time of the first heating mode is>60 minutes; among them, priority is given to determining Condition 1. If Condition 1 is met, the heating MOS tube is disconnected, and the use of the heating film to heat the energy storage battery is stopped; only when Condition 1 is not met will Condition 2 be judged, and Condition 2 is specifically: determining that Tmin>15°C or the execution time of the first heating mode is>60 minutes, that is, the first heating stop condition is preset. If Condition 2 is met, the heating MOS tube is disconnected and heating is stopped. If Condition 2 is not met, the second heating mode is executed until heating is stopped after Condition 2 is met.

[0068] In summary, the execution operation of the heating scheme is realized, and during the execution process of the entire heating scheme, it is assumed that the temperature of the energy storage battery after the execution of the heating scheme can reach the preset ideal operating temperature.

[0069] Optionally, the control method further comprises the following steps:

[0070] S104: Whenever the second heating mode is to be executed, obtaining a data value corresponding to each preset influencing factor at the current moment, inputting the data value into a pre-built analysis model, and analyzing the temperature rise rate using the analysis model; wherein the influencing factor refers to a parameter that affects the temperature rise rate of the energy storage battery during the process of achieving self-heating of the energy storage battery by increasing the charging current, and the influencing factor includes at least the battery internal resistance and heat capacity;

[0071] S105: generating a heating strategy based on the analyzed temperature rise rate and the current battery temperature data, and executing the second heating mode according to the heating strategy, wherein the heating strategy includes at least the adjusted charging current and the execution duration of the second heating mode;

[0072] Among them, "generating a heating strategy based on the analyzed temperature rise rate and current battery temperature data" in S105 specifically includes the following sub-steps:

[0073] Based on the analyzed temperature rise rate and current battery temperature data, the current increment is determined, and multiple current increment curves are generated according to different current change rates. Among them, the current increment curve is a current change curve in which the charging current increases over time, and the increment is consistent with the current increment;

[0074] Executing the second heating mode based on the heating strategy corresponding to each current increasing curve through a pre-built simulation model and outputting simulation results, wherein the simulation results are used to characterize the performance indicators of the energy storage battery after being heated by the second heating mode, and the simulation results at least include temperature data and energy consumption of the energy storage battery after heating;

[0075] Determine the weight of each performance indicator based on the current working scenario of the energy storage battery. Based on the weight of the performance indicators, perform a matching analysis on the simulation results corresponding to each change curve, and use the optimization algorithm to select the optimal incremental curve. Determine the execution time based on the optimal incremental curve.

[0076] Based on the data values ​​corresponding to each current influencing factor, a pre-built prediction model is used to predict the changing trend of the influencing factor during the execution time and the state stage it is in. The same influencing factor has different degrees of influence on the battery temperature rise rate in different state stages.

[0077] If, within the execution duration, among all the influencing factors, there is a target influencing factor that satisfies: the state stage of the target influencing factor changes within the execution duration, then the change moment is used as a change node, and the execution duration is divided into several sub-periods using the change node; based on the predicted state stage corresponding to the influencing factor in each sub-period, a pre-built analysis model is used to analyze and obtain a segmented temperature rise rate for each sub-period, and based on the segmented temperature rise rate corresponding to each sub-period, a segmented charging current is determined for each sub-period, generating a heating strategy that includes all sub-periods, the segmented temperature rise rate corresponding to each sub-period, and the segmented charging current;

[0078] If the target influencing factor does not exist within the execution time, the charging current is determined based on the temperature rise rate, the current battery temperature data, and the execution time, and a heating strategy is generated with the execution time, temperature rise rate, and charging current.

[0079] The “executing the second heating mode according to the heating strategy” in S105 includes:

[0080] The second heating mode is executed according to the heating strategy, and the charging current is adjusted synchronously in real time according to the optimal increasing curve during the execution time.

[0081] In implementation, since the specific execution process steps of the second heating mode and the first heating mode both include the step of "allowing an increase in the current of the charging current to the energy storage battery", the present application proposes that before executing the second heating mode or the first heating mode, the control system will be used to determine the current increment (that is, determine the adjusted charging current) and the execution time of the first heating mode or the second heating mode, and it can be seen from the preset first heating stop condition that the execution time is ≤60 minutes; that is, the control system will first determine the heating strategy with the adjusted charging current and the execution time, and then it can execute the step of "allowing an increase in the current of the charging current to the energy storage battery" involved in the first heating mode or the second heating mode according to the heating strategy.

[0082] However, this application proposes that the prerequisite for determining the heating strategy is to first confirm the temperature rise rate of the energy storage battery in the current working state. Since the temperature rise rate of the energy storage battery is different in different working states, under the same execution time and the same charging current increment, the different temperature rise rates of the energy storage battery will affect the final temperature rise result of the energy storage battery (that is, the temperature of the energy storage battery when the heating is finally completed).

[0083] This application proposes using preset influencing factors to characterize the operating state of an energy storage battery. By obtaining the data value of each preset influencing factor and using a pre-built analysis model to analyze and determine the operating state of the energy storage battery, the analysis model is then used to analyze and determine the temperature rise rate of the energy storage battery under the corresponding operating state after the operating state is determined. Specifically, the influencing factors may include battery internal resistance, battery thermal capacity, etc., to reflect the battery aging state. The analysis model may be a pre-trained model based on various types of data collected over a historical period (such as charging current, ambient temperature, battery initial temperature, battery internal resistance, battery capacity, etc.), constructed using big data analysis techniques (such as machine learning algorithms), and used to analyze and determine the corresponding battery temperature rise rate based on the specific data values ​​of the influencing factors.

[0084] After determining the corresponding temperature rise rate, the required temperature increase (hereinafter referred to as the heating temperature) is determined based on the current battery temperature data and the ideal operating temperature of the energy storage battery; wherein, the heating temperature = |ideal operating temperature - current battery temperature data| / temperature rise rate; and illustratively, in an embodiment of the present application, the control system pre-stores a first correspondence table for storing different heating temperatures and their corresponding current increments. Therefore, the control system can determine the corresponding current increment based on the determined heating temperature, and then generate multiple current increase curves according to different current change rates based on the current charging current before the current increase (hereinafter referred to as the initial charging current), and it is assumed that the charging current increase law corresponding to each current increase curve is a uniform increase, and the final increase result of all current increase curves (that is, the current after the final increase) = the initial charging current - the current increment.

[0085] For the multiple current increase curves generated, the control system will further use a pre-built simulation model to adjust the charging current value in real time according to the current changes in the current increase curve, thereby simulating the heating process of the energy storage battery using increasing charging current. Among them, the simulation model includes a pre-built energy storage battery model (including the energy storage battery physical model, thermal model and electrochemical model).

[0086] Based on the simulation results corresponding to each current increment curve, and further combined with the current operating scenario of the energy storage battery, a weight is assigned to each performance indicator included in the simulation results, and an optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) is used to analyze and determine the optimal increment curve. For example, the control system may pre-store weight values ​​for each performance indicator corresponding to multiple different operating scenarios, and the operating scenarios and weight values ​​may be manually pre-set, allowing the user to set the current operating scenario according to actual needs. In other embodiments, the control system may calculate the matching analysis results for each current increment curve using a weighted summation method, and the current increment curve corresponding to the matching analysis result with the highest value after the weighted summation is determined as the optimal increment curve.

[0087] Next, the control system determines the execution time based on the determined optimal incremental curve, where execution time = current increment / current change rate corresponding to the optimal incremental curve. Then, the prediction model is used to predict the change trend of each influencing factor and the state stage within the execution time, such as predicting the change trend of the battery resistance over time within the future execution time. The prediction model here can specifically be an existing prediction algorithm for predicting data changes over time. This is a prior art and will not be described in detail here. It should be mentioned that the control system stores different state stages of the influencing factors, and each state stage can be represented in the form of a numerical range (for example, when the influencing factor is resistance, each state stage corresponds to a resistance value range, and there is no intersection between the resistance value ranges corresponding to different state stages), and it is believed that the degree of influence of the influencing factor on the battery temperature rise rate is different in different state stages.

[0088] The control system is used to determine whether each influencing factor is the target influencing factor based on all data values ​​corresponding to the influencing factor during the execution period, as predicted by the prediction model. The judgment logic is to determine whether the state of the influencing factor has changed during the execution period. In other words, whether there are two data values ​​that fall within the range of values ​​corresponding to different stages of the influencing factor. If so, the influencing factor is the target influencing factor.

[0089] If there is more than one target influencing factor, a change node is determined for each influencing factor, that is, the change moment when each influencing factor changes its state stage within the specified time is taken as the change node. According to all the change nodes, the execution time is divided into several sub-periods. The endpoint moment of each sub-period can only be the endpoint moment or the change moment of the execution time; if the execution time is [A, B], the change moment is a, b, and A<a<b<B, then the corresponding sub-periods are [a, b], [a, b], and [b, B] respectively.

[0090] Since there is an influencing factor that has undergone a change in state stage within a sub-period, the control system will use the analysis model again to analyze and obtain the segmented temperature rise rate of the corresponding sub-period based on the data value and state stage of the influencing factor predicted in each sub-period. It should be noted that since the input of the analysis model is a single specific data value of the influencing factor, and the sub-period is a time range, the corresponding predicted data value of the influencing factor in the relevant sub-period may be a data range. Therefore, this application proposes to take the mode or average of all data values ​​contained in the data range as the data value of the influencing factor when it is input into the analysis model, so as to realize the analysis of the segmented temperature rise rate of each sub-period by the analysis model.

[0091] Next, the control system determines the temperature rise for each segment based on the temperature rise rate of each segment: temperature rise rate * sub-period duration. The control system then determines the current increment corresponding to each sub-period based on the first correspondence table, thereby deriving the segmented charging current for each sub-period. For example, the segmented charging current corresponding to sub-period [a, b] = [a, b] + the current increment corresponding to [a, b]. This ultimately generates a heating strategy that includes all sub-periods, the segmented temperature rise rate corresponding to each sub-period, and the segmented charging current.

[0092] If all influencing factors are non-target influencing factors, the charging current is determined to be equal to the current increment and the initial charging current, and a heating strategy with execution time, temperature rise rate, and charging current is generated.

[0093] After determining the heating strategy, the "current amount of the charging current of the energy storage battery allowed to be increased" in the first heating mode or the second heating mode is executed according to the heating strategy, that is, the charging current amount of the energy storage battery is adjusted in time within the execution time according to the optimal increasing current curve.

[0094] The present application also discloses a heating control system for an energy storage battery. Figure 3 ,include:

[0095] The battery temperature monitoring module 201 is used to monitor the battery temperature data in real time and analyze whether the battery temperature data meets the preset temperature adjustment conditions;

[0096] A heating plan formulation module 202 is configured to formulate a heating plan based on the analysis results of the current battery temperature data, if the conditions are met, and the heating plan satisfies: enabling the battery temperature to reach a preset ideal operating temperature; wherein the heating plan at least includes: heating the energy storage battery using a preset heat source, and / or regulating the charge and discharge process of the energy storage battery to cause the energy storage battery to self-heat;

[0097] The battery heating execution module 203 is configured to execute the heating plan until the battery temperature reaches a preset ideal operating temperature.

[0098] Optionally, the heating plan formulation module 202 is used to determine whether the lowest battery temperature data and the temperature difference data obtained from the monitoring of the battery temperature data corresponding to all preset points meet the preset first judgment condition; it is also used to execute the first heating mode if the first judgment condition is met, and the first heating mode is to heat the battery module through an external heat source; and in the process of executing the first heating mode, it is determined whether the first stop heating condition is met, and if so, the first heating mode is stopped; if not, the second heating mode is executed until the first stop heating condition is met; wherein the second heating mode is to adjust the charging current of the energy storage battery to achieve self-heating of the energy storage battery; it is also used to execute the third heating mode if the first judgment condition is not met, and stop executing the third heating mode when the preset second stop heating condition is met; wherein the third heating mode refers to using the remaining battery power to achieve self-heating of the energy storage battery when the remaining power of the energy storage battery is sufficient.

[0099] Optionally, a heating strategy processing module is further included, which is used to obtain the data value corresponding to each preset influencing factor at the current moment whenever the second heating mode needs to be executed, input the data value into a pre-built analysis model, and obtain the temperature rise rate through analysis of the analysis model; wherein, the influencing factor refers to the parameter that affects the temperature rise rate of the energy storage battery in the process of achieving self-heating of the energy storage battery by increasing the charging current, and the influencing factor includes at least the internal resistance and heat capacity of the battery; it is also used to generate a heating strategy based on the temperature rise rate obtained by analysis and the current battery temperature data, and execute the second heating mode according to the heating strategy, wherein the heating strategy includes at least the adjusted charging current and the execution time of the second heating mode.

[0100] Optionally, the heating strategy processing module is also used to determine the execution time required to reach the ideal operating temperature based on the temperature rise rate and current battery temperature data obtained through analysis; based on the data value corresponding to each current influencing factor, the pre-built prediction model is used to predict the change trend of the influencing factor during the execution time and the state stage it is in; wherein, the same influencing factor has different degrees of influence on the battery temperature rise rate in different state stages; and it is also used for if within the execution time, among all the influencing factors, there is a target influencing factor that satisfies: the state stage of the target influencing factor changes within the execution time, then the change moment is used as the change node, and the execution time is converted using the change node. The method is divided into several sub-periods; according to the predicted state stage corresponding to the influencing factor in each sub-period, a pre-built analysis model is used to analyze and obtain the segmented temperature rise rate for each sub-period, and according to the segmented temperature rise rate corresponding to each sub-period, the segmented charging current is determined for each sub-period, and a heating strategy with all sub-periods, the segmented temperature rise rate corresponding to each sub-period, and the segmented charging current is generated; it is also used to determine the charging current according to the temperature rise rate, the current battery temperature data, and the execution time if there is no target influencing factor within the execution time, and generate a heating strategy with the execution time, temperature rise rate and charging current.

[0101] Optionally, the heating strategy processing module is further configured to execute the second heating mode according to the heating strategy, and to synchronously adjust the charging current in real time according to the current curve during the execution time.

[0102] Optionally, the heating strategy processing module is also used to determine the current increment based on the temperature rise rate and current battery temperature data obtained through analysis, and to generate multiple current increase curves according to different current change rates; wherein the current increase curve is a current change curve in which the charging current increases with time, and the increment is consistent with the current increment; it is also used to execute the second heating mode based on the heating strategy corresponding to each change curve through a pre-built simulation model, and output simulation results, the simulation results are used to characterize the performance indicators of the energy storage battery after being heated by the second heating mode, and the simulation results include at least the temperature data and energy consumption of the energy storage battery after heating; it is also used to determine the optimal change curve based on the simulation results corresponding to each change curve, and determine the execution time according to the optimal curve.

[0103] Optionally, the heating strategy processing module is also used to determine the weight value of each performance indicator according to the current working scenario of the energy storage battery, and based on the performance indicator weight value, perform matching analysis on the simulation results corresponding to each change curve, and use the optimization algorithm to select the optimal change curve.

[0104] An embodiment of the present application further discloses an energy storage battery heating control device, which includes a memory and a processor. The memory stores a computer program that can be loaded by the processor and execute the above-mentioned energy storage battery heating control method.

[0105] An embodiment of the present application also discloses a computer-readable storage medium, which stores a computer program that can be loaded by a processor and execute the above-mentioned energy storage battery heating control method. The computer-readable storage medium includes, for example: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0106] It should be noted that, in this document, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0107] The above embodiments are intended only to illustrate the technical solutions of this application and are not intended to limit the scope of protection of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on these embodiments, all other embodiments obtained by persons of ordinary skill in the art without inventive effort are also within the scope of protection to be protected by this application.

Claims

1. A method for controlling heating of an energy storage battery, characterized in that: include: Real-time monitoring of battery temperature data, and analysis of whether the battery temperature data meets the preset temperature adjustment conditions; If the conditions are met, a heating plan is formulated based on the analysis results of the current battery temperature data, and the heating plan satisfies: enabling the battery temperature to reach a preset ideal operating temperature; wherein the heating plan at least includes: heating the energy storage battery using a preset heat source, and / or regulating the charge and discharge process of the energy storage battery to cause the energy storage battery to self-heat; Executing the heating scheme until the battery temperature reaches a preset ideal operating temperature; The battery temperature data includes battery temperature data at each preset detection point of the energy storage battery; If the above conditions are met, a heating plan is formulated based on the analysis result of the current battery temperature data, and the heating plan is executed until the battery temperature reaches a preset ideal operating temperature, including: Determine whether the lowest battery temperature data and the temperature difference data among the battery temperature data monitored corresponding to all preset points meet a preset first determination condition; If the first determination condition is met, a first heating mode is executed, wherein the first heating mode heats the battery module through an external heat source; and during the execution of the first heating mode, it is determined whether a first heating stop condition is met. If so, the first heating mode is stopped; if not, the second heating mode is executed until the first heating stop condition is met; wherein the second heating mode adjusts the charging current of the energy storage battery to achieve self-heating of the energy storage battery; If the first determination condition is not met, the third heating mode is executed until the preset second heating stop condition is met, at which time the third heating mode is stopped; wherein the third heating mode refers to utilizing the remaining power of the energy storage battery to achieve self-heating of the energy storage battery when the remaining power of the energy storage battery is sufficient; The method further comprises: Whenever the second heating mode is required, the data value corresponding to each preset influencing factor at the current moment is obtained, the data value is input into a pre-built analysis model, and the temperature rise rate is analyzed by the analysis model to obtain the temperature rise rate; wherein the influencing factor refers to a parameter that affects the temperature rise rate of the energy storage battery during the process of achieving self-heating of the energy storage battery by increasing the charging current, and the influencing factor includes at least the battery internal resistance and heat capacity; generating a heating strategy based on the analyzed temperature rise rate and current battery temperature data, and executing the second heating mode according to the heating strategy, wherein the heating strategy includes at least an adjusted charging current and an execution duration of the second heating mode; The heating strategy is generated based on the analyzed temperature rise rate and the current battery temperature data, including: Determine the execution time required to reach the ideal operating temperature based on the analyzed temperature rise rate and current battery temperature data; Based on the data value corresponding to each of the current influencing factors, a pre-built prediction model is used to predict the change trend of the influencing factor within the execution time and the state stage it is in; wherein the same influencing factor has different degrees of influence on the battery temperature rise rate in different state stages; If, within the execution duration, among all the influencing factors, there is a target influencing factor that satisfies: the state stage of the target influencing factor changes within the execution duration, then the change moment is used as a change node, and the execution duration is divided into a number of sub-periods using the change node; based on the predicted state stage corresponding to the influencing factor in each sub-period, a pre-built analysis model is used to analyze and obtain a segmented temperature rise rate for each sub-period, and based on the segmented temperature rise rate corresponding to each sub-period, a segmented charging current is determined for each sub-period, and a heating strategy is generated with all sub-periods, the segmented temperature rise rate corresponding to each sub-period, and the segmented charging current. If there is no target influencing factor within the execution time, the charging current is determined based on the temperature rise rate, the current battery temperature data, and the execution time, and a heating strategy with the execution time, temperature rise rate, and charging current is generated.

2. The energy storage battery heating control method according to claim 1, characterized in that: The charging current included in the heating strategy is a current curve that changes with time during the execution period; The executing the second heating mode according to the heating strategy includes: The second heating mode is executed according to the heating strategy, and during the execution time, the charging current is synchronously adjusted in real time according to the current curve.

3. The energy storage battery heating control method according to claim 2, characterized in that: Determining the execution duration of the second mode based on the analyzed temperature rise rate and current battery temperature data includes: Determine the current increment based on the analyzed temperature rise rate and current battery temperature data, and generate multiple current increment curves according to different current change rates; wherein the current increment curve is a current change curve in which the charging current increases over time, and the increment is consistent with the current increment; executing the second heating mode based on the heating strategy corresponding to each current increasing curve using a pre-built simulation model, and outputting simulation results, wherein the simulation results are used to characterize performance indicators of the energy storage battery after being heated by the second heating mode, and the simulation results at least include temperature data and energy consumption of the energy storage battery after heating; Based on the simulation results corresponding to each of the current increasing curves, an optimal increasing curve is determined, and the execution time is determined according to the optimal increasing curve.

4. The energy storage battery heating control method according to claim 3, characterized in that: Determining the optimal increasing curve based on the simulation results corresponding to each current increasing curve includes: According to the current working scenario of the energy storage battery, the weight value of each performance indicator is determined, and based on the weight value of the performance indicator, the simulation results corresponding to each change curve are matched and analyzed, and the optimal incremental curve is selected using an optimization algorithm.

5. A heating control system for an energy storage battery, characterized in that: include, A battery temperature monitoring module (201) is used to monitor battery temperature data in real time and analyze whether the battery temperature data meets a preset temperature adjustment condition; A heating scheme formulation module (202) is configured to formulate a heating scheme based on the analysis result of the current battery temperature data if the conditions are met, and the heating scheme satisfies: enabling the battery temperature to reach a preset ideal operating temperature; wherein the heating scheme at least includes: heating the energy storage battery using a preset heat source, and / or regulating the charge and discharge process of the energy storage battery to enable the energy storage battery to self-heat; A battery heating execution module (203) is used to execute the heating scheme until the battery temperature reaches a preset ideal operating temperature; The heating scheme formulation module (202) is also used to determine whether the lowest battery temperature data and the temperature difference data of the battery temperature data obtained by monitoring corresponding to all preset points meet a preset first determination condition; and is also used to execute a first heating mode if the first determination condition is met, and the first heating mode is to heat the battery module through an external heat source; and in the process of executing the first heating mode, determine whether a first heating stop condition is met, and if so, stop executing the first heating mode; if not, execute a second heating mode until the first heating stop condition is met; wherein the second heating mode is to adjust the charging current of the energy storage battery to achieve self-heating of the energy storage battery; and is also used to execute a third heating mode if the first determination condition is not met, and stop executing the third heating mode when the preset second heating stop condition is met; wherein the third heating mode refers to using the remaining power of the energy storage battery to achieve self-heating of the energy storage battery when the remaining power of the energy storage battery is sufficient; a heating strategy processing module, configured to obtain, whenever the second heating mode needs to be executed, the data value corresponding to each preset influencing factor at the current moment, input the data value into a pre-built analysis model, and analyze the temperature rise rate using the analysis model; wherein the influencing factor refers to a parameter that affects the temperature rise rate of the energy storage battery during the process of achieving self-heating of the energy storage battery by increasing the charging current, and the influencing factor includes at least the battery internal resistance and heat capacity; and further configured to generate a heating strategy based on the analyzed temperature rise rate and current battery temperature data, and execute the second heating mode according to the heating strategy, wherein the heating strategy includes at least the adjusted charging current and the execution time of the second heating mode; The heating strategy processing module is also used to determine the execution time required to reach the ideal operating temperature based on the temperature rise rate and current battery temperature data obtained through analysis; based on the data value corresponding to each current influencing factor, the change trend of the influencing factor during the execution time and the state stage it is in are predicted through a pre-built prediction model; wherein, the same influencing factor has different degrees of influence on the battery temperature rise rate in different state stages; and is also used to, if within the execution time, among all the influencing factors, there is a target influencing factor that satisfies: if the state stage of the target influencing factor changes within the execution time, then the change moment is used as the change node, and the execution time is divided into The method is used to divide the battery into several sub-periods; according to the predicted state stage corresponding to the influencing factor in each sub-period, a pre-built analysis model is used to analyze and obtain the segmented temperature rise rate for each sub-period; according to the segmented temperature rise rate corresponding to each sub-period, the segmented charging current is determined for each sub-period, and a heating strategy with all sub-periods, the segmented temperature rise rate corresponding to each sub-period, and the segmented charging current is generated; and the method is also used to determine the charging current according to the temperature rise rate, current battery temperature data, and execution time if there is no target influencing factor within the execution time, and generate a heating strategy with the execution time, temperature rise rate, and charging current.

6. A heating and regulating device for an energy storage battery, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 4.

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