Energy storage battery heating regulation and control method, system and equipment and storage medium

Abstract

The invention relates to an energy storage battery heating regulation and control method, system and device and a storage medium, and belongs to the technical field of battery management.The method comprises the steps that battery temperature data are monitored in real time, and whether the battery temperature data meet preset temperature regulation conditions or not is analyzed; if yes, a heating scheme is formulated based on the analysis result of the current battery temperature data, and the heating scheme meets the condition that the battery temperature can reach the preset ideal working temperature; wherein the heating scheme at least comprises the steps that a preset heat source is used for heating an energy storage battery, and / or the charging and discharging process of the energy storage battery is regulated and controlled, so that the energy storage battery generates heat in a self-heating mode; and executing the heating scheme until the battery temperature reaches a preset ideal working temperature. The method has the advantages that efficient temperature control of the energy storage battery under the low-temperature condition is achieved, and the energy storage battery can be stably maintained in the optimal working state of the energy storage battery.

Description

Technical Field

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

[0002] In recent years, with the development and application of energy storage systems, batteries with various electrolyte components have begun to be widely used. Among them, lithium batteries are currently the most widely used type of battery. According to the characteristics of lithium batteries (optimal operating temperature: about 25°C), therefore, in a low-temperature environment, the chemical reaction rate of lithium batteries is low, and lithium plating is likely to occur, resulting in problems such as a decrease in battery capacity, low charge and discharge efficiency, and even inability to be used normally. However, considering the complexity of the application environment and the diversity of application scenarios of energy storage products, in order to ensure the service life and charge and discharge performance of lithium batteries, lithium batteries applied in low-temperature environments generally adopt external heat source heating methods (such as attaching heating wires to the battery surface or forming a heat-insulating layer by wrapping the battery with foam heat-insulating materials), internal heat generation methods and other heating and heat-insulating methods. Although the foregoing methods can delay the cooling rate to a certain extent and have a certain heating effect, in actual use, it is not easy to accurately and stably maintain the energy storage battery (such as a lithium battery) at the corresponding optimal operating state (i.e., at the optimal operating temperature). Therefore, the 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 an energy storage battery under low-temperature conditions and enable the energy storage battery to be stably maintained at its optimal operating state, the present application provides a method, system, device and storage medium for heating and regulating an energy storage battery.

[0004] In a first aspect, the present application provides a method for heating and regulating an energy storage battery, including: Real-time monitoring of battery temperature data, and analyzing whether the battery temperature data meets a preset temperature regulation condition; If it is satisfied, a heating plan is formulated based on the analysis result of the current battery temperature data, and the heating plan satisfies: being able to make the battery temperature reach a preset ideal operating temperature; wherein, the heating plan at least includes: heating the energy storage battery by using a preset heat source, and / or regulating the charge and discharge process of the energy storage battery to make the energy storage battery generate heat by itself; Executing the heating plan until the battery temperature reaches the preset ideal operating temperature.

[0005] 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 also proposes that the above two heating methods can be used alone as a heating plan, 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, through the above heating control logic, it is ensured that the energy storage battery can efficiently switch to the best operating state in the environment where it is located (especially in a low-temperature environment).

[0006] Optionally, the battery temperature data includes the battery temperature data at each preset detection point of the energy storage battery; If the condition is satisfied, 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 the preset ideal operating temperature, including: Determine whether the lowest battery temperature data and the temperature difference data in the battery temperature data monitored corresponding to all preset points meet the preset first determination condition; If the first determination condition is satisfied, execute the first heating mode, 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, determine whether the first heating stop condition is satisfied. If so, stop executing the first heating mode; if not, execute the second heating mode until the first heating stop condition is satisfied; 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; If the first determination condition is not satisfied, execute the third heating mode until the third heating mode is stopped when the preset second heating stop condition is satisfied; wherein, the third heating mode means that when the remaining power of the energy storage battery is sufficient, the remaining battery power is used to achieve self-heating of the energy storage battery.

[0007] By adopting the above technical solution, the above solution specifically discloses the specific control logic for determining and executing the heating plan. During this period, it is defined that the battery temperature data includes the temperature data at multiple preset detection points on the surface of the energy storage battery, and in the subsequent judgment process, the lowest temperature data and the temperature difference are selected as the judgment basis to improve the comprehensiveness of the battery temperature monitoring. In addition, multiple heating modes (i.e., heating plans) are proposed, including: heating the energy storage battery by using an external heat source, and realizing the self-heating of the energy storage battery by means of the charging process of the external electric energy to the energy storage battery, and realizing self-heating by using the remaining power of the battery itself. Through the independent use of the above multiple heating modes or the heating method of combining multiple modes, the battery temperature is finally controlled at the best operating temperature to extend the service life of the energy storage battery.

[0008] Optionally, the method further includes: Whenever the second heating mode needs to be executed, obtain the data values corresponding to each preset influencing factor at the current moment, input the data values into the pre-constructed analysis model, and analyze the temperature rise rate through the analysis model; wherein, the influencing factor refers to a parameter that affects the magnitude of the temperature rise rate of the energy storage battery during the process of realizing self-heating of the energy storage battery by increasing the charging current, and the influencing factor at least includes battery internal resistance and heat capacity; Based on the analyzed temperature rise rate and the current battery temperature data, generate a heating strategy, and execute the second heating mode according to the heating strategy, wherein the heating strategy at least includes the adjusted charging current amount and the execution duration of the second heating mode.

[0009] By adopting the above technical solution, since the second heating mode realizes the self-heating of the energy storage battery by adjusting the charging current amount of the energy storage battery, it is necessary to determine necessary contents such as the charging current increment and the execution duration of the second heating mode before that, and these contents are affected by the battery temperature rise rate and are not fixed, because the battery temperature rise rate is likely to change with the different working environments and working states of the energy storage battery, and the influencing factor is a specific parameter used to characterize the working environment and working state. For example, when the battery ages, its internal resistance and heat capacity will both change, and these specifically described changes will cause the battery temperature rise rate to change, which in turn will affect the heating efficiency when the energy storage battery is heated to the ideal working temperature. Therefore, this application proposes that whenever the second heating mode needs to be executed, the working state and working environment of the energy storage battery will be analyzed, and the corresponding temperature rise rate and heating strategy will be obtained based on the analysis result, so as to realize the adaptive adjustment of the specific heating strategy of the second heating mode in combination with the working state of the energy storage battery, and ensure that the temperature of the energy storage battery is efficiently adjusted to the optimal working temperature.

[0010] Optionally, generating a heating strategy based on the analyzed temperature rise rate and the current battery temperature data includes: Determine the execution duration required to reach the ideal working temperature according to the analyzed temperature rise rate and the current battery temperature data; According to the data value corresponding to each current influencing factor, predict the change trend and the state stage of the influencing factor during the execution duration through the pre-constructed prediction model; wherein, the influence degree of the same influencing factor on the battery temperature rise rate is different in different state stages; If, within the execution duration, among all influencing factors, there exists a target influencing factor that satisfies: the state stage of the target influencing factor changes within the execution duration, then the moment of change is taken as a change node, and the execution duration is divided into several sub - time periods by using the change node; according to the predicted state stage of the influencing factor in each sub - time period, through a pre - constructed analysis model, the segmented temperature rise rate is analyzed and obtained for each sub - time period respectively. According to the segmented temperature rise rate corresponding to each sub - time period, the segmented charging current is determined for each sub - time period, and a heating strategy is generated that includes all sub - time periods, the segmented temperature rise rate corresponding to each sub - time period, and the segmented charging current. If, within the execution duration, there is no target influencing factor, then the charging current is determined based on the temperature rise rate, the current battery temperature data, and the execution duration, and a heating strategy is generated that includes the execution duration, the temperature rise rate, and the charging current.

[0011] By adopting the above - mentioned technical solution, the data value corresponding to the influencing factor is also likely to change with time, and as the corresponding data value changes, its influence degree on the temperature rise rate is also likely to be different. Therefore, this application proposes to divide the influencing factors into different state stages according to the different influence degrees of the influencing factors on the temperature rise rate, and predict the state stage of each influencing factor within the execution duration through a prediction model. If the state stage of the influencing factor changes within the execution duration, then the execution duration is divided into several sub - time periods based on the moment of change, and then the analysis model is used again based on the state stage of the influencing factor within the sub - time period to re - analyze the influence of the change in the state stage of the influencing factor on the battery temperature rise rate, and finally a more refined and accurate heating strategy is obtained.

[0012] Optionally, the charging current included in the heating strategy is a current curve that changes with time within the execution duration; Executing the second heating mode according to the heating strategy includes: Executing the second heating mode according to the heating strategy, and within the execution duration, adjusting the charging current in real - time and synchronously according to the current curve.

[0013] By adopting the above - mentioned technical solution, when executing the second heating mode according to the heating strategy, this application proposes to use a gradual change 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 the occurrence of the situation that the charging current surges and exacerbates the aging of the energy storage battery.

[0014] Optionally, determining the execution duration of the second mode according to the analyzed temperature rise rate and the current battery temperature data includes: Based on the temperature rise rate and the current battery temperature data obtained from the analysis, determine the current increment, and generate multiple current increasing curves according to different current change rates; wherein, the current increasing curve is a current change curve in which the charging current increases with time, and the increment is consistent with the current increment; Based on the heating strategy corresponding to each current increasing curve through a pre-constructed simulation model, execute the second heating mode and output a simulation result, where the simulation result is used to characterize the performance indicators of the energy storage battery after being heated by the second heating mode, and the simulation result at least includes the temperature data of the energy storage battery after heating and the energy consumption; Based on the simulation results corresponding to each current increasing curve, determine the optimal increasing curve, and determine the execution duration according to the optimal increasing curve.

[0015] By adopting the above technical solution, a simulation model is used to simulate a variety of generated current increasing curves to obtain the heating result of the second heating mode executed according to the heating strategy including the corresponding current increasing curve, and the most suitable current increasing curve is selected by analyzing the heating result. Here, it can be considered that the performance indicators corresponding to the simulation result obtained by executing according to the optimal increasing curve are the best; among them, the difference in the current increasing curves is only the change rate of the charging current with time, that is, under the condition of controlling the gradual increase of the charging current, the simulation model is further used to simulate and determine the optimal current increase rate.

[0016] Optionally, the 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, determine the weight value of each performance indicator, and based on the weight value of the performance indicator, perform a matching analysis on the simulation results corresponding to each change curve, and use an optimization algorithm to select the optimal increasing curve.

[0017] By adopting the above technical solution, the emphasis on the finally obtained performance indicators is different in different working scenarios. Therefore, this application proposes to adaptively adjust the emphasis on the performance indicators in combination with the specific working scenario, that is, analyze and adjust the required importance of each performance indicator in the current working scenario in the form of a weight value, and finally select the change curve corresponding to the simulation result that matches the corresponding required importance as the optimal change curve.

[0018] In a second aspect, this application provides an energy storage battery heating control system, including: A battery temperature monitoring module for real-time monitoring of battery temperature data and analyzing whether the battery temperature data meets a preset temperature adjustment condition; A heating scheme formulation module, configured to formulate a heating scheme based on the analysis result of the current battery temperature data if the condition is met, and the heating scheme satisfies: being able to make the battery temperature 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 make the energy storage battery generate heat by itself; A battery heating execution module, configured to execute the heating scheme until the battery temperature reaches the preset ideal operating temperature.

[0019] In a third aspect, the present application provides an energy storage battery heating and regulation device, including a memory and a processor, and a computer program capable of being loaded and executed by the processor as the method described in the first aspect is stored on the memory.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, storing a computer program capable of being loaded and executed by the processor as the method described in the first aspect.

[0021] In summary, the present application includes the following beneficial technical effects: 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 scheme, and the present application proposes two sets of independent and complementary heating methods, namely: external heat source heating and self-heating of the energy storage battery, and the present application proposes that the foregoing two heating methods can be used alone as a heating scheme, or the foregoing heating methods can be combined as a heating scheme to make the battery temperature maintain at the preset ideal operating temperature; finally, through the foregoing heating control logic, it is ensured that the energy storage battery can efficiently switch to the best working state in the environment (especially in a low-temperature environment). Description of the Drawings In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic flowchart of an energy storage battery heating and regulation method disclosed in an embodiment of the present application.

[0023] Figure 2 It is a flowchart for embodying the formulation and execution logic of the heating scheme in an embodiment of the present application.

[0024] Figure 3 It is a structural block diagram of an energy storage battery heating and regulation system disclosed in an embodiment of the present application.

[0025] Description of the accompanying drawings: 201, battery temperature monitoring module; 202, heating plan formulation module; 203, battery heating execution module. DETAILED DESCRIPTION

[0026] The following is combined with Figures 1 - 3 This application is described in further detail.

[0027] The embodiment of the present application discloses a heating control method for an energy storage battery (hereinafter referred to as the control method), which is used to monitor the working temperature of the energy storage battery in real time in combination with the working environment (especially the low temperature environment) of the energy storage battery, and 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 working temperature to ensure the charging and discharging performance of the energy storage battery. The execution subject of the heating control method is an energy storage battery heating control system (hereinafter referred to as the control system). Figures 1 - 2 The specific process steps of the control system to execute the control method are specifically explained.

[0028] S101, real-time monitoring of battery temperature data, and analysis of whether the battery temperature data meets a preset temperature adjustment condition.

[0029] S102, if satisfied, 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 enable the energy storage battery to self-heat.

[0030] S103, executing the heating scheme until the battery temperature reaches the preset ideal operating temperature Wherein, S102 and S103 specifically include the following sub-steps: 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.

[0031] 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 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.

[0032] If the first judgment condition is not met, the third heating mode is executed until the preset second heating stop condition is met, and 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.

[0033] In implementation, the energy storage battery disclosed in the embodiments of the present application may specifically be a lithium-ion battery. The power source of the energy storage battery is twofold. One is that the inverter converts solar energy into electrical energy through photovoltaic and stores it in the energy storage battery, and the other is to supplement the power of the energy storage battery through the mains. In addition, the energy storage battery disclosed in the embodiments of the present application can be specifically applied to the household energy storage scenario, that is, the energy storage battery is used to supply power to household loads. And the following will exemplarily elaborate on how to heat the energy storage battery when it works in a low-temperature environment, that is, how to implement the temperature control method of the energy storage battery.

[0034] 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 cell temperature monitoring module (such as NTC). The battery temperature data at all preset points (i.e., the battery temperature data described above) is finally transmitted by the cell temperature monitoring module to the regulation system. The regulation system specifically includes a BMS, which is pre-electrically connected to the cell temperature monitoring module to obtain the battery temperature data and formulate and execute a heating plan when the battery temperature data meets the preset temperature regulation conditions.

[0035] Specifically, referring to Figure 2 , the specific content of the preset temperature regulation conditions may be whether the connection between the energy storage battery and the inverter is normal. If so, it is judged whether the lowest battery temperature data Tmin and the temperature difference data among the battery temperature data corresponding to all preset points meet the preset first determination condition. The first determination condition may specifically be 2°C < Tmin < 12°C and the temperature difference data < 15°C; if not, the third heating mode is executed. If the first determination condition is met, it is further judged whether the inverter is connected to the mains or photovoltaic, that is, it is determined whether the charging source when charging the energy storage battery through the inverter is mains charging or photovoltaic converting electrical energy into electrical energy for charging; if so, the first heating mode is executed, if not, the third heating mode is executed.

[0036] Among them, the process steps of executing the first heating mode specifically include: closing the heating MOS transistor, allowing an increase in the current amount for charging the energy storage battery, and closing the heating film temperature control switch. Among them, the heating film can specifically be a PI heating film (i.e., the external heat source described above), which is used to heat the energy storage battery by physically contacting the surface of the energy storage battery after being powered on. The power supply of the PI heating film mainly has two sources. One is electrically connected to the energy storage battery to form a heating circuit, and the BMS is used to control the on / off of the heating circuit by controlling the heating MOS transistor provided in the heating circuit (for example, when the heating MOS transistor is closed, the heating circuit is connected), so as to supply power to the heating film through the power of the energy storage battery itself, so that the heating film conducts heat in the reverse direction to the energy storage battery; the other is to supply power to the heating film by converting the commercial power or photovoltaic power connected through the inverter into direct current. In addition, a heating film temperature control switch is built into the heating film, and the heating film temperature control switch can specifically be a normally closed temperature control switch PTC; the PTC is used to monitor the real-time temperature on the surface 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 connection between the heating film and the energy storage battery, that is, disconnecting the heating circuit, until when the temperature of the heating film drops to the preset temperature threshold (such as 45 °C), the normally closed temperature control switch PTC closes again to reconnect the heating film into the heating circuit; that is, both the heating MOS and the normally closed temperature control switch PTC jointly control the on / off of the heating circuit. Only when both the heating MOS and the normally closed temperature control switch PTC are closed, the heating circuit is in a connected state, that is, the heating scheme of the energy storage battery can be realized by the way of heating the heating film by powering it on.

[0037] In addition, in the process steps of the first heating mode, the solution of "allowing an increase in the current amount for charging the energy storage battery" is also mentioned, which means that during the process of heating the energy storage battery by using the heating film, the energy storage battery itself can also be in a charging state, that is, charging is realized by means of commercial power or photovoltaic power connected through the inverter, and during the charging process, the BMS can adjust the magnitude of the charging current, such as further increasing the charging current on the basis of the current charging current amount, so that the energy storage battery generates heat by itself due to the increase of the internal current and realizes temperature rise. In summary, the execution operation of the first heating mode is realized.

[0038] During the execution of the first heating mode, the control system is also used to judge 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 duration of the first heating mode > 60 minutes; if so, disconnect the heating MOS transistor, that is, stop the first heating mode and complete the heating. If during the execution of the first heating mode, the first heating stop condition is not met, the second heating mode is further executed until the first heating stop condition is met.

[0039] The process steps for specifically executing the second heating mode are as follows: Determine whether Tmin is greater than 5°C. If so, execute: Allow an increase in the current amount of the charging current for the energy storage battery, and after closing the heating film temperature control switch, re-determine 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.

[0040] For the third heating mode executed when the first determination condition is not met, its specific process steps are as follows: First, determine whether -20°C ≤ Tmin ≤ 2°C and the temperature difference data < 15°C are met; if not, if met, disconnect the charging MOS transistor (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 is disconnected from the energy storage battery and charging stops), and further determine whether the remaining power S0C of the energy storage battery is greater than 30%. If so, close the heating MOS transistor, that is, use the remaining power of the energy storage battery to supply power to the heating film, so that the heating film is energized and heated to heat the energy storage battery. During this power supply and heating process, determine whether the preset second heating stop condition is met; Among them, the preset second heating stop condition specifically includes: Condition 1: Determine whether S0C is less than 20%, and Condition 2: Determine whether Tmin > 15°C or the execution duration of the first heating mode > 60 minutes; among them, Condition 1 is preferentially determined. If Condition 1 is met, the heating MOS transistor is disconnected to stop heating the energy storage battery using the heating film; only when Condition 1 is not met, will Condition 2 be judged, and Condition 2 is specifically: Determine whether Tmin > 15°C or the execution duration of the first heating mode > 60 minutes, that is, the preset first heating stop condition. If Condition 2 is met, the heating MOS transistor is disconnected to stop heating. If Condition 2 is not met, the second heating mode is executed until heating stops after Condition 2 is met.

[0041] In summary, the execution operation of the heating scheme is realized, and during the above execution process of the entire heating scheme, it is defaulted that the temperature of the energy storage battery can reach the preset ideal working temperature after executing the above heating scheme.

[0042] Optionally, the control method further includes the following steps: S104, whenever the second heating mode needs to be executed, obtain the data values corresponding to each preset influencing factor at the current moment, input the data values into the pre-constructed analysis model, and analyze the temperature rise rate through the analysis model; among them, the influencing factor refers to a parameter that affects the magnitude of the temperature rise rate of the energy storage battery during the process of self-heating the energy storage battery by increasing the charging current. The influencing factors at least include battery internal resistance and heat capacity; S105. Generate a heating strategy based on the analyzed temperature rise rate and the current battery temperature data, and execute the second heating mode according to the heating strategy, where the heating strategy at least includes the adjusted charging current and the execution duration of the second heating mode; Among them, "generate a heating strategy based on the analyzed temperature rise rate and the current battery temperature data" in S105 specifically includes the following sub-steps: Determine the current increment according to the analyzed temperature rise rate and the current battery temperature data, and generate multiple current increasing curves according to different current change rates; where the current increasing curve is a current change curve in which the charging current increases with time, and the increment is consistent with the current increment; Execute the second heating mode based on the heating strategy corresponding to each current increasing curve through a pre-constructed simulation model, and output the 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. The simulation results at least include the temperature data of the energy storage battery after heating and the energy consumption; Determine the weight value of each performance indicator according to the current working scenario of the energy storage battery, and perform matching analysis on the simulation results corresponding to each change curve based on the weight value of the performance indicator. Use the optimization algorithm to select the optimal increasing curve; determine the execution duration according to the optimal increasing curve; According to the data value corresponding to each current influencing factor, predict the change trend and the state stage of the influencing factor during the execution duration through a pre-constructed prediction model; where the influence degree of the same influencing factor on the battery temperature rise rate is different in different state stages; If among all the influencing factors during the execution duration, there is a target influencing factor that satisfies: the state stage of the target influencing factor changes during the execution duration, then use the change moment as the change node, and divide the execution duration into several sub-periods by using the change node; according to the predicted state stage of the influencing factor in each sub-period, analyze and obtain the segmented temperature rise rate for each sub-period through a pre-constructed analysis model. According to the segmented temperature rise rate corresponding to each sub-period, determine the segmented charging current for each sub-period, and generate a heating strategy 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 during the execution duration, then determine the charging current according to the temperature rise rate, the current battery temperature data, and the execution duration, and generate a heating strategy with the execution duration, the temperature rise rate, and the charging current.

[0043] "Execute the second heating mode according to the heating strategy" in S105 includes: Execute the second heating mode according to the heating strategy, and during the execution duration, adjust the charging current in real time and synchronously according to the optimal increasing curve.

[0044] In implementation, since the specific execution flow steps of the second heating mode and the first heating mode both include the step of "allowing an increase in the current amount of the charging current of the energy storage battery", the present application proposes that whenever the second heating mode or the first heating mode is executed, the control system will be used to determine the current increment (that is, determine the adjusted charging current amount), and the execution duration of the first heating mode or the second heating mode. And it can be known from the preset first heating stop condition that the execution duration ≤ 60 minutes; that is to say, the control system will first determine the heating strategy with the adjusted charging current amount and the execution duration, and then can execute the step of "allowing an increase in the current amount of the charging current of the energy storage battery" involved in the first heating mode or the second heating mode according to this heating strategy.

[0045] The present application proposes that the premise for determining the heating strategy is to first confirm the temperature rise speed of the energy storage battery in the current working state. And since the temperature rise speed of the energy storage battery is different in different working states, and under the same execution duration and the same charging current increment, the different temperature rise speeds 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 heating is finally completed).

[0046] The present application proposes to use a preset influence factor to characterize the working state of the energy storage battery. By obtaining the data value of each preset influence factor, and using a pre-constructed analysis model to analyze and obtain the working state of the energy storage battery, and then using the analysis model to analyze and obtain the temperature rise speed of the energy storage battery in the corresponding working state after determining the working state. Among them, the influence factor can specifically include the internal resistance value of the battery, the heat capacity value of the battery, etc., to be used to reflect the battery aging state. The analysis model can specifically be a model that is pre-trained and constructed based on various types of data collected in historical periods (such as the charging current magnitude, ambient temperature, battery initial temperature, battery internal resistance, battery capacity, etc.) using big data analysis techniques (such as machine learning algorithms) and is used to analyze and obtain the corresponding battery temperature rise speed according to the specific data value of the influence factor.

[0047] After determining the corresponding temperature rise rate, based on the current battery temperature data and the ideal operating temperature of the energy storage battery, determine the temperature to be raised (hereinafter referred to as the temperature rise); where the temperature rise = |ideal operating temperature - current battery temperature data| / temperature rise rate; and by way of example, in the embodiments of the present application, the control system pre-stores a first correspondence table for storing different temperature rises and their corresponding current increments. Therefore, the control system can determine the corresponding current increment based on the determined temperature rise, and then generate multiple current increasing curves according to different current change rates based on the initial charging current (hereinafter referred to as the initial charging current) before the current increase. It is default that the charging current increase rule corresponding to each current increasing curve is a uniform increase, and the final increase result of all current increasing curves (i.e., the finally increased current) = initial charging current - current increment.

[0048] For the multiple generated current increasing curves, the control system will further adjust the charging current value in real time according to the current change in the current increasing curve through a pre-constructed simulation model, so as to simulate the heating process of using the increasing charging current to realize the self-heating of the energy storage battery. The simulation model includes a pre-constructed energy storage battery model (including an energy storage battery physical model, a heat blood model, and an electrochemical model).

[0049] According to the simulation results obtained by simulating each current increasing curve, further combined with the current working scenario of the energy storage battery, match weights to each performance index included in the simulation results, and use an optimization algorithm (such as a genetic algorithm, a particle swarm optimization algorithm) to analyze and obtain the optimal increasing curve. By way of example, the control system can pre-store the weight values of each performance index corresponding to multiple different working scenarios, and the working scenarios and weight values can be preset manually. The user can set the current working scenario according to actual needs; in other embodiments, the control system can calculate the matching analysis results of each current increasing curve in a weighted summation manner, and the current increasing curve corresponding to the matching analysis result with the highest value after weighted summation is used as the optimal increasing curve.

[0050] Next, the control system determines the execution duration according to the determined optimal increasing curve. The execution duration = current increment / current change rate corresponding to the optimal increasing curve. Then, according to the prediction model, it predicts the change trend and the state stage of each influencing factor within the execution duration. For example, it predicts the change trend of the battery resistance over time within the future execution duration. The prediction model here can specifically be an existing prediction algorithm used to predict the change of data over time, which is prior art and will not be elaborated here. It should be noted that different state stages of the influencing factors are stored in the control system, 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 considered that the influencing factors have different degrees of influence on the battery temperature rise rate at different state stages.

[0051] The control system is used to determine whether each influencing factor is a target influencing factor one by one according to all the data values corresponding to the influencing factors predicted by the prediction model within the execution duration. The judgment logic is: determine whether the state stage of the influencing factor changes within the execution duration, that is, whether there are two data values belonging to the numerical ranges corresponding to different stage states of the influencing factor. If so, it means that the influencing factor is the target influencing factor.

[0052] If there is more than one target influencing factor, change nodes are determined for each influencing factor respectively, that is, the change moment when the state stage of each influencing factor changes within the specified duration is used as the change node. According to all the change nodes, the execution duration is divided into several sub-periods, and the end time of each sub-period can only be the end time of the execution duration or the change time; for example, if the execution duration is [A, B], the change times are a and b, and A < a < b < B, then the corresponding sub-periods are [a, b], [a, b], [b, B].

[0053] Since the state stage of the influencing factor changes within the 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 values and state stages of the influencing factors predicted for 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 data values of the influencing factors predicted within the sub-period may be a data range. Therefore, this application proposes to take the mode or average of all the data values included in the data range as the data value of the influencing factor when inputting it into the analysis model, so as to realize the analysis of the segmented temperature rise rate of each sub-period by the analysis model.

[0054] Next, the control system determines the heating temperature for each segment based on the temperature rise rate of each segment, where the heating temperature for each segment = temperature rise rate * sub-period duration. Then, according to the first correspondence table, it determines the current increment corresponding to each sub-period, thereby obtaining the segmented charging current for each sub-period. For example, the segmented charging current corresponding to the sub-period [a, b] = [a, b] + the current increment corresponding to [a, b]. Finally, a heating strategy is generated that includes all sub-periods, the segmented temperature rise rate corresponding to each sub-period, and the segmented charging current.

[0055] If all influencing factors are non-target influencing factors, it determines that the charging current = current increment + initial charging current, and generates a heating strategy that includes the execution duration, temperature rise rate, and charging current.

[0056] After determining the heating strategy, it executes the "allowing the current of the charging current of the energy storage battery to increase" in the first heating mode or the second heating mode according to the heating strategy, that is, adjusts the charging current of the energy storage battery in a timely manner within the execution duration according to the optimal increasing current curve.

[0057] The embodiment of the present application also discloses an energy storage battery heating control system. Refer to Figure 3 , including: 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; The heating scheme formulation module 202 is used to, if it is satisfied, formulate a heating scheme based on the analysis result of the current battery temperature data, and the heating scheme meets: being able to make the battery temperature reach the preset ideal working 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 make the energy storage battery generate heat by itself; The battery heating execution module 203 is used to execute the heating scheme until the battery temperature reaches the preset ideal working temperature.

[0058] Optionally, the heating scheme formulation module 202 is configured to determine whether the lowest battery temperature data and the temperature difference data among the battery temperature data obtained by monitoring corresponding to all preset points meet a preset first determination condition; and is further configured to, if the first determination condition is met, execute a first heating mode, where the first heating mode is to heat the battery module through an external heat source; and during the execution of the first heating mode, determine whether the 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; where 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 further configured to, if the first determination condition is not met, execute a third heating mode until the preset second heating stop condition is met and then stop executing the third heating mode; where the third heating mode means that when the remaining power of the energy storage battery is sufficient, the remaining battery power is used to achieve self-heating of the energy storage battery.

[0059] Optionally, it further includes a heating strategy processing module, configured to, whenever the second heating mode needs to be executed, obtain the data values corresponding to each preset influencing factor at the current moment, input the data values into an analysis model based on a pre-constructed one, and analyze the temperature rise rate through the analysis model; where the influencing factor refers to a parameter that affects the magnitude of 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 factors at least include battery internal resistance and heat capacity; and is further configured to generate a heating strategy based on the analyzed temperature rise rate and the current battery temperature data, and execute the second heating mode according to the heating strategy, where the heating strategy at least includes the adjusted charging current amount and the execution duration of the second heating mode.

[0060] Optionally, the heating strategy processing module is further configured to determine the execution duration required to reach the ideal operating temperature according to the analyzed temperature rise rate and the current battery temperature data; predict the change trend and the state stage of the influencing factor during the execution duration through a pre-constructed prediction model according to the data value corresponding to each current influencing factor; wherein, the influence degree of the same influencing factor on the battery temperature rise rate is different in different state stages; it is further configured to, if among all the influencing factors during the execution duration, there is a target influencing factor that satisfies: the state stage of the target influencing factor changes during the execution duration, then use the change moment as a change node, and divide the execution duration into several sub-periods by using the change node; according to the predicted state stage of the influencing factor in each sub-period, analyze the segmented temperature rise rate for each sub-period through a pre-constructed analysis model, determine the segmented charging current for each sub-period according to the segmented temperature rise rate corresponding to each sub-period, and generate a heating strategy with all sub-periods, the segmented temperature rise rate corresponding to each sub-period, and the segmented charging current; it is further configured to, if there is no target influencing factor during the execution duration, determine the charging current according to the temperature rise rate, the current battery temperature data, and the execution duration, and generate a heating strategy with the execution duration, the temperature rise rate, and the charging current.

[0061] Optionally, the heating strategy processing module is further configured to execute the second heating mode according to the heating strategy, and during the execution duration, synchronously adjust the charging current in real time according to the current curve.

[0062] Optionally, the heating strategy processing module is further configured to determine the current increment according to the analyzed temperature rise rate and the current battery temperature data, and 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 further configured to execute the second heating mode through a pre-constructed simulation model based on the heating strategy corresponding to each change curve, and output a simulation result, where the simulation result is used to characterize the performance index of the energy storage battery after being heated by the second heating mode, and the simulation result at least includes the temperature data of the energy storage battery after heating and the energy consumption; it is further configured to determine the optimal change curve based on the simulation results corresponding to each change curve, and determine the execution duration according to the optimal curve.

[0063] Optionally, the heating strategy processing module is further configured to determine the weight value of each performance index according to the current working scenario of the energy storage battery, and perform matching analysis on the simulation results corresponding to each change curve based on the weight value of the performance index, and select the optimal change curve by using an optimization algorithm.

[0064] An embodiment of the present application also discloses a heating control device for an energy storage battery. The heating control device for an energy storage battery includes a memory and a processor. A computer program capable of being loaded and executed by the processor, such as the above-mentioned heating control method for an energy storage battery, is stored on the memory.

[0065] An embodiment of the present application also discloses a computer-readable storage medium, which stores a computer program capable of being loaded and executed by the processor, such as the above-mentioned heating control method for an energy storage battery. The computer-readable storage medium includes, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0066] It should be noted that, in this article, relational terms such as first and second are only 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.

[0067] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the protection scope of the application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all embodiments. Based on these embodiments, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope to be protected by the present application.

Claims

1. A method for heating and regulating an energy storage battery, characterized in that: include: Real-time monitoring of battery temperature data, and analysis of whether the battery temperature data meets preset temperature adjustment conditions; If satisfied, a heating scheme is formulated based on the analysis result of the current battery temperature data, 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 so that the energy storage battery generates self-heat; The heating scheme is executed until the battery temperature reaches a preset ideal operating temperature.

2. The energy storage battery heating control method according to claim 1, characterized in that: 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 scheme is formulated based on the analysis result of the current battery temperature data, and the heating scheme 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, 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, and 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; If the first judgment condition is not met, the third heating mode is executed until the preset second heating stop condition is met, and 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.

3. The energy storage battery heating control method according to claim 2, characterized in that: The method further comprises: Whenever the second heating mode needs to be executed, 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; wherein the influencing factor refers to a parameter that affects the temperature rise rate of the energy storage battery in the process of realizing 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; 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.

4. The energy storage battery heating control method according to claim 3, characterized in that: 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; According to the data value corresponding to each of the current influencing factors, a change trend of the influencing factor within the execution time and the state stage of the influencing factor are predicted by 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; 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 a change node, and the execution time is divided into a number of sub-periods using the change node; 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 a segmented temperature rise rate for each sub-period, and according to the segmented temperature rise rate corresponding to each sub-period, a 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; If there is no target influencing factor within the execution time, the charging current is determined based on the temperature rise rate, current battery temperature data, and execution time, and a heating strategy with execution time, temperature rise rate, and charging current is generated.

5. The energy storage battery heating control method according to claim 3, characterized in that: The charging current included in the heating strategy is a current curve that changes with time during the execution time; The performing 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 adjusted synchronously in real time according to the current curve.

6. The energy storage battery heating control method according to claim 5, characterized in that: The step of determining the execution time of the second mode according to the analyzed temperature rise rate and the current battery temperature data includes: According to the analyzed temperature rise rate and current battery temperature data, the current increment is determined, and a plurality of current increment curves are generated according to different current change rates; wherein the current increment curve is a current change curve in which the charging current increases with time, and the increment is consistent with the current increment; Execute the second heating mode based on the heating strategy corresponding to each current increasing curve through a pre-built simulation model, and output a simulation result, wherein the simulation result is used to characterize the performance index of the energy storage battery after being heated by the second heating mode, and the simulation result at least includes 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 according to the optimal increasing curve, the execution time is determined.

7. The energy storage battery heating control method according to claim 6, characterized in that: The step of determining the optimal increasing curve based on the simulation result corresponding to each current increasing curve comprises: 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, a matching analysis is performed on the simulation results corresponding to each of the change curves, and the optimal incremental curve is selected using an optimization algorithm.

8. A heating control system for an energy storage battery, characterized in that: include, A battery temperature monitoring module (201), 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 used 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 charging and discharging process of the energy storage battery so that the energy storage battery generates self-heat; The battery heating execution module (203) is used to execute the heating scheme until the battery temperature reaches a preset ideal operating temperature.

9. 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 7.

10. 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 7.

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