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

Abstract

The application discloses an energy storage battery active heating control method and device, a photovoltaic system and a medium, and belongs to the technical field of light storage systems. Reference temperature data is received; battery temperature change data in a target time interval is obtained based on the prediction of the reference temperature data; reference motion working condition data in the target time interval is generated; charging and discharging demand information in the target time interval is obtained based on the prediction of the reference motion working condition data in the target time interval; and the battery heating time interval and the battery heating power source in the battery heating time interval are determined based on the battery temperature change data, the charging and discharging demand information and the reference motion working condition data. By determining the battery heating time interval, invalid heating during a non-charging and discharging period is avoided, and energy invalid loss and energy waste caused by continuous heat preservation or early heating are reduced. By determining the battery heating power source, the heating cost is reduced, and high economy of battery heating control is realized.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic energy storage system technology, and particularly relates to an active heating control method, device, photovoltaic system and dielectric for energy storage batteries. Background Technology

[0002] The electrochemical performance of batteries in photovoltaic-energy storage systems is highly sensitive to temperature. At low temperatures, batteries experience significant capacity reduction, increased internal resistance, increased system losses, reduced charge / discharge capacity, and are prone to thermal runaway. Ambient temperature is a key bottleneck restricting the efficient operation of photovoltaic-energy storage systems across all climates. Therefore, heating of the batteries in energy storage systems is necessary in low-temperature environments.

[0003] In related technologies, a passive heating method based on temperature thresholds is adopted. This method starts heating when the battery temperature is detected to be lower than a set threshold and stops heating when the target temperature is reached. This scheme relies entirely on passive response, does not consider the economics of heating timing, and does not distinguish the source of heating energy. It may consume high-priced energy during peak electricity price periods. Summary of the Invention

[0004] This invention aims to at least partially address one of the technical problems in related technologies. To this end, this invention proposes an active heating control method, device, photovoltaic system, and dielectric for energy storage batteries. Based on battery temperature change data, charge / discharge demand information, and reference operating condition data, it determines the battery heating time interval, avoiding ineffective heating during non-charge / discharge periods when there is no demand for charging or discharging, and reducing energy loss and waste caused by continuous heat preservation or premature heating. Simultaneously, by determining the source of battery heating power, heating costs are reduced, thereby achieving a high degree of economy in battery heating control while ensuring battery charge / discharge performance and vehicle power response.

[0005] In a first aspect, this application provides an active heating control method for an energy storage battery, comprising: Receive reference temperature data; Based on the reference temperature data, predictions are made to obtain battery temperature change data within the target time interval. Generate reference motion condition data within the target time interval; Based on the reference motion condition data within the target time interval, prediction is made to obtain the charging and discharging demand information within the target time interval. Decisions are made based on the battery temperature change data, the charging and discharging demand information, and the reference motion condition data to determine the battery heating time interval and the source of battery heating power within the battery heating time interval.

[0006] The active heating control method for energy storage batteries according to embodiments of the present invention makes decisions based on battery temperature change data, charge / discharge demand information, and reference operating condition data to determine the battery heating time interval. This avoids ineffective heating during non-charge / discharge periods when there is no charge / discharge demand, reducing ineffective energy loss and waste caused by continuous heat preservation or premature heating. Simultaneously, by determining the source of battery heating power, heating costs are reduced, thereby achieving a high degree of economy in battery heating control while ensuring battery charge / discharge performance and vehicle power response.

[0007] According to one embodiment of the present invention, the reference temperature data includes historical battery temperature change data, real-time battery temperature data, historical ambient temperature change data, real-time ambient temperature data, and weather information for the target time interval.

[0008] According to one embodiment of the present invention, the reference motion condition data includes a charge / discharge scheduling scheme, load power change data, photovoltaic power change data, and battery state of charge change data.

[0009] According to one embodiment of the present invention, the charge / discharge demand information includes charge / discharge time, charge / discharge power corresponding to the charge / discharge time, and target temperature data. The step of predicting the charge / discharge demand information within the target time interval based on reference motion condition data within the target time interval includes: Based on the reference motion condition data within the target time interval, prediction is made to obtain the charging and discharging times within the target time interval and the charging and discharging power corresponding to the charging and discharging times. Based on the mapping relationship between charging / discharging power and temperature power, the target temperature data corresponding to the charging / discharging time is determined.

[0010] According to one embodiment of the present invention, the reference motion condition data includes a charge / discharge scheduling scheme, load power change data, and photovoltaic power change data; the step of making a decision based on the battery temperature change data, the charge / discharge demand information, and the reference motion condition data to determine the battery heating time interval and the battery heating power source within the battery heating time interval includes: The battery heating time interval is determined by planning based on the battery temperature change data, the charging and discharging time, the target temperature data, and the weather information of the target time interval; The source of battery heating power within the battery heating time interval is determined based on the battery heating time interval, the charge and discharge scheduling scheme, the load power change data, and the photovoltaic power change data.

[0011] According to one embodiment of the present invention, the step of determining the source of battery heating power within the battery heating time interval based on the battery heating time interval, the charge / discharge scheduling scheme, the load power change data, and the photovoltaic power change data includes: If the photovoltaic power change data satisfies the charge / discharge scheduling scheme and the load power change data within the battery heating time interval, then the source of the battery heating power is determined to be the photovoltaic system. If the photovoltaic power change data does not meet the charging and discharging scheduling scheme and the load power change data within the battery heating time interval, then the source of the battery heating power is determined to be the photovoltaic system and the power grid.

[0012] According to one embodiment of the present invention, generating reference motion condition data within a target time interval includes: Receive reference operating condition data and charge / discharge scheduling scheme; Prediction is made based on the reference operating condition data, and reference motion condition data within the target time interval is obtained by combining the charging and discharging scheduling scheme.

[0013] In a second aspect, the present invention provides an active heating control device for an energy storage battery, the device comprising: Reference temperature data receiving module, used to receive reference temperature data; The battery temperature prediction module is used to predict the battery temperature change data within a target time interval based on the reference temperature data. The reference motion condition data generation module is used to generate reference motion condition data within the target time interval; The charge / discharge demand prediction module is used to predict the charge / discharge demand information within the target time interval based on reference motion condition data within the target time interval. The charge / discharge demand decision module is used to make decisions based on the battery temperature change data, the charge / discharge demand information, and the reference motion condition data, to determine the battery heating time interval and the source of battery heating power within the battery heating time interval.

[0014] The active heating control device for energy storage batteries proposed in this embodiment of the invention makes decisions based on battery temperature change data, charging and discharging demand information, and reference operating condition data to determine the battery heating time interval. This avoids ineffective heating during non-charging and discharging periods when there is no demand for charging or discharging, reducing ineffective energy loss and waste caused by continuous heat preservation or premature heating. Simultaneously, by determining the source of battery heating power, heating costs are reduced, thereby achieving a high degree of economy in battery heating control while ensuring battery charging and discharging performance and vehicle power response.

[0015] Thirdly, the present invention provides a photovoltaic system including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the active heating control method for energy storage batteries as described in the first aspect above.

[0016] Fourthly, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the active heating control method for energy storage batteries as described in the first aspect above.

[0017] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the active heating control method for energy storage batteries as described in the first aspect above.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic flowchart of the active heating control method for energy storage batteries provided in the embodiments of this application; Figure 2 This is a schematic diagram of the process for obtaining charging and discharging demand information within a target time interval, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the process for determining the battery heating time interval and the source of battery heating power within the battery heating time interval, provided in an embodiment of this application. Figure 4 This is a schematic diagram of the process for generating reference motion condition data within a target time interval, provided in an embodiment of this application. Figure 5 This is a schematic diagram of the active heating control device for energy storage batteries provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of the photovoltaic system provided in the embodiments of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of this application.

[0021] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0022] The active heating control method, device, photovoltaic system, and medium for energy storage batteries provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0023] Among them, the active heating control method for energy storage batteries can be applied to the terminal, and can be executed by the hardware or software in the terminal.

[0024] The active heating control method for energy storage batteries provided in this invention can be executed by an electronic device or a functional module or entity in an electronic device that can implement the active heating control method for energy storage batteries. The electronic devices mentioned in this invention include, but are not limited to, mobile phones, tablets, computers, cameras, and wearable devices. The active heating control method for energy storage batteries provided in this application will be described below using an electronic device as an example.

[0025] The electrochemical performance of batteries in photovoltaic-energy storage systems is highly sensitive to temperature. At low temperatures, batteries experience significant capacity reduction, increased internal resistance, increased system losses, reduced charge / discharge capacity, and are prone to thermal runaway. Ambient temperature is a key bottleneck restricting the efficient operation of photovoltaic-energy storage systems across all climates. Therefore, heating of the batteries in energy storage systems is necessary in low-temperature environments.

[0026] In related technologies, a passive heating method based on temperature thresholds is used. This method starts heating when the battery temperature is detected to be below a set threshold and stops heating once the target temperature is reached. This approach relies entirely on passive response, does not consider the economics of heating timing, and does not differentiate between the sources of heating energy. It may consume high-priced electricity during peak electricity price periods. Furthermore, it does not incorporate heating behavior into the energy economic optimization framework of photovoltaic-storage systems.

[0027] In related technologies, a timed heating strategy is adopted, which starts heating at fixed times according to a preset schedule. This scheme has a fixed time and cannot adapt to climate change and electricity price fluctuations. At the same time, it does not take into account the charging and discharging needs of the battery during heating, and may heat up at unnecessary times, resulting in energy waste.

[0028] The above methods failed to effectively utilize low-cost or even zero-cost energy sources such as surplus photovoltaic power and green electricity for battery preheating, and also failed to incorporate grid time-of-use pricing information, resulting in the use of high-priced electricity for heating during peak hours and causing unnecessary economic losses.

[0029] Based on this, the embodiments in this specification provide an active heating control method for energy storage batteries. Please refer to [link / reference]. Figure 1 The medical image artifact removal method may include the following steps: S110, Receive reference temperature data.

[0030] S120: Based on reference temperature data, predict and obtain battery temperature change data within the target time interval.

[0031] The reference temperature data can be historical and current data reflecting and influencing the battery's thermal state. The target time interval can be a period from the current moment to a future moment, such as a 24-hour forecast window. The battery temperature change data can be the evolution of the battery temperature over time within the target time interval. All batteries are energy storage batteries, capable of charging and discharging, and can perform energy storage and discharging operations.

[0032] Specifically, the agent first receives reference temperature data, then uses a pre-built battery thermal characteristic prediction model, taking the received reference temperature data as model input, and outputs a battery temperature change prediction result covering the target time interval through model inference. This battery temperature change data can be represented as a series of discrete temperature prediction values ​​arranged according to a preset time step (such as every 10 minutes or 30 minutes), or as a continuous temperature change curve over time.

[0033] In some implementations, this method can be applied to photovoltaic-storage systems. The heating decision agent utilizes a pre-built battery thermal characteristic prediction model, based on reference temperature data, to predict the natural evolution of battery temperature over a future period without applying any active heating. This yields the natural temperature change trajectory of the battery under unheated conditions, and ultimately obtains battery temperature change data within a target time interval.

[0034] It should be noted that the pre-built battery thermal characteristic prediction model can be established based on the battery's thermal equilibrium relationship. Since different batteries differ in material systems, structural designs, capacities, and operating environments, their thermal characteristics will also vary. Therefore, the thermal models corresponding to different batteries may differ in specific parameters and even model structures. Modeling or parameter calibration should be performed for specific battery types to ensure the accuracy of the prediction results.

[0035] S130, Generate reference motion condition data within the target time interval.

[0036] S140. Based on the reference motion condition data within the target time interval, make predictions to obtain the charging and discharging demand information within the target time interval.

[0037] Among them, the reference operating condition data can be operating condition data related to battery charging and discharging requirements predicted for the target time interval.

[0038] Specifically, the agent first predicts charging and discharging demand based on historical and current data that reflect and influence charging and discharging needs, generating reference motion condition data for the target time interval. Based on this, the agent further performs demand prediction processing using the generated reference motion condition data for the target time interval, thereby obtaining charging and discharging demand information for that time interval. This information characterizes the charging power, discharging power, and duration required by the battery at various future times, enabling reasonable energy management and power scheduling of the battery.

[0039] S150 makes decisions based on battery temperature change data, charging and discharging demand information, and reference motion condition data to determine the battery heating time interval and the source of battery heating power within the battery heating time interval.

[0040] The battery heating power source can be an energy supply source that provides electrical or thermal power for the battery heating process.

[0041] Specifically, battery temperature change data can be the evolution of battery temperature over time within a target time interval. Charge / discharge demand information describes the expected charging or discharging power, duration, and temperature requirements of the battery within the target time interval. Reference motion condition data can be motion condition data predicted for the target time interval and associated with the battery charge / discharge demands. The agent acquires battery temperature change data, charge / discharge demand information, and reference motion condition data to make decisions, determining the battery heating time interval within the target time interval that requires active battery heating, and deciding on the source of battery heating power to be used within that heating time interval.

[0042] The active heating control method for energy storage batteries provided in this application makes decisions based on battery temperature change data, charging and discharging demand information, and reference operating condition data to determine the battery heating time interval. This avoids ineffective heating during non-charging / discharging periods when there is no charging or discharging demand, reducing ineffective energy loss and waste caused by continuous heat preservation or premature heating. Simultaneously, by determining the source of battery heating power, heating costs are reduced, thereby achieving a high degree of economy in battery heating control while ensuring battery charging and discharging performance and vehicle power response.

[0043] In some embodiments, the reference temperature data includes historical battery temperature change data, real-time battery temperature data, historical ambient temperature change data, real-time ambient temperature data, and weather information for the target time interval.

[0044] Specifically, the system can collect and record the battery's temperature change sequence over a historical period as historical battery temperature change data, while simultaneously acquiring the current battery temperature value via sensors as real-time battery temperature data. The system also collects the temperature change sequence of the battery's surrounding environment over the corresponding historical period as historical environmental temperature change data, and acquires the current environmental temperature value via sensors as real-time environmental temperature data. Furthermore, it is necessary to obtain weather information for the target future time interval, such as outdoor temperature and other meteorological parameters. This weather information for the target time interval can be obtained through weather forecasts.

[0045] The intelligent agent uses a pre-built battery thermal characteristic prediction model, taking historical battery temperature change data, real-time battery temperature data, historical ambient temperature change data, real-time ambient temperature data, and weather information for the target time interval as model inputs, and outputs battery temperature change prediction results covering the target time interval through model inference.

[0046] In some embodiments, the reference operating condition data includes charge and discharge scheduling schemes, load power change data, photovoltaic power change data, and battery state of charge change data.

[0047] Specifically, the charge / discharge scheduling scheme represents the pre-planned time and power information for charging or discharging within the target time interval, clarifying the charging / discharging actions and power commands at each future moment. Load power variation data represents the dynamic change in the power required by the load over time within the target time interval. Photovoltaic power variation data characterizes the changes in solar energy within the target time interval. Battery state of charge variation data characterizes the predicted change in the battery's state of charge over time within the target time interval, reflecting the evolution trend of the energy storage system's remaining capacity. Obtaining these parameters facilitates subsequent determinations regarding whether the battery needs heating and the source of that heating.

[0048] In some embodiments, please refer to Figure 2 The charging and discharging demand information includes the charging and discharging time, the corresponding charging and discharging power, and the target temperature data. Based on reference motion condition data within the target time interval, prediction is performed to obtain the charging and discharging demand information for the target time interval. This can include the following steps: S210. Based on the reference motion condition data within the target time interval, make predictions to obtain the charging and discharging times and the corresponding charging and discharging power within the target time interval.

[0049] S220. Based on the mapping relationship between charging / discharging power and temperature power, determine the target temperature data corresponding to the charging / discharging time.

[0050] The temperature-power mapping relationship can be a data table or function obtained in advance based on battery characteristic tests, characterizing the correspondence between charge / discharge power and the battery's permissible operating temperature. Since the safe charging and discharging temperature range of a battery varies under different charging and discharging powers—for example, during high-power charging or discharging, the battery is typically required to be in a relatively high temperature range, while under low-power conditions, the lower limit of the battery's permissible temperature is relatively low—it is necessary to establish and store this temperature-power mapping relationship so that the required battery temperature can be determined based on the actual charging and discharging power during operation. The target temperature data can be the minimum temperature requirement for the battery; that is, the battery temperature must at least reach this target temperature value before performing a charging or discharging operation at the corresponding power to ensure the safety and performance of the charging and discharging process.

[0051] It should be noted that the charge / discharge times in this instruction manual include both charging and discharging times. These times do not overlap in time; that is, for the same energy storage battery, it can only be in either a charging or discharging state at any given time. Charge / discharge power includes both charging and discharging power; each charging time corresponds to a charging power, and each discharging time corresponds to a discharging power.

[0052] Specifically, the agent can acquire reference motion condition data within a target time interval. This reference motion condition data may include, for example, time-series information such as predicted solar irradiance sequences, ambient temperature sequences, and load power demand sequences within the target time interval. The agent can use a pre-trained policy network to predictively process the reference motion condition data, thereby obtaining the charging or discharging times that need to be charged or discharged within the target time interval, and simultaneously providing the charging or discharging power corresponding to each charging or discharging time, thus forming a charging and discharging strategy sequence containing charging and discharging times and charging and discharging power. Based on this, the agent further invokes a pre-established temperature-power mapping relationship, and determines the target temperature data corresponding to each charging and discharging time according to the charging and discharging power corresponding to each charging and discharging time. This target temperature data is used to indicate the expected temperature level of the battery when performing the corresponding charging and discharging action, thereby providing a basis for subsequent thermal management control. The target time interval may include at least one charging or discharging time.

[0053] In some implementations, the reference operating condition data includes charge / discharge scheduling schemes, load power change data, and battery state of charge (SOC) change data. The charge / discharge scheduling scheme represents the pre-planned time and power information for charging or discharging within a target time interval, clarifying the charging / discharging actions and power commands at each future moment. Load power change data indicates the dynamic change of the load's required power over time within the target time interval. Battery SOC change data characterizes the predicted change in the battery's SOC over time within the target time interval, reflecting the evolution trend of the energy storage system's remaining capacity. Based on this, the heating decision-making agent comprehensively analyzes the charge / discharge scheduling scheme, load power change data, and battery SOC change data as inputs to determine whether the energy storage system has charging or discharging needs within the target time interval, thereby determining the charging / discharging times and corresponding charging / discharging powers within the target time interval.

[0054] In this embodiment, predictions are made based on reference motion condition data within the target time interval to obtain the charging and discharging times and corresponding charging and discharging power within the target time interval. Based on the mapping relationship between charging and discharging power and temperature power, the target temperature data corresponding to the charging and discharging times is determined, providing an accurate temperature reference for whether the battery needs to be heated in advance. This is beneficial for coordinating charging and discharging scheduling with battery heating, thereby improving the charging and discharging performance and battery life of the operating battery.

[0055] In some embodiments, please refer to Figure 3 The reference operating condition data includes charge / discharge scheduling schemes, load power change data, and photovoltaic power change data. Decisions are made based on battery temperature change data, charge / discharge demand information, and the reference operating condition data to determine the battery heating time interval and the source of battery heating power within that interval. This may include the following steps: S310: Based on battery temperature change data, charging and discharging times, target temperature data, and weather information for the target time interval, a plan is made to determine the battery heating time interval.

[0056] In some cases, to ensure the effectiveness of the heating process, the temperature change of the energy storage battery needs to be closely synchronized with the charging and discharging schedule. This requires preventing excessive heat loss due to premature heating before the start of charging and discharging, and also preventing delayed heating where the battery temperature hasn't reached the target temperature by the time charging and discharging begins, thus affecting charging and discharging performance and battery life. Ideally, the energy storage battery should be heated to the target temperature required for that period just before the charging and discharging time begins, achieving a reasonable synchronization between temperature and the charging and discharging task.

[0057] Specifically, the agent acquires battery temperature change data, charging / discharging times, target temperature data, and weather information for the target time interval. The battery temperature change data reflects the evolution of the battery temperature over the target time interval, the target temperature data represents the desired temperature level the battery should reach at different charging / discharging times, and the weather information can include ambient temperature. Then, the agent determines whether the battery needs to be heated based on the battery temperature change data, the charging / discharging times, and the corresponding target temperature data. That is, when charging / discharging times exist, the agent determines the battery temperature corresponding to that time based on the battery temperature change data and compares this battery temperature data with the target temperature data for that time. If the battery temperature at that time is lower than the target temperature, it indicates that the battery temperature at that time is insufficient to meet the charging / discharging requirements, and the battery needs to be heated to raise its temperature to the target temperature, ensuring the battery has suitable temperature conditions for successful charging / discharging. It should also be noted that even if the battery temperature is low, if it is predicted that there will be no charging or discharging demand at all during a certain period, the intelligent agent will not consume energy to heat the battery simply to increase its temperature, thereby avoiding unnecessary energy waste.

[0058] When it is determined that battery heating is necessary, the agent uses weather information for the target time interval and target temperature data corresponding to the charging and discharging times as input to a pre-built battery thermal characteristic prediction model. This model can calculate, based on the current weather conditions and target temperature, how long it will take to heat the battery to the required target temperature during the charging and discharging times, thus obtaining a heating duration. Based on this heating duration and the charging and discharging times, a battery heating time interval and the specific start time for heating can be determined to ensure that the battery temperature reaches the target before charging and discharging occurs.

[0059] It should be noted that during operation, there can be multiple different charging and discharging times, each with its own heating requirements. Therefore, multiple independent battery heating time intervals that do not overlap in time can be formed, with each interval corresponding to its own heating start time and heating duration.

[0060] S320 makes decisions based on the battery heating time interval, charge and discharge scheduling scheme, load power change data and photovoltaic power change data to determine the source of battery heating power within the battery heating time interval.

[0061] Specifically, the charge / discharge scheduling scheme represents the pre-planned time and power information for charging or discharging within the target time interval, clarifying the charging / discharging actions and power commands at each future moment. Load power variation data represents the dynamic change in the power required by the load over time within the target time interval. Photovoltaic power variation data characterizes the changes in solar energy within the target time interval. The intelligent agent aligns and analyzes these three types of data with the battery heating time interval on the time axis to determine whether the photovoltaic system has sufficient available power to heat the battery during the required heating period, thereby identifying the source of battery heating power within the battery heating time interval.

[0062] In this embodiment, planning is carried out based on battery temperature change data, charging and discharging time, target temperature data, and weather information for the target time interval to determine the battery heating time interval. Based on the battery heating time interval, charging and discharging scheduling scheme, load power change data, and photovoltaic power change data, decisions are made to determine the source of battery heating power within the battery heating time interval, so that the heating process can both meet the requirements for battery temperature increase and reduce energy costs.

[0063] In some embodiments, the decision to determine the source of battery heating power within the battery heating time interval based on the battery heating time interval, charge and discharge scheduling scheme, load power change data, and photovoltaic power change data may include: if the photovoltaic power change data satisfies the charge and discharge scheduling scheme and load power change data within the battery heating time interval, then the source of battery heating power is determined to be the photovoltaic system.

[0064] Specifically, the source of battery heating power needs to be determined based on energy costs to meet battery heating requirements while considering economic efficiency. After determining the battery heating time interval, the agent first calculates the combined power of at least one sub-heating time interval within that interval based on the set charge / discharge scheduling scheme and load power change data. This combined power represents at least one of the required power specified by the charge / discharge scheduling scheme and the required power of the load. When both exist, the combined power is the sum of the required power and the load power. Next, the agent needs to obtain the photovoltaic power change data corresponding to the same sub-heating time interval. Then, the agent compares the photovoltaic power change data corresponding to the sub-heating time interval with the combined power. When the photovoltaic output level reflected by the photovoltaic power change data reaches the combined power, i.e., the photovoltaic power change data meets the requirements corresponding to the combined power, it is determined that the photovoltaic power is currently sufficient. In this case, battery heating can be completed solely by the photovoltaic system, thus confirming that the battery heating power source is the photovoltaic system. It should be noted that the battery heating time interval may have multiple sub-heating time intervals, and the agent performs the above judgment separately for each sub-heating time interval.

[0065] In this embodiment, if the photovoltaic power change data meets the charging and discharging scheduling scheme and load power change data within the battery heating time interval, then the source of battery heating power is determined to be the photovoltaic system. This allows for the priority use of photovoltaic power generation to meet the heating requirements without affecting the battery heating effect, avoiding or reducing the need to draw power from the grid or utilize energy storage resources. This effectively reduces energy costs or waste during the heating process, achieving a balance between battery heating needs and economic efficiency.

[0066] In some embodiments, the decision to determine the source of battery heating power within the battery heating time interval based on the battery heating time interval, the charge and discharge scheduling scheme, the load power change data, and the photovoltaic power change data may include: if the photovoltaic power change data does not meet the requirements of the charge and discharge scheduling scheme and the load power change data within the battery heating time interval, then the source of battery heating power is determined to be the photovoltaic system and the power grid.

[0067] Specifically, the source of battery heating power needs to be determined based on energy costs to meet battery heating requirements while considering economic efficiency. After determining the battery heating time interval, the agent first calculates the combined power of at least one sub-heating time interval within that interval based on the set charge / discharge scheduling scheme and load power change data. This combined power represents at least one of the required power specified by the charge / discharge scheduling scheme and the required power of the load. When both exist, the combined power is the sum of the required power and the required power of the load. Next, the agent needs to obtain the photovoltaic power change data corresponding to the same sub-heating time interval. Then, the agent compares the photovoltaic power change data corresponding to the sub-heating time interval with the combined power. When the photovoltaic output level reflected in the photovoltaic power change data cannot reach the combined power, i.e., the photovoltaic power change data cannot meet the requirements corresponding to the combined power, it is determined that the current state is one of insufficient photovoltaic power. In this case, the photovoltaic system alone cannot complete battery heating, and an additional energy source must be introduced; therefore, the power grid is added as a supplementary source of heating power. It should be noted that once the grid is connected and provides energy to the load side, part or all of the load power originally borne by the photovoltaic system will be replaced by the grid, thus releasing the corresponding power generation capacity of the photovoltaic system. Therefore, within the battery heating time interval, if the photovoltaic power change data does not meet the power demand determined by the charge / discharge scheduling scheme and the load power change data, the source of the battery heating power can be determined as the joint supply from the photovoltaic system and the grid. It should be noted that the battery heating time interval may contain multiple sub-heating time intervals, and the intelligent agent performs the above judgment separately for each sub-heating time interval.

[0068] In some implementations, the agent acquires time-of-use electricity price information from the power grid and, upon determining that the current sub-heating time interval is in a state of insufficient photovoltaic power, further determines the corresponding electricity price information for that sub-heating time interval. If the sub-heating time interval is during off-peak hours, the power grid is directly used as a supplementary source of heating power during that sub-heating time interval. The power grid provides electricity to the load side to replace the load power originally borne by the photovoltaic system, thereby releasing the photovoltaic system's power generation capacity. In this case, the battery heating power is jointly supplied by the photovoltaic system and the power grid.

[0069] In other implementations, the agent acquires time-of-use electricity price information from the power grid. If it determines that the current sub-heating time interval is in a state of insufficient photovoltaic power and falls within a peak electricity period, it selects an off-peak electricity period and uses the power grid to charge and store energy for the battery during this period. When the sub-heating time interval is reached, the energy stored in the battery is used to power the load, replacing the load power originally borne by the photovoltaic system. This releases the photovoltaic system's power generation capacity for battery heating, at which point the battery heating power is jointly supplied by the photovoltaic system and the battery energy storage.

[0070] In this embodiment, if the photovoltaic power change data does not meet the charging / discharging scheduling scheme and load power change data during the battery heating time interval, the source of battery heating power is determined to be the photovoltaic system and the power grid. Therefore, by utilizing the power grid to provide electricity to the load side to replace the load power originally borne by the photovoltaic system, the photovoltaic system's power generation capacity is released and dedicated to battery heating. This ensures that battery heating needs can still be reliably met even when photovoltaic output is insufficient. Furthermore, the composition of heating power can be optimally selected based on energy cost information such as time-of-use electricity pricing, improving the system's operational economy while ensuring heating reliability.

[0071] It should be noted that mixed battery heating power sources can exist within the battery heating time interval. For different sub-intervals, the agent determines the heating power source based on whether the photovoltaic power change data meets the combined power requirement: if it does, the source is solely the photovoltaic system; otherwise, the grid is introduced as a supplement, forming a combined supply from the photovoltaic system and the grid. Therefore, within the entire battery heating time interval, there may be a mixed configuration where some sub-intervals use only the photovoltaic system, and others use a combined supply from the photovoltaic system and the grid.

[0072] In some embodiments, please refer to Figure 4 Generating reference motion condition data within the target time interval may include the following steps: S410 receives reference operating condition data and charge / discharge scheduling scheme.

[0073] S420: Based on reference operating condition data, make predictions and combine them with the charging and discharging scheduling scheme to obtain reference motion condition data within the target time interval.

[0074] Specifically, the agent receives reference operating condition data and a charge / discharge scheduling scheme. The reference operating condition data may include historical operating data for operating condition prediction, real-time sensor data, and external environmental information. Then, the agent makes predictions based on the reference operating condition data and uses the prediction results together with the charge / discharge scheduler as reference motion operating condition data for the target time interval.

[0075] In some implementations, the reference operating condition data includes charge and discharge scheduling schemes, load power change data, photovoltaic power change data, and battery state of charge change data.

[0076] The heating decision-making intelligent agent can obtain charging and discharging scheduling schemes from the energy storage scheduling and control system based on the instructions input by the user through the human-computer interaction interface or through the communication network.

[0077] Reference operating condition data can include historical load power variation data, historical battery charge / discharge power, real-time load power data, and real-time battery charge / discharge power. The heating decision-making agent can acquire historical load power variation data, historical battery charge / discharge power, and real-time load power data and real-time battery charge / discharge power collected by sensors. Based on this, the heating decision-making agent uses the acquired data to execute a load prediction model and predict the load power variation data within a target time interval.

[0078] Reference operating condition data can include historical photovoltaic (PV) power variation data and its associated historical weather data, real-time PV power data, real-time weather data, and weather information for the target time interval. The heating decision-making agent not only acquires historical PV power variation data and its associated historical weather data, but also receives real-time PV power data and real-time weather data collected by PV power sensors. It also needs to receive weather information for the target time interval, which includes at least sunrise and sunset times, and may further include forecast parameters affecting sunlight such as cloud cover and humidity. Based on the acquired data, the heating decision-making agent uses a pre-built PV power prediction model to predict the PV power variation data within the target time interval.

[0079] Reference operating condition data can include historical battery state of charge (SOC) change data and real-time battery SOC data. The heating decision agent can acquire historical SOC change data and receive real-time SOC data provided by the battery management system. Based on this, the heating decision agent makes predictions using the acquired data to obtain battery SOC change data within a target time interval.

[0080] In this embodiment, reference operating condition data and charge / discharge scheduling scheme are received, prediction is made based on the reference operating condition data, and reference motion operating condition data within the target time interval is obtained by combining the charge / discharge scheduling scheme. This provides a data basis for subsequent judgment on whether the battery needs to be heated, thereby improving the battery's operating efficiency, safety and cycle life in low-temperature environments.

[0081] This specification provides an active heating control device 500 for an energy storage battery. Please refer to [link / reference needed]. Figure 5 The active heating control unit 500 for energy storage batteries includes: a reference temperature data receiving module 510, a battery temperature prediction module 520, a reference motion condition data generation module 530, a charge / discharge demand prediction module 540, and a charge / discharge demand decision module 550.

[0082] Reference temperature data receiving module 510 is used to receive reference temperature data; The battery temperature prediction module 520 is used to make predictions based on the reference temperature data to obtain battery temperature change data within a target time interval. Reference motion condition data generation module 530 is used to generate reference motion condition data within the target time interval; The charge / discharge demand prediction module 540 is used to predict the charge / discharge demand information within the target time interval based on the reference motion condition data within the target time interval. The charge / discharge demand decision module 550 is used to make decisions based on the battery temperature change data, the charge / discharge demand information and the reference motion condition data, to determine the battery heating time interval and the source of battery heating power within the battery heating time interval.

[0083] For a detailed description of the active heating control device for energy storage batteries, please refer to the description of the active heating control method for energy storage batteries above, which will not be repeated here. Each module in the above device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0084] This specification provides a photovoltaic system 600, including a memory 601, a processor 602, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the active heating control method for energy storage batteries as described in any of the above embodiments.

[0085] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described active heating control method embodiment for energy storage batteries and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0086] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described active heating control method for energy storage batteries.

[0087] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0088] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the related technology, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0089] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0090] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0091] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for active heating control of an energy storage battery, characterized in that, include: Receive reference temperature data; Based on the reference temperature data, predictions are made to obtain battery temperature change data within the target time interval. Generate reference motion condition data within the target time interval; Based on the reference motion condition data within the target time interval, prediction is made to obtain the charging and discharging demand information within the target time interval. Decisions are made based on the battery temperature change data, the charging and discharging demand information, and the reference motion condition data to determine the battery heating time interval and the source of battery heating power within the battery heating time interval.

2. The active heating control method for energy storage batteries according to claim 1, characterized in that, The reference temperature data includes historical battery temperature change data, real-time battery temperature data, historical ambient temperature change data, real-time ambient temperature data, and weather information for the target time period.

3. The active heating control method for energy storage batteries according to claim 1, characterized in that, The reference operating condition data includes charge and discharge scheduling schemes, load power change data, photovoltaic power change data, and battery state of charge change data.

4. The active heating control method for energy storage batteries according to claim 1, characterized in that, The charging and discharging demand information includes the charging and discharging time, the charging and discharging power corresponding to the charging and discharging time, and the target temperature data. The step of predicting the charging and discharging demand information within the target time interval based on reference motion condition data includes: Based on the reference motion condition data within the target time interval, prediction is made to obtain the charging and discharging times within the target time interval and the charging and discharging power corresponding to the charging and discharging times. Based on the mapping relationship between charging / discharging power and temperature power, the target temperature data corresponding to the charging / discharging time is determined.

5. The active heating control method for energy storage batteries according to claim 4, characterized in that, The reference operating condition data includes charge / discharge scheduling schemes, load power change data, and photovoltaic power change data; the decision-making process based on the battery temperature change data, the charge / discharge demand information, and the reference operating condition data to determine the battery heating time interval and the battery heating power source within the battery heating time interval includes: The battery heating time interval is determined by planning based on the battery temperature change data, the charging and discharging time, the target temperature data, and the weather information of the target time interval; The source of battery heating power within the battery heating time interval is determined based on the battery heating time interval, the charge and discharge scheduling scheme, the load power change data, and the photovoltaic power change data.

6. The active heating control method for energy storage batteries according to claim 5, characterized in that, The decision-making process based on the battery heating time interval, the charge / discharge scheduling scheme, the load power change data, and the photovoltaic power change data to determine the source of battery heating power within the battery heating time interval includes: If the photovoltaic power change data satisfies the charge / discharge scheduling scheme and the load power change data within the battery heating time interval, then the source of the battery heating power is determined to be the photovoltaic system. If the photovoltaic power change data does not meet the charging and discharging scheduling scheme and the load power change data within the battery heating time interval, then the source of the battery heating power is determined to be the photovoltaic system and the power grid.

7. The active heating control method for energy storage batteries according to claim 3, characterized in that, The generated reference motion condition data within the target time interval includes: Receive reference operating condition data and charge / discharge scheduling scheme; Prediction is made based on the reference operating condition data, and reference motion condition data within the target time interval is obtained by combining the charging and discharging scheduling scheme.

8. An active heating control device for an energy storage battery, characterized in that, The device includes: Reference temperature data receiving module, used to receive reference temperature data; The battery temperature prediction module is used to predict the battery temperature change data within a target time interval based on the reference temperature data. The reference motion condition data generation module is used to generate reference motion condition data within the target time interval; The charge / discharge demand prediction module is used to predict the charge / discharge demand information within the target time interval based on reference motion condition data within the target time interval. The charge / discharge demand decision module is used to make decisions based on the battery temperature change data, the charge / discharge demand information, and the reference motion condition data, to determine the battery heating time interval and the source of battery heating power within the battery heating time interval.

9. A photovoltaic system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the active heating control method for energy storage batteries as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the active heating control method for energy storage batteries as described in any one of claims 1-7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the active heating control method for energy storage batteries as described in any one of claims 1-7.

Images