Photovoltaic energy storage management system

Abstract

The invention provides a photovoltaic energy storage management system, which relates to the field of photovoltaic technology and comprises a state monitoring module, a parameter estimation module, a safety management module, a charging and discharging control module, an equalization module and a heat management module. The state monitoring module is used for detecting performance data of a battery in the system; the parameter estimation module is used for calculating according to the performance data so as to determine state parameters of the battery; the safety management module is used for determining battery faults; the charge-discharge control module is used for controlling the charge-discharge state according to the environment data and the performance data of the battery; the equalization module is used for equalizing the charge states of the batteries; the thermal management module is used for adjusting the temperature of the battery in the system. By detecting the performance data of the battery and analyzing and determining the state parameters of the battery, the problem of the battery is found and processed in time, the situation that the health degree of the battery is reduced due to faults is avoided, it is guaranteed that a photovoltaic system can operate safely and reliably, and the durability and operation efficiency of the photovoltaic energy storage system are improved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic technology, and in particular to a photovoltaic energy storage management system. Background Art

[0002] The photovoltaic system converts solar energy into electrical energy through photovoltaic panels. Since photovoltaic panels generate direct current, it is necessary to convert direct current into alternating current through a photovoltaic inverter to facilitate the transmission and utilization of electricity. The generated electrical energy is input into the photovoltaic energy storage system for storage.

[0003] With the vigorous development of new energy technologies, energy storage technology has also received more and more attention. However, it is difficult for photovoltaic energy storage systems to detect possible faults and problems in a timely manner, resulting in reduced system safety and durability. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first objective of this application is to propose a photovoltaic energy storage management system.

[0006] The second objective of the present application is to provide an electronic device.

[0007] To achieve the above objectives, the first embodiment of the present application proposes a photovoltaic energy storage management system, including:

[0008] State monitoring module, parameter estimation module, safety management module, charge and discharge control module, balancing module, thermal management module;

[0009] The state monitoring module is used to detect the performance data of the battery in the system, and the state monitoring module is connected to other modules to transmit the performance data to other modules;

[0010] The parameter estimation module is used to perform calculations based on the performance data to determine the state parameters of the battery;

[0011] The safety management module is used to perform fault detection according to the performance data to determine battery fault;

[0012] The charge and discharge control module is used to control the charge and discharge state according to the environmental data and performance data of the battery;

[0013] The balancing module is used to balance the charge state between the batteries;

[0014] The thermal management module is used to adjust the temperature of the battery in the system.

[0015] Optionally, the status monitoring module includes:

[0016] Voltage monitoring module, used to detect and obtain the voltage of each battery;

[0017] Current monitoring module, used to detect and obtain the current of each battery;

[0018] The temperature monitoring module is used to detect and obtain the temperature of each battery.

[0019] Optionally, the parameter estimation module includes:

[0020] A battery health status diagnosis module, used to pre-process the performance data and extract features, and match the features to determine the health status of the battery;

[0021] a battery energy state estimation module, configured to perform calculations based on the performance data to determine the current remaining energy of the battery, and determine the energy state of the battery based on the remaining energy;

[0022] A battery state of charge estimation module, used for inputting the performance data into a state of charge estimation model for calculation to determine the state of charge of the battery;

[0023] The battery functional state estimation module is used to input the performance data into a functional state estimation model for calculation to determine the functional state of the battery.

[0024] Optionally, the security management module includes:

[0025] A fault diagnosis module, configured to perform analysis and calculation based on the performance data to determine fault information, wherein the fault information includes at least one of the following: fault type, fault location, and severity;

[0026] The fault alarm module is used to generate and display corresponding alarm information according to the fault information.

[0027] Optionally, the charge and discharge control module is used to control the charging and discharging of the battery according to the battery capacity and ambient temperature conditions.

[0028] Optionally, the balancing module is used to determine the consistency of the charge states of each battery, and perform balancing adjustments according to the consistency to balance the charge states of each battery.

[0029] Optionally, the thermal management module includes: a control module, a heating module and a heat dissipation module;

[0030] The control module is used to determine the temperature of the battery according to the performance data, and to generate control information according to the thermal management strategy, and send it to the heating module and the heat dissipation module;

[0031] The heating module is used to heat the battery;

[0032] The heat dissipation module is used to cool the battery.

[0033] To achieve the above-mentioned purpose, a second aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0034] The memory stores computer-executable instructions;

[0035] The processor executes the computer-executable instructions stored in the memory to implement the system as described in any one of the first aspects.

[0036] The photovoltaic energy storage management system provided in the present application detects battery performance data and analyzes and determines battery status parameters, promptly discovers and handles battery problems, avoids failures that reduce battery health, ensures that the photovoltaic system can operate safely and reliably, and improves the durability and operating efficiency of the photovoltaic energy storage system.

[0037] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0039] Figure 1 A schematic diagram of the structure of a photovoltaic energy storage management system provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of a passive balancing structure provided in an embodiment of the present application;

[0041] Figure 3 A schematic diagram of an active balancing structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0043] The present application embodiment provides a photovoltaic energy storage management system, Figure 1 This is a schematic diagram of the structure of a photovoltaic energy storage management system provided in an embodiment of the present application. Figure 1 As shown, the system includes:

[0044] State monitoring module 10, parameter estimation module 20, safety management module 30, charge and discharge control module 40, balancing module 50, thermal management module 60;

[0045] The state monitoring module 10 is used to detect the performance data of the battery in the system, and the state monitoring module 10 is connected to other modules to transmit the performance data to other modules;

[0046] The parameter estimation module 20 is used to perform calculations based on the performance data to determine the state parameters of the battery;

[0047] The safety management module 30 is used to perform fault detection according to the performance data to determine battery fault;

[0048] The charge and discharge control module 40 is used to control the charge and discharge state according to the environmental data and performance data of the battery;

[0049] The balancing module 50 is used to balance the charge state between the batteries;

[0050] The thermal management module 60 is used to adjust the temperature of the batteries in the system.

[0051] There are many functional modules in this system, and the modules need to communicate with each other to realize the mutual transmission of data and the implementation of commands. At the same time, the MCU (Motor Control Unit) will detect and obtain a lot of data, and these important data need to be stored in order to calculate other important parameters and related analysis of the battery.

[0052] Electromagnetic compatibility: Depending on the working environment of the battery pack, there are corresponding requirements for the system's anti-electromagnetic interference capability and external radiation intensity.

[0053] Since there is a large inconsistency between batteries after multiple uses, the capacity of the battery pack is lower than the capacity of the smallest single battery. In this regard, the balancing module 50 in this embodiment adopts active or passive balancing to make the battery capacity tend to be consistent. Due to the diversity of balancing technology, there are passive balancing and active balancing, so the balancing current can be from ten milliamperes to several amperes, but since passive balancing will generate a lot of heat, the balancing current is selected to be relatively small, generally not exceeding 1A.

[0054] Optionally, the status monitoring module 10 includes:

[0055] Voltage monitoring module, used to detect and obtain the voltage of each battery;

[0056] Current monitoring module, used to detect and obtain the current of each battery;

[0057] The temperature monitoring module is used to detect and obtain the temperature of each battery.

[0058] In this embodiment, battery status detection mainly refers to battery voltage, current, and temperature detection. Generally, the detection range of a single battery is slightly larger than the voltage, current, and temperature range allowed by the battery. The commonly used voltage detection accuracy is ±2-5mV, the current detection accuracy is ±1%, and the temperature detection accuracy is ±2%. In possible scenarios, smoke detection, collision detection, etc. will also be added.

[0059] Optionally, the parameter estimation module 20 includes:

[0060] A battery health status diagnosis module, used to pre-process the performance data and extract features, and match the features to determine the health status of the battery;

[0061] Battery Health (BH or SOH) refers to the degree of performance degradation of a battery relative to a brand new state. Calculating the battery health state usually involves evaluating the battery capacity, internal resistance, voltage response and other aspects. Here are some common methods: 1. Capacity test: · Through a deep discharge test, the actual capacity of the battery is compared with the nominal capacity to calculate the relative capacity of the battery. If the actual capacity is lower than the nominal capacity, it means that the battery has degraded. 2. Internal resistance measurement: · Measure the internal resistance of the battery. An increase in internal resistance indicates that the conductivity of the battery has decreased, which may be caused by the shedding of active materials, changes in electrolyte concentration, etc. The change in internal resistance can reflect the health status of the battery. 3. Voltage response: · Observe the voltage response of the battery under different load conditions. If the voltage response is abnormal, it may mean that the battery has plate sulfation, electrolyte stratification and other problems. 4. Coulomb efficiency: · Calculate the coulomb efficiency of the battery during the charge and discharge process, that is, the ratio of the actual energy released to the actual energy injected. A decrease in coulomb efficiency indicates that the conversion efficiency of the battery is reduced and there may be internal losses. 5. Self-discharge rate: · Compare the voltage changes during the static period of the battery. An increase in the self-discharge rate indicates that the health of the battery has deteriorated. 6. Temperature coefficient: · Analyze the performance of the battery at different temperatures. Increased temperature sensitivity may indicate a decrease in the health of the battery. 7. Pulse test: · Apply an instantaneous large current pulse and observe the voltage response and recovery time of the battery to determine the health of the battery. 8. Model estimation algorithm: · Use mathematical models and machine learning algorithms, combined with historical data and real-time data, to predict the health of the battery. This method requires a large amount of data support and a complex modeling process. 9. Online diagnosis: · Real-time monitoring of various battery indicators through the battery management system (BMS), combined with specific diagnostic algorithms, to evaluate the health of the battery in real time.

[0062] Among them, online diagnosis is a method of real-time monitoring and evaluation of battery health status, which relies on real-time data and corresponding diagnostic algorithms provided by the battery management system (BMS). The main goal of online diagnosis is to identify abnormal battery behavior, predict the future performance of the battery, and provide decision-making basis for battery management and maintenance. The following are the basic steps of online diagnosis:

[0063] 1. Data collection: BMS collects various parameters of the battery through built-in sensors, such as voltage, current, temperature, internal resistance, etc. These data reflect the instant status of the battery and are the basis for online diagnosis.

[0064] 2. Data processing: After preprocessing, the data is fed into the diagnostic algorithm for analysis to remove noise and outliers. Data processing also includes feature extraction, which converts raw data into effective information that can be used for diagnosis.

[0065] 3. Diagnostic algorithms: Diagnostic algorithms evaluate the health status of the battery based on the collected data, combined with the physical characteristics of the battery and historical data. These algorithms can be rule-based expert systems, statistical models, machine learning models, etc.

[0066] 4. Threshold setting: Set a series of thresholds to determine whether the battery is in a normal state. For example, if the battery voltage or internal resistance exceeds a certain threshold, the battery is considered abnormal.

[0067] 5. Alarm and early warning: If the diagnostic results show that there is a problem with the battery's health status, the BMS will issue an alarm to remind the user or system administrator to take appropriate measures. Early warning can notify of possible problems in advance and facilitate timely intervention.

[0068] 6. Decision support: The results of online diagnosis can guide battery usage and maintenance decisions. For example, if the battery health status has significantly deteriorated, the battery may need to be replaced; if the battery health status is still acceptable, it may only require adjusting the charging strategy.

[0069] 7. Feedback and update: The results of online diagnosis can also be used to update the battery model and algorithm to improve the accuracy of diagnosis. For example, if the new data does not match the existing model, it may be necessary to adjust the model parameters or develop new diagnostic methods.

[0070] a battery energy state estimation module, configured to perform calculations based on the performance data to determine the current remaining energy of the battery, and determine the energy state of the battery based on the remaining energy;

[0071] The battery state of energy (SOE) refers to the ratio of the battery's current remaining energy to its full energy, usually expressed as a percentage. SOE is different from the state of charge (SOC), which focuses on the battery's charge level, while SOE focuses more on the battery's total energy. There are many ways to calculate the battery's energy state, and the following are some common methods:

[0072] 1. Cumulative Energy Method: SOE is calculated by accumulating the charge and discharge energy of the battery. The increase and decrease of energy is recorded for each charge and discharge, thereby obtaining the total energy of the battery. This method assumes that the initial energy of the battery is known and that the energy loss of the battery is negligible. 2. Energy Balance Method: Subtract the input energy and output energy of the battery to obtain the net energy loss of the battery. This difference is the energy state of the battery. This method requires accurate measurement of the charge and discharge energy of the battery, otherwise a large error will occur. 3. Model Estimation Algorithm: Using mathematical models and machine learning algorithms, combined with historical data and real-time data, to predict the energy state of the battery. This method requires a large amount of data support and a complex modeling process. 4. Experimental Calibration Method: The energy capacity of the battery is determined experimentally, and then the actual energy of the battery is compared with the calibrated energy to obtain the SOE. This method requires regular experimental calibration to correct the energy state of the battery. 5. Combination Method: Combining multiple methods, such as the cumulative energy method and the model estimation algorithm, can obtain a more accurate SOE. Combination methods usually require more computing resources and higher complexity.

[0073] It should be noted that the calculation of battery energy state is affected by many factors, such as battery self-discharge, aging, temperature changes, etc. Therefore, the calculation of SOE needs to take these factors into consideration and regularly calibrate the calculation results. In addition, the calculation of battery energy state is usually an iterative process that requires continuous accumulation of data and experience. In practical applications, the evaluation of battery energy state should combine multiple methods to obtain comprehensive and accurate results.

[0074] A battery state of charge estimation module, used for inputting the performance data into a state of charge estimation model for calculation to determine the state of charge of the battery;

[0075] The state of charge (SOC) of a battery refers to the current remaining charge of the battery relative to the fully charged state, usually expressed as a percentage. Estimating the state of charge of a battery is an important task because it is critical for the battery management system (BMS) to help us understand the battery's health, remaining battery life, and charge / discharge behavior. Here are some commonly used SOC estimation methods:

[0076] 1.Ah Integration: The ampere-hour integration method is the most basic SOC estimation method, which estimates the SOC by accumulating the battery's charge and discharge current and time. This method is based on the battery's theoretical energy capacity, that is, the battery's rated ampere-hour (Ah) value. However, this method ignores the chemical reaction and self-discharge effect inside the battery, and its accuracy is limited.

[0077] 2. Open circuit voltage method (OCV): The open circuit voltage method uses the open circuit voltage (OCV) of the battery after it has been stationary for a period of time as an indicator of SOC. There is a certain correlation between the open circuit voltage and SOC of the battery, but this relationship is nonlinear and requires the establishment of an accurate OCV-SOC mapping table.

[0078] 3. Coulomb counting method: Coulomb counting method estimates SOC by measuring the charge and discharge current and time of the battery. This method requires accurate current measurement and compensation mechanism to offset self-discharge and measurement errors.

[0079] 4. Kalman filter: Kalman filter is a statistical estimation method that can fuse data from multiple sensors, such as current, voltage, and temperature, to provide an optimal SOC estimate. Kalman filter can handle noise and uncertainty, but requires knowledge of the dynamic model of the battery.

[0080] 5. Neural Networks and Machine Learning: Neural network and machine learning techniques can be used to learn the behavior patterns of batteries from historical data, thereby improving the accuracy of SOC estimation. This approach requires a large amount of training data and a complex model training process.

[0081] 6. Equivalent circuit model: The equivalent circuit model attempts to simulate the physical processes inside the battery, including elements such as resistance, capacitance, and inductance. By fitting these model parameters, a more accurate SOC estimate can be obtained.

[0082] 7. Hybrid methods: Combining multiple methods, such as the ampere-hour integration method and the Kalman filter, can obtain better estimation results. Hybrid methods usually combine simple methods with advanced methods to make the best use of their respective advantages.

[0083] Regardless of the method, the battery characteristics, usage scenarios, and external environmental factors need to be considered. In addition, the accuracy of SOC estimation will also decrease as the battery ages and degrades, so the estimation model needs to be calibrated and updated regularly. In practical applications, multiple methods are usually combined to improve the reliability of SOC estimation.

[0084] The battery functional state estimation module is used to input the performance data into a functional state estimation model for calculation to determine the functional state of the battery.

[0085] The state of function (SOF) of a battery refers to the degree of performance degradation of a battery relative to its new state, and focuses on the power output capability of the battery. Calculating the state of function of a battery usually involves evaluating factors such as the internal resistance, voltage response, and capacity of the battery. The following are some commonly used methods:

[0086] 1. Internal resistance measurement: ·Measure the internal resistance of the battery. An increase in internal resistance indicates that the battery's conductivity has decreased, which may be caused by the shedding of active materials, changes in electrolyte concentration, etc. The change in internal resistance can reflect the health status of the battery. 2. Voltage response: ·Observe the voltage response of the battery under different load conditions. If the voltage response is abnormal, it may mean that there are problems such as battery plate sulfidation and electrolyte stratification. 3. Capacity test: ·Through a deep discharge test, compare the actual capacity of the battery with the nominal capacity and calculate the relative capacity of the battery. If the actual capacity is lower than the nominal capacity, it means that the battery has degraded. 4. Coulomb efficiency: ·Calculate the coulomb efficiency of the battery during the charge and discharge process, that is, the ratio of the actual energy released to the actual energy injected. A decrease in coulomb efficiency indicates that the battery's conversion efficiency has decreased and there may be internal losses. 5. Pulse test: ·Apply an instantaneous high current pulse and observe the voltage response and recovery time of the battery to determine the health status of the battery. 6. Model estimation algorithm: ·Use mathematical models and machine learning algorithms to combine historical data and real-time data to predict the health status of the battery. This method requires a lot of data support and a complex modeling process. 7. Online diagnosis: · Real-time monitoring of various battery indicators through the battery management system (BMS), combined with specific diagnostic algorithms, to evaluate the health status of the battery. Online diagnosis can detect battery problems in a timely manner and give early warnings. 8. Threshold setting: · Set a series of thresholds to determine whether the battery is in a normal state. For example, if the voltage or internal resistance of the battery exceeds a certain threshold, the battery is considered to be abnormal. 9. Feedback and update: · The results of online diagnosis can be used to update the battery model and algorithm to improve the accuracy of diagnosis. For example, if the new data does not match the existing model, it may be necessary to adjust the model parameters or develop new diagnostic methods.

[0087] The calculation of the battery functional status is usually an iterative process that requires high-quality data, reliable diagnostic algorithms, and reasonable threshold settings. In addition, due to the complexity and nonlinearity of the battery, the diagnostic algorithm needs to be continuously learned and optimized to provide accurate evaluation results. In practical applications, the evaluation of the battery functional status should combine multiple methods to obtain comprehensive and accurate results.

[0088] Optionally, the security management module 30 includes:

[0089] A fault diagnosis module, configured to perform analysis and calculation based on the performance data to determine fault information, wherein the fault information includes at least one of the following: fault type, fault location, and severity;

[0090] This module is used for battery safety control, fault diagnosis and alarm, including fault detection, judgment, location, information transmission, alarm and other treatment measures to prevent the system, battery and human body from being damaged by faults such as over-high or under-low temperature, over-low or over-high voltage, over-high current and internal short circuit.

[0091] The fault alarm module is used to generate and display corresponding alarm information according to the fault information.

[0092] Optionally, the charge and discharge control module 40 is used to control the charging and discharging of the battery according to the battery capacity and ambient temperature conditions.

[0093] Optionally, the balancing module 50 is used to determine the consistency of the charge states of each battery, and perform balancing adjustment according to the consistency to balance the charge states of each battery.

[0094] For batteries, the life of a group of batteries is often much shorter than that of a single battery. This is due to the basic design principle of the battery pack: when a single battery in the battery pack is fully charged, the battery pack must stop charging to avoid overcharging of the battery; when the voltage of a single battery in the battery pack is lower than the discharge cut-off voltage, the battery pack must stop discharging to avoid over-discharging of the battery. Each time the battery pack undergoes a charge and discharge cycle, some batteries in the battery pack will not be fully charged when charged, and will not be fully discharged when discharged. Over time, the capacity difference between weak batteries and strong batteries will increase, hindering the charge and discharge capabilities of the battery pack. To solve the above problems, the concept of balanced management was proposed.

[0095] The basis of balancing management is the balancing method, which is divided into passive balancing and active balancing. Passive balancing is also called lossy balancing, which consumes excess energy through energy consumption to keep the battery pack voltage or SOC value consistent; active balancing is also called lossless balancing, which transfers the high-energy part of the single cell to the low-energy cell through energy transfer, so that the energy is redistributed and finally achieves battery balancing. Passive balancing mainly uses the resistance energy consumption method, while there are many active balancing methods, including capacitive balancing, inductive balancing, transformer balancing and parallel balancing.

[0096] Figure 2 A schematic diagram of a passive balancing structure provided in an embodiment of the present application; Figure 2As shown, the batteries are connected in series to form a battery pack. When using resistance energy consumption balancing, a resistor and a switch are connected in parallel at both ends of each single battery. When the voltage or SOC of the single battery is detected to be too high, the switch connected in parallel is controlled to close, so that the parallel resistor is shunted to consume the excess energy until the voltage or SOC value of all batteries in the entire battery pack is consistent, and the switch is disconnected to end the balancing. Since this balancing method only needs to control a single switch, it has the advantages of simple hardware structure, common and cheap components, easy to implement and control software, small balancing current, and high energy balancing efficiency. At the same time, since the balancing circuit is only associated with the battery being balanced, this balancing method has strong scalability and can add batteries and passive balancing circuits without destroying other circuits.

[0097] Passive balancing has many shortcomings. Since passive balancing is based on the lowest battery voltage or SOC, it cannot increase the capacity of low-voltage batteries. At the same time, the excess electrical energy is converted into heat and consumed, which not only causes energy waste, but also may cause heat and affect the normal operation of the battery. In order to solve the shortcomings of passive balancing, active balancing has been proposed and continuously developed. Compared with passive balancing, the active balancing structure is more complicated. Most of them are to connect an energy storage element (inductor or capacitor) and a switch in parallel at both ends of each single cell. The high energy part of the single cell is transferred to the energy storage element through the closed switch, and then the energy storage element transfers the energy to the single cell with low energy. Through this process, the energy is redistributed and the battery is finally balanced.

[0098] Figure 3 A schematic diagram of an active balancing structure provided in an embodiment of the present application is shown in FIG. Figure 3As shown, Figure (a) and (b) are respectively the concentrated capacitor structure and the concentrated inductor structure. Only one capacitor / inductor is needed in the entire battery pack as the medium for energy transfer, so as to realize the energy transfer from the battery with high capacity to the common capacitor / inductor, and then to the battery with low capacity, which is the balance between the single cell and the battery. Both of these balancing methods have high energy transfer, but there are great difficulties in control. The realization of the capacitor structure requires a large voltage difference between the single cells as a prerequisite for balancing, so the energy balancing efficiency is low. When the inductor is used as an energy storage element, it can be balanced even if the voltage difference between the batteries is small, making up for the shortcomings of the capacitor balancing structure, and the energy balancing efficiency is high. Figure (c) is a bidirectional multi-winding transformer structure, which transfers the energy of the single cell to the transformer, uses the transformer to increase the voltage, and then transfers it to the entire battery pack, evenly distributing it to each single cell; conversely, it can also obtain energy from the battery pack as a whole, and transfer it to the single cell with low capacity. It is difficult to control. At the same time, due to the certain leakage magnetic field and energy loss of the transformer, the energy balancing transfer of this method is medium. Figure (d) is an active parallel structure. Since the battery series structure needs to be disconnected during balancing, this balancing structure can only be used in a static state. It has few usage scenarios, but it has higher energy transfer and balancing efficiency.

[0099] The core of balancing management is balancing strategy. The normal operation of a system often requires the cooperation of hardware and software, with hardware as the foundation and software as the core. Therefore, corresponding balancing algorithms will be used for different balancing structures. The traditional high-release and low-charge balancing algorithm in active balancing, also known as the mean-difference balancing algorithm, is widely used, but there are different judgment criteria for high and low:

[0100] (1) Real-time voltage is the equalization target. This judgment target is simple and easy to achieve. Because the battery management system has the function of detecting the voltage of single cells, the voltage data can be directly obtained. However, there are disadvantages. The battery has a platform voltage. When the voltage is at this stage, there is a difference in capacity, but the voltage difference is very small. If the voltage detection accuracy is not enough, it will cause an error in the equalization judgment.

[0101] (2) The final voltage is the balancing target. The voltage value at the end of charging or discharging is used as the judgment. It is basically the same as the real-time voltage method, but the application scenario is relatively simple, so this judgment basis is not often used.

[0102] (3) SOC is the balance target. This is the most accurate basis for judgment, but also the most complex. At present, there are many SOC estimation algorithms, which require data such as battery voltage, current, operating temperature, internal resistance, and capacity. After the fusion calculation of multiple battery data, the battery status can be more clearly displayed.

[0103] Optionally, the thermal management module 60 includes: a control module, a heating module and a heat dissipation module;

[0104] The control module is used to determine the temperature of the battery according to the performance data, and to generate control information according to the thermal management strategy, and send it to the heating module and the heat dissipation module;

[0105] The heating module is used to heat the battery;

[0106] The heat dissipation module is used to cool the battery.

[0107] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the system provided by the above embodiments.

[0108] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the system provided by the above embodiments.

[0109] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the system provided by the above embodiments when executed by a processor.

[0110] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0111] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0112] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0113] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0114] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0115] Any process or system description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0117] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or systems can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented with hardware, as in another embodiment, it can be implemented with any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0118] A person skilled in the art may understand that all or part of the steps of the system implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the system embodiment.

[0119] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0120] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A photovoltaic energy storage management system, characterized in that: include: State monitoring module, parameter estimation module, safety management module, charge and discharge control module, balancing module, thermal management module; The state monitoring module is used to detect the performance data of the battery in the system, and the state monitoring module is connected to other modules to transmit the performance data to other modules; The parameter estimation module is used to perform calculations based on the performance data to determine the state parameters of the battery; The safety management module is used to perform fault detection according to the performance data to determine battery fault; The charge and discharge control module is used to control the charge and discharge state according to the environmental data and performance data of the battery; The balancing module is used to balance the charge state between the batteries; The thermal management module is used to adjust the temperature of the battery in the system.

2. The system according to claim 1, characterized in that The status monitoring module comprises: Voltage monitoring module, used to detect and obtain the voltage of each battery; Current monitoring module, used to detect and obtain the current of each battery; The temperature monitoring module is used to detect and obtain the temperature of each battery.

3. The system according to claim 1, characterized in that The parameter estimation module comprises: A battery health status diagnosis module, used to pre-process the performance data and extract features, and match the features to determine the health status of the battery; a battery energy state estimation module, configured to perform calculations based on the performance data to determine the current remaining energy of the battery, and determine the energy state of the battery based on the remaining energy; A battery state of charge estimation module, used for inputting the performance data into a state of charge estimation model for calculation to determine the state of charge of the battery; The battery functional state estimation module is used to input the performance data into a functional state estimation model for calculation to determine the functional state of the battery.

4. The system according to claim 1, characterized in that The security management module includes: A fault diagnosis module, configured to perform analysis and calculation based on the performance data to determine fault information, wherein the fault information includes at least one of the following: fault type, fault location, and severity; The fault alarm module is used to generate and display corresponding alarm information according to the fault information.

5. The system according to claim 1, characterized in that The charge and discharge control module is used to control the charging and discharging of the battery according to the battery capacity and the ambient temperature conditions.

6. The system according to claim 3, characterized in that The balancing module is used to determine the consistency of the charge states of each battery, and perform balancing adjustment according to the consistency to balance the charge states of each battery.

7. The system according to claim 1, characterized in that The thermal management module includes: a control module, a heating module and a heat dissipation module; The control module is used to determine the temperature of the battery according to the performance data, and to generate control information according to the thermal management strategy, and send it to the heating module and the heat dissipation module; The heating module is used to heat the battery; The heat dissipation module is used to cool the battery.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the system according to any one of claims 1 to 7.

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