Energy storage EMS system data remote acquisition management system based on cloud computing

Abstract

The invention relates to the technical field of energy storage management, in particular to an energy storage EMS system data remote acquisition management system based on cloud computing, and the system comprises a data acquisition module which is used for collecting the voltage and current of an energy storage system during working, and carrying out the preliminary cleaning and formatting of the collected data; the charging judgment module is used for identifying and analyzing the data acquired by the data acquisition module and calculating load conversion coefficients corresponding to input and output power of the energy storage system; the state monitoring module is used for acquiring the energy storage state of the energy storage system and adjusting the operation mode of the energy storage system according to the energy storage state and the load conversion coefficient; monitoring the charging and discharging frequency in the operation mode and the time of each operation, and determining a state monitoring coefficient corresponding to the operation mode; the numerical value detection module is used for analyzing whether various data of the energy storage system in the charging process exceed limit values or not and calculating an over-limit coefficient in the current operation mode; and the operation efficiency, the safety and the stability of the energy storage system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage management, and in particular to a cloud computing-based energy storage EMS system data remote acquisition and management system. Background Art

[0002] Energy storage is an indispensable supporting technology for the development of smart grids, renewable energy access, distributed generation, and microgrids. At present, its applications mainly involve: (1) Smoothing the grid: smoothing short-term output fluctuations, improving the certainty, predictability, and economy of renewable energy generation; (2) realizing functions such as peak shaving, frequency and voltage regulation, hot standby, and power quality management, and improving the system's own regulation capabilities; (3) Emergency power backup: when the power grid is out of power, the energy storage management system will cut off the connection between the load and the grid and provide load energy. The EMS system controls the diesel generator to start in time and provides guarantee for the stable operation of the power system.

[0003] For example, Chinese patent publication number CN117477728A discloses an EMS energy storage management system, which relates to the field of EMS energy management technology, including a power monitoring module, a charging acquisition module, an electrical analysis module and an operation and maintenance management module; the power monitoring module is used to monitor the remaining power of the battery module in real time; when the remaining power is lower than the preset power threshold, a charging instruction is generated; when the battery module is in a charging state, the charging acquisition module is used to collect the charging parameters of the battery module, and transmit the charging parameters to the electrical analysis module for safety early warning analysis, calculate the early warning index, judge whether the battery module is in a dangerous state, and improve power safety; the operation and maintenance management module is used to analyze the endurance deviation index according to the endurance information with timestamps stored in the database; if the endurance deviation index is greater than the preset protection threshold, a battery maintenance signal is generated.

[0004] However, the existing technology focuses on the resistance value and the corresponding endurance information, resulting in unclear understanding of the operating status of the battery itself. It is necessary to comprehensively consider the comprehensive status of the battery under the corresponding capacity, current, voltage, etc. to adjust the utilization efficiency of the current energy storage system and timely reduce the rate of battery degradation. Summary of the invention

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a cloud computing-based energy storage EMS system data remote acquisition and management system, including: a data acquisition module for collecting the voltage and current of the energy storage system when it is working, and preliminarily cleaning and formatting the collected data.

[0006] The charging judgment module is used to identify and analyze the data collected by the data acquisition module and calculate the load conversion coefficient corresponding to the input and output power of the energy storage system.

[0007] The status monitoring module is used to obtain the energy storage status of the energy storage system, adjust the operation mode of the energy storage system according to the energy storage status and load conversion coefficient; monitor the charging and discharging frequency under the operation mode, and the time of each operation, and determine the status monitoring coefficient corresponding to the operation mode.

[0008] The numerical detection module is used to analyze whether the various data of the energy storage system exceed the limit value during the charging process, and calculate the over-limit coefficient under the current operating mode.

[0009] The strategy setting module is used to obtain the demand control requirements under the current operation mode according to the energy storage state of the current energy storage system, adjust the charging and discharging frequency according to the demand control requirements, and determine the currently set charging strategy.

[0010] The beneficial effects of the present invention are as follows: by real-time monitoring of the state of the energy storage system and adjusting the operation mode, the system can ensure that the energy storage system operates in the best state, thereby improving the operation efficiency of the energy storage system; by predicting the battery degradation rate and taking corresponding preventive measures, the system can extend the battery life and reduce the cost of battery replacement; through over-limit detection and strategy adjustment, the system can timely discover and handle abnormal conditions of the energy storage system during the charging process, thereby avoiding potential safety hazards and enhancing the safety and stability of the system; through data analysis, the system can realize intelligent management and control of the energy storage system, improve the intelligence level of the system, and provide strong support for the optimized operation of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0012] Figure 1 This is the system framework diagram of the energy storage EMS system data remote acquisition management system based on cloud computing.

[0013] Figure 2 It is a system diagram of a cloud computing-based energy storage EMS system data remote acquisition and management system.

[0014] Figure 3 It is a flow chart of the status monitoring module of the energy storage EMS system data remote acquisition management system based on cloud computing. DETAILED DESCRIPTION

[0015] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. If no specific techniques or conditions are specified in the embodiments, the techniques or conditions described in the literature in the art or the product specifications are used.

[0016] See also Figure 1 , Figure 2 The cloud computing-based energy storage EMS system data remote acquisition and management system includes: a data acquisition module, a charging judgment module, a state monitoring module, a numerical detection module, and a strategy setting module; the output end of the data acquisition module is connected to the charging judgment module, the output end of the charging judgment module is connected to the state monitoring module, the output end of the state monitoring module is connected to the numerical detection module, and the output end of the numerical detection module is connected to the strategy setting module, thereby completing the setting of the charging and discharging strategy.

[0017] The data acquisition module is used to collect the voltage and current of the energy storage system when it is working, and to perform preliminary cleaning and formatting of the collected data.

[0018] The charging judgment module is used to identify and analyze the data collected by the data acquisition module, calculate the load conversion coefficient of the energy storage system corresponding to the input and output power, determine the current charging and discharging status of the energy storage system, and evaluate the energy storage system's current processing efficiency.

[0019] The status monitoring module is used to obtain the energy storage status of the energy storage system, adjust the operation mode of the energy storage system according to the energy storage status and load conversion coefficient; monitor the charging and discharging frequency under the operation mode, and the time of each operation, and determine the status monitoring coefficient corresponding to the operation mode.

[0020] The numerical detection module is used to analyze whether the various data of the energy storage system exceed the limit value during the charging process, and calculate the over-limit coefficient under the current operating mode.

[0021] The strategy setting module is used to obtain the demand control requirements under the current operation mode according to the energy storage state of the current energy storage system, adjust the charging and discharging frequency according to the demand control requirements, and determine the currently set charging strategy.

[0022] In one embodiment of the present invention, the data acquisition module is used to collect the voltage and current of the energy storage system when it is working, and to perform preliminary cleaning and formatting on the collected data.

[0023] The purpose of collecting voltage, current and other data is to determine the corresponding numerical conditions of the energy storage system during charging and discharging, and whether the corresponding output and input power fluctuate at this time, to determine what kind of problems the energy storage system will have when working, and whether the energy storage state and load state of the energy storage system will change, so as to control the energy storage system at this time. The corresponding data can be collected by voltmeter and ammeter, and the energy storage state mainly represents SOC (state of charge) and SOH (state of health) to identify whether the current energy storage system is healthy at the corresponding charging and discharging frequency, and whether the current system's charging and discharging strategy needs to be adjusted.

[0024] SOC (State of Charge): It indicates the percentage of the current remaining capacity of the battery to the total capacity. In simple terms, it is how much power is left in the battery. The Kalman filter method can be used to estimate the change in SOC at this time to determine the specific working conditions of the current energy storage system.

[0025] SOH (State of Health): Indicates the current health status of the battery, reflecting the degree of performance degradation of the battery compared to a new battery; usually expressed as a percentage, 100% means that the battery is in a brand new state, and less than 100% means that the battery performance has declined; the internal resistance of the battery in the energy storage system can be used to calculate the current battery SOH, and the number of charge and discharge cycles can also be obtained, combined with the battery's service life, to estimate the current battery SOH, so as to accurately identify the current energy storage state of the energy storage system.

[0026] In one embodiment of the present invention, the charging judgment module is used to identify and analyze the data collected by the data acquisition module, calculate the load conversion coefficient of the energy storage system corresponding to the input and output power, determine the current charging and discharging status of the energy storage system, and evaluate the energy storage system's processing efficiency for current.

[0027] In the charging judgment module, the coefficient related to the load and power of the current energy storage system is obtained mainly by calculating the corresponding power from the current and voltage, and associating the current power conversion with the load of the energy storage system. At this time, when obtaining the load conversion coefficient, the maximum power point of the current energy storage system when the load exists will be obtained first. The maximum power point represents the point corresponding to the output power. This point is usually located at the position where the slope of the characteristic curve corresponding to the current and voltage is zero. At this time, the output power and input power of the time period corresponding to the maximum power point are integrated to obtain the corresponding load conversion coefficient at this time; when calculating the integral, it will be calculated according to the time required for the energy storage system to change from one state to another. These states can be charging and discharging states, connection states, fault states, working modes, other states, etc. These states will affect the energy storage system's conversion of its own power, so as to evaluate whether the charging and discharging of the energy storage system is normal at this time, and whether it has the load conversion coefficient under normal conditions.

[0028] The status mentioned at this time can be explained using the following explanation.

[0029] The charge and discharge state is used to describe whether the energy storage system is currently in the charging stage or the discharging stage; in the charging state, the system receives energy from an external power source and stores it in the energy storage device; in the discharging state, the system releases the energy in the energy storage device to meet the load demand; this state change is usually closely related to the change in SOC, because charging increases SOC, while discharging reduces SOC.

[0030] The connection state indicates the connection between the energy storage system and other systems (such as the power grid, load, etc.); the system may be in a state of being connected to the grid, connected to the load, or connected to both. In addition, the system may also be in a disconnected state, that is, disconnected from all external systems. This state change has an important impact on the system's operating mode and energy management strategy.

[0031] The fault state indicates that there is some kind of fault or abnormality in the energy storage system. This may include battery failure, controller failure, communication failure, etc. When the system detects a fault, it usually triggers corresponding protection measures (such as disconnection, stopping charging and discharging, etc.) to ensure the safe and stable operation of the system. The change of fault state is an important indicator for system reliability and safety assessment.

[0032] The working mode indicates the current operating state or task mode of the energy storage system. This may include normal operating mode, maintenance mode, test mode, etc. Different working modes correspond to different control strategies and energy management strategies. For example, in normal operating mode, the system will meet the load demand as efficiently as possible; while in maintenance mode, the system may perform self-test, calibration, or update operations.

[0033] Other states: In addition to the above states, the energy storage system may also have other specific state representations, such as temperature state (indicating the temperature of the system or energy storage device), voltage state (indicating the voltage level of the system or energy storage device), etc. The changes in these states have an important impact on the performance, efficiency and safety of the system.

[0034] Since the load conversion factor at this time may also be affected by the current value, and the current value will show different situations when the load is cut in and when the load is cut out, the load conversion factor at this time is expressed as shown below.

[0035] Where η represents the load conversion factor, P in (t) represents the input power at time t, t represents the time point, P out (t) represents the output power at time t, I q Indicates the current value after the load is cut in, I qφ Indicates the current value before the load is cut in, I qφ′ It indicates the current value after the load is cut off. α1, α2 and α3 indicate adjustment factors, and the set values ​​can be 0.4375, 0.25 and 0.3125 respectively.

[0036] At this time, I q , which means the average value of the current after the load is switched on when the current is stable, I qφ It indicates the average value of the current when it is stable before the load is cut in, Iqφ′ It represents the average value of the current when it is stable after the load is cut off, to quantify the change level of the current value when the load is cut in and out. Here, you can choose the stable stage after the load is cut in (0.1s later) and the stable stage after the load is cut out (0.2s later); the load conversion coefficient used at this time is quantified by the average value of the current value in these stages; these obtained current values ​​only focus on the numerical change of the current when the load exists, so as to use the load conversion coefficient to evaluate the current state of the energy storage system when the load is working.

[0037] In one embodiment of the present invention, a state monitoring module is used to obtain the energy storage state of the energy storage system, adjust the operation mode of the energy storage system according to the energy storage state and the load conversion coefficient; monitor the charging and discharging frequency under the operation mode, and the time of each operation, and determine the state monitoring coefficient corresponding to the operation mode.

[0038] The energy storage status at this time is the same as described above, indicating the two states of SOC (state of charge) and SOH (state of health). At this time, the main thing to judge is the remaining power, the charging and discharging conditions under the upper limit of the available power, and whether the current operating mode used by the energy storage system is in a normal state; based on these contents, the energy storage system is controlled and adjusted in time.

[0039] The above-mentioned operating modes can be expressed as charging mode, discharging mode, standby mode, etc. Each mode corresponds to different charging and discharging conditions and will be adjusted according to the current energy storage state.

[0040] like Figure 3 As shown, in the state monitoring module, the implementation method of adjusting the operation mode of the energy storage system includes: inferring the internal state parameters of the energy storage system according to the current and voltage of the energy storage system, and calculating the first relationship between the internal state parameters and the battery degradation rate, wherein the internal state parameters include the battery capacity and the battery internal resistance; taking the energy storage state as a basis, calculating the second relationship between the current, voltage and the battery degradation rate under different energy storage state values; taking the load conversion coefficient as a supplementary condition, calculating the third relationship between the load conversion coefficient and the battery degradation rate under different energy storage state values, and selecting the operation mode of the energy storage system according to the obtained first relationship, second relationship and third relationship.

[0041] The battery degradation rate can generally be described by the change in battery capacity, battery internal resistance, and battery performance parameters. For example, when the battery deteriorates, the difference between its current, voltage and the maximum upper limit will increase, the charging efficiency will decrease, and the battery capacity and battery internal resistance will change significantly. At this time, the selected operating mode can be linked to the parameters corresponding to the degradation rate, such as the relationship between the degradation rate and the charging and discharging conditions (such as charging current, charging voltage, temperature, etc.); the relationship between the degradation rate and the battery status (such as SOH, capacity, internal resistance, etc.); the relationship between the degradation rate and the load conversion coefficient. These three relationships are used to quantify the battery status at this time, so that the corresponding changes in the energy storage system can be more accurately understood during remote monitoring.

[0042] When calculating the corresponding first, second and third relationships, the time corresponding to the load conversion coefficient is selected, so the data existing at time t is processed emphatically to calculate the specific situation of the energy storage system in this time period.

[0043] First calculate the battery capacity and battery internal resistance. The battery capacity can be Where C represents the battery capacity, I(t) represents the current value at time point t, t0 and t represent the time range of integration; the battery internal resistance can be obtained by the voltage difference and current difference in the corresponding time. The battery internal resistance is as follows: Among them, R int Represents the internal resistance of the battery, △V represents the voltage difference, and △I represents the current difference. At this time, the degradation speed is evaluated and the working mode is selected. The capacity attenuation rate coefficient and the internal resistance growth rate coefficient are calculated through the battery capacity and the battery internal resistance to make a judgment, and a comprehensive judgment is completed by combining the current, voltage and load conversion coefficient.

[0044] The first relationship can be realized in the following manner, and the capacity attenuation rate coefficient and the internal resistance growth rate coefficient corresponding to the first relationship between the battery capacity and the battery internal resistance are respectively obtained.

[0045] The capacity decay rate coefficient is expressed as: Among them, k C1 represents the capacity decay coefficient of the first relationship, and C represents the battery capacity.

[0046] The internal resistance growth rate coefficient is expressed as: Among them, k R1 The first relationship represents the internal resistance growth rate coefficient, R int Indicates the internal resistance of the battery.

[0047] The second relationship adds current and voltage to obtain the relationship between current, voltage and degradation rate. At this time, the second relationship is expressed as the additional introduction of current and voltage values ​​in the calculation method of the above capacity decay rate coefficient and internal resistance growth rate coefficient for quantification. For example, the influence between current and battery degradation rate can be converted into the influence of current on battery capacity and the influence of current on battery internal resistance.

[0048] The second relationship between current, voltage and battery degradation rate can be expressed as the influence of current on battery capacity, the influence of current on battery internal resistance, the influence of voltage on battery capacity, and the influence of voltage on battery internal resistance, and the capacity attenuation rate coefficient and internal resistance growth rate coefficient corresponding to the second relationship are calculated.

[0049] The effect of current on battery capacity can be: Among them, k C2,I represents the capacity decay rate coefficient corresponding to the current in the second relationship, C represents the battery capacity, T represents the number of time points, t ranges from 1 to T, I(t) represents the current value at time point t, I min Indicates the minimum value of the current, I std Indicates the standard value of the current value. At this time, the standard value of the current value selects the average value of the current value in the historical data under the same load conversion coefficient.

[0050] The effect of current on the internal resistance of the battery can be: Among them, k R2,I represents the internal resistance growth rate coefficient corresponding to the current in the second relationship, I(t) represents the current value at time point t, and I std Indicates the standard value of the current value, R int Indicates the internal resistance of the battery.

[0051] The effect of voltage on battery capacity can be: Among them, k C2,V represents the capacity decay rate coefficient corresponding to the voltage in the second relationship, V end Indicates the voltage value to complete the work, V min Indicates the minimum voltage value during operation, W total Indicates the total electrical energy consumed during operation.

[0052] The effect of voltage on the internal resistance of the battery can be: Among them, k R2,V represents the internal resistance growth rate coefficient corresponding to the voltage in the second relationship, V(t) represents the voltage value at time point t, V std It represents the standard value of the voltage value. At this time, the standard value of the voltage value is the average value of the voltage value in the historical data under the same load conversion coefficient.

[0053] Therefore, the capacity decay rate coefficient and the internal resistance growth rate coefficient in the second relationship are expressed as the average values ​​of the capacity decay rate coefficient and the internal resistance growth rate coefficient corresponding to the current and voltage.

[0054] That is, the capacity decay rate coefficient k in the second relationship C2 for The internal resistance growth rate coefficient k in the second relation R2 for Among them, k C2,I represents the capacity decay rate coefficient corresponding to the current in the second relationship, k R2,I represents the internal resistance growth rate coefficient corresponding to the current in the second relationship, k C2,V represents the capacity decay rate coefficient corresponding to the voltage in the second relationship, k R2,V It represents the internal resistance growth rate coefficient corresponding to the voltage in the second relationship.

[0055] The third relationship between the load conversion coefficient and the battery degradation rate is expressed as follows: the influence of the load conversion coefficient on the battery capacity and the influence of the load conversion coefficient on the battery internal resistance are calculated to obtain the capacity attenuation rate coefficient and the internal resistance growth rate coefficient corresponding to the third relationship.

[0056] The effect of load conversion factor on battery capacity is expressed as: Among them, k C3 represents the capacity attenuation rate coefficient corresponding to the third relationship, C represents the battery capacity, α η It represents the weight factor of the load conversion coefficient, which is generally set to 0.1 or 0.2. e represents the exponential constant, η represents the load conversion coefficient, and η′ represents the standard value of the load conversion coefficient. The standard value of the load conversion coefficient is the standard deviation of the load conversion coefficient in the historical data.

[0057] The effect of load conversion coefficient on battery internal resistance is expressed as: in, The internal resistance growth rate coefficient corresponding to the third relationship, R int Indicates the internal resistance of the battery.

[0058] According to the obtained first, second and third relationships, when selecting the operation mode of the energy storage system, all the obtained capacity decay rate coefficients and internal resistance growth rate coefficients are comprehensively calculated, and the basic capacity decay rate coefficient and basic internal resistance growth rate coefficient at this time are selected; the operation mode with the highest similarity to the basic capacity decay rate coefficient and basic internal resistance growth rate coefficient in the historical data is used as the currently used operation mode; the similarity here is expressed by using cosine similarity, taking the currently calculated basic capacity decay rate coefficient, basic internal resistance growth rate coefficient and historical data as input, and calculating a similarity value, so as to select the operation mode to be used.

[0059] The basic capacity decay rate coefficient is expressed as: Among them, k′ C Indicates the basic capacity attenuation rate coefficient, k C1 The capacity decay coefficient of the first relation, k C2 represents the capacity decay rate coefficient in the second relation, k C3 represents the capacity decay rate coefficient corresponding to the third relationship; They respectively represent the weight factors of the capacity attenuation coefficient, and the values ​​of the weight factors are 0.4, 0.3, and 0.3, respectively.

[0060] The basic internal resistance growth rate coefficient is expressed as: Among them, k′ R Indicates the basic internal resistance growth rate coefficient, k R1 The first relation represents the internal resistance growth rate coefficient, k R2 represents the internal resistance growth rate coefficient in the second relation, represents the internal resistance growth rate coefficient corresponding to the third relationship; They respectively represent the weight factors of the internal resistance growth rate coefficient, and the values ​​of the weight factors are 0.3, 0.4, and 0.3 respectively.

[0061] Then calculate the similarity of the basic capacity decay rate coefficient, the basic internal resistance growth rate coefficient and the historical data. At this time, there are two coefficients, the basic capacity decay rate coefficient and the basic internal resistance growth rate coefficient. There will be two similarities in the final calculation. At this time, you need to select the operating mode with the largest comprehensive value of the two similarities as the current operating mode. What is regulated at this time is when the energy storage system is charged and discharged. The discharge time and frequency will be set according to the restrictions of the operating mode, so that you can choose the operating mode that matches the battery internal resistance and battery capacity to improve the current battery life and the utilization efficiency of the overall energy storage system.

[0062] After obtaining the operating mode, it is necessary to monitor the charge and discharge frequency and the time of each operation at this time. It can be assumed that the operation time at this time is CT, and the state monitoring coefficient at this time is expressed as the basic capacity decay rate coefficient and the basic internal resistance growth rate coefficient. The charging frequency and the time of each operation are combined to obtain the state monitoring coefficient.

[0063] The condition monitoring coefficient can be expressed as.

[0064] Where CM represents the state monitoring coefficient, k′ C Indicates the basic capacity attenuation rate coefficient, k′ R represents the basic internal resistance growth rate coefficient, They represent the weight factors of the basic capacity attenuation rate coefficient and the basic internal resistance growth rate coefficient, which can be set to 0.5 and 0.5 respectively; f represents the charge and discharge frequency, fstd Indicates the standard value of the charge and discharge frequency, CT indicates the duration of each operation, CT std A standard time value that represents the duration of each operation.

[0065] The standard values ​​of the charge and discharge frequency and the time of each operation can be obtained by selecting the standard deviation in the corresponding situation in the historical data to determine whether the current state monitoring coefficient can reflect the current state of the system, and control the energy storage device or related batteries according to the state of the system to assist remote personnel monitoring.

[0066] In one embodiment of the present invention, the numerical detection module is used to analyze whether various data of the energy storage system exceeds the limit value during the charging process, and calculate the over-limit coefficient under the current operation mode.

[0067] At this time, the method of obtaining the over-limit coefficient includes obtaining the corresponding eigenvalues ​​of the current, voltage, and state monitoring coefficient, and forming a eigenvector of the current, voltage, and state monitoring coefficient. Each eigenvector contains three elements, and the set of eigenvectors can be {x1,x2,x3,x4,,,,x n}, calculate the difference between the current, voltage and state monitoring coefficient at this time to get the over-limit coefficient. At this time, the difference between these data in continuous calculation is used to determine whether they exceed the standard limit. For example, the data after multiple processing can be obtained to obtain the current, voltage and state monitoring coefficient in n time periods. At this time, the current and voltage of each time period will be taken as the average value of the corresponding time period, so that the data volume of the current and voltage when calculated is consistent with the number of state monitoring coefficients. At the same time, the three are combined into a feature vector, and these extracted data are processed to finally determine whether the obtained over-limit coefficient meets the normal processing specifications.

[0068] The overrun factor can be expressed as follows: Among them, i represents the over-limit coefficient in the i-th time period, n represents the number of time periods, i ranges from 1 to n, x i Represented as the value of the feature vector in the i-th time period, x i+1 It is represented as the value of the eigenvector of the i+1th time period, and exp() represents the natural exponential function.

[0069] At this time, the proportional relationship between the difference between the continuous current, voltage, and state monitoring coefficient and the average difference is judged, and whether the calculated value at this time shows a significant increase or decrease, so as to identify the part of the energy storage system that has significantly increased or decreased, and determine whether the system is abnormal at this time. The cube used at the same time means that the currently set feature vector is a third-order feature vector, that is, a feature vector with three elements; for example, the feature vector composed of current, voltage, and state monitoring coefficient at this time is 3×3, in which each row represents current, voltage, and state monitoring coefficient from top to bottom, and each column represents the average value, maximum value, and minimum value from left to right. These values ​​are calculated from the data within the collected time period; the state monitoring coefficient changes according to different times. At this time, if multiple values ​​of the state monitoring coefficient are required, the collected time period needs to be adjusted so that the average value, maximum value, and minimum value of the state monitoring coefficient exist within the selected time period, so that the corresponding over-limit coefficient can be obtained.

[0070] In one embodiment of the present invention, the strategy setting module is used to obtain the demand control requirements in the current operation mode according to the energy storage state of the current energy storage system, adjust the charging and discharging frequency according to the demand control requirements, and determine the currently set charging and discharging strategy.

[0071] The demand control requirements under the corresponding operating mode are expressed as charging limit, charging rate, charging temperature, discharge capacity, discharge rate and remaining capacity management; setting the charging and discharging strategy is to select the content related to the current charging and discharging strategy from the demand control requirements under the premise of the currently set state monitoring coefficient, over-limit coefficient and continuous coefficient, so as to realize the real-time load demand monitoring, charging and discharging control and charging and discharging strategy adjustment; for example, according to the state monitoring coefficient, over-limit coefficient and continuous coefficient, set an adjustment coefficient, and modify and adjust the overall content of the plan according to this adjustment coefficient. At the same time, it can also be sent to the external control center according to this adjustment coefficient. The external control center can remotely control the energy storage system by observing the changes in this coefficient.

[0072] The above-mentioned charging limit refers to the maximum input power or current allowed by the battery during the charging process. Exceeding this limit may cause battery overheating, damage or even safety accidents. The purpose of setting the charging limit is usually to prevent the battery from overcharging and avoid excessive chemical reactions inside the battery, thereby protecting the battery's health and extending its service life; ensuring the safety of the charging process and preventing thermal runaway caused by excessive current.

[0073] The above charging rate refers to the amount of charge that the battery can accept per unit time, usually expressed as a C rate, for example, 1C means that the battery is fully charged within one hour, and 0.5C means that it is fully charged within two hours; the charging rate determines the speed of the charging process, but too high a charging rate may cause the battery temperature to rise and affect the battery life. The purpose of setting the charging rate is usually to balance the charging speed and battery life. Choosing a suitable charging rate can reduce damage to the battery while ensuring charging efficiency; adapt to different application scenarios, for example, fast charging stations require a higher charging rate, while home charging piles can use a lower charging rate.

[0074] The above-mentioned charging temperature refers to the temperature of the battery during the charging process. The optimal operating temperature range of the battery is usually between 20℃ and 45℃. Too high or too low temperature will affect the performance and life of the battery; too high charging temperature may cause the internal chemical reaction of the battery to intensify, generate more heat, and then cause thermal runaway; too low temperature may cause the internal resistance of the battery to increase and the charging efficiency to decrease; the purpose of charging temperature setting is usually to monitor and control the battery temperature to ensure that it is within a safe operating range and avoid damage to the battery due to excessively high or low temperature; the battery temperature is adjusted by a cooling or heating system to keep it within the optimal operating temperature range.

[0075] The above-mentioned discharge capacity refers to the total amount of electricity that the battery can provide during the discharge process, usually in ampere-hours (Ah). The discharge capacity reflects the actual available energy of the battery, and the discharge capacity will gradually decrease with the use and aging of the battery; the purpose of setting the discharge capacity is usually to evaluate the health status and remaining life of the battery. The reduction in discharge capacity means a decline in battery performance, optimize the energy management and scheduling of the energy storage system, and ensure sufficient energy supply when needed.

[0076] The above discharge rate refers to the amount of electricity that a battery can release per unit time. It is also expressed in C rate, for example, 1C means that the battery is fully discharged within one hour, and 0.5C means that it is fully discharged within two hours. The discharge rate determines the speed of the discharge process, but too high a discharge rate may cause the battery temperature to rise and affect the battery life; the purpose of setting the discharge rate is usually to balance the discharge rate and battery life. Choosing a suitable discharge rate can reduce damage to the battery while ensuring discharge efficiency; adapt to different application scenarios, for example, electric vehicles require a higher discharge rate, while home energy storage systems can use a lower discharge rate.

[0077] The above-mentioned remaining capacity management refers to monitoring and managing the current remaining capacity SOC of the battery. SOC is a percentage value, which indicates the ratio of the current power of the battery to its fully charged state. Remaining capacity management is the key to ensuring efficient and safe operation of the battery. It also prevents overcharging and over-discharging of the battery. Overcharging will cause the internal pressure of the battery to increase, while over-discharging may cause irreversible changes in the chemical substances inside the battery.

[0078] Therefore, the method for obtaining the demand control requirements at this time can be to obtain the changing trend of the energy storage state, and obtain the demand control requirements based on the changing trend of the energy storage state. At this time, the energy storage state is SOC and SOH, which can directly display the current charge and discharge rate, charge and discharge amount, and the remaining amount. At the same time, the temperature can be obtained using a temperature sensor. At this time, the changing trend of the energy storage state is used as an index for classification, and the demand control requirements are obtained based on the classified energy storage state.

[0079] The classification method uses the exponential smoothing method to calculate the trend quantity of the energy storage state, calculates the similarity of the energy storage state based on the value of the trend quantity, and selects the demand control requirement according to the similarity of the energy storage state.

[0080] For example, at time point t, the exponential smoothing method can be expressed as: S t =β×Y t +(1-β)×S t-1 ; Among them, S t represents the predicted value at time point t, Y t represents the actual value at time point t, S t-1 It represents the predicted value at time point t-1. When t=1, S0 represents the average value of historical data, and β represents the smoothing factor, which ranges from 0 to 1.

[0081] Therefore, the changing trend of the energy storage state can be deduced in turn according to the smoothing factor, and the similarity of the energy storage state can be calculated by the predicted value calculated at time point t. According to the similarity, the demand control requirement corresponding to the maximum similarity value is selected as the demand control requirement selected at this time.

[0082] Therefore, the method for obtaining the charging and discharging strategy is to quantify the demand control requirements according to the charging limit, charging rate, charging temperature, discharge capacity, discharge rate and remaining capacity management, obtain the score value of the energy storage state in the demand control requirements, calculate the demand control coefficient, and use the demand control coefficient, over-limit coefficient and state monitoring coefficient as conditions to calculate the adjustment factor for adjusting the charging and discharging frequency. The retrieved charging and discharging strategies are sorted according to the adjustment factor, and the charging and discharging strategy with the highest sorting priority is used as the set charging and discharging strategy.

[0083] The demand control coefficient may be expressed as a weighted average of the score values ​​of the charging limit, the charging rate, the charging temperature, the discharge capacity, the discharge rate, and the remaining capacity management.

[0084] The adjustment factor can be expressed as shown below. The data calculated by the demand control coefficient and the state monitoring coefficient are matched one by one with the time period of the over-limit coefficient to obtain the corresponding changes of the demand control coefficient and the state monitoring coefficient in different time periods, thereby obtaining the comprehensive adjustment factor at this time.

[0085] τ=lnτ(i,r)-lnτ(i-1,r); where τ represents the adjustment factor, τ(i,r) represents the index value of the adjustment factor when the number of retention steps in time period i is r, τ(i-1,r) represents the index value of the adjustment factor when the number of retention steps in time period i-1 is r, and the value range of i is 1 to nr.

[0086] The calculation method of τ(i,r) is the same as τ(i-1,r), where τ(i,r) can be expressed as.

[0087] Among them, RF i represents the demand control coefficient in the i-th time period, CM i represents the state monitoring coefficient in the i-th time period, ζ i represents the over-limit coefficient in the i-th time period; α RF represents the demand control coefficient weight, α CM represents the weight of the state monitoring coefficient, α ζ It represents the weight of the over-limit coefficient, n represents the number of time periods, and r represents the number of retention steps. The number of retention steps at this time represents several time periods. It is mainly used to find out whether the moving average value in several time periods will change with time, and use this change as an adjustment factor to adjust the current charging and discharging frequency.

[0088] To calculate τ(i-1,r), you only need to replace i with i-1. We will not explain this formula here.

[0089] The sorting priority for the retrieved charging and discharging strategies indicates that the confidence and correlation of the demand control coefficient, the over-limit coefficient and the state monitoring coefficient in the charging and discharging strategy library are calculated respectively. The requirements for the confidence and correlation of these three numbers are 0.7. The correlation is the Pearson correlation coefficient calculated using the current demand control coefficient, the over-limit coefficient and the state monitoring coefficient with the pre-set values ​​in the charging and discharging strategy library. At this time, the charging and discharging strategy with the largest comprehensive value of confidence and correlation is selected as the charging and discharging strategy with the highest sorting priority.

[0090] At this time, the current energy storage system is tested in terms of demand, status, and whether the data exceeds the limit, so that the energy storage system can be remotely controlled based on the collected data, and whether there are large fluctuations in the energy storage system during operation, and whether the fluctuations will affect the energy storage system, and based on the multiple coefficients received at this time, the status management of the energy storage system is completed.

[0091] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. The energy storage EMS system data remote acquisition and management system based on cloud computing is characterized by: include: The data acquisition module is used to collect the voltage and current of the energy storage system when it is working, and to perform preliminary cleaning and formatting of the collected data; The charging judgment module is used to identify and analyze the data collected by the data acquisition module and calculate the load conversion coefficient corresponding to the input and output power of the energy storage system; The state monitoring module is used to obtain the energy storage state of the energy storage system, and adjust the operation mode of the energy storage system according to the energy storage state and the load conversion coefficient; monitor the charging and discharging frequency under the operation mode, and the time of each operation, and determine the state monitoring coefficient corresponding to the operation mode; The numerical detection module is used to analyze whether the various data of the energy storage system exceed the limit value during the charging process, and calculate the over-limit coefficient under the current operating mode; The strategy setting module is used to obtain the demand control requirements under the current operation mode according to the energy storage state of the current energy storage system, adjust the charging and discharging frequency according to the demand control requirements, and determine the currently set charging strategy.

2. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 1, characterized in that: The load conversion factor is expressed as follows: Where η represents the load conversion factor, P in (t) represents the input power at time t, t represents the time point, P out (t) represents the output power at time t, I q Indicates the current value after the load is cut in, I qφ Indicates the current value before the load is cut in, I qφ′ It indicates the current value after the load is cut off, and α1, α2, and α3 indicate the adjustment factors.

3. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 1, characterized in that: The implementation method of adjusting the operation mode of the energy storage system includes: inferring the internal state parameters of the energy storage system based on the current and voltage of the energy storage system, and calculating the first relationship between the internal state parameters and the battery degradation rate, wherein the internal state parameters include the battery capacity and the battery internal resistance; taking the energy storage state as a basis, calculating the second relationship between the current, voltage and the battery degradation rate under different energy storage state values; taking the load conversion coefficient as a supplementary condition, calculating the third relationship between the load conversion coefficient and the battery degradation rate under different energy storage state values, and selecting the operation mode of the energy storage system based on the obtained first relationship, second relationship and third relationship.

4. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 3 is characterized in that: The first relationship is realized by the following method, respectively obtaining the capacity attenuation rate coefficient and the internal resistance growth rate coefficient corresponding to the first relationship between the battery capacity and the battery internal resistance; The capacity decay rate coefficient is expressed as: Among them, k C1 represents the capacity attenuation coefficient of the first relationship, C represents the battery capacity; The internal resistance growth rate coefficient is expressed as: Among them, k R1 The first relationship represents the internal resistance growth rate coefficient, R int Indicates the internal resistance of the battery.

5. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 3, characterized in that: The second relationship between current, voltage and battery degradation rate can be expressed as the influence of current on battery capacity, the influence of current on battery internal resistance, the influence of voltage on battery capacity, and the influence of voltage on battery internal resistance, and the capacity attenuation rate coefficient and internal resistance growth rate coefficient corresponding to the second relationship are calculated; The effect of current on battery capacity can be: Among them, k C2,I represents the capacity decay rate coefficient corresponding to the current in the second relationship, C represents the battery capacity, T represents the number of time points, t ranges from 1 to T, I(t) represents the current value at time point t, I min Indicates the minimum value of the current, I std Indicates the standard value of the current value; The effect of current on the internal resistance of the battery can be: Among them, k R2,I represents the internal resistance growth rate coefficient corresponding to the current in the second relationship, I std Indicates the standard value of the current value, R int Indicates the internal resistance of the battery; The effect of voltage on battery capacity can be: Among them, k C2,V represents the capacity decay rate coefficient corresponding to the voltage in the second relationship, V end Indicates the voltage value to complete the work, V min Indicates the minimum voltage value during operation, W total Indicates the total electrical energy consumed during operation; The effect of voltage on the internal resistance of the battery can be: Among them, k R2,V represents the internal resistance growth rate coefficient corresponding to the voltage in the second relationship, V(t) represents the voltage value at time point t, V std Indicates the standard value of voltage; The capacity fading rate coefficient k in the second relation C2 for The internal resistance growth rate coefficient k in the second relation R2 for 6. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 3 is characterized in that: The third relationship between the load conversion coefficient and the battery degradation rate is expressed as follows: the influence of the load conversion coefficient on the battery capacity and the influence of the load conversion coefficient on the battery internal resistance are calculated to obtain the capacity attenuation rate coefficient and the internal resistance growth rate coefficient corresponding to the third relationship; The effect of load conversion factor on battery capacity is expressed as: Among them, k C3 represents the capacity attenuation rate coefficient corresponding to the third relationship, C represents the battery capacity, α η represents the weight factor of the load conversion coefficient, e represents the exponential constant, η represents the load conversion coefficient, and η′ represents the standard value of the load conversion coefficient; The effect of load conversion coefficient on battery internal resistance is expressed as: Among them, k R3 represents the internal resistance growth rate coefficient corresponding to the third relationship, η represents the load conversion coefficient, η′ represents the standard value of the load conversion coefficient, R int Indicates the internal resistance of the battery.

7. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 1, characterized in that: The condition monitoring coefficient can be expressed as: Where CM represents the state monitoring coefficient, k′ C Indicates the basic capacity attenuation rate coefficient, k′ R represents the basic internal resistance growth rate coefficient, They represent the weight factors of the basic capacity attenuation rate coefficient and the basic internal resistance growth rate coefficient respectively; f represents the charge and discharge frequency, f std Indicates the standard value of the charge and discharge frequency, CT indicates the duration of each operation, CT std A standard time value that represents the duration of each operation.

8. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 1, characterized in that: The over-limit coefficient can be expressed as shown below. The corresponding eigenvalues ​​of the current, voltage, and state monitoring coefficient are obtained, and the current, voltage, and state monitoring coefficient are combined into a eigenvector. The difference between the current, voltage, and state monitoring coefficient is calculated to obtain the over-limit coefficient: Among them, i represents the over-limit coefficient in the i-th time period, n represents the number of time periods, i ranges from 1 to n, x i Represented as the value of the feature vector in the i-th time period, x i+1 It is represented as the value of the eigenvector of the i+1th time period, and exp() represents the natural exponential function.

9. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 1, characterized in that: The charging and discharging strategy is obtained in the following ways: The demand control requirements are expressed as charging limit, charging rate, charging temperature, discharge capacity, discharge rate and remaining capacity management. The demand control requirements are quantified according to the charging limit, charging rate, charging temperature, discharge capacity, discharge rate and remaining capacity management, and the score value of the energy storage state in the demand control requirements is obtained. The demand control coefficient is calculated, and the demand control coefficient, the over-limit coefficient and the state monitoring coefficient are used as conditions to calculate the adjustment factor for adjusting the charging and discharging frequency. The retrieved charging and discharging strategies are sorted according to the adjustment factor, and the charging and discharging strategy with the highest sorting priority is used as the set charging and discharging strategy.

10. The cloud computing-based energy storage EMS system data remote acquisition and management system according to claim 9, characterized in that: The demand control coefficient can be expressed as a weighted average of the score values ​​of charging limit, charging rate, charging temperature, discharge capacity, discharge rate and remaining capacity management; The adjustment factor can be expressed as shown below. The data calculated by the demand control coefficient and the state monitoring coefficient are matched one by one with the time period of the over-limit coefficient to obtain the adjustment factor; τ=lnτ(i,r)-lnτ(i-1,r); Among them, τ represents the adjustment factor, τ(i,r) represents the index value of the adjustment factor when the number of retention steps is r in time period i, τ(i-1,r) represents the index value of the adjustment factor when the number of retention steps is r in time period i-1, and the value range of i is 1 to nr; where τ(i,r) can be expressed as: Among them, RF i represents the demand control coefficient in the i-th time period, CM i represents the state monitoring coefficient in the i-th time period, ζ i represents the over-limit coefficient in the i-th time period; α RF represents the demand control coefficient weight, α CM represents the weight of the state monitoring coefficient, α ζ represents the weight of the over-limit coefficient, n represents the number of time periods, and r represents the number of retention steps.

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