Energy storage system sensor fault diagnosis method based on EMS

Abstract

The invention discloses an energy storage system sensor fault diagnosis method based on an EMS. The method comprises the following steps: respectively acquiring temperature real-time values and voltage real-time values of a plurality of energy storage units in a current detection period; calculating a voltage estimation value of the current energy storage unit based on the real-time temperature value in combination with a charge-discharge rate and a state-of-charge value of the corresponding energy storage unit, and obtaining an absolute value of a difference value between the real-time voltage value and the voltage estimation value; when the absolute values of the difference values are larger than or equal to a preset voltage threshold standard value, the charge-discharge rate and / or the charge state value of the energy storage unit are / is adjusted according to preset adjustment times, and when the multiple absolute values of the difference values obtained after adjustment are all larger than or equal to the preset voltage threshold standard value and the temperature real-time value is within a preset temperature interval, the energy storage unit is adjusted. Determining that the voltage sensor is in an abnormal state; and when the absolute value of the difference value is smaller than a preset voltage threshold standard value or any absolute value of the difference value obtained after adjustment is smaller than the preset voltage threshold standard value, judging that the voltage sensor is in a normal state.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system detection, and in particular to an energy storage system sensor fault diagnosis method based on EMS. Background Art

[0002] With the vigorous promotion and application of renewable energy, such as solar energy and wind energy, the scale and complexity of energy storage systems, as key components for balancing energy supply and demand and improving grid stability, are growing rapidly. Energy storage systems are usually composed of a large number of energy storage units, such as lithium-ion battery packs and lead-acid battery packs. The stable operation of these energy storage units is directly related to the performance and reliability of the entire energy storage system.

[0003] In order to accurately control the working status of energy storage units during operation, temperature sensors and voltage sensors are widely deployed. The data collected in real time is an important basis for judging the health of energy storage units. However, due to the complex and changeable environment of energy storage systems, including temperature fluctuations, frequent switching of charge and discharge rates, and long-term use and aging, the accuracy of data collected by sensors faces many challenges.

[0004] On the one hand, temperature has a profound impact on battery performance. Under different temperature conditions, the battery's chemical reaction rate, internal resistance, ion migration rate, etc. will change, which will cause the battery's voltage output to show nonlinear fluctuations. For example, in a high temperature environment, the side reactions inside the battery will intensify, which may cause the electrolyte to decompose and the electrode material to corrode, causing the internal resistance to increase and the voltage to drop; at low temperatures, ion conduction is hindered and the battery polarization phenomenon is serious, which will also cause the voltage to deviate from the normal range.

[0005] On the other hand, changes in charge and discharge rates also bring significant changes to the battery voltage. When charging and discharging at high rates, the current density inside the battery increases, the polarization effect is enhanced, and the voltage drop across the battery increases. This effect is intertwined with factors such as temperature and the battery's state of charge (SOC). Different SOCs mean that the content of active substances that can participate in the reaction inside the battery is different. At high rates, when transitioning from low SOC to high SOC or vice versa, the voltage change pattern is complex and difficult to describe with a simple linear model.

[0006] As the battery is used continuously, its state of health (SOH) gradually declines, manifesting as capacity attenuation, increased internal resistance and other problems. Under the same temperature, charge and discharge rate and SOC conditions, the voltage output characteristics of an aged battery are completely different from those of a new battery, which makes the traditional sensor fault judgment method based on a fixed threshold completely ineffective. In the past, we simply relied on setting a voltage upper and lower limit range to determine whether the voltage sensor was faulty. Once the actual voltage exceeded the range due to the above complex factors, it was mistakenly judged as a sensor abnormality; or in the early stages of battery aging, small voltage changes were masked by the fixed threshold, and potential sensor problems could not be detected in time until the fault worsened and affected system operation. Summary of the invention

[0007] The purpose of the embodiment of the present invention is to provide an energy storage system sensor fault diagnosis method based on EMS, which calculates the voltage estimation value by comprehensively considering multiple factors such as temperature, charge and discharge rate, and state of charge value, and compares it with the real-time voltage value. At the same time, it combines the adjustment of the working condition of the energy storage unit and the corresponding difference change to make a judgment, thereby avoiding the misjudgment caused by relying solely on single voltage data to judge the fault.

[0008] To solve the above technical problems, a first aspect of an embodiment of the present invention provides a method for diagnosing sensor faults in an energy storage system based on an EMS, wherein the energy storage system includes a plurality of energy storage units, each of which is provided with a temperature sensor and a voltage sensor that are data-connected to the EMS of the energy storage system, and the method includes the following steps:

[0009] Based on the energy storage system EMS, respectively obtain the real-time temperature values ​​and the real-time voltage values ​​of the plurality of energy storage units in the current detection cycle;

[0010] Based on the real-time temperature value, combined with the charge / discharge ratio and the state of charge value of the corresponding energy storage unit, the estimated voltage value of the current energy storage unit is calculated, and the absolute value of the difference between the real-time voltage value and the estimated voltage value is obtained;

[0011] When the absolute value of the difference is greater than or equal to the preset voltage threshold standard value, the charge and discharge rate and / or the state of charge value of the energy storage unit is adjusted according to the preset adjustment times, and when the absolute values ​​of the differences obtained after the adjustment are greater than or equal to the preset voltage threshold standard value and the real-time temperature value is within the preset temperature range, it is determined that the voltage sensor is in an abnormal state;

[0012] When the absolute value of the difference is smaller than the preset voltage threshold standard value or when any absolute value of the difference obtained after adjustment is smaller than the preset voltage threshold standard value, it is determined that the voltage sensor is in a normal state.

[0013] Furthermore, the current estimated voltage value V of the energy storage unitest The calculation formula is:

[0014]

[0015] Among them, V 0_ij is the open circuit voltage of the current energy storage unit under standard conditions, i is the serial number of the current detection cycle, j is the serial number of the energy storage unit, T ij is the real-time temperature value of the current energy storage unit in the current detection cycle, C ij is the charge and discharge rate of the current energy storage unit in the current detection cycle, SOC ij is the state of charge value of the current energy storage unit in the current detection cycle, λ1-λ 12 are the corresponding weight coefficients of temperature, charge and discharge rate, state of charge and their higher-order terms and interaction terms in the calculation formula.

[0016] Furthermore, before calculating the estimated voltage value of the current energy storage unit, the method further includes:

[0017] Obtaining a health status value and a self-discharge rate of the current energy storage unit;

[0018] The estimated voltage value of the current energy storage unit is calculated by combining the health status value, the self-discharge rate, and the number of cycles and the storage time of the current energy storage unit.

[0019] Furthermore, the current estimated voltage value V of the energy storage unit est_ij The calculation formula is:

[0020]

[0021] Among them, SOH ij is the health status value of the current energy storage unit in the current detection cycle, CC ij The number of cycles of the current energy storage unit in the current detection cycle, ST ij SDR is the storage time of the current energy storage unit in the current detection cycle. ij SDR is the self-discharge rate of the current energy storage unit in the current detection cycle. min is the minimum discharge rate of the current energy storage unit, SDR max is the maximum discharge rate of the current energy storage unit, λ 13 -λ 20 It is the corresponding weight coefficient of health status value, number of cycles, storage time and self-discharge rate in the calculation formula.

[0022] Furthermore, before comparing the difference between the current estimated voltage value of the energy storage unit and the average value with the preset voltage threshold standard value, the method further includes:

[0023] Calibrate the preset voltage threshold standard value according to the current state data of the energy storage unit to obtain a preset voltage threshold calibration value;

[0024] The current state data of the energy storage unit includes: a real-time value of internal resistance and a real-time value of capacity.

[0025] Furthermore, the preset voltage threshold calibration value V threshold_ij The calculation formula is:

[0026]

[0027] Among them, V threshold_initial_ij is the preset voltage threshold initial value of the current energy storage unit, R current_ij is the real-time value of the internal resistance of the energy storage unit, R initial_ij is the initial value of the internal resistance of the current energy storage unit, P current_ij is the real-time value of the capacity of the current energy storage unit, P initial_ij is the initial value of the capacity of the current energy storage unit, t ij is the usage time of the current energy storage unit, OM ij is the working mode parameter of the current energy storage unit, CC ij is the number of cycles of the current energy storage unit in the current detection cycle, τ is the time required for the real-time capacity of the current energy storage unit to drop to the preset capacity value, τ cycle is the characteristic time constant related to the number of cycles of the current energy storage unit, and θ1-θ4 are the weight coefficient values ​​of internal resistance, capacity, working mode and number of cycles in the calculation formula.

[0028] Furthermore, the current working modes of the energy storage unit include: low-rate charging and discharging scenarios, high-rate charging and discharging scenarios, peak-valley electricity price arbitrage scenarios, emergency standby scenarios, and maintenance status scenarios.

[0029] Further, after determining that the current energy storage unit is in an abnormal state, the method further includes:

[0030] Send abnormal state alarm information, wherein the abnormal state alarm information includes: the sequence number, location, real-time temperature value, voltage estimation value of the energy storage unit, and the difference between the voltage estimation value and the average voltage value of several adjacent energy storage units.

[0031] Accordingly, a second aspect of an embodiment of the present invention provides an energy storage system sensor fault diagnosis device based on EMS, wherein the energy storage system includes a plurality of energy storage units, each of which is provided with a temperature sensor and a voltage sensor in data communication with the energy storage system EMS, and the device includes:

[0032] A data acquisition module, which is used to respectively acquire the real-time temperature values ​​and the real-time voltage values ​​of the several energy storage units in the current detection cycle based on the energy storage system EMS;

[0033] A data calculation module, which is used to calculate the estimated voltage value of the current energy storage unit based on the real-time temperature value, combined with the charge and discharge rate and the state of charge value of the corresponding energy storage unit, and obtain the absolute value of the difference between the real-time voltage value and the estimated voltage value;

[0034] A first control module, which is used to adjust the charge and discharge rate and / or the state of charge value of the energy storage unit according to a preset adjustment number when the absolute value of the difference is greater than or equal to the preset voltage threshold standard value, and when the absolute values ​​of the differences obtained after adjustment are greater than or equal to the preset voltage threshold standard value and the real-time temperature value is within a preset temperature range, determine that the voltage sensor is in an abnormal state;

[0035] The second control module is used to determine that the voltage sensor is in a normal state when the absolute value of the difference is smaller than the preset voltage threshold standard value or when any absolute value of the difference obtained after adjustment is smaller than the preset voltage threshold standard value.

[0036] Accordingly, a third aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the above-mentioned EMS-based energy storage system sensor fault diagnosis method.

[0037] Accordingly, a fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned EMS-based energy storage system sensor fault diagnosis method.

[0038] The above technical solution of the embodiment of the present invention has the following beneficial technical effects:

[0039] 1. The voltage estimate is calculated by comprehensively considering multiple factors such as temperature, charge and discharge rate, and state of charge, and compared with the real-time voltage value. At the same time, the adjustment of the energy storage unit working condition and the corresponding difference change are combined to make a judgment, avoiding the misjudgment caused by relying solely on a single voltage data to judge the fault. This multi-dimensional and dynamic diagnosis method can more accurately identify whether it is a voltage sensor failure or a voltage fluctuation caused by changes in normal working conditions, significantly improving the accuracy of sensor fault diagnosis;

[0040] 2. Timely and accurate determination of whether the voltage sensor is in an abnormal state helps to discover potential problems in advance. When a voltage sensor fault is detected, corresponding measures can be taken quickly, such as notifying the operation and maintenance personnel to repair or replace it, to prevent the erroneous voltage data caused by the sensor fault from affecting the charging and discharging control strategy and the overall operating status of the energy storage system, ensuring that the energy storage system continues to work in a safe and stable state, reducing a series of problems such as battery overcharging and over-discharging caused by the failure to discover the fault in time, and extending the service life of the energy storage unit;

[0041] 3. During the diagnosis process, the charge and discharge rate and state of charge value of the energy storage unit will be adjusted for a preset number of times to simulate the voltage performance of the battery under different working conditions, fully considering the complex and changeable working conditions in the actual operation of the energy storage system. Whether under different charge and discharge intensities or different power levels, the state of the voltage sensor can be effectively judged, making the diagnostic method highly adaptable and widely used in energy storage systems with different application scenarios and working modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a method for diagnosing a fault of an energy storage system sensor based on EMS provided by an embodiment of the present invention;

[0043] Figure 2 It is a module block diagram of an energy storage system sensor fault diagnosis device based on EMS provided in an embodiment of the present invention.

[0044] Reference numerals:

[0045] 1. Data acquisition module, 2. Data calculation module, 3. First control module, 4. Second control module. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0047] Please refer to Figure 1 A first aspect of an embodiment of the present invention provides a method for diagnosing sensor faults in an energy storage system based on an EMS. The energy storage system includes a plurality of energy storage units, each of which is provided with a temperature sensor and a voltage sensor that are in data communication with the energy storage system EMS. The method includes the following steps:

[0048] Step S100, based on the energy storage system EMS, respectively obtain the real-time temperature values ​​and voltage values ​​of several energy storage units in the current detection cycle.

[0049] The setting of the detection cycle needs to take into account the application characteristics of the energy storage system, the chemical characteristics of the battery, and the requirements for fault sensitivity. For example, for energy storage systems participating in real-time frequency regulation of the power grid, due to the frequent switching of charging and discharging conditions, a detection cycle can be set every few seconds to capture the rapid changes in battery status; while for distributed household energy storage, which is used to smooth out peaks and valleys in electricity consumption, the detection cycle can be extended to several minutes to find a reasonable value between balancing monitoring effects and data processing burdens.

[0050] After obtaining the real-time values ​​of temperature and voltage, preliminary verification is required. For temperature data, different energy storage battery technologies (such as lithium-ion, lead-acid, nickel-metal hydride, etc.) have their own suitable operating temperature ranges. Taking lithium-ion batteries as an example, the normal operating temperature is mostly between -20°C and 50°C. If the collected value exceeds this range, it may be a sensor failure, data transmission packet loss, or abnormal overheating / overcooling of the environment. It needs to be marked as suspicious data for further investigation and verification. Similarly, the voltage data should be judged for rationality against the rated voltage parameters of the energy storage unit to prevent abnormal voltage values ​​caused by overcharging, over-discharging or sensor deviation from being mixed into the subsequent diagnostic process.

[0051] Step S200, based on the real-time temperature value, combined with the charge and discharge rate and the state of charge value of the corresponding energy storage unit, calculate the voltage estimation value of the current energy storage unit, and obtain the absolute value of the difference between the real-time voltage value and the voltage estimation value.

[0052] The charge and discharge rate is a key dynamic factor affecting the battery voltage performance. It can be converted according to the rated capacity by real-time monitoring of the charge and discharge current of the energy storage unit. This requires that the current sensor installed in the energy storage unit has high-precision measurement capabilities, and the EMS can quickly and accurately process the current data and update the charge and discharge rate information in real time. The state of charge (SOC) can be determined by the coulomb counting method, which is based on the integral calculation of the amount of electricity in the battery charging and discharging process, starting from the initial SOC, and accumulating or deducting the charge and discharge electricity in real time. However, there is a cumulative error in long-term use, and regular calibration is required; in addition, the open circuit voltage method can also be used to estimate the SOC with the help of the inherent characteristic curve of the battery open circuit voltage and SOC. In actual application, it can be combined with the temperature compensation model and aging correction factor optimization.

[0053] The effect of temperature on battery voltage is complex and nonlinear. At low temperatures, the internal resistance of the battery increases and the ion diffusion rate slows down, resulting in a decrease in voltage output; at high temperatures, side reactions intensify and the stability of the electrolyte decreases, which also affects the voltage. In addition, temperature is closely intertwined with the charge and discharge rate. High-rate charge and discharge in a high-temperature environment will aggravate polarization and cause a more severe voltage drop. SOC is also involved. The electrochemical characteristics and internal resistance of the battery vary at different SOC stages, and the temperature and charge and discharge rate work together to shape the voltage performance. These interaction terms are fully considered when calculating the voltage estimate to accurately capture the voltage dynamics of the battery under different operating conditions.

[0054] Obtaining the absolute value of the difference between the real-time voltage value and the estimated value is intended to eliminate the interference caused by the positive and negative signs of the difference and focus on the degree of deviation between the two. Regardless of whether the measured voltage value is higher or lower than the estimated value, as long as the absolute value of the difference exceeds the preset range, it may indicate that there is an abnormality in the battery or sensor, providing an intuitive quantitative indicator for subsequent accurate judgment.

[0055] Step S300, when the absolute value of the difference is greater than or equal to the preset voltage threshold standard value, the charge and discharge rate and / or charge state value of the energy storage unit is adjusted according to the preset adjustment number of times, and when the absolute values ​​of several difference values ​​obtained after adjustment are greater than or equal to the preset voltage threshold standard value and the real-time temperature value is in the preset temperature range, it is determined that the voltage sensor is in an abnormal state.

[0056] When the absolute value of the difference exceeds the threshold, the first suspicion is that the voltage deviation may be caused by abnormal operating conditions rather than sensor failure. The setting of the preset number of adjustments is based on the understanding of battery characteristics and the range of common operating conditions. For example, to adjust the charge and discharge rate, you may first gradually increase or decrease a certain proportion (such as 10%, 20%, etc.) from the current rate to observe the voltage change; for the state of charge, you can perform short-term charge and discharge fine-tuning to simulate the battery performance at different power levels. During the adjustment process, continuously monitor the change in the absolute value of the difference. If the difference is always greater than or equal to the threshold after multiple adjustments, and the temperature is stable in the preset range at this time (excluding voltage anomalies caused by temperature runaway), the problem can basically be locked in as the voltage sensor. Because under different reasonable operating conditions, the normal battery voltage should be able to approach the estimated value, and sensor failure will cause the measured voltage to continue to deviate, which cannot be corrected by operating condition adjustment.

[0057] Repeat the above-mentioned charge and discharge rate and state of charge adjustment and monitoring process within the preset number of adjustments. Each round of adjustment is regarded as a "stress test" of the battery voltage characteristics under different working conditions. Taking a typical three-time adjustment as an example, after the first round of adjustment, the absolute value of the difference and related voltage, temperature and other data are recorded; the second round optimizes the adjustment direction and amplitude based on the results of the first round, and observes again; the third round serves as the final verification to ensure that the battery voltage response is fully understood after multiple attempts. If after these three rounds of adjustment, the absolute value of the difference is still greater than or equal to the preset voltage threshold standard value, it can be determined that the voltage sensor is in a fault state.

[0058] The preset temperature range is set based on the temperature range of normal battery operation. Generally, lithium-ion batteries may be between -20℃ and 50℃. When adjusting the operating conditions, if the temperature exceeds this range, on the one hand, it may be that the battery thermal management system has failed. On the other hand, too high or too low temperature will make the battery voltage characteristics extremely complicated, and it is difficult to simply judge the voltage sensor problem. Only when the temperature is stable in the preset range and the interference of temperature factors on the voltage is eliminated, can the voltage sensor be more confidently judged to be abnormal based on the absolute value of the difference. For example, in a round of adjustments, the temperature rises rapidly to the upper limit after the charge and discharge rate is increased. At this time, even if the absolute value of the difference is abnormal, the sensor failure cannot be hastily judged. The temperature problem must be solved first, and the voltage sensor must be retested after the temperature returns to normal.

[0059] Step S400: When the absolute value of the difference is less than the preset voltage threshold standard value or when any absolute value of the difference obtained after adjustment is less than the preset voltage threshold standard value, it is determined that the voltage sensor is in a normal state.

[0060] When the absolute value of the difference is less than the threshold, it indicates that under the current operating conditions, the actual battery voltage is highly consistent with the estimated value, the voltage sensor measurement is reliable, and the sensor is preliminarily judged to be normal, which means that the battery voltage performance meets expectations under the coordination of multiple factors such as temperature, charge and discharge rate, and state of charge, and the system can continue to operate according to the established strategy.

[0061] After determining that the voltage sensor is in a normal state, the energy management system (EMS) of the energy storage system can maintain the current charging and discharging strategy based on this reliable information to ensure that the energy storage unit continues to operate efficiently. For example, if the system is originally in a discharge mode to supply power to the grid, and the discharge rate is set at 0.8C, since the voltage sensor feedback is normal, the EMS does not need to adjust the discharge rate due to concerns about battery abnormalities, ensuring stable power output and meeting power demand. At the same time, based on the judgment that the sensor is normal, some voltage-related control parameters can be appropriately fine-tuned to further optimize the operating efficiency of the energy storage system, such as fine-tuning the charge and discharge cut-off voltage, so that the battery can work in a more reasonable power range and extend battery life.

[0062] Furthermore, the current estimated voltage value V of the energy storage unit est The calculation formula is:

[0063]

[0064] Among them, V 0_ij is the open circuit voltage of the current energy storage unit under standard conditions, i is the serial number of the current detection cycle, j is the serial number of the energy storage unit, T ij is the real-time temperature value of the current energy storage unit in the current detection cycle, C ij is the charge and discharge rate of the current energy storage unit in the current detection cycle, SOC ij is the state of charge value of the current energy storage unit in the current detection cycle, λ1-λ 12 are the corresponding weight coefficients of temperature, charge and discharge rate, state of charge and their higher-order terms and interaction terms in the calculation formula.

[0065] The calculation formula of the above voltage estimation value covers the key factors that have a significant impact on the battery voltage: temperature, charge and discharge rate, state of charge and other parameters, and accurately captures the comprehensive impact of various influencing factors on the voltage under different working conditions through mathematical function terms and their interactive combination. Based on the open circuit voltage under standard conditions, the remaining items are dynamically adjusted according to the real-time operating parameters, so that the calculated estimated voltage can closely match the actual working voltage of the battery, providing the necessary data support for the precise management and control of the energy storage system.

[0066] Among them, the linear term -λ1×T ij It reflects the linear effect of temperature on voltage. The coefficient λ1 determines the rate at which voltage changes with temperature. Generally, as temperature rises, the rate of chemical reactions inside the battery changes, and characteristics such as internal resistance also change, resulting in voltage fluctuations. When λ1>0, it means that as temperature rises, the voltage decreases linearly, which is consistent with the basic negative correlation between temperature and voltage in the normal operating temperature range of most batteries. The linear term λ2×C ij It reflects the linear correlation between the charge and discharge rate and the voltage, and the coefficient λ2 represents the speed of voltage change with the increase or decrease of the charge and discharge rate. During the battery charge and discharge process, the increase in rate usually intensifies the battery polarization phenomenon, increases the internal resistance voltage drop, and thus affects the terminal voltage. If λ2>0, it means that as the charge and discharge rate increases, the voltage rises accordingly.

[0067] Quadratic term -λ3×T ij 2Used to accurately simulate the nonlinear effect of temperature on battery voltage, especially under extreme temperature conditions. Compared with linear terms, quadratic terms can capture more complex changes in battery performance caused by temperature changes. When the temperature deviates from the appropriate range, whether it is high or low temperature, the physical and chemical processes inside the battery will change significantly, such as electrolyte decomposition at high temperature, increased structural damage to electrode materials, and a sharp drop in ion conduction rate at low temperature, resulting in an accelerated voltage drop rate. The quadratic term fits this nonlinear deterioration process well through its parabolic function characteristics.

[0068] The quadratic term λ4×C ij 2 , for the charge and discharge rate, in the high rate region, the changes in the polarization and internal resistance of the battery show nonlinear characteristics, and a simple linear relationship cannot accurately describe the voltage change. The quadratic term can reflect the change in the slope of the voltage change as the charge and discharge rate increases further. For example, when switching from a low rate to a high rate, the voltage drop may be small at first. As the rate square effect becomes more prominent, the voltage drop intensifies, which more accurately conforms to the actual performance of the battery under high rate conditions.

[0069] Index term It is used to capture the exponential effect of temperature on battery performance, especially in high temperature environments. When the temperature rises to a certain level, the side reactions inside the battery grow exponentially, such as the rapid decomposition of the electrolyte and the irreversible loss of active substances, which will cause the battery voltage to drop sharply. The characteristics of the exponential function enable it to sensitively reflect this avalanche drop in voltage near the high temperature threshold, providing key voltage warning information for battery thermal management. Once the value changes dramatically, it indicates that the battery may face the risk of overheating and needs to adjust the operating conditions in time.

[0070] The logarithmic term λ7×ln(1+λ8×C ij ) is used to deal with the sensitivity changes of the charge and discharge rate in the range of near zero value and small rate. When the charge and discharge rate is extremely low, the battery voltage is little affected by the rate and can almost be regarded as a constant; but as the rate slowly increases, the voltage begins to show more sensitive changes. The logarithmic function can delicately capture this transition process from almost no effect to gradually significant effect, ensuring that in low-rate application scenarios (such as some precision equipment powered by microcurrents), the voltage estimation is still accurate, avoiding the error accumulation in the low-rate range caused by simple linear models.

[0071] Logarithmic term λ9×T ij ×ln(1+λ 10 ×SOC ij), which relates temperature and state of charge through a logarithmic function, reflecting the complex interaction between the two. At different temperatures, the SOC-voltage relationship of the battery is not fixed. At low temperatures, as the SOC increases, the ion diffusion resistance inside the battery increases, and the voltage is more affected; at high temperatures, the thermal stability and voltage performance of batteries in different SOC ranges vary. This logarithmic function, combined with the temperature variable, accurately captures the nonlinear characteristics of the impact of SOC changes on voltage under different temperature backgrounds, and can give a more realistic voltage estimate than considering temperature or SOC alone.

[0072] Logarithmic term λ 11 ×C ij ×ln(1+λ 12 ×T ij ), focusing on the synergistic effects of charge and discharge rate and temperature. Under high-rate discharge and high-temperature conditions, the internal polarization and thermal effects of the battery are superimposed on each other, which has a catastrophic effect on the voltage; while under low-rate and low-temperature conditions, the effects of the two are relatively mild. This logarithmic function effectively simulates the complex variation trajectory of voltage under different rate and temperature combinations, provides an accurate basis for battery voltage estimation under variable conditions, and helps optimize charge and discharge strategies.

[0073] In addition, the above weight coefficients can be finely fitted in combination with historical data and updated in real time.

[0074] Furthermore, before calculating the estimated voltage value of the current energy storage unit in step S200, the method further includes:

[0075] Step S110, obtaining the health status value and self-discharge rate of the current energy storage unit.

[0076] The health status value is a key indicator to measure the current performance of the battery compared with the new state, reflecting the overall "health" of the battery. Its value can be obtained through regular capacity testing, comparing the current measured battery available capacity with the rated capacity of the battery when it leaves the factory. The battery internal resistance measurement can also be used to assist in judgment. As the battery ages, the internal resistance usually increases gradually. By establishing an empirical relationship model between internal resistance and SOH, the SOH can be estimated based on the real-time measured internal resistance.

[0077] The self-discharge rate reflects the loss of battery power when the battery is not connected to an external circuit, and has a significant impact on the long-term performance and actual available capacity of the battery. The self-discharge rate is generally measured by the static method, that is, the fully charged battery is left at a specific ambient temperature (such as 25°C) for a period of time (for example, 7 days), and the battery voltage or capacity changes before and after the static method are measured. If the initial voltage is V1 and the voltage after static is V2, the SOC change ΔSOC before and after the static method is calculated based on the corresponding relationship curve between the open circuit voltage and SOC of the battery. The self-discharge rate can be calculated according to the formula calculate.

[0078] Step S120 , calculating the estimated voltage value of the current energy storage unit by combining the health status value, the self-discharge rate, and the number of cycles and storage time of the current energy storage unit.

[0079] Specifically, when the SOH is high, the electrode material structure inside the battery is intact, the electrochemical reaction activity is close to the ideal state, and the ion transmission is smooth. At this time, under the same temperature, charge and discharge rate and SOC conditions, the battery can output a more stable voltage close to the theoretical value. For example, under standard operating conditions, the open circuit voltage of a new battery (SOH = 100%) is . As the SOH drops to 80%, due to the reduction of active substances on the electrode surface and the increase of internal resistance, the voltage at the same SOC may decrease by ΔV1. The specific reduction value depends on the battery type and operating conditions.

[0080] The self-discharge process is essentially a series of irreversible chemical reactions that occur inside the battery. These reactions consume the power inside the battery, causing the SOC to gradually decrease, which in turn affects the voltage. Even if the battery is in a static state, a high self-discharge rate will cause the SOC to decrease over time. According to the relationship between the open circuit voltage and SOC of the battery, the voltage will also decrease accordingly. For example, if the self-discharge rate of a battery is 5% per month and it is static for one month, if the initial SOC is 60%, the actual SOC becomes 60%*(1-5%)=57%, and the corresponding voltage will drop to a new value according to the SOC-voltage curve.

[0081] Each charge and discharge cycle will cause a certain degree of damage to the internal structure of the battery. As the number of cycles increases, the crystal structure of the electrode material gradually deforms, the active material falls off, the electrolyte decomposes, and other problems become more serious, resulting in increased internal resistance of the battery and intensified polarization, which directly affects the voltage output. Generally speaking, in the early cycle stage, the voltage changes relatively slowly, but when the number of cycles exceeds a certain threshold (such as after the lithium-ion battery is cycled), the voltage drop trend accelerates significantly. For example, from cycle 0 to cycle 100, at the same temperature, rate and SOC, the voltage may only drop by ΔV2; and from cycle 500 to cycle 600, the voltage drop may increase to ΔV3, and ΔV3>ΔV2.

[0082] Even if the battery has not undergone a charge and discharge cycle, long-term storage will cause the battery to age. The main reasons include the slow chemical reaction between the electrode material and the electrolyte, and the accumulation of self-discharge. The longer the storage time, the more obvious the battery performance declines, which is manifested as capacity decay and increased internal resistance. The final reflection in the voltage is that the voltage decreases under the same conditions. Similar to the number of cycles, the effect of storage time on voltage is also staged, with a small initial impact and gradually significant over time.

[0083] Furthermore, the current estimated voltage value V of the energy storage unitest_ij The calculation formula is:

[0084]

[0085] Among them, SOH ij is the health status value of the current energy storage unit in the current detection cycle, CC ij The number of cycles of the current energy storage unit in the current detection cycle, ST ij SDR is the storage time of the current energy storage unit in the current detection cycle. ij SDR is the self-discharge rate of the current energy storage unit in the current detection cycle. min is the minimum discharge rate of the current energy storage unit, SDR max is the maximum discharge rate of the current energy storage unit, λ 13 -λ 20 It is the corresponding weight coefficient of health status value, number of cycles, storage time and self-discharge rate in the calculation formula.

[0086] Among them, the SOH related term (λ 13 ×SOH ij 2 ) describes the impact of SOH on the target quantity (assuming it is voltage), based on the fact that during actual use of the battery, as the health state decreases, its voltage change does not show a simple linear relationship. When the SOH is high, the battery performance is relatively stable and the voltage changes relatively slowly; however, once the SOH begins to decrease, it means that the internal structure of the battery is gradually deteriorating, such as the loss of active substances in the electrode material and the increase in internal resistance. The impact of these changes on the voltage will become more and more significant as the SOH further decreases, showing a trend of accelerated change. The characteristics of the quadratic function can just capture this nonlinear change, that is, every time the SOH decreases by a certain percentage, the voltage drop will become larger and larger, which is more in line with the actual voltage response characteristics during battery aging.

[0087] In the early stage of battery use, assuming that the SOH drops from 100% to 90%, due to slight aging inside the battery, the voltage may only drop by ΔV4; but as the use time increases, when the SOH drops from 80% to 70%, the voltage drop may increase to ΔV5 (and ΔV5>ΔV4) for the same 10% SOH drop. This is because as the SOH continues to decrease, the chemical reaction inside the battery is more hindered, and ion transmission becomes more difficult, making the voltage more sensitive to changes in SOH, and the quadratic term SOH 2 The characteristic of the curve slope changing continuously can accurately simulate the phenomenon of nonlinear acceleration of voltage drop as SOH decreases. The coefficient λ 13The strength of this influence is further adjusted, and its specific value is determined by fitting according to different battery types and actual test data to ensure that the quantitative relationship between SOH and voltage in the battery system is accurately reflected.

[0088] Cycle number related items The Sigmoid function is used to describe the effect of the number of cycles on the voltage. In the early stage of the cycle, the battery performance is relatively good, the increase in the number of cycles has a relatively small effect on the voltage, and the voltage drops slowly; as the number of cycles continues to accumulate, the structural damage inside the battery gradually accumulates, such as the destruction of the crystal structure of the electrode material, the decomposition of the electrolyte, etc., causing the voltage to begin to drop rapidly; but when the number of cycles reaches a certain level, the battery performance gradually tends to a relatively stable and poor state. At this time, even if the number of cycles is increased, the amplitude of the voltage drop will gradually become gentle, no longer changing as fast as in the middle period, and presenting a state close to saturation. The "S" curve characteristics of the Sigmoid function can simulate this process of slow changes at first, accelerated changes in the middle, and gentle changes in the later period, which conforms to the actual law of voltage changes of the battery as the number of cycles increases during the entire life cycle.

[0089] Taking lithium-ion batteries as an example, in the first few dozen cycles, due to the initial stability of the internal materials of the battery and the margin of the battery design, each cycle may only cause the voltage to drop by a very small amount, such as a voltage drop of ΔV per cycle. init When the number of cycles reaches hundreds, the aging problem inside the battery becomes prominent, and the voltage drop may increase to ΔV per cycle. mid (ΔV mid >ΔV init ); When the number of cycles increases further and exceeds a certain critical value (for example, thousands of times), although the battery performance is already very poor, the voltage drop rate will gradually slow down because the aging process inside the battery has approached the limit, and the voltage drop amplitude of each cycle may return to a value similar to ΔV late (ΔV late <ΔV mid ) is a smaller value. The Sigmoid function is 15 and λ 16 To adjust the shape of the curve, that is, to adjust the node where the voltage starts to drop rapidly when the number of cycles changes and the degree to which it eventually tends to saturation, and the coefficient λ 14 This determines the intensity of the impact of the overall number of cycles on the voltage.

[0090] Storage time related items The impact on the battery voltage also shows a saturation effect, and the Sigmoid function form is also used. Even if the battery does not undergo a charge and discharge cycle, but is only stored for a long time, a series of slow chemical reactions will occur inside it, such as the interaction between the electrode material and the electrolyte, self-discharge, etc., which will cause the battery performance to gradually decline, thereby affecting the voltage. At the beginning of storage, the battery voltage is less affected by the storage time and changes slowly; as the storage time continues to increase, the battery ages more and the voltage drops faster; but after a long time, the aging reaction inside the battery tends to stabilize, and the voltage drop will gradually slow down. This change trend is similar to the effect of the number of cycles on the voltage, so the Sigmoid function can well simulate this nonlinear process that changes with time.

[0091] Self-discharge rate related terms The square term is used to describe the effect of the self-discharge rate on the voltage because the self-discharge process itself is an irreversible chemical reaction that will gradually consume the power inside the battery, thereby affecting the battery's state of charge (SOC) and voltage. When the self-discharge rate is low, its effect on the voltage is relatively small, and the battery voltage can remain relatively stable for a long time; but as the self-discharge rate increases, the battery power loss rate accelerates, which will cause the battery's SOC to drop rapidly. There is a close correlation between the battery voltage and the SOC, so the voltage will also change more significantly, and this change is not a simple linear relationship, but as the self-discharge rate increases, the voltage drops more and more, showing the characteristics of nonlinear accelerated change. The square term can capture this change trend well, so that the formula can more accurately reflect the actual impact of the self-discharge rate on the voltage.

[0092] In addition, before comparing the difference between the current estimated voltage value of the energy storage unit and the average value with the preset voltage threshold standard value, the method further includes:

[0093] According to the current state data of the energy storage unit, the preset voltage threshold standard value is calibrated to obtain the preset voltage threshold calibration value. The current state data of the energy storage unit includes: the real-time value of the internal resistance and the real-time value of the capacity.

[0094] When the preset voltage threshold standard value is initially set, it is usually determined based on general knowledge of battery characteristics, past experience, and test data under some standard working conditions. However, there are large individual differences in energy storage units in actual operation, and the state of the battery will change dynamically with factors such as usage time, number of charge and discharge cycles, and changes in environmental conditions. Relying solely on fixed preset voltage threshold standard values ​​for abnormal judgment is likely to result in misjudgment or missed judgment. Therefore, dynamic calibration of the threshold based on real-time acquired energy storage unit status data, such as real-time internal resistance and capacity values, can greatly improve the accuracy and reliability of fault diagnosis.

[0095] Considering the continuity of battery status changes, the calibration process needs to have a certain degree of real-time performance. Before each comparison of the difference between the estimated voltage value and the average value, the calibration should be completed in time based on the latest real-time value of the internal resistance and the real-time value of the capacity to ensure the accuracy of the judgment. However, real-time calibration requires high computing resources and data processing speed. In order to balance resource consumption and diagnostic accuracy, a reasonable calibration cycle can be set. For example, when the battery is in a stable charge and discharge stage, it is calibrated every 10 minutes; and after the battery has just experienced fast charging, high temperature environment operation, deep discharge and other working conditions, due to the drastic change in battery status, a calibration is performed immediately, and 2-3 more calibrations are performed within the next 5 minutes to quickly track battery status changes, dynamically adjust the preset voltage threshold calibration value, and ensure the reliable operation of the energy storage system.

[0096] Furthermore, the preset voltage threshold calibration value V threshold_ij The calculation formula is:

[0097]

[0098] Among them, V threshold_initial_ij is the preset voltage threshold initial value of the current energy storage unit, R current_ij is the real-time value of the internal resistance of the energy storage unit, R initial_ij is the initial value of the internal resistance of the current energy storage unit, P current_ij is the real-time value of the capacity of the current energy storage unit, P initial_ij is the initial value of the capacity of the current energy storage unit, t ij is the usage time of the current energy storage unit, OM ij is the working mode parameter of the current energy storage unit, CC ij is the number of cycles of the current energy storage unit in the current detection cycle, τ is the time required for the real-time capacity of the current energy storage unit to drop to the preset capacity value, τ cycle is the characteristic time constant related to the number of cycles of the current energy storage unit, and θ1-θ4 are the weight coefficient values ​​of internal resistance, capacity, working mode and number of cycles in the calculation formula.

[0099] In the above calculation formula, the internal resistance related terms The square form of the relative internal resistance change is used to highlight the nonlinear effect of the internal resistance change on the threshold. When the initial change in internal resistance is small, its effect on the threshold is relatively gentle; however, once the internal resistance increases significantly, the square term will cause the threshold to rise rapidly. This is because in actual situations, a larger change in internal resistance means that the physical and chemical properties inside the battery have deteriorated more significantly, the voltage consistency of the battery during charging and discharging has deteriorated, and larger voltage differences are more likely to occur between adjacent battery cells, so the threshold needs to be relaxed to avoid misjudgment. For example, in the early stage of battery use, assuming R initial_ij =10mΩ, when R current_ij It only increases to 11mΩ, with a relative change of 10%. The increase in the threshold value is relatively small according to this calculation; however, as the battery ages further, if R current_ij When it increases to 15mΩ, the relative change reaches 50%. At this time, under the action of the square term, the threshold will be greatly increased to adapt to the increased voltage fluctuation caused by internal degradation of the battery. The coefficient θ1 is obtained through experimental data fitting based on the differences in the sensitivity to internal resistance in different battery systems and application scenarios, and accurately adjusts the weight of the impact of internal resistance changes on the threshold.

[0100] Aging factor related items As time goes by, battery performance gradually degrades. The above degradation process is simulated by using exponential decay. The exponential function characteristics make the initial performance decline relatively slowly, and the threshold increase is also relatively gentle; but as time approaches the characteristic time, performance degradation accelerates, and the threshold will increase exponentially. This is consistent with the actual aging process of the battery. In the early stage of aging, the battery can still maintain a relatively stable voltage consistency, but in the later stage, due to the reduction of internal chemical reaction activity and the destruction of electrode material structure, the voltage fluctuation range expands, and the threshold needs to be increased accordingly. Taking a lithium-ion battery as an example, the initial capacity P initial_ij =100mA, if after a period of time t, the current capacity P current_ij =90mA, and the characteristic time τ = 500h (assumed value) is known. As time accumulates, the adjustment of the threshold will gradually become more prominent. In the first few dozen hours, the capacity decay is not obvious, and the threshold is almost maintained at a close to the initial value; but when it approaches or exceeds 500h, the capacity drops sharply and the threshold increases rapidly to prevent normal voltage changes caused by aging from being misjudged as faults. The coefficient θ2 is also determined based on the battery type and aging characteristic experiments to ensure accurate reflection of the quantitative relationship between performance degradation and threshold.

[0101] Work mode related items The working mode has a significant impact on the battery voltage stability. During high-rate charge and discharge, the internal polarization of the battery intensifies, the internal resistance voltage drop increases, and the uneven current density distribution causes more severe voltage fluctuations.ij As a quantitative indicator of the working mode, the parameter increases in value in high-rate scenarios and is multiplied by the internal resistance ratio, which means that not only the change of the internal resistance itself is taken into account, but also the adjustment of the threshold is strengthened in combination with the working mode. When the working mode becomes severe (such as high rate) and the internal resistance increases at the same time, the threshold will increase rapidly to adapt to the larger normal voltage difference between batteries under such conditions. Assuming that at low rate charging and discharging, OM ij =0.8, the internal resistance of a battery does not change much relative to the initial value; but when switching to high rate charge and discharge, OM ij =2.5, if the internal resistance also rises to a certain extent at this time, the threshold value will be significantly increased after this calculation. This ensures that under different working intensities, the fault diagnosis threshold value can dynamically adapt to the actual voltage performance of the battery to avoid false alarms or missed judgments caused by working mode switching. The coefficient is optimized based on the experimental fitting of the voltage response characteristics of the battery under different working modes.

[0102] Furthermore, the current working modes of energy storage units include: low-rate charge and discharge scenario, high-rate charge and discharge scenario, peak-valley electricity price arbitrage scenario, emergency standby scenario, and maintenance status scenario. ij The value is 0.8, high rate charge and discharge scenario OM ij The value is 0.8, peak-valley electricity price arbitrage scenario OM ij The value is 1.2, emergency backup scenario OM ij The value is 1.8, maintenance status scenario OM ij The value is 0.6.

[0103] Cycle number related items The number of cycles directly reflects its usage intensity and degree of aging. Each charge and discharge cycle will cause damage to the internal structure of the battery, such as deformation of the crystal structure of the electrode material, shedding of active substances, etc., which will lead to increased internal resistance and poor voltage consistency. The items related to the number of cycles use an exponential growth form. In the early stage of the cycle, although there is a certain amount of aging accumulation, the impact on the voltage threshold is relatively small; as the number of cycles increases and time passes, the aging effect gradually appears and accelerates, and the exponential term accelerates the threshold to rise, which is in line with the law that the impact of cycle aging on voltage fluctuations gradually increases during the battery's life cycle. For example, for the first few dozen cycles of a new battery, since the internal structure of the battery is relatively intact, the effect of this item on improving the threshold is limited; but when the number of cycles reaches hundreds of times, the battery is seriously aged, such as CC ij =500, and combined with the time factor (assuming τ cycle =1000h, etc.), the exponential term is close to saturation, and the threshold value will be greatly increased to adapt to the voltage change characteristics of the aging battery during the cycle process. The coefficient θ4 is fitted based on a large amount of cycle life test experimental data to accurately reflect the relationship between the number of cycles and the threshold value.

[0104] Further, after determining in step S400 that the current energy storage unit is in an abnormal state, the method further includes:

[0105] Step S410, sending abnormal state alarm information, the abnormal state alarm information includes: the sequence number, location, real-time temperature value, voltage estimation value of the energy storage unit and the difference between the voltage estimation value and the average voltage value of several adjacent energy storage units.

[0106] Once an abnormality is determined, the multi-level alarm mechanism is immediately triggered. The EMS system quickly pushes detailed alarm information to the operation and maintenance personnel, covering key data such as the abnormal unit number, physical location, current temperature, voltage estimation, difference details, etc., to assist in quickly locating the root cause of the fault; at the same time, it automatically performs preliminary emergency operations according to the preset strategy, suspends the charging and discharging of the abnormal unit, prevents the fault from worsening, ensures the normal operation of the rest of the system, and buys time for subsequent in-depth maintenance.

[0107] Accordingly, please refer to Figure 2 A second aspect of an embodiment of the present invention provides an energy storage system sensor fault diagnosis device based on an EMS, wherein the energy storage system includes a plurality of energy storage units, each of which is provided with a temperature sensor and a voltage sensor in data communication with the energy storage system EMS, and the device includes:

[0108] A data acquisition module 1, which is used to respectively acquire the real-time temperature values ​​and the real-time voltage values ​​of the several energy storage units in the current detection cycle based on the energy storage system EMS;

[0109] A data calculation module 2 is used to calculate the estimated voltage value of the current energy storage unit based on the real-time temperature value and the charge and discharge rate and state of charge value of the corresponding energy storage unit, and obtain the absolute value of the difference between the real-time voltage value and the estimated voltage value;

[0110] A first control module 3, which is used to adjust the charge and discharge rate and / or the state of charge value of the energy storage unit according to a preset adjustment number when the absolute value of the difference is greater than or equal to the preset voltage threshold standard value, and when the absolute values ​​of the differences obtained after adjustment are all greater than or equal to the preset voltage threshold standard value and the real-time temperature value is within a preset temperature range, determine that the voltage sensor is in an abnormal state;

[0111] The second control module 4 is used to determine that the voltage sensor is in a normal state when the absolute value of the difference is smaller than the preset voltage threshold standard value or when any absolute value of the difference obtained after adjustment is smaller than the preset voltage threshold standard value.

[0112] Accordingly, a third aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the above-mentioned EMS-based energy storage system sensor fault diagnosis method.

[0113] Accordingly, a fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned EMS-based energy storage system sensor fault diagnosis method.

[0114] The embodiment of the present invention aims to protect a sensor fault diagnosis method for an energy storage system based on EMS, wherein the energy storage system includes a plurality of energy storage units, each of which is provided with a temperature sensor and a voltage sensor that are data-connected to the EMS of the energy storage system, and the method includes the following steps: based on the EMS of the energy storage system, respectively obtain the real-time temperature values ​​and real-time voltage values ​​of a plurality of energy storage units in the current detection cycle; based on the real-time temperature value, in combination with the charge and discharge ratio and the state of charge value of the corresponding energy storage unit, calculate the voltage estimation value of the current energy storage unit, and obtain the absolute value of the difference between the real-time voltage value and the estimated voltage value; when the absolute value of the difference is greater than or equal to the preset voltage threshold standard value, adjust the charge and discharge ratio and / or state of charge value of the energy storage unit according to the preset number of adjustments, and when the absolute values ​​of the differences obtained after adjustment are all greater than or equal to the preset voltage threshold standard value and the real-time temperature value is in the preset temperature range, determine that the voltage sensor is in an abnormal state; when the absolute value of the difference is less than the preset voltage threshold standard value or when any absolute value of the difference obtained after adjustment is less than the preset voltage threshold standard value, determine that the voltage sensor is in a normal state. The above technical scheme has the following effects:

[0115] 1. The voltage estimate is calculated by comprehensively considering multiple factors such as temperature, charge and discharge rate, and state of charge, and compared with the real-time voltage value. At the same time, the adjustment of the energy storage unit working condition and the corresponding difference change are combined to make a judgment, avoiding the misjudgment caused by relying solely on a single voltage data to judge the fault. This multi-dimensional and dynamic diagnosis method can more accurately identify whether it is a voltage sensor failure or a voltage fluctuation caused by changes in normal working conditions, significantly improving the accuracy of sensor fault diagnosis;

[0116] 2. Timely and accurate determination of whether the voltage sensor is in an abnormal state helps to discover potential problems in advance. When a voltage sensor fault is detected, corresponding measures can be taken quickly, such as notifying the operation and maintenance personnel to repair or replace it, to prevent the erroneous voltage data caused by the sensor fault from affecting the charging and discharging control strategy and the overall operating status of the energy storage system, ensuring that the energy storage system continues to work in a safe and stable state, reducing a series of problems such as battery overcharging and over-discharging caused by the failure to discover the fault in time, and extending the service life of the energy storage unit;

[0117] 3. During the diagnosis process, the charge and discharge rate and state of charge value of the energy storage unit will be adjusted for a preset number of times to simulate the voltage performance of the battery under different working conditions, fully considering the complex and changeable working conditions in the actual operation of the energy storage system. Whether under different charge and discharge intensities or different power levels, the state of the voltage sensor can be effectively judged, making the diagnostic method highly adaptable and widely used in energy storage systems with different application scenarios and working modes.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for diagnosing energy storage system sensor faults based on EMS, characterized in that: The energy storage system includes a plurality of energy storage units, each of which is provided with a temperature sensor and a voltage sensor which are in data communication with the energy storage system EMS. The method includes the following steps: Based on the energy storage system EMS, respectively obtain the real-time temperature values ​​and the real-time voltage values ​​of the plurality of energy storage units in the current detection cycle; Based on the real-time temperature value, combined with the charge / discharge ratio and the state of charge value of the corresponding energy storage unit, the estimated voltage value of the current energy storage unit is calculated, and the absolute value of the difference between the real-time voltage value and the estimated voltage value is obtained; When the absolute value of the difference is greater than or equal to the preset voltage threshold standard value, the charge and discharge rate and / or the state of charge value of the energy storage unit is adjusted according to the preset adjustment times, and when the absolute values ​​of the differences obtained after the adjustment are greater than or equal to the preset voltage threshold standard value and the real-time temperature value is within the preset temperature range, it is determined that the voltage sensor is in an abnormal state; When the absolute value of the difference is smaller than the preset voltage threshold standard value or when any absolute value of the difference obtained after adjustment is smaller than the preset voltage threshold standard value, it is determined that the voltage sensor is in a normal state.

2. The energy storage system sensor fault diagnosis method based on EMS according to claim 1 is characterized in that: The current estimated voltage value V of the energy storage unit est The calculation formula is: Among them, V 0_ij is the open circuit voltage of the current energy storage unit under standard conditions, i is the serial number of the current detection cycle, j is the serial number of the energy storage unit, T ij is the real-time temperature value of the current energy storage unit in the current detection cycle, C ij is the charge and discharge rate of the current energy storage unit in the current detection cycle, SOC ij is the state of charge value of the current energy storage unit in the current detection cycle, λ1-λ 12 are the corresponding weight coefficients of temperature, charge and discharge rate, state of charge and their higher-order terms and interaction terms in the calculation formula.

3. The EMS-based energy storage system sensor fault diagnosis method according to claim 2, characterized in that: Before calculating the estimated voltage value of the current energy storage unit, the method further includes: Obtaining a health status value and a self-discharge rate of the current energy storage unit; The estimated voltage value of the current energy storage unit is calculated by combining the health status value, the self-discharge rate, and the number of cycles and the storage time of the current energy storage unit.

4. The energy storage system sensor fault diagnosis method based on EMS according to claim 3 is characterized in that: The current estimated voltage value V of the energy storage unit est_ij The calculation formula is: Among them, SOH ij is the health status value of the current energy storage unit in the current detection cycle, CC ij The number of cycles of the current energy storage unit in the current detection cycle, ST ij SDR is the storage time of the current energy storage unit in the current detection cycle. ij SDR is the self-discharge rate of the current energy storage unit in the current detection cycle. min is the minimum discharge rate of the current energy storage unit, SDR max is the maximum discharge rate of the current energy storage unit, λ 13 -λ 20 It is the corresponding weight coefficient of health status value, number of cycles, storage time and self-discharge rate in the calculation formula.

5. The energy storage system sensor fault diagnosis method based on EMS according to claim 1, characterized in that: Before comparing the difference between the current estimated voltage value of the energy storage unit and the average value with the preset voltage threshold standard value, the method further includes: Calibrate the preset voltage threshold standard value according to the current state data of the energy storage unit to obtain a preset voltage threshold calibration value; The current state data of the energy storage unit includes: a real-time value of internal resistance and a real-time value of capacity.

6. The method for diagnosing energy storage system sensor faults based on EMS according to claim 5, characterized in that: The preset voltage threshold calibration value V threshold_ij The calculation formula is: Among them, V threshold_initial_ij is the preset voltage threshold initial value of the current energy storage unit, R current_ij is the real-time value of the internal resistance of the energy storage unit, R initial_ij is the initial value of the internal resistance of the current energy storage unit, P current_ij is the real-time value of the capacity of the current energy storage unit, P initial_ij is the initial value of the capacity of the current energy storage unit, t ij is the usage time of the current energy storage unit, OM ij is the working mode parameter of the current energy storage unit, CC ij is the number of cycles of the current energy storage unit in the current detection cycle, τ is the time required for the real-time capacity of the current energy storage unit to drop to the preset capacity value, τ cycle is the characteristic time constant related to the number of cycles of the current energy storage unit, and θ1-θ4 are the weight coefficient values ​​of internal resistance, capacity, working mode and number of cycles in the calculation formula.

7. The EMS-based energy storage system sensor fault diagnosis method according to claim 6, characterized in that: The current working modes of the energy storage unit include: low-rate charging and discharging scenario, high-rate charging and discharging scenario, peak-valley electricity price arbitrage scenario, emergency standby scenario and maintenance status scenario.

8. The energy storage system sensor fault diagnosis method based on EMS according to any one of claims 1 to 7, characterized in that: After determining that the voltage sensor is in an abnormal state, the method further includes: Send abnormal state alarm information, wherein the abnormal state alarm information includes: the serial number, position, real-time voltage value, estimated voltage value and absolute value of difference of the voltage sensor.

9. An electronic device, characterized in that: include: at least one processor; And a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the EMS-based energy storage system sensor fault diagnosis method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the instructions are executed by a processor, an EMS-based energy storage system sensor fault diagnosis method as described in any one of claims 1-8 is implemented.

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