A compensation system and method for sensor aging drift in an energy storage system

Abstract

The application belongs to the technical field of energy storage systems, and particularly relates to a compensation system and method for sensor aging drift in an energy storage system. The compensation system comprises a plurality of working battery units, a first MEMS sensor integrated on a shell of the working battery unit; one or more reference compensation units, the reference compensation unit comprising an aluminum shell and a positive pole and a negative pole connected to an upper end of the aluminum shell, the aluminum shell being filled with inert gas and integrated with a second MEMS sensor, and the positive pole and the negative pole being connected with a resistor; and a data acquisition and processing module for acquiring data of the first MEMS sensor and the second MEMS sensor and executing a compensation algorithm. By introducing the reference compensation unit, the application can accurately quantify and separate the internal aging drift of the sensor, and further decouple the environmental induced drift from the total drift of the actual sensor, so as to realize double, dynamic and high-precision compensation for the zero point and the sensitivity.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system technology, and in particular relates to a compensation system and method for sensor aging drift in an energy storage system. Background Technology

[0002] With the continuous improvement of energy density and increasingly stringent safety requirements of lithium-ion batteries for energy storage, the need for accurate and real-time monitoring of the battery's internal state is becoming increasingly urgent. Integrating microelectromechanical systems (MEMS) pressure and temperature sensors directly into the battery casing has become a cutting-edge battery management technology. This technology can directly acquire key pressure and temperature data generated inside the cell due to electrochemical reactions, gas production, and precursors to thermal runaway, providing unprecedented high-precision input to the battery management system (BMS) and energy management system (EMS), thereby greatly improving the battery's safety early warning capabilities and the accuracy of state assessments (such as SOH and SOP).

[0003] While MEMS sensors offer advantages such as small size and high integration, their inherent physical characteristics lead to an unavoidable long-term drift problem. This drift mainly consists of two parts: 1. Intrinsic aging drift: This is caused by the aging and creep of the sensor's own materials (such as silicon-based sensing elements and packaging materials) and the slow degradation of the performance of internal circuit components (such as reference voltage sources). This drift is cumulative and systematic over time, and can occur even under constant operating conditions, affecting both the sensor's zero point and sensitivity.

[0004] 2. Environmentally Induced Drift: In the harsh operating environment inside the battery, the sensor is subjected to continuous temperature cycling, thermomechanical stress, and corrosion from corrosive gases (such as hydrogen fluoride, HF) generated by electrolyte evaporation. These external factors accelerate sensor performance degradation, not only causing zero-point drift but also significantly altering the sensor's sensitivity (i.e., the input-output slope).

[0005] Technical problems with existing technical solutions: Currently, most methods for compensating for sensor drift rely on periodic offline calibration or compensation based on simple external references. However, in large-scale, long-cycle operation scenarios such as energy storage power stations, offline calibration of tens of thousands of cells is impractical. Furthermore, existing simple compensation models suffer from the following core technical problems: Unable to decouple drift sources: Existing technologies cannot distinguish online between "intrinsic aging drift" and "environmentally induced drift" caused by factors such as electrolyte corrosion. This leads to inaccurate compensation models, especially when assessing sensor sensitivity degradation caused by corrosion, which is almost nonexistent.

[0006] Limited Compensation Dimensions: Most compensation schemes only perform simple offset correction for zero-point drift, ignoring the more critical sensitivity drift. The lack of sensitivity drift correction means that when real, drastic changes occur in the battery's internal pressure (such as precursors to thermal runaway), the sensor's output signal gain may already be distorted, leading to misjudgments or missed detections by the BMS, potentially causing serious safety incidents.

[0007] Lack of online real-time capability: The compensation process relies on a factory-preset, static experience model, which cannot be adaptively adjusted according to the actual, dynamic drift process of the sensor, resulting in the compensation effect deteriorating over time.

[0008] Therefore, there is an urgent need for a system and method that can decouple and compensate for the complex long-term drift (including zero-point drift and sensitivity drift) of battery-embedded MEMS sensors in an online, real-time and accurate manner. Summary of the Invention

[0009] To address the problems of existing technologies, such as inability to decouple drift sources, limited compensation dimensions, and lack of online real-time capability, this invention proposes a compensation system and method for sensor aging drift in energy storage systems.

[0010] The present invention solves the above problems through the following technical solutions: In a first aspect, the present invention proposes a compensation system for sensor aging drift in an energy storage system, comprising: Multiple working battery cells, wherein a first MEMS sensor is integrated on the housing of each working battery cell; One or more reference compensation units, each reference compensation unit comprising an aluminum shell and a positive terminal and a negative terminal connected to the upper end of the aluminum shell, the interior of the aluminum shell being filled with an inert gas and integrating a second MEMS sensor, and a resistor being connected between the positive terminal and the negative terminal; The data acquisition and processing module is used to acquire data from the first MEMS sensor and the second MEMS sensor, and to execute a compensation algorithm.

[0011] In some implementations, the first MEMS sensor and the second MEMS sensor are sensors from the same production batch.

[0012] In some implementations, there are multiple second MEMS sensors; The data acquisition and processing module acquires data from multiple second MEMS sensors and outputs the average value or performs median filtering.

[0013] In some implementations, the resistance value of the resistor is designed to simulate the ohmic heat generation of the working battery cell during the charging and discharging process when a set current value flows through it.

[0014] In some implementations, the reference compensation unit is located in each battery module or battery cluster in the energy storage system.

[0015] In some implementations, the reference compensation unit is placed in the battery module or battery cluster at a location representing the average temperature change.

[0016] In some implementations, the location of the average temperature change is obtained through thermal simulation calculations.

[0017] Secondly, the present invention also provides a method for compensating for sensor aging drift in an energy storage system, applied to the compensation system described in any of the above claims, comprising: The working battery unit and the reference compensation unit are initially calibrated, including initial zero-point calibration and initial sensitivity factor calibration. Zero-point drift quantization and sensitivity factor quantization are performed on the reference compensation unit; Collect the original air pressure reading of the working battery unit; Based on the zero-point drift quantization and the sensitivity factor quantization, the original gas pressure of the working battery cell is intrinsically compensated for aging drift.

[0018] In some implementations, the method further includes: Calculate the apparent sensitivity of the first MEMS sensor; Based on the apparent sensitivity and initial sensitivity, an environment-induced sensitivity drift factor is defined. Based on the zero-point drift quantization, the sensitivity factor quantization, and the environmentally induced sensitivity drift factor, the original air pressure of the working battery unit is fully compensated to obtain the compensated internal air pressure value.

[0019] In some implementations, the original air pressure of the working battery cell is fully compensated based on the zero-point drift quantization, the sensitivity factor quantization, and the environmentally induced sensitivity drift factor to obtain the compensated internal air pressure value. The calculation formula for the compensated internal air pressure value is as follows:

[0020] in, The compensated internal air pressure value, This is the initial air pressure of the working battery cell. Quantization of zero-point drift For sensitivity factor quantification, This refers to the environmentally induced sensitivity drift factor.

[0021] The beneficial effects of the compensation system and method for sensor aging drift in an energy storage system according to the present invention are: 1. Achieved precise decoupling of large-scale drift sources in highly corrosive environments for sensors: The method can distinguish and quantify the sensor's "intrinsic aging drift" and "environment-induced drift" online, solving the fundamental problem that existing technologies cannot separate composite drift sources.

[0022] 2. Achieves dual compensation for zero-point drift and sensitivity drift: This invention not only compensates for conventional zero-point drift, but more importantly, through an innovative sensitivity drift factor, it achieves real-time calibration of the sensor's dynamic response capability (gain), greatly improving the reliability of monitoring pressure surge events (such as precursors to thermal runaway).

[0023] 3. Improved battery safety: Through precise drift compensation, the BMS is able to rely on accurate internal pressure data for early thermal runaway warnings, minimizing the false alarm and missed alarm rates of safety alarms.

[0024] 4. Improved accuracy of battery status assessment: Accurate internal pressure data is an important input for advanced SOH (State of Health) and RUL (Relative Life) assessment models. The compensated data provided by this invention will significantly improve the accuracy of these models throughout the battery's life cycle and optimize the economic benefits of energy storage systems. Attached Figure Description

[0025] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention. In the drawings: Figure 1 This is a system structure diagram of the compensation system provided in Embodiment 1 of the present invention; Figure 2 A flowchart of a compensation method provided in Embodiment 2 of the present invention; Figure 3 This is a flowchart of a compensation method provided in Embodiment 3 of the present invention. Detailed Implementation

[0026] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0027] Example 1: like Figure 1 As shown, this embodiment proposes a compensation system for sensor aging drift in an energy storage system, including multiple working battery units, one or more reference compensation units, and a data acquisition and processing module.

[0028] Specifically, the first MEMS sensor is integrated into the casing of each working battery cell. Multiple working battery cells are standard energy storage lithium-ion battery cells, including an aluminum casing, electrolyte, separator, active material, and positive and negative electrode terminals connected to the upper end of the aluminum casing. The first MEMS sensor is integrated into the aluminum casing. Multiple working battery cells are arranged and connected sequentially to form a battery module or battery cluster.

[0029] The reference compensation unit includes an aluminum shell and a positive terminal and a negative terminal connected to the upper end of the aluminum shell. The aluminum shell is filled with inert gas and integrates a second MEMS sensor. A resistor is connected between the positive terminal and the negative terminal.

[0030] Specifically, the structural design of the reference compensation unit is the same as that of the working battery unit. Both include an aluminum shell and positive and negative terminals connected to the upper end of the aluminum shell. The upper end of the aluminum shell is also sealed by welding. However, the difference is that the reference compensation unit does not include electrolyte, separator and active material, and its interior is filled with inert gas to near standard atmospheric pressure to simulate the gas pressure of the actual working battery unit's application environment. Here, the inert gas can be nitrogen or argon.

[0031] In this embodiment, the reference compensation unit is set up as a data reference for the actual working battery cell. Therefore, the structural design and working air pressure environment of the reference compensation unit are simulated to be the same as those of the working battery cell. In its internal design, the second MEMS sensor operates in an environment without electrochemical reaction. In this way, the inherent aging drift of the second MEMS sensor can be accurately quantified and separated, which is equivalent to the inherent aging drift of the first MEMS sensor. Thus, the environmentally induced drift is decoupled from the total drift of the first MEMS sensor in the actual working battery cell, achieving dual, dynamic and high-precision compensation for zero point and sensitivity.

[0032] Both the first and second MEMS sensors possess the functions of traditional MEMS sensors. Specifically, a MEMS sensor is a miniature device or system that integrates sensors, actuators, mechanical mechanisms, signal processing, and control circuits, and has gas pressure and temperature sensing functions.

[0033] The data acquisition and processing module is used to acquire data from the first MEMS sensor and the second MEMS sensor, and to execute compensation algorithms. The data acquisition and processing module can be integrated into the BMS or deployed as a standalone functional module in the EMS or cloud platform; this embodiment does not impose specific limitations on this.

[0034] This invention introduces a reference compensation unit to place the second MEMS sensor in an environment without electrochemical reaction. This allows the data acquisition and processing module to collect data from the first and second MEMS sensors, quantify and separate the intrinsic aging drift of the second MEMS sensor based on the data from the second MEMS sensor, and decouple the environmentally induced drift from the total drift of the first MEMS sensor, thereby achieving dual, dynamic and high-precision compensation for zero point and sensitivity.

[0035] In some implementations, the first MEMS sensor and the second MEMS sensor are sensors from the same production batch.

[0036] To ensure accurate quantification and separation of the intrinsic aging drift of the second MEMS sensor, and thus decouple environmentally induced drift from the total drift of the first MEMS sensor, achieving dual, dynamic, and high-precision compensation for zero point and sensitivity, the first and second MEMS sensors are made from the same production batch to ensure that their initial performance and aging characteristics are highly consistent, thereby achieving accurate compensation.

[0037] Furthermore, there can be multiple second MEMS sensors, such as 3, 4 or 5. When there are 3 second MEMS sensors, the data acquisition and processing module acquires data from the 3 second MEMS sensors and outputs the average value of the acquired data from the 3 second MEMS sensors. When there is an odd number of second MEMS sensors, median filtering can be performed to output the median.

[0038] By integrating multiple second MEMS sensors into the reference compensation unit, and by averaging or using median filtering, the random errors or failures of individual second MEMS sensors can be eliminated, ensuring the stability and reliability of the reference benchmark and thus improving the accuracy of compensation.

[0039] Furthermore, the resistance value is designed to simulate the ohmic heat generation of the working battery cell during the charging and discharging process when a set current value flows through it.

[0040] Considering the impact of temperature changes on the reference compensation unit, the resistance value of this resistor is set to a specific value to simulate the ohmic heat generation of the working battery cell during the charging and discharging process when a set current value is flowing through it.

[0041] Specifically, the resistance value of this resistor Designed to flow with a current similar to that of the working battery cell At that time, the Joule heat power it generates It can simulate the ohmic heat generation of the working battery cell during the charging and discharging process, so that the temperature change trend inside the reference compensation cell is highly consistent with that of the working battery cell.

[0042] It should be noted that the resistance value is designed so that the current flowing through it is approximately the same as that flowing through the working battery cell. Here, "approximate current" can be a set current difference threshold, meaning the difference between the current flowing through the resistor and the current flowing through the working battery cell is within the set current difference threshold. This allows the resistor to simulate the ohmic heat generated by the working battery cell during charging and discharging. The resistor can be a non-inductive resistance wire or a power resistor.

[0043] Here, in addition to setting the resistor, a heat transfer model between the reference compensation unit and the adjacent working battery unit can be established. The temperature data of the working battery unit can be used to estimate the internal temperature of the reference compensation unit in real time, thereby eliminating the need for resistor and current acquisition and simplifying the hardware design.

[0044] In some implementations, the reference compensation unit is located in each battery module or battery cluster in the energy storage system. Setting a reference compensation unit in each battery module or battery cluster can improve the reference value and avoid the impact of random errors in single data.

[0045] Furthermore, the reference compensation unit is placed in the battery module or battery cluster at a location representing the average temperature change.

[0046] The location of the average temperature change was obtained through thermal simulation calculations.

[0047] Specifically, the placement of the reference compensation unit directly determines the compensation accuracy. This application uses digital simulation to overcome the limitations of physical testing, finds the system's "temperature reference point," and ensures that the location of the reference compensation unit can truly reflect the overall thermal state, thereby achieving efficient and accurate performance compensation.

[0048] It should be noted that for the reference compensation unit, dummy cells can be avoided. Instead, a working cell from the battery cluster can be selected and equipped with a proven "reference-level" sensor that is more expensive and offers significantly higher accuracy and stability than ordinary sensors. This could be a high-end sensor employing a special anti-corrosion coating and temperature compensation circuitry. The readings of this reference-level sensor can be considered the "true values" and used to calibrate surrounding ordinary sensors.

[0049] This embodiment provides a compensation system for sensor aging drift in an energy storage system. By introducing a reference compensation unit, the inherent aging drift of the sensor can be accurately quantified and separated, thereby decoupling the environmentally induced drift from the total drift of the actual sensor, and achieving dual, dynamic and high-precision compensation for zero point and sensitivity.

[0050] Example 2: like Figure 2 As shown, this embodiment also provides a compensation method for sensor aging drift in an energy storage system, which is applied to the compensation system in Embodiment 1.

[0051] The compensation method includes: Step 100: Perform initial state calibration on the working battery unit and the reference compensation unit. The initial state calibration includes initial zero-point calibration and initial sensitivity factor calibration.

[0052] Before the battery system leaves the factory or in the initial stage of operation (e.g., the first 5 full charge-discharge cycles), initial state calibration is performed on all working battery cells and reference compensation cells.

[0053] Regarding the initial zero-point calibration, under a preset constant temperature and static condition (e.g., ambient temperature...), =25℃, standing time >2 hours), record the initial gas pressure value of all working battery cells. Initial air pressure value of the reference compensation unit .

[0054] Regarding the initial sensitivity factor calibration, during the initial charge-discharge cycle, when the system experiences significant temperature changes, the sensor's pressure and temperature response sensitivity are calibrated. The internal pressure change of the reference compensation unit primarily follows the ideal gas law, and its initial sensitivity factor... (Initial air pressure, temperature sensitivity factor) Defined as: (1) In formula (1), and For two different temperature points in a typical temperature rise process, since all first and second MEMS sensors are from the same batch, we assume the initial sensitivity factor of all first MEMS sensors. Initial sensitivity factor of the second MEMS sensor It should be noted that this is merely an assumption for illustrative purposes; more preferably, each first MEMS sensor should be calibrated individually. Here, in addition to acquiring and recording the raw data, a reference compensation unit can be used as a zero point for data processing and storage.

[0055] Step 200: Perform zero-point drift quantization and sensitivity factor quantization on the reference compensation unit.

[0056] Specifically, during the long-term operation of the battery system, the BMS continuously monitors the second MEMS sensor of the reference compensation unit to quantify the drift caused by inherent aging in real time.

[0057] Regarding zero-point drift quantization The system periodically (e.g., every 24 hours) searches for the quiescent state of the battery clusters (current below a threshold, temperature change gradual). When the temperature of the reference compensation unit stabilizes at a certain value... At that time, the BMS reads its air pressure value. The intrinsic zero-point drift is calculated as: (2) In formula (2), It is calculated based on initial calibration data or the ideal gas law, at a temperature The drift-free initial air pressure value, i.e. .

[0058] Regarding the quantization of sensitivity drift factor During each charge and discharge cycle, the BMS utilizes the current flowing through the internal resistance wire of the reference compensation unit. and its temperature change To evaluate its sensitivity. The reference sensitivity at the current moment. for: Intrinsic sensitivity drift factor The reference sensitivity is defined as the current time. With initial sensitivity factor The ratio: .

[0059] Sensitivity drift factor quantization This value intuitively reflects the degree of "sluggish response" caused by the sensor's own aging. Typically, this value starts at 1 and slowly decreases over time. Based on experience, for automotive-grade or industrial-grade MEMS sensors, the "optimal value" range for this value is expected to be between 1 and 0.9 during normal operating life.

[0060] Step 300: Collect the original air pressure reading of the working battery unit.

[0061] Specifically, the working battery cells are collected through the BMS. Original air pressure .

[0062] Step 400: Based on the zero-point drift quantization and the sensitivity factor quantization, perform intrinsic aging drift compensation on the original gas pressure of the working battery cell.

[0063] First, the original air pressure Preliminary corrections were made for intrinsic aging drift to obtain intermediate values ​​that included only "environmentally induced drift" and "real electrochemical pressure". It simultaneously corrects for the inherent aging of both the zero point and sensitivity. Among these, The calculation formula is as follows: (3) In formula (3), Original air pressure Quantization of zero-point drift Quantization of sensitivity drift factor.

[0064] Example 3: Based on Example 2, such as Figure 3 As shown, this embodiment also provides the following method, which further includes: Step 500: Calculate the apparent sensitivity of the first MEMS sensor.

[0065] Establish a simplified reference pressure model This model describes the normal pressure behavior of a healthy battery under different SOC and temperature conditions. On a short timescale... We assume that the pressure change caused by the SOC change is relatively slow. Therefore, rapid pressure changes are mainly contributed by temperature. We calculate the apparent sensitivity of the sensor in the working battery cell. ,in, Represents a time window Inside, the modified working battery unit The change in internal pressure Represents the exact same time window Inside, the working battery cells are measured by the same integrated MEMS sensor. By comparing this apparent sensitivity to its initial sensitivity at the factory, based on the internal temperature change, we can precisely quantify the additional performance degradation caused by the harsh internal environment of the battery. Theoretically, without environmental corrosion, this apparent sensitivity should be approximately equal to the initial sensitivity. .

[0066] Step 600: Based on the apparent sensitivity and initial sensitivity, define the environment-induced sensitivity drift factor.

[0067] Theoretically, without environmental corrosion, this apparent sensitivity should be approximately equal to the initial sensitivity. Any deviation is primarily attributed to the attenuation of sensitivity to environmentally induced (corrosion) factors, quantified by the environmentally induced sensitivity drift factor. The pressure change inside the working cell is a coupling result of electrochemical gas generation and temperature effects. Here, we define the environmentally induced sensitivity drift factor. for: (4) In formula (4), It is an environmentally induced sensitivity drift factor , It is apparent sensitivity. This is the initial sensitivity of the first MEMS sensor. It is the temperature sensitivity factor.

[0068] To improve the robustness of the calculation, this factor can be averaged or filtered over a sliding time window. Based on accelerated aging experiments, it is inferred that when the sensor is subjected to significant HF gas corrosion, the "optimal value" of this factor may decrease from 1 to 0.8 or even lower within two years, serving as a "direct indicator of the sensor's health status".

[0069] Step 700: Based on the zero-point drift quantization, the sensitivity factor quantization, and the environmentally induced sensitivity drift factor, the original air pressure of the working battery unit is fully compensated to obtain the compensated internal air pressure value.

[0070] Specifically, in step 700, based on the zero-point drift quantization, the sensitivity factor quantization, and the environmentally induced sensitivity drift factor, the original air pressure of the working battery cell is fully compensated to obtain the compensated internal air pressure value. The calculation formula is as follows: (5) In formula (5), The compensated internal air pressure value, This is the initial air pressure reading for the working battery cell. Quantization of zero-point drift For sensitivity factor quantification, This refers to the environmentally induced sensitivity drift factor.

[0071] Compensated internal air pressure value The dual drift effects of zero point and sensitivity have been eliminated, and it can be directly used for battery safety warning and state assessment models.

[0072] It should be noted that, in addition to the above formula, more complex algorithm models such as Kalman filtering or neural networks can also be used. The data from the reference compensation unit is used as part of the observation model to optimally estimate the true state (including drift parameters) of the first MEMS sensor of the working battery cell.

[0073] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. Similarly, for the sake of brevity and to aid in understanding one or more aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0074] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0075] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for compensating for sensor aging drift in an energy storage system, characterized in that, Includes a compensation system, the compensation system comprising: Multiple working battery cells, wherein a first MEMS sensor is integrated on the housing of each working battery cell; One or more reference compensation units, each reference compensation unit comprising an aluminum shell and a positive terminal and a negative terminal connected to the upper end of the aluminum shell, the interior of the aluminum shell being filled with an inert gas and integrating a second MEMS sensor, and a resistor being connected between the positive terminal and the negative terminal; The data acquisition and processing module is used to acquire data from the first MEMS sensor and the second MEMS sensor, and to execute a compensation algorithm. The working battery unit and the reference compensation unit are initially calibrated, including initial zero-point calibration and initial sensitivity factor calibration. Zero-point drift quantization and sensitivity drift factor quantization are performed on the reference compensation unit; Collect the original air pressure reading of the working battery unit; Based on the zero-point drift quantization and the sensitivity drift factor quantization, the original gas pressure of the working battery cell is intrinsically compensated for aging drift. Calculate the apparent sensitivity of the first MEMS sensor; Based on the apparent sensitivity and initial sensitivity, an environment-induced sensitivity drift factor is defined. Based on the zero-point drift quantization, the sensitivity drift factor quantization, and the environmentally induced sensitivity drift factor, the original air pressure of the working battery unit is fully compensated to obtain the compensated internal air pressure value. Regarding the initial zero-point calibration, under a preset constant temperature and static condition, the initial gas pressure values ​​of all working battery cells are recorded. Initial air pressure value of the reference compensation unit ; The initial sensitivity factor is defined as: (1), in formula (1), and These are two different temperature points during a typical temperature rise process; Regarding zero-point drift quantization When the temperature of the reference compensation unit stabilizes at a certain value At that time, the BMS reads its air pressure value. The intrinsic zero-point drift is calculated as: （2） In formula (2), It is calculated based on initial calibration data or the ideal gas law, at a temperature The drift-free initial air pressure value, i.e. ; Regarding the quantization of sensitivity drift factor During each charge and discharge cycle, the BMS utilizes the current flowing through the internal resistance wire of the reference compensation unit. and its temperature change To assess its sensitivity, the reference sensitivity at the current moment. for: Intrinsic sensitivity drift factor The reference sensitivity is defined as the current time. With initial sensitivity factor The ratio: ; Regarding the original air pressure Preliminary corrections were made for intrinsic aging drift to obtain intermediate values ​​that included only "environmentally induced drift" and "real electrochemical pressure". , The calculation formula is as follows: （3） In formula (3), Original air pressure Quantization of zero-point drift Quantization of sensitivity drift factor; Calculate the apparent sensitivity of the working battery cell sensor. ,in, Represents a time window Inside, the modified working battery unit The change in internal pressure Represents the exact same time window Inside, the working battery cells are measured by the same integrated MEMS sensor. The amount of internal temperature change; Define the environment-induced sensitivity drift factor for: （4） In formula (4), It is an environmentally induced sensitivity drift factor , It is apparent sensitivity. This is the initial sensitivity of the first MEMS sensor. It is the initial sensitivity factor.

2. The compensation method for sensor aging drift in an energy storage system according to claim 1, characterized in that, The first MEMS sensor and the second MEMS sensor are from the same production batch.

3. The compensation method for sensor aging drift in an energy storage system according to claim 1, characterized in that, There are multiple second MEMS sensors; The data acquisition and processing module acquires data from multiple second MEMS sensors and outputs the average value or performs median filtering.

4. The compensation method for sensor aging drift in an energy storage system according to claim 1, characterized in that, The resistance value is designed to simulate the ohmic heat generation of the working battery cell during the charging and discharging process when a set current value flows through it.

5. The method for compensating for sensor aging drift in an energy storage system according to any one of claims 1-4, characterized in that, The reference compensation unit is located in each battery module or battery cluster in the energy storage system.

6. The compensation method for sensor aging drift in an energy storage system according to claim 5, characterized in that, The reference compensation unit is placed in the battery module or battery cluster at a position representing the average temperature change.

7. The compensation method for sensor aging drift in an energy storage system according to claim 6, characterized in that, The location of the average temperature change was obtained through thermal simulation calculation.

8. The method for compensating for sensor aging drift in an energy storage system according to claim 1, characterized in that, The method for comprehensively compensating the original air pressure of the working battery cell based on the zero-point drift quantization, the sensitivity drift factor quantization, and the environmentally induced sensitivity drift factor to obtain the compensated internal air pressure value is as follows: in, The compensated internal air pressure value, This is the initial air pressure of the working battery cell. Quantization of zero-point drift Quantization of sensitivity drift factor, This refers to the environmentally induced sensitivity drift factor.

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