Battery early warning detection system and method based on power supply side energy storage system

Abstract

The invention relates to the technical field of batteries, and discloses a battery early warning detection system and method based on a power supply side energy storage system, and the method comprises the steps: obtaining the state data of a battery, and obtaining the electrochemical impedance and thermal diffusion coefficient according to the state data; obtaining a charge state change rate according to the electrochemical impedance; obtaining chemical component data according to the gas release condition; comprehensively evaluating the health condition of the battery according to the thermal diffusion coefficient, the charge state change rate and the chemical component data; and performing early warning according to the comprehensive evaluation result. The health condition of the battery can be monitored in real time, and potential problems can be found in advance, so that sudden faults and safety accidents are avoided. Through accurate analysis of electrochemical impedance, thermal diffusion coefficient and chemical component data, the early warning detection system and method can timely discover tiny change of battery performance, and provide scientific basis for battery maintenance and replacement.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery early warning detection system and method for a power supply side energy storage system. Background Art

[0002] With the rapid development of renewable energy, the role of energy storage systems in power systems has become increasingly prominent. As an important part of the power grid, the safe and stable operation of the power supply side energy storage system has an important impact on the reliability of the entire power system. As the core component of the energy storage system, the health of the battery is directly related to the performance and life of the energy storage system. However, since the battery will be affected by various internal and external factors during long-term operation, such as temperature, number of cycles, depth of charge and discharge, etc., performance degradation, capacity reduction and even safety accidents may occur. Therefore, establishing an effective battery early warning detection system is crucial to ensure the safe operation of the power supply side energy storage system.

[0003] At present, battery early warning detection technology mainly includes monitoring based on battery internal resistance, voltage, current, temperature and other parameters, as well as analysis based on battery charging and discharging characteristics. However, existing early warning detection systems often have the following problems: First, single parameter monitoring is difficult to fully reflect the health of the battery; second, there is a lack of in-depth understanding of the battery aging mechanism, and the early warning threshold setting is not accurate enough; third, the early warning system is not highly integrated with the battery management system (BMS), making it difficult to achieve intelligent early warning. Therefore, there is an urgent need for an innovative early warning detection method that can comprehensively consider multiple parameters and combine the battery aging mechanism to improve the accuracy of the early warning and the intelligence level of the system. Summary of the invention

[0004] In view of this, the present invention proposes a battery early warning detection system and method based on a power supply side energy storage system, aiming to improve the accuracy and reliability of battery early warning detection and ensure the stable operation of the power grid.

[0005] The present invention proposes a battery early warning detection method based on a power supply side energy storage system, comprising:

[0006] Acquiring battery status data, and obtaining electrochemical impedance and thermal diffusion coefficient according to the status data;

[0007] Obtaining a rate of change of state of charge according to the electrochemical impedance;

[0008] Obtain chemical composition data based on gas release;

[0009] Perform a comprehensive assessment of the battery health status based on the thermal diffusivity, state of charge change rate and chemical composition data;

[0010] An early warning is issued based on the comprehensive assessment results.

[0011] Preferably, the state data includes electrochemical impedance, electrolyte resistance, charge transfer resistance, double layer capacitance, angular frequency, Warburg impedance, rate of change of charge state, proportionality constant, time, thermal diffusion coefficient, distance of the measuring point from the heat source, time required for the temperature to rise to half of the applied heat, absorbance of the gas to infrared light, gas concentration, absorption value of the gas sample, absorbance under gas-free conditions, concentration of the reference gas, change in gas concentration, current gas concentration, initial gas concentration and gas weight.

[0012] Preferably, the calculation formula of the electrochemical impedance is:

[0013]

[0014] ω=2πf;

[0015] Among them, Z(ω) represents the total impedance of the battery at different frequencies, in ohms; Rs represents the electrolyte resistance of the battery, which represents the series resistance of the electrolyte, wires and contacts; Rct represents the charge transfer resistance; Cdl represents the double layer capacitance; ω represents the angular frequency; Zw(ω) represents the Warburg impedance; and f represents the test frequency.

[0016] Preferably, the calculation formula of the Warburg impedance is:

[0017]

[0018] Where σ represents the Warburg coefficient; j represents the imaginary unit,

[0019] Preferably, the calculation formula for the charge state change rate is:

[0020]

[0021] in, represents the rate of change of state of charge, in % / s; k represents the proportional constant; Rct represents the charge transfer resistance; t represents time.

[0022] Preferably, the thermal diffusion characteristics are obtained by:

[0023] Apply a constant or pulsed heat source to the surface of the battery, measure the temperature change inside or on the surface of the battery, and obtain the temperature distribution of the battery;

[0024] The overall thermal diffusion coefficient is estimated by the time and temperature distribution of the heat diffusion in the battery. The thermal diffusion coefficient estimation formula is:

[0025]

[0026] Where α represents the thermal diffusion coefficient; x represents the distance between the measurement point and the heat source; t 1 / 2 Indicates the time required for the temperature to rise to half the amount of heat applied.

[0027] Preferably, when obtaining chemical composition data according to gas release conditions, the following steps are included:

[0028] The absorbance of infrared light by the gas is measured, and a comprehensive index is calculated according to the following formula, which is used to quantify the overall change of the gas per unit time:

[0029]

[0030]

[0031] I=∑ i (w i ×ΔC i );

[0032] Among them, I represents the comprehensive index; Ci represents the concentration of gas i; ΔI represents the absorption value of the gas sample; I0 represents the absorbance under gas-free conditions; Cref represents the concentration of the reference gas; ΔCi represents the concentration change of gas i; Ci,current represents the current gas concentration; Ci,initial represents the initial gas concentration; ωi represents the weight of gas i.

[0033] Preferably, when comprehensively evaluating the health status of the battery according to the thermal diffusion coefficient, the charge state change rate and the chemical composition data, it includes:

[0034] Preset the thermal diffusion coefficient safety value, charge state change rate safety value and chemical composition data safety value, which are denoted as T, R and C respectively;

[0035] For α and T, Compare with R, I and C,

[0036] When α<T, When I<C, it is determined that the battery health condition is normal;

[0037] Otherwise, it is determined that the battery health condition is abnormal.

[0038] Preferably, when the battery health condition is abnormal, a comprehensive evaluation of the battery health condition is performed based on the thermal diffusion coefficient, the charge state change rate and the chemical composition data, and the comprehensive evaluation formula is:

[0039]

[0040]

[0041]

[0042]

[0043] Among them, H represents the comprehensive health index; △T represents the abnormal degree of thermal diffusion characteristics; △R represents the abnormal degree of charge state change rate; △C represents the abnormal degree of chemical composition data; ωT represents the weight coefficient of thermal diffusion characteristics abnormality; ωR represents the weight coefficient of charge state change rate abnormality; ωC represents the weight coefficient of chemical composition data abnormality;

[0044] When H is greater than a preset comprehensive health index safety value, the battery abnormality is determined to be serious.

[0045] The present invention also proposes a battery early warning detection system for a power supply side energy storage system, which is used to implement the above-mentioned battery early warning detection method for a power supply side energy storage system.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present invention collects battery status data, such as electrochemical impedance, thermal diffusion coefficient, etc., and combines the charge state change rate with chemical composition data to comprehensively evaluate the health status of the battery, thereby improving the accuracy and reliability of early warning detection.

[0048] 2. The early warning detection method adopted by the present invention can timely identify potential battery failures and performance degradation, and then take corresponding measures to avoid the impact of battery failures on the stable operation of the power grid.

[0049] 3. The present invention achieves a comprehensive assessment of the health status of the battery by accurately calculating the electrochemical impedance, Warburg impedance and charge state change rate, and obtaining chemical composition data by measuring gas release.

[0050] 4. The early warning detection system of the present invention can automatically execute the above method to achieve real-time monitoring of the health status of the battery, thereby improving work efficiency and reducing labor costs.

[0051] 5. The present invention quantitatively evaluates the health status of the battery by setting safety thresholds and weight coefficients, making the early warning more scientific and objective.

[0052] 6. The early warning detection method and system of the present invention are applicable to batteries in the energy storage system on the power supply side, which helps to ensure the safe and stable operation of the energy storage system and is of great significance to the widespread application of new energy.

[0053] In summary, the present invention provides an innovative battery early warning detection method and system, which not only significantly improves the operating safety and reliability of the battery, but also provides strong technical support for the efficient management of the power supply side energy storage system. The application of this technology will have a far-reaching impact on the power industry and promote the technological progress and sustainable development of the entire industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0055] Figure 1 The present invention is a flow chart of a battery early warning detection method for a power supply side energy storage system. DETAILED DESCRIPTION

[0056] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0057] See also Figure 1 This embodiment provides a battery early warning detection method based on a power supply side energy storage system, including:

[0058] Acquiring battery status data, and obtaining electrochemical impedance and thermal diffusion coefficient according to the status data;

[0059] Obtaining a rate of change of state of charge according to the electrochemical impedance;

[0060] Obtain chemical composition data based on gas release;

[0061] Perform a comprehensive assessment of the battery health status based on the thermal diffusivity, state of charge change rate and chemical composition data;

[0062] An early warning is issued based on the comprehensive assessment results.

[0063] It is understandable that this embodiment proposes a warning detection method based on a battery for a power supply side energy storage system. The method involves multiple steps to ensure the stable operation of the battery and extend its service life. The specific steps are as follows:

[0064] First, in order to fully understand the operating status of the battery, we need to obtain the battery status data. This data covers multiple key parameters such as battery voltage, current, temperature, etc. By monitoring these parameters in real time, we can have a preliminary understanding of the operating status of the battery. This step is crucial because it provides basic data for our subsequent analysis.

[0065] Next, we will calculate the electrochemical impedance based on the acquired status data. Electrochemical impedance is a very important indicator that can measure the rate and efficiency of the chemical reaction inside the battery. By calculating and analyzing the electrochemical impedance, we can further understand the electrochemical reaction inside the battery. This step can help us deeply understand the working mechanism of the battery, thereby providing a basis for subsequent maintenance and optimization.

[0066] Then, we will obtain the state of charge change rate based on the electrochemical impedance. The state of charge change rate is an important parameter that reflects the dynamic change of charge during the battery charging and discharging process. By monitoring this change rate, we can evaluate the battery's charge and discharge performance and remaining capacity. This step is crucial to ensure the performance of the battery in actual applications because it can help us detect the degradation of battery performance in time and take appropriate measures.

[0067] In addition, we also need to obtain chemical composition data based on gas release. During the charge and discharge process of the battery, some gases may be released. The composition and content of these gases can reflect the chemical reactions inside the battery. By analyzing these chemical composition data, we can further understand the health of the battery. This step is of great significance for preventing battery failure and extending battery life.

[0068] Finally, we will comprehensively evaluate the health of the battery by considering the thermal diffusivity, state of charge change rate, and chemical composition data. The thermal diffusivity reflects the efficiency of heat transfer inside the battery and is closely related to the thermal stability of the battery. By combining these parameters, we can more comprehensively evaluate the health of the battery. This step can help us fully understand the operating status of the battery, thereby providing a scientific basis for subsequent maintenance and optimization.

[0069] Based on the above comprehensive evaluation results, we can give early warning. If the health of the battery is abnormal, the system will send out early warning signals in time to remind the operator to take corresponding maintenance measures. This can effectively avoid the occurrence of battery failures and ensure the stable operation of the energy storage system on the power side. This step is of great significance to ensure the safety and reliability of the entire system, because it can detect potential problems in advance and take measures to prevent them.

[0070] In some embodiments of the present application, the state data includes electrochemical impedance, electrolyte resistance, charge transfer resistance, double layer capacitance, angular frequency, Warburg impedance, rate of change of charge state, proportionality constant, time, thermal diffusion coefficient, the distance of the measurement point from the heat source, the time required for the temperature to rise to half of the applied heat, the absorbance of the gas to infrared light, the gas concentration, the absorption value of the gas sample, the absorbance under gas-free conditions, the concentration of the reference gas, the change in gas concentration, the current gas concentration, the initial gas concentration, and the weight of the gas.

[0071] It is understandable that the state data of this embodiment includes a variety of different parameters, which can comprehensively reflect the electrochemical characteristics, thermal characteristics and gas characteristics of the system. Specifically, electrochemical impedance refers to the impedance characteristics of the system under the action of alternating current, which can reflect the difficulty of the electrochemical reaction. Electrolyte resistance refers to the resistance value inside the electrolyte, which is closely related to the conductivity of the electrolyte. Charge transfer resistance refers to the difficulty of the charge transfer process during the electrochemical reaction. Double-layer capacitance refers to the capacitance value of the double layer formed on the electrode surface, which is related to the charge storage capacity of the electrode surface. Angular frequency refers to the frequency of alternating current change, which is related to the dynamic response characteristics of the system. Warburg impedance refers to the impedance characteristics of the diffusion process in the electrochemical reaction in the low-frequency region. The rate of change of state of charge refers to the rate of change of the state of charge (SOC) during the battery charging and discharging process, which is closely related to the charge and discharge performance of the battery. The proportional constant refers to the proportional relationship between different parameters in certain physical or chemical processes. Time refers to the duration from a specific moment to the occurrence of an event. Thermal diffusion coefficient refers to the ability of heat transfer inside a substance, which is closely related to the thermal conductivity of the material. The distance between the measuring point and the heat source refers to the spatial distance between the measuring point and the heat source in the measurement of thermal characteristics. The time required for the temperature to rise to half of the applied heat refers to the time required for the temperature to rise from the initial value to half of the applied heat during the heating process. The absorbance of a gas to infrared light refers to the gas's ability to absorb infrared light of a specific wavelength, which is related to the type and concentration of the gas. The gas concentration refers to the proportion of a specific gas in a mixture of a certain volume or mass. The absorption value of a gas sample refers to the degree of absorption of a gas sample to light of a specific wavelength in spectral analysis. The absorbance under gas-free conditions refers to the degree of absorption of light when it passes through a medium in the absence of gas. The concentration of the reference gas refers to the concentration value of the gas used as a reference standard. The change in gas concentration refers to the change in gas concentration within a certain time interval. The current gas concentration refers to the actual concentration of the gas at the moment of measurement. The initial gas concentration refers to the initial concentration of the gas at the beginning of the measurement. The weight of a gas refers to the importance or proportion of a certain gas relative to other gases in a mixed gas. The comprehensive analysis of these parameters can provide comprehensive and accurate data support for system status monitoring and performance evaluation.

[0072] In some embodiments of the present application, the calculation formula of the electrochemical impedance is:

[0073]

[0074] ω=2πf;

[0075] Among them, Z(ω) represents the total impedance of the battery at different frequencies, in ohms; Rs represents the electrolyte resistance of the battery, which represents the series resistance of the electrolyte, wires and contacts; Rct represents the charge transfer resistance; Cdl represents the double layer capacitance; ω represents the angular frequency; Zw(ω) represents the Warburg impedance; and f represents the test frequency.

[0076] It is understood that in the present embodiment, Z(ω) represents the total impedance of the battery at different frequencies, and its unit is ohm (Ω). Rs represents the electrolyte resistance of the battery, which covers the series resistance of the electrolyte, the wire and the contact point. Rct represents the charge transfer resistance, which reflects the resistance encountered when the charge is transferred on the electrode surface. Cdl represents the double layer capacitance, which is related to the charge storage capacity of the double layer formed on the electrode surface. ω represents the angular frequency, and its unit is radians per second (rad / s). It is related to the test frequency f and can be calculated by ω=2πf. Zw(ω) represents the Warburg impedance, which describes the impedance change caused by the diffusion process in the low-frequency region. f represents the frequency used when the test is performed, and its unit is Hertz (Hz). Through this formula, the total impedance of the battery at different frequencies can be calculated more accurately, so as to better understand and optimize the electrochemical performance of the battery.

[0077] In some embodiments of the present application, the calculation formula of the Warburg impedance is:

[0078]

[0079] Where σ represents the Warburg coefficient; j represents the imaginary unit,

[0080] It can be understood that in this embodiment, σ represents the Warburg coefficient, j represents the imaginary unit, and ω represents the angular frequency. Through this formula, the Warburg impedance value at a specific frequency can be calculated. Among them, the imaginary unit j plays a key role in this formula because it allows us to deal with the phase difference of the impedance related to the frequency.

[0081] In some embodiments of the present application, the calculation formula of the charge state change rate is:

[0082]

[0083] in, represents the rate of change of state of charge, in % / s; k represents the proportional constant; Rct represents the charge transfer resistance; t represents time.

[0084] It can be understood that, in this embodiment, ΔSOC / Δt represents the rate of change of the state of charge, and its unit is percentage per second (% / s); k represents a proportional constant, which is used to adjust the magnitude of the calculation result; Rct represents the charge transfer resistance, which reflects the ease of charge transfer during the battery charging and discharging process; t represents time, which is usually in seconds. Through this formula, the change of the state of charge of the battery in a specific time period can be quantitatively described, so as to better understand and control the charging and discharging behavior of the battery.

[0085] In some embodiments of the present application, the thermal diffusion characteristics are obtained by:

[0086] Apply a constant or pulsed heat source to the surface of the battery, measure the temperature change inside or on the surface of the battery, and obtain the temperature distribution of the battery;

[0087] The overall thermal diffusion coefficient is estimated by the time and temperature distribution of the heat diffusion in the battery. The thermal diffusion coefficient estimation formula is:

[0088]

[0089] Where α represents the thermal diffusion coefficient; x represents the distance between the measurement point and the heat source; t 1 / 2 Indicates the time required for the temperature to rise to half the amount of heat applied.

[0090] It can be understood that the method of obtaining the thermal diffusion characteristics of the battery in this embodiment includes the following steps: First, a constant heat source or a series of pulsed heat sources are applied to the surface of the battery. Then, the temperature distribution of the battery is obtained by measuring the temperature change inside or on the surface of the battery. This temperature distribution measurement can be achieved by various temperature sensors, such as thermocouples, infrared sensors, etc. Through these data, we can further analyze the thermal conduction behavior inside the battery.

[0091] Next, by analyzing the time and temperature distribution of the diffusion of conductive heat inside the battery, the thermal diffusion coefficient of the entire battery can be estimated. The thermal diffusion coefficient is an important parameter that characterizes the thermal conductivity of a material, which reflects the speed at which heat propagates inside the material. In this application, in the estimation formula for the thermal diffusion coefficient, α represents the thermal diffusion coefficient, x represents the distance from the measuring point to the heat source, and t1 / 2 represents the time required for the temperature to rise to half of the applied heat. Through this formula, the thermal diffusion coefficient can be calculated based on the distance from the measuring point to the heat source and the time required for the temperature to rise to half. This coefficient can help users better understand the thermal management performance of the battery under different working conditions, thereby providing an important reference for the design and optimization of the battery.

[0092] In some embodiments of the present application, obtaining chemical composition data according to gas release conditions includes the following steps:

[0093] The absorbance of infrared light by the gas is measured, and a comprehensive index is calculated according to the following formula, which is used to quantify the overall change of the gas per unit time:

[0094]

[0095]

[0096] I=∑ i (w i ×ΔC i );

[0097] Among them, I represents the comprehensive index; Ci represents the concentration of gas i; ΔI represents the absorption value of the gas sample; I0 represents the absorbance under gas-free conditions; Cref represents the concentration of the reference gas; ΔCi represents the concentration change of gas i; Ci,current represents the current gas concentration; Ci,initial represents the initial gas concentration; ωi represents the weight of gas i.

[0098] It can be understood that in order to obtain chemical composition data based on gas release, this embodiment can take the following steps: first, by measuring the absorbance of the gas to infrared light, and then calculating a comprehensive index according to the following calculation formula, which can quantify the overall change in the gas per unit time.

[0099] This method can effectively monitor and quantify the changes in various gas components during the gas release process, thereby providing reliable data support for subsequent analysis and processing. This comprehensive index calculation method can not only reflect the changes in a single gas component, but also comprehensively consider the overall changes in multiple gas components, making the monitoring of the gas release process more comprehensive and accurate.

[0100] In some embodiments of the present application, when comprehensively evaluating the health status of the battery according to the thermal diffusion coefficient, the charge state change rate and the chemical composition data, it includes:

[0101] Preset the thermal diffusion coefficient safety value, charge state change rate safety value and chemical composition data safety value, which are denoted as T, R and C respectively;

[0102] For α and T, Compare with R, I and C,

[0103] When α<T, When I<C, it is determined that the battery health condition is normal;

[0104] Otherwise, it is determined that the battery health condition is abnormal.

[0105] It is understandable that in order to comprehensively evaluate the health of the battery, this embodiment uses three key parameters: thermal diffusion coefficient, charge state change rate, and chemical composition data. Through this comprehensive evaluation method, the health of the battery can be effectively monitored, potential problems can be discovered in time, and corresponding measures can be taken to ensure the safe operation of the battery.

[0106] In some embodiments of the present application, when the battery health condition is abnormal, the battery health condition is comprehensively evaluated based on the thermal diffusion coefficient, the charge state change rate and the chemical composition data, and the comprehensive evaluation formula is:

[0107]

[0108]

[0109]

[0110]

[0111] Among them, H represents the comprehensive health index; △T represents the abnormal degree of thermal diffusion characteristics; △R represents the abnormal degree of charge state change rate; △C represents the abnormal degree of chemical composition data; ωT represents the weight coefficient of thermal diffusion characteristics abnormality; ωR represents the weight coefficient of charge state change rate abnormality; ωC represents the weight coefficient of chemical composition data abnormality;

[0112] When H is greater than a preset comprehensive health index safety value, the battery abnormality is determined to be serious.

[0113] It can be understood that in this embodiment, when the health status of the battery is abnormal, a comprehensive assessment of the health status of the battery can be performed by comprehensively considering the thermal diffusion coefficient, the rate of change of the state of charge, and the chemical composition data. This comprehensive assessment can be achieved through the above formula. Through the above formula, a comprehensive assessment of the health status of the battery can be performed. Specifically, when the comprehensive health index H is greater than the pre-set comprehensive health index safety value, the degree of abnormality of the battery can be determined to be serious. This evaluation method not only takes into account the influence of a single factor, but also comprehensively considers the degree of influence of different factors on the battery health status through the introduction of weight coefficients, thereby improving the accuracy and reliability of the evaluation.

[0114] In summary, the present invention relates to a battery early warning detection method for a power supply side energy storage system, and its advantages are mainly reflected in the following points:

[0115] 1. Improved accuracy of battery status monitoring: By collecting battery status data, such as electrochemical impedance, thermal diffusion coefficient, etc., the health of the battery can be assessed more accurately.

[0116] 2. A comprehensive assessment of the battery health status is achieved: combining electrochemical impedance, charge state change rate, chemical composition data and other information to conduct a comprehensive assessment of the battery health status.

[0117] 3. Early warning of potential battery problems: Based on the comprehensive assessment results, abnormal battery conditions can be identified in a timely manner and early warnings can be issued to avoid grid operation risks caused by battery failures.

[0118] 4. Optimized battery maintenance strategy: The early warning detection method provides a scientific basis for battery maintenance and replacement, prolongs battery life and reduces maintenance costs.

[0119] 5. Improved the stability of grid operation: By timely identifying and handling battery problems, the stable operation of the power supply side energy storage system is ensured, enhancing the overall stability of the grid.

[0120] 6. Enhanced flexibility of data processing: The optimal status data covers multiple parameters, and different parameter combinations can be selected according to actual needs to flexibly respond to different battery types and application scenarios.

[0121] 7. Simplified detection process: Through specific calculation formulas, such as electrochemical impedance, Warburg impedance, and charge state change rate, the data processing process is simplified and the detection efficiency is improved.

[0122] 8. Provides quantitative evaluation standards: By setting safety thresholds for thermal diffusion coefficient, charge state change rate and chemical composition data, a quantitative standard is provided for the assessment of battery health status.

[0123] In summary, the battery early warning detection method of the present invention can effectively improve the operating efficiency and safety of the power supply side energy storage system, and has significant technical advantages and application value.

[0124] The present invention also proposes a battery early warning detection system for a power supply side energy storage system, which is used to implement the above-mentioned battery early warning detection method for a power supply side energy storage system.

[0125] 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 battery early warning detection method for a power supply side energy storage system, characterized in that: include: Acquiring battery status data, and obtaining electrochemical impedance and thermal diffusion coefficient according to the status data; Obtaining a rate of change of state of charge according to the electrochemical impedance; Obtain chemical composition data based on gas release; Perform a comprehensive assessment of the battery health status based on the thermal diffusivity, state of charge change rate and chemical composition data; An early warning is issued based on the comprehensive assessment results.

2. The battery early warning detection method based on the power supply side energy storage system according to claim 1 is characterized in that: The state data include electrochemical impedance, electrolyte resistance, charge transfer resistance, double layer capacitance, angular frequency, Warburg impedance, charge state change rate, proportionality constant, time, thermal diffusion coefficient, distance of the measurement point from the heat source, time required for the temperature to rise to half of the applied heat, gas absorbance of infrared light, gas concentration, absorption value of gas sample, absorbance under gas-free conditions, reference gas concentration, gas concentration change, current gas concentration, initial gas concentration and gas weight.

3. The battery early warning detection method based on the power supply side energy storage system according to claim 2 is characterized in that: The calculation formula of the electrochemical impedance is: ω=2πf; Among them, Z(ω) represents the total impedance of the battery at different frequencies, in ohms; Rs represents the electrolyte resistance of the battery, which represents the series resistance of the electrolyte, wires and contacts; Rct represents the charge transfer resistance; Cdl represents the double layer capacitance; ω represents the angular frequency; Zw(ω) represents the Warburg impedance; and f represents the test frequency.

4. The battery early warning detection method based on the power supply side energy storage system according to claim 3 is characterized in that: The calculation formula of the Warburg impedance is: Where σ represents the Warburg coefficient; j represents the imaginary unit, 5. The battery early warning detection method based on the power supply side energy storage system according to claim 3 or 4, characterized in that: The calculation formula of the charge state change rate is: in, represents the rate of change of state of charge, in % / s; k represents the proportional constant; Rct represents the charge transfer resistance; t represents time.

6. The battery early warning detection method based on the power supply side energy storage system according to claim 5 is characterized in that: The thermal diffusion characteristics are obtained by: Apply a constant or pulsed heat source to the surface of the battery, measure the temperature change inside or on the surface of the battery, and obtain the temperature distribution of the battery; The overall thermal diffusion coefficient is estimated by the time and temperature distribution of the heat diffusion in the battery. The thermal diffusion coefficient estimation formula is: Where α represents the thermal diffusion coefficient; x represents the distance between the measurement point and the heat source; t 1 / 2 Indicates the time required for the temperature to rise to half the amount of heat applied.

7. The battery early warning detection method based on the power supply side energy storage system according to claim 6 is characterized in that: The following steps are involved in obtaining chemical composition data based on gas release: The absorbance of infrared light by the gas is measured, and a comprehensive index is calculated according to the following formula, which is used to quantify the overall change of the gas per unit time: I=∑ i (In i ×ΔC i ); Among them, I represents the comprehensive index; Ci represents the concentration of gas i; ΔI represents the absorption value of the gas sample; I0 represents the absorbance under gas-free conditions; Cref represents the concentration of the reference gas; ΔCi represents the concentration change of gas i; Ci,current represents the current gas concentration; Ci,initial represents the initial gas concentration; ωi represents the weight of gas i.

8. The battery early warning detection method based on the power supply side energy storage system according to claim 7 is characterized in that: When comprehensively evaluating the health status of the battery based on the thermal diffusivity, state of charge change rate and chemical composition data, it includes: Preset the thermal diffusion coefficient safety value, charge state change rate safety value and chemical composition data safety value, which are denoted as T, R and C respectively; For α and T, Compare with R, I and C, When α<T, When I<C, it is determined that the battery health condition is normal; Otherwise, it is determined that the battery health condition is abnormal.

9. The battery early warning detection method based on the power supply side energy storage system according to claim 8 is characterized in that: When the battery health condition is abnormal, a comprehensive evaluation of the battery health condition is performed based on the thermal diffusion coefficient, the charge state change rate and the chemical composition data. The comprehensive evaluation formula is: Among them, H represents the comprehensive health index; △T represents the abnormal degree of thermal diffusion characteristics; △R represents the abnormal degree of charge state change rate; △C represents the abnormal degree of chemical composition data; ωT represents the weight coefficient of thermal diffusion characteristics abnormality; ωR represents the weight coefficient of charge state change rate abnormality; ωC represents the weight coefficient of chemical composition data abnormality; When H is greater than a preset comprehensive health index safety value, the battery abnormality is determined to be serious.

10. A battery early warning detection system based on a power supply side energy storage system, characterized in that: The battery early warning detection system for the power supply side energy storage system is used to implement the battery early warning detection method for the power supply side energy storage system according to any one of claims 1 to 9.

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