Lithium ion battery health state estimation method and system based on electrochemical model

Abstract

The invention provides a lithium ion battery health state estimation method and system based on an electrochemical model, and relates to the technical field of battery health state estimation, and the method specifically comprises the steps: collecting key parameters of a lithium ion battery in real time, and calculating an open-circuit voltage through an electrochemical reaction kinetic formula; calculating a health index, calculating a temperature influence index according to a set optimal working temperature and a current working environment temperature mean value, and calculating an internal resistance influence index according to the battery capacity, the cycle index and the internal resistance data of the lithium ion battery; calculating a battery health score for reflecting the health state of the lithium ion battery to be tested according to the health index, the temperature influence index and the internal resistance influence index; and comparing the battery health score with a preset health assessment threshold, and assessing the health state of the lithium ion battery according to the comparison result, thereby providing a scientific battery health assessment mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of battery health state estimation, and in particular to a lithium-ion battery health state estimation method and system based on an electrochemical model. Background Art

[0002] Lithium-ion batteries are widely used in portable electronic devices, electric vehicles, and energy storage due to their high energy density and long cycle life. However, as the use time increases, the performance of lithium-ion batteries gradually declines, especially during the charging and discharging process, there will be problems such as capacity decay, increased internal resistance, and safety hazards. Existing technologies usually rely on simple parameters such as battery capacity, charging voltage, and discharge voltage for monitoring, which cannot fully reflect the true health status of the battery. In addition, traditional monitoring systems often lack dynamic analysis of battery status and cannot detect potential faults or performance degradation in a timely manner, resulting in unreliability and unsafety of the battery in actual use.

[0003] At present, although some battery management systems on the market have introduced temperature monitoring and cycle count statistics, these systems still have certain limitations. For example, they usually ignore the complexity of the internal chemical reactions and electrical characteristics of the battery and cannot accurately model the electrochemical behavior of the battery. In addition, the evaluation methods for battery health status are mostly empirical or based on simple statistical models, lacking systematicity and scientificity, resulting in insufficient accuracy of the evaluation results. These deficiencies make it difficult for existing technologies to meet users' high requirements for battery safety, stability and service life in practical applications. Therefore, it is urgent to develop a more accurate and comprehensive battery health status estimation method to address the deficiencies of current technologies in battery management and monitoring and achieve effective management of lithium-ion batteries.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method and system for estimating the health status of a lithium-ion battery based on an electrochemical model to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method and system for estimating the health status of a lithium-ion battery based on an electrochemical model, the specific steps comprising:

[0008] Step 1: Real-time collection of key parameters of the lithium-ion battery to be estimated, including battery capacity, number of cycles, average working environment temperature and internal resistance data, as well as a discharge voltage curve during the discharge process within the time length t closest to the current moment, calculating the voltage flatness index according to the discharge voltage curve, and calculating the open circuit voltage using an electrochemical reaction kinetics formula;

[0009] Step 2: Calculate the health index based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, calculate the temperature impact index based on the set optimal working temperature and the average of the current working environment temperature, and calculate the internal resistance impact index based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery;

[0010] Step 3: Combine the health index, temperature impact index and internal resistance impact index to generate a battery health assessment model, generate weights in the battery health assessment model through the hierarchical analysis method, use the 9-scale method to evaluate the importance relationship between the indicators, form a judgment matrix, normalize the judgment matrix to calculate the weight vector, and use the model to generate a battery health score that reflects the health status of the lithium-ion battery to be tested;

[0011] Step 4: Compare the battery health score with the preset health assessment threshold, and based on the comparison results, evaluate the health status of the lithium-ion battery and guide management decisions.

[0012] Furthermore, the sensor is used to collect the average value of the battery capacity and the working environment temperature at the current moment, and the battery capacity is recorded as C actual , the mean working environment temperature is recorded as T env ; Among them, the average working environment temperature refers to the average temperature of the environment where the battery is located;

[0013] Obtain the discharge voltage curve during the discharge process within the time length t closest to the current moment, calculate the derivative of the voltage curve within the time length t, obtain the voltage change rate, calculate the standard deviation of the voltage change rate, and use the standard deviation as the voltage flatness index, recorded as VSI;

[0014] The built-in counter of the BMS is used to collect the number of cycles, that is, the number of times the battery has undergone a complete charge and discharge cycle, recorded as N, and at the end of each cycle, the internal resistance of the battery is measured by the small signal test method, recorded as R int , get R int The specific logic is:

[0015] Apply a 1kHz AC test signal to both ends of the lithium-ion battery and measure the current I under the AC test signal. ac and voltage V ac , calculate the internal resistance of the battery according to Ohm's law:

[0016]

[0017] Among them, R int is the internal resistance of the battery, I ac is the current under the AC test signal, V ac is the voltage under the AC test signal.

[0018] Furthermore, the open circuit voltage is calculated using the electrochemical reaction kinetics formula, and the formula is as follows:

[0019]

[0020] Among them, V oc is the open circuit voltage, E0 is the standard electrode potential, which is obtained by looking up the table according to the battery type, R is the gas constant, which is 8.314 J / (mol*K), n is the electron transfer rate, which is determined according to the reaction type, F is the Faraday constant, and a ox and a red are the activities of the oxidized and reduced states, respectively, and T is the absolute temperature;

[0021] Get a ox 、a red The specific logic is:

[0022] Under laboratory conditions, a complete charge and discharge experiment is carried out on the battery, and the SOC value is estimated in real time using the ampere-hour integration method. The formula is as follows:

[0023]

[0024] Among them, SOC(t) represents the state of charge at the current time t, SOC(t o ) represents the initial time t o The state of charge, C rated is the rated capacity of the battery, I(τ) represents the charge and discharge current at time τ;

[0025] The activities of the oxidized and reduced states were calculated according to the following formula:

[0026] a ox =C ox *SOC

[0027] a red =C red *(1-SOC)

[0028] Among them, a ox and a red are the activities of the oxidized and reduced states, respectively, C ox and C red It is a constant related to the battery's chemical characteristics, obtained through experimental calibration, and SOC is the real-time state of charge of the battery;

[0029] The calculation formula of T is as follows:

[0030] T=T env +273.15

[0031] Where T is the absolute temperature, T env is the average operating environment temperature of the battery.

[0032] Furthermore, the health index is calculated based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, and the formula is as follows:

[0033]

[0034] Among them, HI is the health index, V oc is the open circuit voltage, V oc,ref is the standard open circuit voltage of the battery, C actual is the current battery capacity, C rated is the rated capacity of the battery, e is the natural constant, N is the number of cycles, R int is the battery internal resistance, VSI is the voltage flatness index, μ, k1, k2 and k3 are preset proportional coefficients, and μ>k1>k2>k3>0;

[0035] Calculate the temperature impact index based on the set optimal operating temperature and current temperature data.

[0036]

[0037] Among them, TI is the temperature influence index, e is the natural constant, T env is the working environment temperature of the battery, T ideal is the optimal operating temperature of the battery, set to 25°C, σ is the correction coefficient, k4 is the preset proportional coefficient, and k4>0;

[0038] The internal resistance impact index is calculated based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery, and the formula is as follows:

[0039]

[0040] Among them, RI is the internal resistance influence index, C actual is the current battery capacity, C rated is the rated capacity of the battery, e is the natural constant, N is the number of cycles, R int is the internal resistance of the battery, k5 and k6 are preset proportional coefficients, and k6>k5>0.

[0041] Furthermore, the health index, temperature impact index and internal resistance impact index are combined to generate a battery health assessment model, and the model expression is:

[0042] Among them, PI is the battery health score of the lithium-ion battery to be tested, HI is the health index, TI is the temperature influence index, RI is the internal resistance influence index, ω1, ω2 and ω3 are proportional coefficients, which are obtained according to the hierarchical analysis method;

[0043] The proportional coefficient is determined by the hierarchical analysis method. The specific logic is as follows: the three indicators of health index, temperature impact index and internal resistance impact index are marked, and the relative importance of each other is determined by the nine-scale method. The judgment matrix is ​​constructed, in which the index of the health index is marked as 1, the index of the temperature impact index is marked as 2, and the index of the internal resistance impact index is marked as 3. The constructed judgment matrix is:

[0044]

[0045] Where f and v both represent indexes of the exponent, and f∈[1,3], v∈[1,3], b fv Indicates the importance of the index with index f relative to the index with index v. The importance is scaled from 1 to 9, and b fv The larger the value, the more important the index with index f is compared to the index with index v, and

[0046] Each element value in the judgment matrix is ​​divided by the sum of its columns to obtain a normalized judgment matrix. The mean of the element values ​​in each row of the normalized judgment matrix is ​​calculated, and the mean of the element values ​​in the first row is used as the weight of the health index, the mean of the element values ​​in the second row is used as the weight of the temperature influence index, and the mean of the element values ​​in the third row is used as the weight of the internal resistance influence index. With the constraint that the sum of the scaled values ​​is equal to 1, the three weights are scaled in equal proportions, and the scaled weights are used as the proportional coefficients of the corresponding indexes.

[0047] Furthermore, the battery health score is compared with the preset health assessment threshold, based on the following specific logic:

[0048] If PI ≥ PI threshold , it indicates that the health status of the lithium-ion battery to be tested is good, and the battery parameters should be monitored regularly to keep it in the best working conditions;

[0049] If PI <PI threshold , it indicates that the health status of the lithium-ion battery to be tested is not good, and further testing is needed to analyze the reasons for the battery's performance degradation and decide whether the battery needs to be replaced;

[0050] Among them, PI is the battery health score, PI threshold is the health assessment threshold.

[0051] The present invention further provides a lithium-ion battery health state estimation system based on an electrochemical model, wherein the lithium-ion battery health state estimation system based on an electrochemical model is used to execute the above-mentioned lithium-ion battery health state estimation method based on an electrochemical model, comprising:

[0052] A data acquisition module is used to collect key parameters of the lithium-ion battery to be estimated in real time, wherein the key parameters include battery capacity, number of cycles, average working environment temperature and internal resistance data, as well as a discharge voltage curve during the discharge process within the time length t closest to the current moment, calculate the voltage flatness index according to the discharge voltage curve, and calculate the open circuit voltage using an electrochemical reaction kinetics formula;

[0053] A data processing module is used to calculate the health index based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, calculate the temperature influence index based on the set optimal working temperature and the average of the current working environment temperature, and calculate the internal resistance influence index based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery;

[0054] A scoring calculation module is used to combine the health index, temperature impact index and internal resistance impact index to generate a battery health assessment model, generate weights in the battery health assessment model through a hierarchical analysis method, use a 9-scale method to evaluate the importance relationship between the indicators, form a judgment matrix, normalize the judgment matrix to calculate the weight vector, and use the model to generate a battery health score that reflects the health status of the lithium-ion battery to be tested;

[0055] The comprehensive assessment module is used to compare the battery health score with the preset health assessment threshold, evaluate the health status of the lithium-ion battery based on the comparison results, and guide management decisions.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] This solution collects key parameters including battery capacity, voltage, current, temperature, number of cycles and internal resistance in real time, so as to fully reflect the operating status of the battery. Through the electrochemical reaction kinetics formula, the system can accurately calculate the open circuit voltage, thus laying the foundation for the calculation of the health index. Through the dimensionless processed parameters, the system can effectively calculate the health index, temperature impact index and internal resistance impact index. These indicators, combined with the weights determined by the hierarchical analysis method, together constitute the health score of the battery, providing a scientific evaluation mechanism. By comparing the battery health score with the preset health assessment threshold, the system can promptly identify the health status of the battery and guide subsequent maintenance and management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1It is a schematic diagram of the overall method flow of the present invention;

[0059] Figure 2 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0061] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0062] Example:

[0063] See also Figure 1 , the present invention provides a technical solution:

[0064] A method for estimating the health status of a lithium-ion battery based on an electrochemical model, the specific steps comprising:

[0065] Step 1: Real-time collection of key parameters of the lithium-ion battery to be estimated, including battery capacity, number of cycles, average working environment temperature and internal resistance data, as well as a discharge voltage curve during the discharge process within the time length t closest to the current moment, calculating the voltage flatness index according to the discharge voltage curve, and calculating the open circuit voltage using an electrochemical reaction kinetics formula;

[0066] In this embodiment, a sensor is used to collect the average value of the battery capacity and the working environment temperature at the current moment, and the battery capacity is recorded as C actual , the mean working environment temperature is recorded as T env ; Among them, the average working environment temperature refers to the average temperature of the environment where the battery is located;

[0067] Obtain the discharge voltage curve during the discharge process within the time length t closest to the current moment, calculate the derivative of the voltage curve within the time length t, obtain the voltage change rate, calculate the standard deviation of the voltage change rate, and use the standard deviation as the voltage flatness index, recorded as VSI;

[0068] The built-in counter of the BMS is used to collect the number of cycles, that is, the number of times the battery has undergone a complete charge and discharge cycle, recorded as N. At the end of each cycle, the internal resistance of the battery is measured by the small signal test method, and this measured internal resistance is used as the internal resistance at the current moment, recorded as R int , get R int The specific logic is:

[0069] Apply a 1kHz AC test signal to both ends of the lithium-ion battery and measure the current I under the AC test signal. ac and voltage V ac , calculate the internal resistance of the battery according to Ohm's law:

[0070]

[0071] Among them, R int is the internal resistance of the battery, I ac is the current under the AC test signal, V ac is the voltage under the AC test signal.

[0072] The open circuit voltage is calculated using the electrochemical reaction kinetics formula, based on the following formula:

[0073]

[0074] Among them, V oc is the open circuit voltage, E0 is the standard electrode potential, which is obtained by looking up the table according to the battery type, R is the gas constant, which is 8.314 J / (mol*K), n is the electron transfer rate, which is determined according to the reaction type, F is the Faraday constant, and a ox and a red are the activities of the oxidized and reduced states, respectively, and T is the absolute temperature;

[0075] Get a ox 、a red The specific logic is:

[0076] Under laboratory conditions, a complete charge and discharge experiment is carried out on the battery, and the SOC value is estimated in real time using the ampere-hour integration method. The formula is as follows:

[0077]

[0078] Among them, SOC(t) represents the state of charge at the current time t, SOC(t o ) represents the initial time to The state of charge, C rated is the rated capacity of the battery, I(τ) represents the charge and discharge current at time τ;

[0079] The activities of the oxidized and reduced states were calculated according to the following formula:

[0080] a ox =C ox *SOC

[0081] a red =C red *(1-SOC)

[0082] Among them, a ox and a red are the activities of the oxidized and reduced states, respectively, C ox and C red It is a constant related to the battery's chemical characteristics, obtained through experimental calibration, and SOC is the real-time state of charge of the battery;

[0083] The calculation formula of T is as follows:

[0084] T=T env +273.15

[0085] Where T is the absolute temperature, T env is the average operating environment temperature of the battery.

[0086] The advantage of step 1 is that it can fully reflect the actual operating status of the battery by collecting key battery parameters in real time, such as battery capacity, voltage, current, temperature, number of cycles and internal resistance. Compared with traditional technologies that only rely on simple voltage and current monitoring, this comprehensive parameter collection and analysis method can more effectively identify battery performance degradation and potential failures, thereby improving the safety and reliability of battery management.

[0087] In this solution, adopting step 1 can lay a solid foundation for the overall solution. Through real-time monitoring of key parameters, the subsequent calculation of health index, temperature impact index and internal resistance impact index will be based on accurate data, thereby improving the effectiveness and reliability of the entire health status assessment model. This systematic approach not only enhances the ability to monitor the real-time status of lithium-ion batteries, but also provides strong support for the intelligent development of battery management systems, ensuring that batteries operate under optimal working conditions and extending their service life.

[0088] Step 2: Calculate the health index based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, calculate the temperature impact index based on the set optimal working temperature and the average of the current working environment temperature, and calculate the internal resistance impact index based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery;

[0089] In this embodiment, the health index is calculated based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, and the formula used is as follows:

[0090]

[0091] Among them, HI is the health index, V oc is the open circuit voltage, V oc,ref is the standard open circuit voltage of the battery, C actual is the current battery capacity, C rated is the rated capacity of the battery, e is the natural constant, N is the number of cycles, R int is the battery internal resistance, VSI is the voltage flatness index, μ, k1, k2 and k3 are preset proportional coefficients, and μ>k1>k2>k3>0;

[0092] ,This is This item contains comprehensive information on electrochemical properties and discharge behavior, and voltage change is usually the most intuitive manifestation of battery performance degradation, so it is given the highest weight; battery capacity is a key indicator to measure the health status of the battery, which directly reflects the storage capacity and performance of the battery. Assigning a larger weight value can make the impact of battery capacity changes on the health index more obvious; the number of cycles has an impact on the health status of the battery, but compared with the battery capacity, its impact is usually smaller. By setting an exponential decay form, the negative impact of the increase in the number of cycles on the health index can be appropriately reduced; battery internal resistance is also an important parameter that affects battery performance, but compared with capacity and number of cycles, its impact is usually regarded as a secondary factor, so it is assigned a smaller weight value.

[0093] When C actual When it increases, it means that the current battery capacity is closer to the rated capacity, the battery health status is better, and the HI is larger; as the number of cycles N increases, the battery performance will gradually decline, and the battery health index HI will become smaller; the increase in internal resistance usually means that the battery performance decreases, and it may also cause energy loss and increased heat. Therefore, when R int When VSI increases, HI decreases accordingly; when VSI decreases, it means that the voltage changes more slowly and more steadily; that is, V oc , C actual Positively correlated with HI, VSI, N, R int Negatively correlated with HI.

[0094] Calculate the temperature impact index based on the set optimal operating temperature and current temperature data.

[0095]

[0096] Among them, TI is the temperature influence index, e is the natural constant, T env is the working environment temperature of the battery, T ideal is the optimal operating temperature of the battery, set to 25°C, σ is the correction coefficient, and in this solution, σ is set to 5. Its significance and function are to describe the sensitivity of the change of ambient temperature to the battery performance. It is a parameter in the temperature impact index model, which is used to adjust the attenuation amplitude after the temperature deviates from the optimal operating temperature. k4 is the preset proportional coefficient, and k4>0; when (T env -T ideal ) 2 When it increases, it means that the deviation between the current working environment temperature of the lithium-ion battery and the optimal working temperature increases, the performance of the battery at the current ambient temperature is increasingly negatively affected, the health state will also decrease, and the TI will become smaller; that is, (T env -T ideal ) 2 Negatively correlated with TI.

[0097] The internal resistance impact index is calculated based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery, and the formula is as follows:

[0098]

[0099] Among them, RI is the internal resistance influence index, C actual is the current battery capacity, C rated is the rated capacity of the battery, e is the natural constant, N is the number of cycles, R int is the internal resistance of the battery, k5 and k6 are preset proportional coefficients, and k6>k5>0. This is because internal resistance and number of cycles are key factors affecting battery performance. Higher internal resistance will lead to energy loss, heat and reduced battery efficiency. Therefore, when evaluating the health of the battery, the impact of internal resistance and number of cycles is very significant, so they are assigned a larger weight. Although battery capacity has a significant impact on the overall performance of the battery, compared with internal resistance, its impact is often regarded as a secondary factor, so it is assigned a smaller weight value.

[0100] When C actual When the current battery capacity is closer to the rated capacity, it means that the health of the battery has improved, and the internal resistance influence index increases accordingly; as the number of cycles N increases, the battery performance will gradually decline, and the internal resistance influence index will decrease accordingly; a larger internal resistance usually leads to a decrease in battery performance, so the internal resistance R int The increase of will make RI decrease; that is, C actual It is positively correlated with RI, R int , N is negatively correlated with RI.

[0101] The advantage of step 2 is that it systematically calculates the health index, temperature impact index and internal resistance impact index for key parameters such as open circuit voltage, voltage, battery capacity and battery internal resistance through dimensionless processing. This detailed indicator calculation can more accurately evaluate the health status of the battery and avoid misjudgment caused by a single parameter in traditional methods. Compared with existing technologies that only rely on simple voltage or capacity monitoring, step 2 comprehensively considers multiple influencing factors, making battery health assessment more comprehensive and scientific.

[0102] In this solution, adopting step 2 can effectively improve the accuracy and reliability of the overall solution. Through the careful calculation of the health index, temperature impact index and internal resistance impact index, the subsequent battery health status assessment model can be based on more accurate data, thereby improving the credibility of the assessment results. This not only helps to identify potential problems of lithium-ion batteries in a timely manner, but also provides a more solid decision-making basis for the battery management system, ensuring the safety and stability of the battery during use, thereby extending the battery life.

[0103] Step 3: Combine the health index, temperature impact index and internal resistance impact index to generate a battery health assessment model, generate weights in the battery health assessment model through the hierarchical analysis method, use the 9-scale method to evaluate the importance relationship between the indicators, form a judgment matrix, normalize the judgment matrix to calculate the weight vector, and use the model to generate a battery health score that reflects the health status of the lithium-ion battery to be tested;

[0104] In this embodiment, the health index, temperature impact index and internal resistance impact index are combined to generate a battery health assessment model, and the model expression is:

[0105]

[0106] Among them, PI is the battery health score of the lithium-ion battery to be tested, HI is the health index, TI is the temperature influence index, RI is the internal resistance influence index, ω1, ω2 and ω3 are proportional coefficients, which are obtained according to the hierarchical analysis method;

[0107] When HI increases, it usually means that the capacity, performance or life of the battery has been improved. This improvement can come from good usage conditions, reasonable charging and discharging strategies or effective battery management systems, and PI will increase accordingly; when TI increases, it means that the battery is working in a more suitable temperature range, or the battery management system effectively controls temperature changes to prevent overheating or overcooling from having a negative impact on battery performance, and PI will increase accordingly; when RI increases, it usually means that the battery's conductivity is improved, which helps to improve charging and discharging efficiency, reduce heat generation, and thus improve battery performance and service life, and PI will increase accordingly; that is, HI, TI, RI and PI are positively correlated.

[0108] The specific logic for determining the proportionality coefficient by analytic hierarchy process is as follows:

[0109] The three indicators of health index, temperature impact index and internal resistance impact index are marked, and the relative importance values ​​between them are determined by the nine-scale method to construct a judgment matrix, in which the index of the health index is marked as 1, the index of the temperature impact index is marked as 2, and the index of the internal resistance impact index is marked as 3. The constructed judgment matrix is:

[0110]

[0111] Where f and v both represent indexes of the exponent, and f∈[1,3], v∈[1,3], b fv Indicates the importance of the index with index f relative to the index with index v. The importance is scaled from 1 to 9, and b fv The larger the value, the more important the index f is compared to the index v, and b ff =1,

[0112] Each element value in the judgment matrix is ​​divided by the sum of its columns to obtain a normalized judgment matrix. The mean of the element values ​​in each row of the normalized judgment matrix is ​​calculated, and the mean of the element values ​​in the first row is used as the weight of the health index, the mean of the element values ​​in the second row is used as the weight of the temperature influence index, and the mean of the element values ​​in the third row is used as the weight of the internal resistance influence index. With the constraint that the sum of the scaled values ​​is equal to 1, the three weights are scaled in equal proportions, and the scaled weights are used as the proportional coefficients of the corresponding indexes.

[0113] The advantage of step 3 is that by building a battery health status assessment model, the health index, temperature impact index and internal resistance impact index are integrated into a comprehensive score, making the overall health status of the battery easier to understand and evaluate. The weights generated by the hierarchical analysis method can scientifically reflect the relative importance of each influencing factor to the battery health status. Compared with the traditional single indicator evaluation, this method provides a more comprehensive and accurate health status judgment, thereby reducing the possibility of misjudgment.

[0114] In this solution, adopting step 3 can effectively improve the evaluation capability and decision support of the overall solution. By weighting and integrating multiple indicators to generate a battery health score, the subsequent health status assessment becomes more systematic and scientific. This process can not only improve the efficiency of monitoring and management of battery performance, but also provide users or management systems with clear maintenance and usage recommendations, which helps to promptly identify potential problems and take corresponding measures, thereby significantly improving the safety and reliability of the battery and extending its service life.

[0115] Step 4: Compare the battery health score with the preset health assessment threshold, and based on the comparison results, evaluate the health status of the lithium-ion battery and guide management decisions.

[0116] In this embodiment, the battery health score is compared with a preset health assessment threshold, and the specific logic is as follows:

[0117] If PI ≥ PI threshold , it indicates that the health status of the lithium-ion battery to be tested is good, and the battery parameters should be monitored regularly to keep it in the best working conditions;

[0118] If PI <PI thresho1d , it indicates that the health status of the lithium-ion battery to be tested is not good, and further testing is needed to analyze the reasons for the battery's performance degradation and decide whether the battery needs to be replaced;

[0119] Among them, PI is the battery health score, PI threshold is the health assessment threshold, PI threshold Determined based on specific experimental tests.

[0120] The advantage of step 4 is that by comparing the battery health score with the preset health assessment threshold, an effective assessment of the health status of the lithium-ion battery is achieved. This method not only simplifies the assessment process, but also can promptly identify whether the battery is in good condition. Compared with the single indicator judgment commonly used in the prior art, the comprehensive comparison mechanism of step 4 improves the accuracy of the assessment and can more comprehensively reflect the actual health status of the battery.

[0121] In this solution, the use of step 4 can significantly enhance the practicality and effectiveness of the overall solution. By regularly monitoring the battery health score and comparing it with the set threshold, potential problems with battery performance can be quickly discovered so that timely measures can be taken. This process provides a dynamic feedback mechanism for the battery management system, which not only optimizes the battery usage strategy, but also effectively extends the battery life and improves user safety and economic benefits.

[0122] See also Figure 2 , a lithium-ion battery health status estimation system based on an electrochemical model, comprising:

[0123] A data acquisition module is used to collect key parameters of the lithium-ion battery to be estimated in real time, wherein the key parameters include battery capacity, number of cycles, average working environment temperature and internal resistance data, as well as a discharge voltage curve during the discharge process within the time length t closest to the current moment, calculate the voltage flatness index according to the discharge voltage curve, and calculate the open circuit voltage using an electrochemical reaction kinetics formula;

[0124] A data processing module is used to calculate the health index based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, calculate the temperature influence index based on the set optimal working temperature and the average of the current working environment temperature, and calculate the internal resistance influence index based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery;

[0125] A scoring calculation module is used to combine the health index, temperature impact index and internal resistance impact index to generate a battery health assessment model, generate weights in the battery health assessment model through a hierarchical analysis method, use a 9-scale method to evaluate the importance relationship between the indicators, form a judgment matrix, normalize the judgment matrix to calculate the weight vector, and use the model to generate a battery health score that reflects the health status of the lithium-ion battery to be tested;

[0126] The comprehensive assessment module is used to compare the battery health score with the preset health assessment threshold, evaluate the health status of the lithium-ion battery based on the comparison results, and guide management decisions.

[0127] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0128] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0129] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for estimating the health status of a lithium-ion battery based on an electrochemical model, characterized in that: The specific steps include: Step 1: Real-time collection of key parameters of the lithium-ion battery to be estimated, including battery capacity, number of cycles, average working environment temperature and internal resistance data, as well as a discharge voltage curve during the discharge process within the time length t closest to the current moment, calculating the voltage flatness index according to the discharge voltage curve, and calculating the open circuit voltage using an electrochemical reaction kinetics formula; Step 2: Calculate the health index based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, calculate the temperature impact index based on the set optimal working temperature and the average of the current working environment temperature, and calculate the internal resistance impact index based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery; Step 3: Combine the health index, temperature impact index and internal resistance impact index to generate a battery health assessment model, generate weights in the battery health assessment model through the hierarchical analysis method, use the 9-scale method to evaluate the importance relationship between the indicators, form a judgment matrix, normalize the judgment matrix to calculate the weight vector, and use the model to generate a battery health score that reflects the health status of the lithium-ion battery to be tested; Step 4: Compare the battery health score with the preset health assessment threshold, and based on the comparison results, evaluate the health status of the lithium-ion battery and guide management decisions.

2. The method for estimating the health status of a lithium-ion battery based on an electrochemical model according to claim 1, characterized in that: Use the sensor to collect the battery capacity and the average working environment temperature at the current moment, and record the battery capacity as C actual , the mean working environment temperature is recorded as T env ; Among them, the average working environment temperature refers to the average temperature of the environment where the battery is located; Obtain the discharge voltage curve during the discharge process within the time length t closest to the current moment, calculate the derivative of the voltage curve within the time length t, obtain the voltage change rate, calculate the standard deviation of the voltage change rate, and use the standard deviation as the voltage flatness index, recorded as VSI; The built-in counter of the BMS is used to collect the number of cycles, that is, the number of times the battery has undergone a complete charge and discharge cycle, recorded as N, and the internal resistance of the battery is measured by the small signal test method after the most recent historical charge and discharge cycle ends, recorded as R int , get R int The specific logic is: Apply a 1kHz AC test signal to both ends of the lithium-ion battery and measure the current I under the AC test signal. ac and voltage V ac , calculate the internal resistance of the battery according to Ohm's law: Among them, R int is the internal resistance of the battery, I ac is the current under the AC test signal, V ac is the voltage under the AC test signal.

3. The method for estimating the health status of a lithium-ion battery based on an electrochemical model according to claim 1, characterized in that: The open circuit voltage is calculated using the electrochemical reaction kinetics formula, based on the following formula: Among them, V oc is the open circuit voltage, E0 is the standard electrode potential, which is obtained by looking up the table according to the battery type, R is the gas constant, which is 8.314 J / (mol*K), n is the electron transfer rate, which is determined according to the reaction type, F is the Faraday constant, and a ox and a red are the activities of the oxidized and reduced states, respectively, and T is the absolute temperature; Get a ox 、a red The specific logic is: Under laboratory conditions, a complete charge and discharge experiment is carried out on the battery, and the SOC value is estimated in real time using the ampere-hour integration method. The formula is as follows: Among them, SOC(t) represents the state of charge at the current time t, SOC(t o ) represents the initial time t o The state of charge, C rated is the rated capacity of the battery, I(τ) represents the charge and discharge current at time τ; The activities of the oxidized and reduced states were calculated according to the following formula: a ox =C ox *SOC a red =C red *(1-SOC) Among them, a ox and a red are the activities of the oxidized and reduced states, respectively, C ox and C red It is a constant related to the battery's chemical characteristics, obtained through experimental calibration, and SOC is the real-time state of charge of the battery; The calculation formula of T is as follows: T=T rnv +273.15 Where T is the absolute temperature, T env is the average operating environment temperature of the battery.

4. The method for estimating the health status of a lithium-ion battery based on an electrochemical model according to claim 1, characterized in that: The health index is calculated based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, and the formula is as follows: Among them, HI is the health index, V oc is the open circuit voltage, V oc,ref is the standard open circuit voltage of the battery, C actual is the current battery capacity, C rated is the rated capacity of the battery, e is the natural constant, N is the number of cycles, R int is the battery internal resistance, VSI is the voltage flatness index, μ, k1, k2 and k3 are preset proportional coefficients, and μ>k1>k2>k3>0; Calculate the temperature impact index based on the set optimal operating temperature and current temperature data. Among them, TI is the temperature influence index, e is the natural constant, T env is the working environment temperature of the battery, T ideal is the optimal operating temperature of the battery, set to 25°C, σ is the correction coefficient, k4 is the preset proportional coefficient, and k4>0; The internal resistance impact index is calculated based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery, and the formula is as follows: Among them, RI is the internal resistance influence index, C actual is the current battery capacity, C rated is the rated capacity of the battery, e is the natural constant, N is the number of cycles, R int is the internal resistance of the battery, k5 and k6 are preset proportional coefficients, and k6>k5>0.

5. The method for estimating the health status of a lithium-ion battery based on an electrochemical model according to claim 4, characterized in that: The health index, temperature impact index and internal resistance impact index are combined to generate a battery health assessment model. The model expression is: Among them, PI is the battery health score of the lithium-ion battery to be tested, HI is the health index, TI is the temperature influence index, RI is the internal resistance influence index, ω1, ω2 and ω3 are proportional coefficients, which are obtained according to the hierarchical analysis method; The proportional coefficient is determined by the hierarchical analysis method. The specific logic is as follows: the three indicators of health index, temperature impact index and internal resistance impact index are marked, and the relative importance of each other is determined by the nine-scale method. The judgment matrix is ​​constructed, in which the index of the health index is marked as 1, the index of the temperature impact index is marked as 2, and the index of the internal resistance impact index is marked as 3. The constructed judgment matrix is: Where f and v both represent indexes of the exponent, and f∈[1,3], v∈[1,3], b fv Indicates the importance of the index with index f relative to the index with index v. The importance is scaled from 1 to 9, and b fv The larger the value, the more important the index f is compared to the index v, and b ff =1, Each element value in the judgment matrix is ​​divided by the sum of its columns to obtain a normalized judgment matrix. The mean of the element values ​​in each row of the normalized judgment matrix is ​​calculated, and the mean of the element values ​​in the first row is used as the weight of the health index, the mean of the element values ​​in the second row is used as the weight of the temperature influence index, and the mean of the element values ​​in the third row is used as the weight of the internal resistance influence index. With the constraint that the sum of the scaled values ​​is equal to 1, the three weights are scaled in equal proportions, and the scaled weights are used as the proportional coefficients of the corresponding indexes.

6. The method for estimating the health status of a lithium-ion battery based on an electrochemical model according to claim 1, characterized in that: The battery health score is compared with the preset health assessment threshold, based on the following logic: If PI ≥ PI threshold , it indicates that the health status of the lithium-ion battery to be tested is good, and the battery parameters should be monitored regularly to keep it in the best working conditions; If PI <PI threshold , it indicates that the health status of the lithium-ion battery to be tested is not good, and further testing is needed to analyze the reasons for the battery's performance degradation and decide whether the battery needs to be replaced; Among them, PI is the battery health score, PI threshold is the health assessment threshold.

7. A lithium-ion battery health status estimation system based on an electrochemical model, characterized in that: The lithium-ion battery health state estimation system based on an electrochemical model is used to execute the lithium-ion battery health state estimation method based on an electrochemical model according to any one of claims 1 to 6, comprising: A data acquisition module is used to collect key parameters of the lithium-ion battery to be estimated in real time, wherein the key parameters include battery capacity, number of cycles, average working environment temperature and internal resistance data, as well as a discharge voltage curve during the discharge process within the time length t closest to the current moment, calculate the voltage flatness index according to the discharge voltage curve, and calculate the open circuit voltage using an electrochemical reaction kinetics formula; A data processing module is used to calculate the health index based on the dimensionless voltage flatness index, open circuit voltage, battery capacity and battery internal resistance, calculate the temperature influence index based on the set optimal working temperature and the average of the current working environment temperature, and calculate the internal resistance influence index based on the battery capacity, number of cycles and internal resistance data of the lithium-ion battery; A scoring calculation module is used to combine the health index, temperature impact index and internal resistance impact index to generate a battery health assessment model, generate weights in the battery health assessment model through a hierarchical analysis method, use a 9-scale method to evaluate the importance relationship between the indicators, form a judgment matrix, normalize the judgment matrix to calculate the weight vector, and use the model to generate a battery health score that reflects the health status of the lithium-ion battery to be tested; The comprehensive assessment module is used to compare the battery health score with the preset health assessment threshold, evaluate the health status of the lithium-ion battery based on the comparison results, and guide management decisions.

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