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

Abstract

The application provides a lithium ion battery health state estimation method and system based on an electrochemical model, relates to the technical field of battery health state estimation, and specifically comprises the following steps: collecting key parameters of a lithium ion battery in real time, and calculating an open circuit voltage by using an electrochemical reaction kinetics 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 battery capacity, cycle number and 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 measured according to the health index, the temperature influence index and the internal resistance influence index; comparing the battery health score with a preset health evaluation threshold value, and evaluating the health state of the lithium ion battery according to a comparison result, so that a scientific battery health evaluation mechanism is provided.

Description

Technical Field

[0001] This invention relates to the field of battery health state estimation technology, specifically to a method and system for estimating the health state of lithium-ion batteries based on an electrochemical model. Background Technology

[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, with increasing usage time, the performance of lithium-ion batteries gradually declines, especially during charge and discharge processes, leading to problems such as capacity degradation, increased internal resistance, and safety hazards. Existing technologies typically rely on monitoring simple parameters such as battery capacity, charging voltage, and discharging voltage, which cannot fully reflect the battery's true health status. Furthermore, traditional monitoring systems often lack dynamic analysis of battery status, failing to detect potential faults or performance degradation in a timely manner, thus resulting in unreliability and safety issues in actual battery use.

[0003] Currently, while some battery management systems on the market have incorporated temperature monitoring and cycle count statistics, these systems still have certain limitations. For example, they often ignore the complexity of internal chemical reactions and electrical characteristics of the battery, failing to accurately model the battery's electrochemical behavior. Furthermore, the methods for assessing battery health are mostly empirical or based on simple statistical models, lacking systematicity and scientific rigor, resulting in insufficient accuracy of the assessment results. These shortcomings make it difficult for existing technologies to meet users' high requirements for battery safety, stability, and lifespan in practical applications. Therefore, there is an urgent need to develop a more accurate and comprehensive method for estimating battery health to address the shortcomings of current technologies in battery management and monitoring, and to achieve effective management of lithium-ion batteries.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for estimating the state of health of lithium-ion batteries based on an electrochemical model, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method and system for estimating the state of health of lithium-ion batteries based on an electrochemical model, the specific steps of which include:

[0008] Step 1: Real-time acquisition of key parameters of the lithium-ion battery to be estimated, including battery capacity, cycle number, average operating ambient temperature and internal resistance data, as well as the discharge voltage curve during the discharge process within the time length t closest to the current moment. Calculate the voltage smoothness index based on the discharge voltage curve and calculate the open circuit voltage using electrochemical reaction kinetics formulas.

[0009] Step 2: Calculate the health index based on the dimensionless voltage smoothing index, open circuit voltage, battery capacity, and battery internal resistance; calculate the temperature influence index based on the set optimal operating temperature and the average current operating ambient temperature; and calculate the internal resistance influence index based on the battery capacity, cycle number, and lithium-ion battery internal resistance data.

[0010] Step 3: Combine the health index, temperature influence index, and internal resistance influence index to generate a battery health assessment model. Use the analytic hierarchy process (AHP) to generate the weights within the battery health assessment model. Use the 9-scale method to assess the importance relationship between the indicators to form a judgment matrix. Normalize the judgment matrix to calculate the weight vector. Use the model to generate a battery health score that reflects the health status of the lithium-ion battery under test.

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

[0012] Furthermore, sensors are used to collect the average battery capacity and ambient temperature at the current moment, and the battery capacity is denoted as C. actual The average working environment temperature is denoted as T. env The average operating ambient temperature refers to the average temperature of the environment in which the battery is located.

[0013] Obtain the discharge voltage curve during the discharge process within the time t closest to the current time, calculate the derivative of the voltage curve within the time t, obtain the voltage change rate, calculate the standard deviation of the voltage change rate, and use the standard deviation as the voltage smoothness index, denoted as VSI.

[0014] The battery cycle count, denoted as N, is collected using the BMS's built-in counter. At the end of each cycle, the battery's internal resistance, denoted as R, is measured using a small-signal test method. int , obtain R int The specific logic is as follows:

[0015] A 1kHz AC test signal is applied across the two ends of the lithium-ion battery, and the current I under the AC test signal is measured. ac and voltage V ac Calculate the battery's internal resistance using Ohm's law:

[0016]

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

[0018] Furthermore, the open-circuit voltage is calculated using electrochemical reaction kinetics formulas, based on the following formula:

[0019]

[0020] Among them, V oc E0 is the open-circuit voltage, E0 is the standard electrode potential (obtained from a table based on the cell type), R is the gas constant (8.314 J / (mol·K)), n is the electron transfer rate (determined based on the reaction type), F is the Faraday constant, and a ox and a red Here, T represents the activity in the oxidized and reduced states, respectively, and T is the absolute temperature.

[0021] Get a ox a red The specific logic underlying this is as follows:

[0022] Under laboratory conditions, a complete charge-discharge experiment was conducted on the battery, and the State of Charge (SOC) value was estimated in real time using the ampere-hour integration method. The formula used is as follows:

[0023]

[0024] Where 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 Let I(τ) be the rated capacity of the battery, and let I(τ) represent the charging and discharging current at time τ.

[0025] The activities of the oxidized and reduced states are calculated using the following formula:

[0026] a ox =C ox *SOC

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

[0028] Among them, a ox and a red The activities of the oxidized and reduced states, respectively, are C. ox and C red These are constants related to the chemical properties of the battery, obtained through experimental calibration; SOC is the real-time state of charge of the battery.

[0029] The formula for calculating T is as follows:

[0030] T = T env +273.15

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

[0032] Furthermore, the health index is calculated based on the dimensionless voltage smoothness index, open-circuit voltage, battery capacity, and battery internal resistance, using the following formula:

[0033]

[0034] Where HI stands for Health Index, and V... oc V is the open-circuit voltage. oc,ref C is the standard open-circuit voltage of the battery. actual For the current battery capacity, C rated Where e is the battery's rated capacity, N is the natural constant, and R is the number of cycles. int VSI is the battery internal resistance, μ is the voltage smoothing index, and k1, k2 and k3 are preset proportional coefficients, where μ>k1>k2>k3>0.

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

[0036]

[0037] Where TI is the temperature effect index, e is the natural constant, and T env The operating ambient temperature of the battery, T ideal The optimal operating temperature of the battery is set to 25℃, σ is the correction coefficient, k4 is the preset proportional coefficient, and k4>0;

[0038] The internal resistance influence index is calculated based on battery capacity, cycle life, and internal resistance data of lithium-ion batteries, using the following formula:

[0039]

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

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

[0042] Wherein, PI is the battery health score of the lithium-ion battery under test, HI is the health index, TI is the temperature influence index, RI is the internal resistance influence index, and ω1, ω2 and ω3 are proportional coefficients, obtained according to the analytic hierarchy process.

[0043] The proportional coefficient is determined using the analytic hierarchy process (AHP). The specific logic is as follows: The three indicators—health index, temperature influence index, and internal resistance influence index—are labeled. The relative importance of each pair of indicators is determined using the nine-scale method, and a judgment matrix is ​​constructed. The health index is labeled as 1, the temperature influence index as 2, and the internal resistance influence index as 3. The constructed judgment matrix is ​​as follows:

[0044]

[0045] Where f and v both represent the index of the exponent, and f∈[1,3], v∈[1,3], b fv This represents the importance of the index f relative to the index v, using a 1-9 scale, and b fv The larger the value, the greater the importance of the index f compared to the index v.

[0046] Divide each element value in the judgment matrix by the sum of its columns to obtain a normalized judgment matrix. Calculate the mean of the element values ​​in each row of the normalized judgment matrix. Use the mean of the first row as the weight of the health index, the mean of the second row as the weight of the temperature influence index, and the mean of the third row as the weight of the internal resistance influence index. With the constraint that the sum of the scaled values ​​equals 1, scale the three weights proportionally and use the scaled weights as the scaling coefficients for the corresponding indices.

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

[0048] If PI ≥ PI threshold This indicates that the lithium-ion battery under test is in good health, and that battery parameters should be monitored regularly to keep it in optimal working conditions.

[0049] If PI <PI threshold If the test result is negative, it indicates that the lithium-ion battery under test is in poor health and requires further testing to analyze the cause of the battery's performance degradation and decide whether the battery needs to be replaced.

[0050] PI stands for Battery Health Score. threshold This is the threshold for health assessment.

[0051] This invention also provides a lithium-ion battery health state estimation system based on an electrochemical model. This system is used to execute the aforementioned lithium-ion battery health state estimation method based on an electrochemical model, and includes:

[0052] The data acquisition module is used to collect key parameters of the lithium-ion battery to be estimated in real time. The key parameters include battery capacity, cycle number, average operating ambient temperature and internal resistance data, as well as the discharge voltage curve during the discharge process within the time length t closest to the current moment. The voltage smoothness index is calculated based on the discharge voltage curve, and the open circuit voltage is calculated using the electrochemical reaction kinetic formula.

[0053] The data processing module is used to calculate the health index based on the dimensionless voltage smoothness index, open circuit voltage, battery capacity and battery internal resistance; calculate the temperature influence index based on the set optimal operating temperature and the average current operating ambient temperature; and calculate the internal resistance influence index based on the battery capacity, cycle number and lithium-ion battery internal resistance data.

[0054] The scoring calculation module combines the health index, temperature influence index, and internal resistance influence index to generate a battery health assessment model. It uses the analytic hierarchy process to generate the weights within the battery health assessment model, employs the 9-scale method to assess the importance relationship between the indicators, forms a judgment matrix, normalizes the judgment matrix to calculate the weight vector, and uses the model to generate a battery health score that reflects the health status of the lithium-ion battery under test.

[0055] The comprehensive assessment module compares the battery health score with preset health assessment thresholds, evaluates the health status of the lithium-ion battery based on the comparison results, and guides management decisions.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] This solution collects key parameters in real time, including battery capacity, voltage, current, temperature, cycle count, and internal resistance, thus comprehensively reflecting the battery's operating status. Using electrochemical reaction kinetics formulas, the system can accurately calculate the open-circuit voltage, laying the foundation for calculating the health index. Through dimensionless parameter processing, the system can effectively calculate the health index, temperature influence index, and internal resistance influence index. These indicators, combined with weights determined by the analytic hierarchy process (AHP), together constitute the battery's health score, providing a scientific evaluation mechanism. By comparing the battery health score with preset health assessment thresholds, the system can promptly identify the battery's health status, guiding subsequent maintenance and management decisions. Attached Figure Description

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

[0059] Figure 2 This is a schematic diagram of the system modules of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0061] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only 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] Please see Figure 1 The present invention provides a technical solution:

[0064] A method for estimating the state of health of lithium-ion batteries based on an electrochemical model, comprising the following steps:

[0065] Step 1: Real-time acquisition of key parameters of the lithium-ion battery to be estimated, including battery capacity, cycle number, average operating ambient temperature and internal resistance data, as well as the discharge voltage curve during the discharge process within the time length t closest to the current moment. Calculate the voltage smoothness index based on the discharge voltage curve and calculate the open circuit voltage using electrochemical reaction kinetics formulas.

[0066] In this embodiment, a sensor is used to collect the average battery capacity and ambient temperature at the current moment, and the battery capacity is denoted as C. actual The average working environment temperature is denoted as T. env The average operating ambient temperature refers to the average temperature of the environment in which the battery is located.

[0067] Obtain the discharge voltage curve during the discharge process within the time t closest to the current time, calculate the derivative of the voltage curve within the time t, obtain the voltage change rate, calculate the standard deviation of the voltage change rate, and use the standard deviation as the voltage smoothness index, denoted as VSI.

[0068] The battery cycle count, denoted as N, is collected using the BMS's built-in counter. At the end of each cycle, the battery's internal resistance is measured using a small-signal test method. This measured internal resistance is then recorded as the current internal resistance, denoted as R. int , obtain R int The specific logic is as follows:

[0069] A 1kHz AC test signal is applied across the two ends of the lithium-ion battery, and the current I under the AC test signal is measured. ac and voltage V ac Calculate the battery's internal resistance using Ohm's law:

[0070]

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

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

[0073]

[0074] Among them, V oc E0 is the open-circuit voltage, E0 is the standard electrode potential (obtained from a table based on the cell type), R is the gas constant (8.314 J / (mol·K)), n is the electron transfer rate (determined based on the reaction type), F is the Faraday constant, and a ox and a red Here, T represents the activity in the oxidized and reduced states, respectively, and T is the absolute temperature.

[0075] Get a ox a red The specific logic underlying this is as follows:

[0076] Under laboratory conditions, a complete charge-discharge experiment was conducted on the battery, and the State of Charge (SOC) value was estimated in real time using the ampere-hour integration method. The formula used is as follows:

[0077]

[0078] Where 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 Let I(τ) be the rated capacity of the battery, and let I(τ) represent the charging and discharging current at time τ.

[0079] The activities of the oxidized and reduced states are calculated using the following formula:

[0080] a ox =C ox *SOC

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

[0082] Among them, a ox and a red The activities of the oxidized and reduced states, respectively, are C. ox and C red These are constants related to the chemical properties of the battery, obtained through experimental calibration; SOC is the real-time state of charge of the battery.

[0083] The formula for calculating T is as follows:

[0084] T = T env +273.15

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

[0086] The advantage of step 1 lies in its ability to comprehensively reflect the actual operating status of the battery by collecting key parameters in real time, such as battery capacity, voltage, current, temperature, cycle count, and internal resistance. This comprehensive parameter acquisition and analysis method, compared to traditional techniques that rely solely on simple voltage and current monitoring, can more effectively identify battery performance degradation and potential faults, thereby improving the safety and reliability of battery management.

[0087] In this solution, step 1 lays a solid foundation for the overall approach. Real-time monitoring of key parameters ensures that subsequent calculations of the health index, temperature impact index, and internal resistance impact index are based on accurate data, thereby enhancing the effectiveness and reliability of the entire health status assessment model. This systematic approach not only strengthens the monitoring capability of 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 conditions and extending their lifespan.

[0088] Step 2: Calculate the health index based on the dimensionless voltage smoothing index, open circuit voltage, battery capacity, and battery internal resistance; calculate the temperature influence index based on the set optimal operating temperature and the average current operating ambient temperature; and calculate the internal resistance influence index based on the battery capacity, cycle number, and lithium-ion battery internal resistance data.

[0089] In this embodiment, the health index is calculated based on the dimensionless voltage smoothness index, open-circuit voltage, battery capacity, and battery internal resistance, using the following formula:

[0090]

[0091] Where HI stands for Health Index, and V... oc V is the open-circuit voltage. oc,ref C is the standard open-circuit voltage of the battery. actual For the current battery capacity, C rated Where e is the battery's rated capacity, N is the natural constant, and R is the number of cycles. int VSI is the battery internal resistance, μ is the voltage smoothing index, and k1, k2 and k3 are preset proportional coefficients, where μ>k1>k2>k3>0.

[0092] ,This is This item includes comprehensive information on electrochemical performance and discharge behavior, and voltage changes are usually the most direct manifestation of battery performance degradation, so it is given the highest weight. Battery capacity is a key indicator for measuring battery health, directly reflecting the battery's storage capacity and performance. Allocating a larger weight value makes the impact of battery capacity changes on the health index more obvious. Cycle count affects battery health, but its impact is usually smaller than that of battery capacity. By setting it in an exponential decay form, the negative impact of increasing cycle count on the health index can be appropriately reduced. Battery internal resistance is also an important parameter affecting battery performance, but compared to capacity and cycle count, its impact is usually considered a secondary factor, so it is assigned a smaller weight value.

[0093] When C actual An increase in R indicates that the current battery capacity is closer to the rated capacity, the better the battery health, and the higher the HI. As the number of cycles N increases, battery performance gradually declines, and the battery health index HI decreases. An increase in internal resistance usually means a decrease in battery performance and may also lead to increased energy loss and heat generation. Therefore, when R... int When VSI increases, HI will decrease accordingly; when VSI decreases, it means that the voltage change is more gradual and stable; that is, it indicates that V oc C actual VSI, N, and R are positively correlated with HI. int It is negatively correlated with HI.

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

[0095]

[0096] Where TI is the temperature effect index, e is the natural constant, and T env T represents the operating ambient temperature of the battery. ideal The optimal operating temperature for the battery is set to 25℃. σ is a correction coefficient, which is set to 5 in this scheme. Its significance and function are to describe the sensitivity of battery performance to changes in ambient temperature. It is a parameter in the temperature effect index model, used to adjust the rate of performance degradation after the temperature deviates from the optimal operating temperature. k4 is a preset proportional coefficient, and k4>0; when (T env -T ideal ) 2 An increase in TI indicates a larger deviation between the current operating temperature and the optimal operating temperature of the lithium-ion battery. This means the battery's performance is negatively impacted at the current ambient temperature, its health deteriorates, and TI decreases accordingly. In other words, (T) env -T ideal ) 2 It is negatively correlated with TI.

[0097] The internal resistance influence index is calculated based on battery capacity, cycle life, and internal resistance data of lithium-ion batteries, using the following formula:

[0098]

[0099] Where RI is the internal resistance influence index, C actual For the current battery capacity, C rated Where e is the battery's rated capacity, N is the natural constant, and R is the number of cycles. int K5 and K6 are preset proportional coefficients, with K6 > K5 > 0. This is because internal resistance and cycle count are key factors affecting battery performance. Higher internal resistance leads to energy loss, heat generation, and decreased battery efficiency. Therefore, internal resistance and cycle count have a significant impact on battery health and are thus assigned a larger weight. Although battery capacity has a significant impact on overall battery performance, it is often considered a secondary factor compared to internal resistance and is therefore assigned a smaller weight.

[0100] When C actual As the current battery capacity approaches its rated capacity, the battery's health improves, and the internal resistance effect index increases accordingly. With increasing cycle number N, battery performance gradually degrades, and the internal resistance effect index decreases accordingly. Higher internal resistance typically leads to decreased battery performance; therefore, internal resistance R... int An increase in C will decrease RI; that is, it indicates that C actual It is positively correlated with RI, R int N and RI are negatively correlated.

[0101] Step 2's advantage lies in its dimensionless processing, systematically calculating health indices, temperature influence indices, and internal resistance influence indices for key parameters such as open-circuit voltage, voltage, battery capacity, and battery internal resistance. This detailed index calculation enables a more accurate assessment of the battery's health status, avoiding misjudgments caused by single parameters in traditional methods. Compared to existing technologies that rely solely 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, step 2 effectively improves the accuracy and reliability of the overall solution. Through detailed calculations of the health index, temperature influence index, and internal resistance influence 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 in lithium-ion batteries in a timely manner but also provides a more solid basis for decision-making in the battery management system, ensuring the safety and stability of the battery during use, and ultimately extending the battery's lifespan.

[0103] Step 3: Combine the health index, temperature influence index, and internal resistance influence index to generate a battery health assessment model. Use the analytic hierarchy process (AHP) to generate the weights within the battery health assessment model. Use the 9-scale method to assess the importance relationship between the indicators to form a judgment matrix. Normalize the judgment matrix to calculate the weight vector. Use the model to generate a battery health score that reflects the health status of the lithium-ion battery under test.

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

[0105]

[0106] Wherein, PI is the battery health score of the lithium-ion battery under test, HI is the health index, TI is the temperature influence index, RI is the internal resistance influence index, and ω1, ω2 and ω3 are proportional coefficients, obtained according to the analytic hierarchy process.

[0107] When HI increases, it usually means that the battery's capacity, performance, or lifespan has improved. This improvement can come from good usage conditions, reasonable charge and discharge strategies, or an effective battery management system, and PI will increase accordingly. When TI increases, it means that the battery is operating within a more suitable temperature range, or that the battery management system has effectively controlled temperature changes, preventing the negative impact of overheating or overcooling on battery performance, and PI will increase accordingly. When RI increases, it usually means that the battery's conductivity has improved, which helps to improve charge and discharge efficiency, reduce heat generation, and thus improve battery performance and lifespan, and PI will increase accordingly. This shows that HI, TI, RI, and PI are positively correlated.

[0108] The specific logic behind determining the proportional coefficient using the analytic hierarchy process (AHP) is as follows:

[0109] The three indicators—health index, temperature influence index, and internal resistance influence index—are labeled. The relative importance of each pair of indicators is determined using the nine-scale method, and a judgment matrix is ​​constructed. The health index is labeled as 1, the temperature influence index as 2, and the internal resistance influence index as 3. The constructed judgment matrix is ​​as follows:

[0110]

[0111] Where f and v both represent the index of the exponent, and f∈[1,3], v∈[1,3], b fv This represents the importance of the index f relative to the index v, using a 1-9 scale, and b fv The larger the value, the greater the importance of the index f compared to the index v, and b ff =1,

[0112] Divide each element value in the judgment matrix by the sum of its columns to obtain a normalized judgment matrix. Calculate the mean of the element values ​​in each row of the normalized judgment matrix. Use the mean of the first row as the weight of the health index, the mean of the second row as the weight of the temperature influence index, and the mean of the third row as the weight of the internal resistance influence index. With the constraint that the sum of the scaled values ​​equals 1, scale the three weights proportionally and use the scaled weights as the scaling coefficients for the corresponding indices.

[0113] Step 3's advantage lies in constructing a battery health status assessment model that integrates the health index, temperature influence index, and internal resistance influence index into a comprehensive score, making the overall battery health status easier to understand and assess. The weights generated using the analytic hierarchy process (AHP) scientifically reflect the relative importance of each influencing factor to the battery's health status. Compared to traditional single-indicator assessments, this method provides a more comprehensive and accurate assessment of the health status, thereby reducing the possibility of misjudgment.

[0114] In this solution, step 3 effectively enhances the overall assessment capabilities and decision support. By weighted integration of multiple indicators to generate a battery health score, subsequent health status assessments become more systematic and scientific. This process not only improves the efficiency of battery performance monitoring and management but also provides users or management systems with clear maintenance and usage recommendations, helping to promptly identify potential problems and take corresponding measures, thereby significantly improving battery safety and reliability and extending its lifespan.

[0115] Step 4: Compare the battery health score with the preset health assessment threshold. Based on the comparison results, assess 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 used is as follows:

[0117] If PI ≥ PI threshold This indicates that the lithium-ion battery under test is in good health, and that battery parameters should be monitored regularly to keep it in optimal working conditions.

[0118] If PI <PI thresho1d If the test result is negative, it indicates that the lithium-ion battery under test is in poor health and requires further testing to analyze the cause of the battery's performance degradation and decide whether the battery needs to be replaced.

[0119] PI stands for Battery Health Score. threshold PI is the threshold for health assessment. threshold Determined based on specific experimental tests.

[0120] The advantage of step 4 lies in its ability to effectively assess the health status of lithium-ion batteries by comparing the battery health score with preset health assessment thresholds. This method not only simplifies the assessment process but also enables timely identification of whether the battery is in good condition. Compared to the single-indicator judgment typically used in existing technologies, the comprehensive comparison mechanism in step 4 improves the accuracy of the assessment and can more comprehensively reflect the actual health status of the battery.

[0121] In this solution, step 4 significantly enhances the overall practicality and effectiveness. By regularly monitoring the battery health score and comparing it with set thresholds, potential battery performance issues can be quickly identified, allowing for timely intervention. This process provides a dynamic feedback mechanism for the battery management system, optimizing battery usage strategies, effectively extending battery life, and improving user safety and economic benefits.

[0122] Please see Figure 2 A lithium-ion battery health state estimation system based on an electrochemical model, comprising:

[0123] The data acquisition module is used to collect key parameters of the lithium-ion battery to be estimated in real time. The key parameters include battery capacity, cycle number, average operating ambient temperature and internal resistance data, as well as the discharge voltage curve during the discharge process within the time length t closest to the current moment. The voltage smoothness index is calculated based on the discharge voltage curve, and the open circuit voltage is calculated using the electrochemical reaction kinetic formula.

[0124] The data processing module is used to calculate the health index based on the dimensionless voltage smoothness index, open circuit voltage, battery capacity and battery internal resistance; calculate the temperature influence index based on the set optimal operating temperature and the average current operating ambient temperature; and calculate the internal resistance influence index based on the battery capacity, cycle number and lithium-ion battery internal resistance data.

[0125] The scoring calculation module combines the health index, temperature influence index, and internal resistance influence index to generate a battery health assessment model. It uses the analytic hierarchy process to generate the weights within the battery health assessment model, employs the 9-scale method to assess the importance relationship between the indicators, forms a judgment matrix, normalizes the judgment matrix to calculate the weight vector, and uses the model to generate a battery health score that reflects the health status of the lithium-ion battery under test.

[0126] The comprehensive assessment module compares the battery health score with preset health assessment thresholds, evaluates the health status of the lithium-ion battery based on the comparison results, and guides management decisions.

[0127] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0128] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software 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 separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for estimating the state of health of lithium-ion batteries based on an electrochemical model, characterized in that, The specific steps include: Step 1: Real-time acquisition of key parameters of the lithium-ion battery to be estimated, including battery capacity, cycle number, average operating ambient temperature and internal resistance data, as well as the discharge voltage curve during the discharge process within the time length t closest to the current moment. Calculate the voltage smoothness index based on the discharge voltage curve and calculate the open circuit voltage using electrochemical reaction kinetics formulas. Step 2: Calculate the health index based on the dimensionless voltage smoothing index, open circuit voltage, battery capacity, and battery internal resistance; calculate the temperature influence index based on the set optimal operating temperature and the average current operating ambient temperature; and calculate the internal resistance influence index based on the battery capacity, cycle number, and lithium-ion battery internal resistance data. Step 3: Combine the health index, temperature influence index, and internal resistance influence index to generate a battery health assessment model. Use the analytic hierarchy process (AHP) to generate the weights within the battery health assessment model. Use the 9-scale method to assess the importance relationship between the indicators to form a judgment matrix. Normalize the judgment matrix to calculate the weight vector. Use the model to generate a battery health score that reflects the health status of the lithium-ion battery under test. Step 4: Compare the battery health score with the preset health assessment threshold, assess the health status of the lithium-ion battery based on the comparison results, and guide management decisions. The health index is calculated based on the dimensionless voltage smoothness index, open-circuit voltage, battery capacity, and battery internal resistance, using the following formula: Where HI stands for Health Index, and V... oc V is the open-circuit voltage. oc,ref C is the standard open-circuit voltage of the battery. actual For the current battery capacity, C rated Where e is the battery's rated capacity, N is the natural constant, and R is the number of cycles. int VSI is the battery internal resistance, μ is the voltage smoothing index, and k1, k2 and k3 are preset proportional coefficients, where μ>k1>k2>k3>0. The temperature impact index is calculated based on the set optimal operating temperature and the current temperature data. Where TI is the temperature effect index, e is the natural constant, and T env The operating ambient temperature of the battery, T ideal The optimal operating temperature of the battery is set to 25℃, σ is the correction coefficient, k4 is the preset proportional coefficient, and k4>0; The internal resistance influence index is calculated based on battery capacity, cycle life, and internal resistance data of lithium-ion batteries, using the following formula: Where RI is the internal resistance influence index, C actual For the current battery capacity, C rated Where e is the battery's rated capacity, N is the natural constant, and R is the number of cycles. int K5 and K6 are preset proportional coefficients, and K6 > K5 > 0. A battery health assessment model is generated by combining the health index, temperature effect index, and internal resistance effect index. The model expression is as follows: Wherein, PI is the battery health score of the lithium-ion battery under test, HI is the health index, TI is the temperature influence index, RI is the internal resistance influence index, and ω1, ω2 and ω3 are proportional coefficients, obtained according to the analytic hierarchy process. The proportional coefficient is determined using the analytic hierarchy process (AHP). The specific logic is as follows: The three indicators—health index, temperature influence index, and internal resistance influence index—are labeled. The relative importance of each pair of indicators is determined using the nine-scale method, and a judgment matrix is ​​constructed. The health index is labeled as 1, the temperature influence index as 2, and the internal resistance influence index as 3. The constructed judgment matrix is ​​as follows: Where f and v both represent the index of the exponent, and f∈[1,3], v∈[1,3], b fv This represents the importance of the index f relative to the index v, using a 1-9 scale, and b fv The larger the value, the greater the importance of the index f compared to the index v, and b ff =1, Divide each element value in the judgment matrix by the sum of its columns to obtain a normalized judgment matrix. Calculate the mean of the element values ​​in each row of the normalized judgment matrix. Use the mean of the first row as the weight of the health index, the mean of the second row as the weight of the temperature influence index, and the mean of the third row as the weight of the internal resistance influence index. With the constraint that the sum of the scaled values ​​equals 1, scale the three weights proportionally and use the scaled weights as the scaling coefficients for the corresponding indices.

2. The method for estimating the state of health of a lithium-ion battery based on an electrochemical model according to claim 1, characterized in that: The battery capacity and average ambient temperature at the current moment are collected using sensors, and the battery capacity is denoted as C. actual The average working environment temperature is denoted as T. env The average operating ambient temperature refers to the average temperature of the environment in which the battery is located. Obtain the discharge voltage curve during the discharge process within the time t closest to the current time, calculate the derivative of the voltage curve within the time t, obtain the voltage change rate, calculate the standard deviation of the voltage change rate, and use the standard deviation as the voltage smoothness index, denoted as VSI. The battery cycle count, denoted as N, is collected using the BMS's built-in counter. After the most recent historical charge-discharge cycle, the battery's internal resistance, denoted as R, is measured using a small-signal test method. int , obtain R int The specific logic is as follows: A 1kHz AC test signal is applied across the two ends of the lithium-ion battery, and the current I under the AC test signal is measured. ac and voltage V ac Calculate the battery's internal resistance using Ohm's law: Among them, R int I is the internal resistance of the battery. ac For the current under an AC test signal, V ac The voltage is the voltage under an AC test signal.

3. The method for estimating the state of health of a lithium-ion battery based on an electrochemical model according to claim 1, characterized in that: The open-circuit voltage is calculated using electrochemical reaction kinetics formulas, based on the following formula: Among them, V oc E0 is the open-circuit voltage, E0 is the standard electrode potential (obtained from a table based on the cell type), R is the gas constant (8.314 J / (mol*K)), n is the electron transfer rate (determined based on the reaction type), F is the Faraday constant, and a is the open-circuit voltage. ox and a red Here, T represents the activity in the oxidized and reduced states, respectively, and T is the absolute temperature. Get a ox a red The specific logic underlying this is as follows: Under laboratory conditions, a complete charge-discharge experiment was conducted on the battery, and the State of Charge (SOC) value was estimated in real time using the ampere-hour integration method. The formula used is as follows: Where 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 Let I(τ) be the rated capacity of the battery, and let I(τ) represent the charging and discharging current at time τ. The activities of the oxidized and reduced states are calculated using the following formula: a ox =c ox *SOC a red =C red *(1-SOC) Among them, a ox and a red The activities of the oxidized and reduced states, respectively, are C. ox and C red These are constants related to the chemical properties of the battery, obtained through experimental calibration; SOC is the real-time state of charge of the battery. The formula for calculating T is as follows: T=T env +273.15 Where T is the absolute temperature, T env This represents the average operating temperature of the battery.

4. The method for estimating the state of health of a lithium-ion battery based on an electrochemical model according to claim 1, characterized in that: The battery health score is compared with a preset health assessment threshold, based on the following logic: If PI ≥ PI threshold This indicates that the lithium-ion battery under test is in good health, and that battery parameters should be monitored regularly to keep it in optimal working conditions. If PI <PI threshold If the test result is negative, it indicates that the lithium-ion battery under test is in poor health and requires further testing to analyze the cause of the battery's performance degradation and decide whether the battery needs to be replaced. PI stands for Battery Health Score. threshold This is the threshold for health assessment.

5. A lithium-ion battery health state estimation system based on an electrochemical model, characterized in that: The aforementioned 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 as described in any one of claims 1-4, comprising: The data acquisition module is used to collect key parameters of the lithium-ion battery to be estimated in real time. The key parameters include battery capacity, cycle number, average operating ambient temperature and internal resistance data, as well as the discharge voltage curve during the discharge process within the time length t closest to the current moment. The voltage smoothness index is calculated based on the discharge voltage curve, and the open circuit voltage is calculated using the electrochemical reaction kinetic formula. The data processing module is used to calculate the health index based on the dimensionless voltage smoothness index, open circuit voltage, battery capacity and battery internal resistance; calculate the temperature influence index based on the set optimal operating temperature and the average current operating ambient temperature; and calculate the internal resistance influence index based on the battery capacity, cycle number and lithium-ion battery internal resistance data. The scoring calculation module combines the health index, temperature influence index, and internal resistance influence index to generate a battery health assessment model. It uses the analytic hierarchy process to generate the weights within the battery health assessment model, employs the 9-scale method to assess the importance relationship between the indicators, forms a judgment matrix, normalizes the judgment matrix to calculate the weight vector, and uses the model to generate a battery health score that reflects the health status of the lithium-ion battery under test. The comprehensive assessment module compares the battery health score with preset health assessment thresholds, evaluates the health status of the lithium-ion battery based on the comparison results, and guides management decisions.

Images