A method and system for evaluating the state of charge of a lead-carbon battery

Abstract

The present invention relates to the technical field of lead-carbon batteries, and specifically includes a method and system for evaluating the state of charge of a lead-carbon battery, including: establishing a first-order RC equivalent circuit model, setting dynamic indicators to simulate the open-circuit voltage and current of the lead-carbon battery, and formulating an SOC-OCV curve; constructing a charge and discharge mode analysis model, obtaining an adaptive adjustment strategy based on the data set, and evaluating the state of charge of the lead-carbon battery, solving the technical problem that the state of charge of the lead-carbon battery is affected by various factors and cannot comprehensively reflect the actual state of charge of the lead-carbon battery under different working conditions, resulting in deviation in the evaluation results. By comprehensively considering the internal resistance change, polarization voltage, transient response time, and voltage recovery speed of the lead-carbon battery under different SOCs, the accuracy of the state of charge evaluation is improved. At the same time, it adapts to different application environments and working conditions, and realizes the technical effect of accurately and reliably evaluating the state of charge of the lead-carbon battery on the premise of controllable loss of protecting the lead-carbon battery.

Description

Technical Field

[0001] The present invention relates to the technical field of lead-carbon batteries, and particularly to a method and system for evaluating the state of charge of a lead-carbon battery. Background Art

[0002] With the rapid development of energy technology and the rise of the new energy industry, batteries, as key components for energy storage and conversion, are increasingly widely used in various fields. As a new type of energy storage battery with high performance and high safety, lead-carbon batteries are widely used in energy storage power stations, electric vehicles, smart grids, etc. due to their excellent charge and discharge performance, cycle life, and environmental adaptability. However, at present, in the evaluation and management of the state of charge of lead-carbon batteries, conventional evaluation methods have problems such as insufficient accuracy and poor adaptability. For example, evaluation methods based on a single parameter cannot comprehensively reflect the actual state of lead-carbon batteries, while evaluation methods based on empirical formulas lack sufficient accuracy and reliability. In addition, with the wide application of lead-carbon batteries in complex and variable application environments, higher requirements are put forward for the real-time and accurate evaluation of the state of charge. However, existing evaluation technologies are difficult to meet the actual needs, resulting in possible safety problems such as overcharging and over-discharging during the operation of the battery, affecting the service life and performance of lead-carbon batteries.

[0003] In summary, in the prior art, during the charge and discharge process of lead-carbon batteries, due to the complexity of internal chemical reactions and the diversity of external environments, the state of charge of lead-carbon batteries is affected by various factors, and cannot comprehensively reflect the actual state of charge of lead-carbon batteries under different working conditions, resulting in deviation in evaluation results. Summary of the Invention

[0004] The present application provides a system for evaluating the state of charge of a lead-carbon battery, aiming to solve the technical problem in the prior art that during the charge and discharge process of lead-carbon batteries, due to the complexity of internal chemical reactions and the diversity of external environments, the state of charge of lead-carbon batteries is affected by various factors, and cannot comprehensively reflect the actual state of charge of lead-carbon batteries under different working conditions, resulting in deviation in evaluation results.

[0005] In view of the above problems, the technical solution of the present application is as follows:

[0006] On the one hand, the present application provides a method for evaluating the state of charge of a lead-carbon battery. The method includes: establishing a first-order RC equivalent circuit model, setting a first type of dynamic characteristic index using a capacity instance, and setting a second type of dynamic characteristic index using a pulse instance; based on the first type of dynamic characteristic index and the second type of dynamic characteristic index, simulating the open-circuit voltage and current of the lead-carbon battery, and formulating an SOC-OCV curve between the open-circuit voltage and the state of charge; extracting the battery model parameters of the first-order RC equivalent circuit model, and constructing a charge-discharge mode analysis model, where the charge-discharge mode analysis model includes a constant-current charge-discharge mode analysis network, a constant-voltage charge-discharge mode analysis network, and a health impact joint calibration unit; the charge-discharge mode analysis model obtains an adaptive adjustment strategy according to the mode selection data set of the lead-carbon battery during the charge-discharge cycle, and the adaptive adjustment strategy is used to regulate the charge operation parameters in the current charge cycle / discharge operation parameters in the current discharge cycle of the lead-carbon battery; based on the SOC-OCV curve, evaluating the state of charge of the lead-carbon battery on the premise of protecting the loss of the lead-carbon battery within a controllable range, and obtaining the state of charge evaluation result.

[0007] On the other hand, the present application provides an evaluation system for the state of charge of a lead-carbon battery. The system includes: a characteristic index setting module for establishing a first-order RC equivalent circuit model, setting a first type of dynamic characteristic index using a capacity instance, and setting a second type of dynamic characteristic index using a pulse instance; a curve formulating module for simulating the open-circuit voltage and current of the lead-carbon battery based on the first type of dynamic characteristic index and the second type of dynamic characteristic index, and formulating an SOC-OCV curve between the open-circuit voltage and the state of charge; a battery model parameter extraction module for extracting the battery model parameters of the first-order RC equivalent circuit model and constructing a charge-discharge mode analysis model, where the charge-discharge mode analysis model includes a constant-current charge-discharge mode analysis network, a constant-voltage charge-discharge mode analysis network, and a health impact joint calibration unit; an adjustment module for the charge-discharge mode analysis model to obtain an adaptive adjustment strategy according to the mode selection data set of the lead-carbon battery during the charge-discharge cycle, and the adaptive adjustment strategy is used to regulate the charge operation parameters in the current charge cycle / discharge operation parameters in the current discharge cycle of the lead-carbon battery; a state of charge evaluation module for evaluating the state of charge of the lead-carbon battery based on the SOC-OCV curve, on the premise of protecting the loss of the lead-carbon battery within a controllable range, and obtaining the state of charge evaluation result.

[0008] In summary, in one or more technical solutions provided in this application, by introducing a class of dynamic characteristic indicators and a second class of dynamic characteristic indicators, the internal resistance change, polarization voltage, transient response time, and voltage recovery speed of the lead-carbon battery under different SOCs are comprehensively considered, thereby improving the accuracy of state-of-charge assessment. At the same time, according to the dataset selected for the charging and discharging cycles of the battery, the charging or discharging operation parameters are adaptively adjusted to adapt to different application environments and working conditions, achieving the technical effect of accurately and reliably assessing the state of charge of the lead-carbon battery on the premise that the loss of the lead-carbon battery is controllable. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 FIG. is a schematic flowchart of a method for evaluating the state of charge of a lead-carbon battery provided in this application;

[0010] Figure 2 FIG. is a schematic structural diagram of an evaluation system for the state of charge of a lead-carbon battery provided in this application;

[0011] Description of reference numerals: characteristic index setting module M100, curve drafting module M200, battery model parameter extraction module M300, adjustment module M400, state-of-charge evaluation module M500. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] Example 1: The present application will be specifically described below with reference to the accompanying drawings. As Figure 1 shown, the present application provides a method for evaluating the state of charge of a lead-carbon battery, wherein the method includes:

[0013] S1: Establish a first-order RC equivalent circuit model, set a class of dynamic characteristic indicators using capacity instances, and at the same time, set a second class of dynamic characteristic indicators using pulse instances; S2: Based on the class of dynamic characteristic indicators and the second class of dynamic characteristic indicators, simulate the open-circuit voltage and current of the lead-carbon battery, and draft an SOC-OCV curve between the open-circuit voltage and the state of charge.

[0014] Specifically, a first-order RC equivalent circuit model is used to describe the electrochemical behavior of the battery, which consists of a voltage source (representing the open-circuit voltage of the battery), a resistor (representing the internal resistance of the battery), and an RC branch (representing the polarization effect of the battery). By adjusting the model parameters, the dynamic response of the battery under different operating conditions can be simulated; one type of dynamic characteristic index is extracted through capacity instances, reflecting the steady-state characteristics of the battery under different states of charge (SOC), such as internal resistance variation and polarization voltage; the second type of dynamic characteristic index is extracted through pulse instances, reflecting the transient characteristics of the battery under different SOCs, such as transient response time and voltage recovery speed; the open-circuit voltage versus state of charge (SOC-OCV) curve refers to that by simulating and analyzing the dynamic characteristic indexes, the relationship curve between the open-circuit voltage (OCV) and the state of charge (SOC) can be established, which can intuitively reflect the voltage characteristics of the battery under different SOCs.

[0015] Use a battery test device to conduct constant-current charge and discharge experiments on the lead-carbon battery, and record the voltage and current data at different SOCs; according to the experimental data, fit the parameters of the first-order RC equivalent circuit model, including the open-circuit voltage (OCV), internal resistance (R0), and polarization resistance (Rp) and polarization capacitance (Cp); conduct constant-current charge and discharge experiments at different SOCs, record the internal resistance and polarization voltage of the battery, and determine the first type of dynamic characteristic indexes. In a similar implementation manner, apply a pulse current to the battery (such as a 1A pulse current with a duration of 10 seconds), record the transient response time and voltage recovery speed of the battery, and determine the second type of dynamic characteristic indexes.

[0016] Use the extracted first type of dynamic characteristic indexes (internal resistance and polarization voltage), combined with the first-order RC equivalent circuit model, to simulate the open-circuit voltage at different SOCs; fit the simulated open-circuit voltage with the corresponding SOC values to obtain the SOC-OCV curve; by establishing the first-order RC equivalent circuit model and extracting two types of dynamic characteristic indexes, the dynamic characteristics of the lead-carbon battery under different SOCs can be more comprehensively reflected. Based on these indexes, the open-circuit voltage is simulated and the SOC-OCV curve is drawn up, providing a reference basis for accurately evaluating the state of charge of the lead-carbon battery, not only improving the accuracy of the evaluation, but also enhancing the reliability and adaptability of the evaluation results.

[0017] S3: Extract the battery model parameters of the first-order RC equivalent circuit model and construct a charge-discharge mode analysis model. The charge-discharge mode analysis model includes a constant-current charge-discharge mode analysis network, a constant-voltage charge-discharge mode analysis network, and a health impact joint tuning unit; S4: The charge-discharge mode analysis model obtains an adaptive adjustment strategy based on the mode selection data set of the lead-carbon battery during the charge-discharge cycle. The adaptive adjustment strategy is used to control the charging operation parameters during the current charging cycle / the discharging operation parameters during the current discharging cycle of the lead-carbon battery; S5: Based on the SOC-OCV curve, evaluate the state of charge of the lead-carbon battery under the premise of ensuring that the loss of the lead-carbon battery is controllable according to the adaptive adjustment strategy, and obtain the state of charge evaluation result.

[0018] Specifically, through data fitting, the key parameters of the first-order RC equivalent circuit model are extracted, including the open-circuit voltage (OCV), internal resistance (R0), polarization resistance (Rp), and polarization capacitance (Cp), which are used to reflect the dynamic characteristics of the battery under different working conditions; the charge-discharge mode analysis model includes a constant-current charge-discharge mode analysis network, a constant-voltage charge-discharge mode analysis network, and a health impact joint tuning unit. Among them, the constant-current charge-discharge mode analysis network is used to analyze the performance parameters of the battery under the constant-current charge-discharge working condition, such as charging / discharging current, voltage change, etc.; the constant-voltage charge-discharge mode analysis network is used to analyze the performance parameters of the battery under the constant-voltage charge-discharge working condition, such as charging / discharging voltage, current change, etc.; the health impact joint tuning unit is used to combine the health state of the battery (such as internal resistance change, voltage recovery speed, etc.) to optimize and adjust the charge-discharge mode to extend the battery life; the adaptive adjustment strategy is based on the mode selection data set during the charge-discharge cycle (including historical charge-discharge data, battery health state information, etc.), and through data analysis and machine learning algorithms, an adaptive adjustment strategy is generated, which can dynamically adjust the charge-discharge parameters according to the actual operating state of the battery to ensure that the battery loss is controllable.

[0019] Through constant current charge and discharge experiments and pulse charge and discharge experiments, record the voltage, current, and time data at different states of charge (SOC). Use these data to fit the parameters of the first-order RC equivalent circuit model: open-circuit voltage (OCV), internal resistance (R0, calculated by the ratio of voltage drop to current at different SOC), polarization resistance (Rp), and polarization capacitance (Cp). According to the constant current charge and discharge experiment data, analyze the changes in charge / discharge current and voltage at different SOC, collect the historical charge and discharge data of the battery, including charge current, charge voltage, discharge current, discharge voltage, etc., record the battery health state information, such as internal resistance change, voltage recovery speed, etc., and use machine learning algorithms (such as decision trees, neural networks, etc.) to analyze the data set and generate an adaptive adjustment strategy to dynamically adjust the charge and discharge parameters. For example, if the battery internal resistance increases, appropriately reduce the charge current and extend the charge time; if the voltage recovery speed slows down, adjust the discharge current to optimize the discharge process.

[0020] During the charging or discharging process, real-time monitor parameters such as the voltage, current, and temperature of the battery; according to the SOC-OCV curve and combined with the adaptive adjustment strategy, dynamically adjust the charge and discharge parameters to ensure that the battery operates within a safe range; through real-time monitoring and adjustment, accurately evaluate the state of charge (SOC) of the battery and generate an evaluation result; according to the adaptive adjustment strategy, dynamically optimize the charge and discharge parameters to reduce overcharging and over-discharging phenomena of the battery; through the health impact joint calibration unit, extend the service life of the battery. For example, by adjusting the charge current and discharge current, keep the change in the battery internal resistance within a reasonable range; by extracting the battery model parameters of the first-order RC equivalent circuit model, construct a charge and discharge mode analysis model and generate an adaptive adjustment strategy, which can dynamically adjust the charge and discharge parameters according to the actual operating state of the battery, improve the accuracy and reliability of the state of charge evaluation, and also protect the battery loss within a controllable range and extend the service life of the battery by optimizing the charge and discharge process.

[0021] Furthermore, the method of the present application includes:

[0022] Based on the basic operating parameters of the lead-carbon battery, using a first-order RC equivalent circuit model and capacity instances, extract a first type of dynamic characteristic indexes between the positive electrode, negative electrode, electrolyte and state of charge. The first type of dynamic characteristic indexes include internal resistance change and polarization voltage at different SOC; using a first-order RC equivalent circuit model and pulse instances, extract a second type of dynamic characteristic indexes between the positive electrode, negative electrode, electrolyte and state of charge. The second type of dynamic characteristic indexes include transient response time and voltage recovery speed at different SOC.

[0023] Specifically, the first-order RC equivalent circuit model is a simplified electrochemical model used to describe the dynamic behavior of lead-carbon batteries, including: a voltage source (OCV, representing the open-circuit voltage of the battery, which is closely related to the state of charge SOC), an internal resistance (R0, representing the ohmic internal resistance of the battery, which affects the instantaneous voltage response of the battery), and an RC branch (Rp and Cp, representing the polarization effect of the battery, which affects the dynamic response of the battery); a type of dynamic characteristic index refers to being extracted through capacity instances (constant current charge and discharge experiments) and reflecting the steady-state characteristics of the battery at different SOCs, including: internal resistance variation (the variation of the battery internal resistance with SOC), polarization voltage (the variation of the battery polarization voltage with SOC); a second type of dynamic characteristic index refers to being extracted through pulse instances (pulse charge and discharge experiments) and reflecting the transient characteristics of the battery at different SOCs, including: transient response time (the voltage response time of the battery under the action of a pulse current), voltage recovery speed (the speed at which the battery voltage recovers to the steady state after the pulse current ends).

[0024] Through constant current charge and discharge experiments, record the voltage and current data at different SOCs, and calculate the internal resistance and polarization voltage. Specifically, conduct constant current charging and constant current discharging, set the charging current to 2A and the discharging current to 2A respectively, record the voltage and current data at different SOCs, and calculate the internal resistance of the battery at different SOCs. The internal resistance can be calculated by the ratio of the voltage drop to the current; the polarization voltage can be calculated by the difference between the open-circuit voltage and the actual voltage.

[0025] Through pulse charge and discharge experiments, record the voltage and current changes at different SOCs, and calculate the transient response time and voltage recovery speed. Specifically, use a pulse current (such as a 1A pulse current with a duration of 10 seconds) to conduct charge and discharge experiments, record the voltage changes at different SOCs. The transient response time refers to the time when the battery voltage reaches the steady state under the action of the pulse current, and the voltage recovery speed refers to the speed at which the battery voltage recovers to the steady state after the pulse current ends.

[0026] By extracting the first type of dynamic characteristic index and the second type of dynamic characteristic index, it can comprehensively reflect the dynamic behavior of lead-carbon batteries at different SOCs, provide an important basis for establishing the SOC-OCV curve, and also provide data support for subsequent charge and discharge mode analysis and adaptive adjustment strategies. Through these dynamic characteristic indexes, the state of charge of the battery can be more accurately evaluated, the charge and discharge process can be optimized, and the service life of the battery can be extended.

[0027] Furthermore, the method of this application includes:

[0028] The radiator is linked and controlled with the lead-carbon battery to differentially adjust the speeds of multiple fans integrated in the radiator according to a heat distribution map, and one or more fans corresponding to the heat concentration area are preferentially started each time it is started; analyze the abnormal operating states of the multiple fans integrated in the radiator. The multiple fans are alternatives to each other. When any one fan fails, a fault switching strategy is generated.

[0029] Specifically, during high-rate charge and discharge or long-term operation, the lead-carbon battery generates heat during the charge and discharge process. Through the linked control of the radiator and the lead-carbon battery, the working state of the radiator can be dynamically adjusted according to the real-time temperature and heat distribution of the battery to ensure that the battery operates within a safe temperature range; the heat distribution map is a temperature distribution map drawn by collecting temperature data on the surface or inside of the battery through temperature sensors. According to the heat distribution map, the area where heat is concentrated can be identified, and differential adjustment can be made to the multiple fans integrated in the radiator. For example, the fans corresponding to the heat concentration area are preferentially started to improve the heat dissipation efficiency; the multiple fans integrated in the radiator are alternatives to each other. By real-time monitoring of the operating states of the fans, abnormal situations can be detected in a timely manner. Once a certain fan fails, a fault switching strategy is automatically generated to switch the load to other normally operating fans to ensure the reliability of the heat dissipation system.

[0030] Install multiple temperature sensors on the surface or inside of the battery pack to collect temperature data in real time; draw a heat distribution map based on the collected temperature data. For example, it is found that the temperature in the middle area of the battery pack is relatively high and the heat is concentrated; according to the heat distribution map, the fans corresponding to the heat concentration area are preferentially started. For example, start the two fans in the middle area, and set the rotation speed to a higher value (such as 2000 RPM); the fans in other areas operate at a low speed (such as 1500 RPM) to maintain the overall heat dissipation effect. At the same time, during the operation, continuously monitor the temperature change. If the temperature in the middle area drops, appropriately reduce the rotation speed of the fans in that area; if the temperature in other areas rises, increase the rotation speed of the corresponding fans.

[0031] Monitor the operating status of each fan through sensors, including parameters such as rotational speed, current, and temperature; when it is found that the rotational speed of a certain fan (such as Fan A) drops abnormally or stops working, it is judged as a fault; the system automatically identifies the faulty fan and generates a fault switching strategy. For example, the load of Fan A is switched to adjacent Fans B and C; adjust the rotational speeds of Fans B and C to compensate for the loss of Fan A. For example, increase the rotational speeds of Fans B and C from 1500 RPM to 1800 RPM; send out a fault signal to prompt the maintenance personnel to conduct inspections and repairs. Before the maintenance personnel arrive, continue to operate through the fault switching strategy to ensure that the temperature control is not affected; through the linkage control of the radiator and the lead-carbon battery, combined with the heat distribution map for differential adjustment, the heat dissipation efficiency can be effectively improved, ensuring that the battery operates within a safe temperature range. At the same time, through the analysis of abnormal operating states and the fault switching strategy, the reliability and fault tolerance of the heat dissipation system are improved, and the service life of the battery is extended.

[0032] Furthermore, the charge and discharge mode analysis model includes a constant current charge and discharge mode analysis network, a constant voltage charge and discharge mode analysis network, and a health impact joint calibration unit. The method of the present application includes:

[0033] Set the charging threshold information, where the charging threshold information includes a preset charging voltage threshold and a preset charging current threshold under the charging rate condition; obtain the safe operating range of the lead-carbon battery, where the safe operating range includes the lowest safe SOC and the highest safe SOC under the charging task constraint; based on the charging threshold information and the safe operating range, combined with the battery protection board, perform synchronous cyclic monitoring, and the battery protection board is communicatively connected to the constant current charge and discharge mode analysis network and the constant voltage charge and discharge mode analysis network.

[0034] Specifically, the charging threshold information is a key parameter to ensure that the lead-carbon battery is charged within a safe range, including a preset charging voltage threshold and a preset charging current threshold, which are set according to the characteristics of the battery (such as rated voltage, rated capacity) and the charging rate condition (such as 0.5C, etc.) to prevent overcharging and overcurrent phenomena; the safe operating range refers to the range of the lowest and highest SOC (state of charge) allowed for the battery during the charging process, which is determined according to the health state of the battery and the application scenario to ensure that the battery operates within a safe range and avoid the impact of overcharging and over-discharging on the battery life; through the communication connection between the battery protection board and the charge and discharge mode analysis network, the voltage, current, SOC and other parameters of the battery are monitored in real time. The battery protection board can dynamically adjust the charging parameters according to the charging threshold information and the safe operating range to ensure the safety and efficiency of the charging process.

[0035] Determine the charging rate conditions and set the charging threshold information. Specifically, according to the rated voltage of the battery (2.4V / cell), set the charging voltage threshold to 2.5V (per cell) to prevent overcharging. At the same time, set the charging current threshold to 50A to prevent overcurrent. Under the constraint of the charging task, the minimum safe SOC is 20%. Values lower than this may cause the battery to over-discharge, affecting the battery life. Under the constraint of the charging task, the maximum safe SOC is 90%. Values exceeding this may cause the battery to overcharge, leading to safety issues. The battery protection board is connected to the constant current charge and discharge mode analysis network and the constant voltage charge and discharge mode analysis network through a communication interface (such as CAN bus or I2C bus), and transmits data such as the voltage, current, and SOC of the battery in real-time. During the charging process, the battery protection board monitors the voltage and current of the battery in real-time. If the voltage approaches the preset charging voltage threshold (2.5V), the charging mode is adjusted from constant current charging to constant voltage charging to prevent overcharging.

[0036] If the current exceeds the preset charging current threshold (50A), the charging current is reduced to ensure the safety of the charging process. At the same time, the SOC of the battery is monitored to ensure that it is within the safe operating range (20% - 90%). If the SOC approaches the minimum or maximum safe value, the charging parameters are adjusted to avoid exceeding the safe range. By setting the charging threshold information and the safe operating range, and combining with the synchronous cyclic monitoring of the battery protection board, it can effectively ensure that the lead-carbon battery is charged within the safe range, improving the safety and reliability of the charging process. At the same time, by dynamically adjusting the charging parameters, the charging efficiency is optimized, and the charging time is reasonably optimized.

[0037] Furthermore, the charge and discharge mode analysis model obtains an adaptive adjustment strategy based on the mode selection data set of the lead-carbon battery during the charge and discharge cycle. The method of this application includes:

[0038] Based on the data storage unit, extract historical charge and discharge data and battery health status information, and establish an initial data set. Identify the core characteristic indicators through the initial data set, conduct mode analysis, and obtain the health impact factor. The health impact factor reflects the change trend of the health status of the lead-carbon battery during the charge and discharge process and the degree of influence on the battery performance. Based on the health impact factor, the initial data set is elastically expanded to obtain the mode selection data set of the lead-carbon battery during the charge and discharge cycle.

[0039] Specifically, the data storage unit is used to store the historical charge-discharge data and health status information of the lead-carbon battery, including charge-discharge current, voltage, temperature, SOC change, cycle times, etc. By extracting these data, an initial data set can be established to provide a basis for subsequent analysis and modeling. Through the initial data set, core characteristic indicators closely related to the battery health status are identified, including internal resistance change, voltage recovery speed, charge-discharge efficiency, etc. Through pattern analysis, health impact factors can be obtained, and these factors can reflect the change trend of the battery health status during the charge-discharge process and its influence degree on the battery performance. Based on the health impact factors, elastic expansion is performed on the initial data set. Elastic expansion refers to generating more representative data points through methods such as simulation or interpolation to enrich the data set. The expanded data set can better reflect the charge-discharge behavior of the battery under different working conditions and provide more comprehensive data support for the adaptive adjustment strategy.

[0040] Based on the data storage unit, historical charge and discharge data are extracted, including: charging current, charging voltage, charging time, discharging current, discharging voltage, discharging time, and number of cycles; battery health status information is extracted, including: internal resistance (initially 0.15Ω, increasing to 0.35Ω after 500 cycles), voltage recovery speed (initially 0.8V / s, decreasing to 0.3V / s after 500 cycles), charge and discharge efficiency (initially 90%, decreasing to 80% after 500 cycles); the above data are organized in tabular form, with each row representing the data of one charge and discharge cycle, including parameters such as current, voltage, time, internal resistance, voltage recovery speed, and charge and discharge efficiency; by analyzing the initial data set, core characteristic indicators associated with the number of cycles are identified, including but not limited to changes in internal resistance, changes in voltage recovery speed, and changes in charge and discharge efficiency; principal component analysis (PCA) is used to perform dimensionality reduction on the initial data set to extract the main features; through clustering analysis (such as the K-means algorithm), the data are divided into different states (such as healthy state, aging state, etc.) to obtain health impact factors, and the health impact factors reflect the changing trend of the health state of the lead-carbon battery during charge and discharge and the degree of influence on the battery performance; in a high-temperature environment, the rate of increase in the internal resistance of the battery accelerates, and the rate of decrease in the voltage recovery speed accelerates; through simulation, charge and discharge data in a high-temperature environment are generated, including parameters such as internal resistance, voltage recovery speed, and charge and discharge efficiency; the simulated data are combined with the initial data set to form an expanded mode selection data set; the expanded data set can reflect the charge and discharge behavior of the battery under different environmental conditions (such as normal temperature, high temperature, low temperature) and different load conditions; by establishing the initial data set, identifying core characteristic indicators, performing pattern analysis, and elastically expanding the data set, the changing trend of the health state of the lead-carbon battery during charge and discharge and its impact on performance are comprehensively reflected, and the expanded data set provides richer data support for the adaptive adjustment strategy, and can dynamically adjust the charge and discharge parameters according to the actual health state and working conditions of the battery, thereby optimizing the use performance of the battery.

[0041] Furthermore, based on the health impact factor, the initial data set is elastically expanded, and the method of the present application includes:

[0042] Set the normal operation scenarios of the lead-carbon battery, and the normal operation scenarios include the operation states under constant temperature environment, high temperature environment, low temperature environment, and different load conditions; based on the normal operation scenarios, simulate the charge and discharge behavior of the lead-carbon battery to generate a simulated data set; fuse the simulated data set with the initial data set, and perform directional optimization analysis on the charge and discharge mode of the lead-carbon battery according to the health impact factor to obtain an expanded mode selection data set.

[0043] Specifically, the conventional operation scenario refers to the working state of lead-carbon batteries under different environmental conditions and load conditions, including: constant temperature environment (such as performing charge and discharge tests at room temperature, e.g., 25°C), high temperature environment (such as performing charge and discharge tests at a high temperature of 45°C to observe the changes in battery performance), low temperature environment (such as performing charge and discharge tests at a low temperature of -10°C to evaluate the battery's performance under cold conditions), and different load conditions, including charge and discharge tests under light load (such as 0.2C), medium load (such as 0.5C), and heavy load (such as 0.8C); by establishing a battery model and combining different environmental and load conditions, simulate the charge and discharge behavior of lead-carbon batteries, including parameters such as battery voltage, current, SOC change, and internal resistance change; fuse the generated simulation data set with the initial data set to form an expanded mode selection data set; according to the health impact factors, conduct targeted optimization analysis on the charge and discharge mode to improve the performance and lifespan of the battery.

[0044] Set the conventional operation scenario of the lead-carbon battery, use the first-order RC equivalent circuit model, and based on the conventional operation scenario, simulate the charge and discharge behavior of the lead-carbon battery, and record data such as voltage, current, SOC change, and internal resistance change under each condition; under different environmental and load conditions, simulate the charge and discharge process to generate a data set and a simulation data set; fuse the simulation data set with the initial data set to form an expanded mode selection data set, and the fused data set contains charge and discharge behavior data under different environmental and load conditions; according to the health impact factors (such as internal resistance change, voltage recovery speed, charge and discharge efficiency, etc.), conduct targeted optimization analysis on the charge and discharge mode. For example, it is found that the internal resistance increase rate accelerates in a high temperature environment, and the charging current and voltage are adjusted to reduce the impact of internal resistance on battery performance. Through optimization analysis, an expanded mode selection data set is generated, which contains the best charge and discharge strategies under different environmental conditions and load conditions; the mode selection data set provides a basis for subsequent adaptive adjustment strategies, improving the performance and service life of the battery; by setting the conventional operation scenario, simulating the charge and discharge behavior, fusing the data set, and conducting targeted optimization analysis, the performance of the lead-carbon battery under different environmental and load conditions can be comprehensively evaluated, providing rich data support for the adaptive adjustment strategy, and thus optimizing the charge and discharge process of the lead-carbon battery.

[0045] Furthermore, the method of the present application further includes:

[0046] Based on the pattern selection dataset, evaluate the charge and discharge efficiency of the lead-carbon battery to generate an efficiency evaluation report; according to the efficiency evaluation report, optimize the parameter configurations of the constant-current charge and discharge mode analysis network and the constant-voltage charge and discharge mode analysis network, and calculate the energy utilization rate of the lead-carbon battery; based on the energy utilization rate of the lead-carbon battery, combined with the health impact factor, count the charge and discharge cycle times of the lead-carbon battery to establish a cycle life prediction model, and the cycle life prediction model supports the operation management of the lead-carbon battery during multiple charge and discharge cycles.

[0047] Specifically, by analyzing the pattern selection dataset, evaluate the efficiency of the lead-carbon battery under different charge and discharge modes. The charge and discharge efficiency is usually calculated by comparing the charging energy with the discharging energy, which reflects the energy loss of the battery during the charge and discharge process; according to the efficiency evaluation report, adjust the parameter configurations of the constant-current charge and discharge mode analysis network and the constant-voltage charge and discharge mode analysis network to improve the charge and discharge efficiency, including charging current, charging voltage, discharging current, discharging voltage, etc.; the energy utilization rate refers to the ratio of the actual energy used by the battery during the charge and discharge process to the theoretical maximum energy. By optimizing the charge and discharge parameters, the energy utilization rate can be improved and the energy loss can be reduced; combined with the health impact factor and the energy utilization rate, count the charge and discharge cycle times of the lead-carbon battery to establish a cycle life prediction model. The cycle life prediction model can predict the performance changes of the battery during multiple charge and discharge cycles and provide support for the operation management of the battery.

[0048] Extract the energy data during the charging and discharging processes from the pattern selection dataset, calculate the charge and discharge efficiency, and generate an efficiency evaluation report. The efficiency evaluation report details the charge and discharge efficiency under different environmental and load conditions; according to the efficiency evaluation report, adjust the constant-current charging current and discharging current. For example, in a high-temperature environment, reduce the charging current from 2A to 1.8A to reduce heat generation and improve efficiency. Use the constant-voltage charge and discharge mode analysis network to adjust the constant-voltage charging voltage and discharging voltage. At the same time, according to the pattern selection dataset, count the charge and discharge cycle times of the battery under different environmental and load conditions. Use the counted cycle times and the health impact factor to establish a cycle life prediction model to predict the performance changes of the battery during multiple charge and discharge cycles. For example, in a constant-temperature environment, the expected battery life is 1000 cycles; in a high-temperature environment, the expected battery life is 800 cycles; by evaluating the charge and discharge efficiency, optimizing the charge and discharge parameter configurations, calculating the energy utilization rate, and establishing a cycle life prediction model combined with the health impact factor, the operation status of the lead-carbon battery can be comprehensively managed, and the charge and discharge efficiency and energy utilization rate of the battery are improved.

[0049] Furthermore, to establish a cycle life prediction model, the method of this application further includes:

[0050] Based on the above cycle life prediction model, estimate the replacement cycle of the lead-carbon battery and generate a replacement reminder message; through the heat distribution map, combined with the replacement reminder message, dynamically narrow the upper and lower limit ranges of the safe operating interval of the lead-carbon battery, and evaluate the thermal runaway risk; based on the thermal runaway risk, generate a temperature control strategy, which is used to optimize the operating parameters of the radiator.

[0051] Specifically, based on the cycle life prediction model, combined with the actual usage of the battery (such as the number of charge and discharge cycles, environmental conditions, etc.), estimate the replacement cycle of the lead-carbon battery and generate a replacement reminder message to plan the maintenance and replacement of the battery in advance and avoid system downtime caused by battery failure; through the heat distribution map, combined with the replacement reminder message, dynamically adjust the safe operating interval (such as the SOC range) of the lead-carbon battery. As the battery approaches the replacement cycle, its performance degrades, and the operating interval needs to be controlled more strictly to reduce the thermal runaway risk; thermal runaway refers to the uncontrollable temperature rise of the battery due to overheating, which may cause battery damage or even fire. Evaluate the thermal runaway risk through the heat distribution map and the health status of the battery, and generate the corresponding temperature control strategy; according to the thermal runaway risk, generate a temperature control strategy to optimize the operating parameters of the radiator (such as fan speed, coolant flow, etc.) to ensure that the battery operates within a safe temperature range.

[0052] According to the cycle life prediction model, estimate the remaining life of the battery. For example, a certain lead-carbon battery is expected to complete 1000 cycles in a constant temperature environment. Currently, 800 cycles have been completed, and the remaining life is expected to be 200 cycles; generate a replacement reminder message. When the battery approaches the replacement cycle (such as the remaining life is less than 100 cycles), generate a replacement reminder message. For example, the remaining life of the battery is expected to be 70 cycles, and it is recommended to arrange for replacement as soon as possible.

[0053] According to the heat distribution map, identify the heat concentration area of the battery. For example, it is found that the temperature in the middle area of the battery is relatively high; as the battery approaches the replacement cycle, dynamically narrow the safe operating interval. For example, adjust the SOC safe operating interval from 20% - 90% to 30% - 80% to reduce the charge and discharge depth of the battery and lower the thermal runaway risk. Evaluate the thermal runaway risk according to the heat distribution map and the health status of the battery. For example, if the temperature in the middle area of the battery continues to rise and the internal resistance increases significantly, the thermal runaway risk is relatively high; according to the thermal runaway risk, generate a temperature control strategy. For example, increase the fan speed in the heat concentration area from 1500 RPM to 2000 RPM to enhance the heat dissipation effect; the radiator adjusts the operating parameters according to the temperature control strategy. For example, start an additional fan in the middle area to ensure that the battery temperature is within a safe range.

[0054] By predicting the replacement cycle of lead-carbon batteries and generating reminder messages, the maintenance and replacement of batteries can be planned in advance to avoid system downtime caused by battery failures. Dynamically adjusting the safe operating range and assessing the risk of thermal runaway can effectively reduce the safety risks during battery operation. Combining with the temperature control strategy to optimize the operating parameters of the radiator can further ensure that the battery operates within a safe temperature range, extend the battery life, and improve the reliability and safety of the system.

[0055] In summary, the beneficial effects of the embodiments of this application are as follows:

[0056] Since a first-order RC equivalent circuit model is established, dynamic characteristic indexes are set, the open-circuit voltage and current of the lead-carbon battery are simulated, and the SOC-OCV curve is drawn; the model parameters are extracted, a charge-discharge mode analysis model is constructed, and an adaptive adjustment strategy is obtained based on the data set to evaluate the state of charge of the lead-carbon battery. By providing a method and system for evaluating the state of charge of a lead-carbon battery, by introducing a class of dynamic characteristic indexes and a second class of dynamic characteristic indexes, comprehensively considering the internal resistance change, polarization voltage, transient response time and voltage recovery speed of the lead-carbon battery under different SOCs, the accuracy of the state of charge evaluation is improved. At the same time, according to the data set selected for the charge-discharge cycle mode of the battery, the charging or discharging operation parameters are adaptively adjusted to adapt to different application environments and working conditions, and the technical effect of accurately and reliably evaluating the state of charge of the lead-carbon battery is achieved on the premise that the loss of the lead-carbon battery is controllable.

[0057] Embodiment 2: Based on the same inventive concept as the method for evaluating the state of charge of a lead-carbon battery in the foregoing embodiment, as Figure 2 shown, the embodiment of this application provides a system for evaluating the state of charge of a lead-carbon battery, wherein the system includes:

[0058] A characteristic index setting module M100, configured to establish a first-order RC equivalent circuit model, set a class of dynamic characteristic indexes using capacity instances, and at the same time, set a second class of dynamic characteristic indexes using pulse instances.

[0059] A curve drawing module M200, configured to simulate the open-circuit voltage and current of the lead-carbon battery based on the class of dynamic characteristic indexes and the second class of dynamic characteristic indexes, and draw an SOC-OCV curve between the open-circuit voltage and the state of charge.

[0060] A battery model parameter extraction module M300, configured to extract the battery model parameters of the first-order RC equivalent circuit model and construct a charge-discharge mode analysis model, wherein the charge-discharge mode analysis model includes a constant current charge-discharge mode analysis network, a constant voltage charge-discharge mode analysis network, and a health impact joint calibration unit.

[0061] The adjustment module M400 is used for the charge-discharge mode analysis model to obtain an adaptive adjustment strategy based on the mode selection data set of the lead-carbon battery in the charge and discharge cycle. The adaptive adjustment strategy is used to regulate the charging operating parameters in the current charging cycle / discharging operating parameters in the current discharging cycle of the lead-carbon battery.

[0062] The state of charge assessment module M500 is used to assess the state of charge of the lead-carbon battery based on the SOC-OCV curve and according to the adaptive adjustment strategy under the premise of protecting the loss of the lead-carbon battery under control, and obtain a state of charge assessment result.

[0063] Furthermore, the system is configured to perform the following method:

[0064] Based on the basic operating parameters of lead-carbon batteries, a first-order RC equivalent circuit model is used to extract a type of dynamic characteristic indicators between the positive electrode, negative electrode, electrolyte and state of charge using a capacity example. The first type of dynamic characteristic indicators includes the internal resistance change and polarization voltage under different SOCs. A first-order RC equivalent circuit model is used to extract a second type of dynamic characteristic indicators between the positive electrode, negative electrode, electrolyte and state of charge using a pulse example. The second type of dynamic characteristic indicators includes the transient response time and voltage recovery speed under different SOCs.

[0065] Furthermore, the system is configured to perform the following method:

[0066] The radiator is controlled in conjunction with the lead-carbon battery, and the rotation speeds of the multiple fans integrated in the radiator are differentially adjusted based on the heat distribution map. One or more fans corresponding to the heat concentration area are started first each time the radiator is started. The abnormal operating status of the multiple fans integrated in the radiator is analyzed. The multiple fans serve as alternatives to each other. When any fan fails, a fault switching strategy is generated.

[0067] Furthermore, the system is configured to perform the following method:

[0068] Charging threshold information is set, and the charging threshold information includes a preset charging voltage threshold and a preset charging current threshold under charging rate conditions; a safe operating range of the lead-carbon battery is obtained, and the safe operating range includes a minimum safe SOC and a maximum safe SOC under charging task constraints; based on the charging threshold information and the safe operating range, synchronous cycle monitoring is performed in combination with a battery protection board, and the battery protection board is communicatively connected to the constant current charge and discharge mode analysis network and the constant voltage charge and discharge mode analysis network.

[0069] Furthermore, the system is configured to perform the following method:

[0070] Based on the data storage unit, historical charge and discharge data and battery health status information are extracted, and an initial data set is established; core characteristic indicators are identified through the initial data set, pattern analysis is performed, and health impact factors are obtained. The health impact factors reflect the change trend of the health status of the lead-carbon battery during charge and discharge and the degree of influence on the battery performance; based on the health impact factors, the initial data set is elastically expanded to obtain a pattern selection data set of the lead-carbon battery during charge and discharge cycles.

[0071] Further, the system is used to execute the following method:

[0072] Set the normal operation scenarios of the lead-carbon battery, where the normal operation scenarios include the operation states under constant temperature environment, high temperature environment, low temperature environment, and different load conditions; based on the normal operation scenarios, simulate the charge and discharge behaviors of the lead-carbon battery to generate a simulation data set; fuse the simulation data set with the initial data set, and perform directional optimization analysis on the charge and discharge modes of the lead-carbon battery according to the health impact factors to obtain an expanded pattern selection data set.

[0073] Further, the system is also used to execute the following method:

[0074] Based on the pattern selection data set, evaluate the charge and discharge efficiency of the lead-carbon battery to generate an efficiency evaluation report; according to the efficiency evaluation report, optimize the parameter configurations of the constant current charge and discharge mode analysis network and the constant voltage charge and discharge mode analysis network, and calculate the energy utilization rate of the lead-carbon battery; based on the energy utilization rate of the lead-carbon battery, combined with the health impact factors, count the number of charge and discharge cycles of the lead-carbon battery to establish a cycle life prediction model, and the cycle life prediction model supports the operation management of the lead-carbon battery during multiple charge and discharge cycles.

[0075] Further, the system is also used to execute the following method:

[0076] Based on the cycle life prediction model, estimate the replacement cycle of the lead-carbon battery to generate a replacement reminder message; through the heat distribution map, combined with the replacement reminder message, dynamically narrow the upper and lower limit ranges of the safe operation interval of the lead-carbon battery, and evaluate the thermal runaway risk; based on the thermal runaway risk, generate a temperature control strategy, and the temperature control strategy is used to optimize the operation parameters of the radiator.

[0077] In summary, any step can be stored as computer instructions or programs in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor, and no additional restrictions are imposed here.

[0078] Furthermore, the above technical solutions only represent the preferred technical solutions of the technical solutions of the embodiments of the present application. Some changes that those skilled in the art may make to some parts thereof all reflect the principles of the novel embodiments of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A method for evaluating the state of charge of a lead-carbon battery, characterized in that, The method includes: Establish a first-order RC equivalent circuit model, set a type of dynamic characteristic index using a capacity instance, and at the same time, set a second type of dynamic characteristic index using a pulse instance; Based on the first type of dynamic characteristic index and the second type of dynamic characteristic index, simulate the open-circuit voltage and current of the lead-carbon battery, and draw the SOC-OCV curve between the open-circuit voltage and the state of charge; Extract the battery model parameters of the first-order RC equivalent circuit model, and construct a charge and discharge mode analysis model, where the charge and discharge mode analysis model includes a constant current charge and discharge mode analysis network, a constant voltage charge and discharge mode analysis network, and a health impact joint calibration unit; The charge and discharge mode analysis model obtains an adaptive adjustment strategy according to the mode selection data set of the lead-carbon battery in the charge and discharge cycle, and the adaptive adjustment strategy is used to control the charge operation parameters in the current charge cycle / discharge operation parameters in the current discharge cycle of the lead-carbon battery; The charge and discharge mode analysis model obtains an adaptive adjustment strategy according to the mode selection data set of the lead-carbon battery in the charge and discharge cycle, including: Based on the data storage unit, extract historical charge and discharge data and battery health status information, and establish an initial data set; Identify core characteristic indicators through the initial data set, perform mode analysis, and obtain a health impact factor, where the health impact factor reflects the change trend of the health status of the lead-carbon battery during charge and discharge and the degree of influence on the battery performance; Based on the health impact factor, perform elastic expansion on the initial data set to obtain a mode selection data set of the lead-carbon battery in the charge and discharge cycle; The elastic expansion of the initial data set based on the health impact factor includes: Set the normal operation scenarios of the lead-carbon battery, and the normal operation scenarios include the operation states under constant temperature environment, high temperature environment, low temperature environment, and different load conditions; Based on the normal operation scenarios, simulate the charge and discharge behaviors of the lead-carbon battery to generate a simulation data set; Fuse the simulation data set with the initial data set, and perform directional optimization analysis on the charge and discharge modes of the lead-carbon battery according to the health impact factor to obtain an expanded mode selection data set; Based on the SOC-OCV curve, perform state of charge assessment on the lead-carbon battery under the premise of controllable loss of the lead-carbon battery according to the adaptive adjustment strategy, and obtain the state of charge assessment result.

2. The method for evaluating the state of charge of a lead-carbon battery according to claim 1, wherein Based on the basic operation parameters of the lead-carbon battery, using a first-order RC equivalent circuit model and a capacity instance, extract a first type of dynamic characteristic index between the positive electrode, negative electrode, electrolyte and the state of charge, and the first type of dynamic characteristic index includes the internal resistance change and polarization voltage at different SOCs; Using a first-order RC equivalent circuit model and a pulse instance, extract a second type of dynamic characteristic index between the positive electrode, negative electrode, electrolyte and the state of charge, and the second type of dynamic characteristic index includes the transient response time and voltage recovery speed at different SOCs.

3. The method for evaluating the state of charge of a lead-carbon battery according to claim 1, wherein The radiator is linked with the lead-carbon battery to differentially adjust the rotation speeds of multiple fans integrated in the radiator according to the heat distribution map, and preferentially start one or more fans corresponding to the heat concentration area each time it is started; Analyze the abnormal operating states of multiple fans integrated in the radiator. The multiple fans are alternatives to each other, and a fault switching strategy is generated when any one of the fans fails.

4. The method for evaluating the state of charge of a lead-carbon battery according to claim 3, characterized in that, The charge-discharge mode analysis model includes a constant-current charge-discharge mode analysis network, a constant-voltage charge-discharge mode analysis network, and a health impact joint calibration unit. The method includes: Set charge threshold information, which includes a preset charge voltage threshold and a preset charge current threshold under a charging rate condition. Obtain the safe operating range of the lead-carbon battery, which includes the lowest safe SOC and the highest safe SOC under the constraint of the charging task. Based on the charge threshold information and the safe operating range, and in combination with the battery protection board, perform synchronous cyclic monitoring. The battery protection board is communicatively connected to the constant-current charge-discharge mode analysis network and the constant-voltage charge-discharge mode analysis network.

5. The method for evaluating the state of charge of a lead-carbon battery according to claim 1, wherein The method further includes: Evaluate the charge-discharge efficiency of the lead-carbon battery based on the mode selection data set and generate an efficiency evaluation report. According to the efficiency evaluation report, optimize the parameter configurations of the constant-current charge-discharge mode analysis network and the constant-voltage charge-discharge mode analysis network, and calculate the energy utilization rate of the lead-carbon battery. Based on the energy utilization rate of the lead-carbon battery and in combination with the health impact factor, count the number of charge-discharge cycles of the lead-carbon battery and establish a cycle life prediction model. The cycle life prediction model supports the operation management of the lead-carbon battery during multiple charge-discharge cycles.

6. The method for evaluating the state of charge of a lead-carbon battery according to claim 5, wherein Establish a cycle life prediction model. The method includes: Estimate the replacement cycle of the lead-carbon battery based on the cycle life prediction model and generate a replacement reminder message. Through the heat distribution map, in combination with the replacement reminder message, dynamically narrow the upper and lower limit ranges of the safe operating range of the lead-carbon battery and evaluate the thermal runaway risk. Based on the thermal runaway risk, generate a temperature control strategy, which is used to optimize the operating parameters of the radiator.

7. An evaluation system for the state of charge of a lead-carbon battery, characterized in that, A system for implementing the method for evaluating the state of charge of a lead-carbon battery according to any one of claims 1-6, the system includes: A characteristic index setting module, which is used to establish a first-order RC equivalent circuit model, set a type of dynamic characteristic index using a capacity instance, and at the same time, set a second type of dynamic characteristic index using a pulse instance. A curve drafting module, which is used to simulate the open-circuit voltage and current of the lead-carbon battery based on the type of dynamic characteristic index and the second type of dynamic characteristic index, and draft an SOC-OCV curve between the open-circuit voltage and the state of charge. A battery model parameter extraction module, which is used to extract the battery model parameters of the first-order RC equivalent circuit model and construct a charge-discharge mode analysis model. The charge-discharge mode analysis model includes a constant-current charge-discharge mode analysis network, a constant-voltage charge-discharge mode analysis network, and a health impact joint calibration unit. An adjustment module, which is used to obtain an adaptive adjustment strategy for the charge-discharge mode analysis model according to the mode selection data set of the lead-carbon battery during the charge-discharge cycle. The adaptive adjustment strategy is used to regulate the charging operation parameters during the current charging cycle / discharging operation parameters during the current discharging cycle of the lead-carbon battery. The state of charge (SOC) evaluation module is used to evaluate the SOC of the lead-carbon battery based on the SOC-OCV curve and the adaptive adjustment strategy, on the premise of controllable loss of protecting the lead-carbon battery, so as to obtain the SOC evaluation result.

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