Lithium battery intelligent management and temperature adaptive adjustment method and system in severe environment

Abstract

The invention relates to the technical field of battery management, and discloses a lithium battery intelligent management and temperature adaptive adjustment method and system in a severe environment. The method comprises the following steps: synchronously acquiring multi-dimensional environment data to establish a monitoring database aiming at a severe environment of a subway UPS (Uninterrupted Power Supply) system; calculating an environmental adaptability threat index by adopting a Kalman filtering algorithm; generating differentiated charging and discharging management parameters in combination with the threat index and the UPS switching demand; inverting the actual working temperature of the battery through the internal resistance change; when the temperature affects the power supply capacity, the carbon fiber heating film self-heating system is started, and the optimal working state of the battery is maintained by adopting a PID algorithm. According to the application, a threat evaluation system and a differentiated charging and discharging management strategy based on multi-environmental factor fusion are established, and through internal resistance inversion temperature detection and PID self-heating control, the power supply reliability and the battery service life of the UPS system in severe environments such as a subway river-crossing tunnel and the like are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a method and system for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments. Background Art

[0002] Existing technology primarily uses lead-acid batteries as backup power sources, coupled with simple battery management circuits for charge and discharge control. Traditional battery management methods typically rely on single voltage or temperature monitoring, employing fixed charge and discharge parameter settings, and lack comprehensive consideration of complex and harsh environmental factors. Regarding temperature management, existing technology primarily relies on passive heat dissipation designs or simple fan cooling, lacking effective active heating measures for seasonal low-temperature environments.

[0003] However, existing technologies have significant shortcomings in applications in harsh environments such as subway cross-river tunnels. First, traditional lead-acid batteries are prone to electrolyte leakage and thermal runaway risks in high humidity and salt spray corrosion environments, and their low-temperature performance degrades severely, resulting in high full-cycle costs. Secondly, existing battery management methods lack the ability to monitor and fuse data for multiple environmental factors in real time, and are unable to accurately assess the comprehensive impact of complex harsh environments on battery performance. In addition, fixed charge and discharge management strategies cannot adapt to the special operating conditions of the UPS system's floating charge-constant current and voltage limiting switching, and can easily lead to rapid degradation of battery performance in harsh environments. Summary of the Invention

[0004] This application provides a method and system for intelligent management and adaptive temperature regulation of lithium batteries in harsh environments, addressing the existing lack of intelligent management and adaptive temperature regulation of lithium batteries in complex harsh environments. This application establishes a threat assessment system based on the fusion of multiple environmental factors and a differentiated charge and discharge management strategy. Furthermore, through internal resistance inversion temperature detection and PID self-heating control, it significantly improves the power supply reliability and battery life of UPS systems in harsh environments such as subway cross-river tunnels.

[0005] In a first aspect, the present application provides a method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments, the method comprising: Step S1: For the cross-river tunnel environment of the subway UPS system, high humidity, salt spray corrosion and seasonal low temperature environment data are collected simultaneously to establish a composite harsh environment monitoring database; Step S2: Based on the composite harsh environment monitoring database, a Kalman filter algorithm is used to fuse the influence of multiple environmental factors to calculate the UPS lithium battery environmental adaptability threat index; Step S3: generating differentiated lithium battery charge and discharge management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system floating charge-constant current and voltage limiting switching requirements; Step S4: Based on the differentiated lithium battery charge and discharge management parameters, inverting the actual operating temperature of the lithium battery in a low temperature environment by measuring the change in the battery internal resistance; Step S5: When the actual operating temperature of the lithium battery affects the power supply capacity of the UPS system, the carbon fiber heating film self-heating system is started, and the PID temperature control algorithm is used to maintain the optimal working state of the battery.

[0006] In a second aspect, the present application provides a system for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments, the system comprising: The acquisition module is used to simultaneously collect data on high humidity, salt spray corrosion, and seasonal low temperature environments in the cross-river tunnel environment of the subway UPS system, and establish a database for monitoring composite harsh environments; A calculation module is used to calculate the environmental adaptability threat index of the UPS lithium battery based on the composite harsh environment monitoring database and by using a Kalman filter algorithm to fuse the influence of multiple environmental factors; A switching module is used to generate differentiated lithium battery charge and discharge management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system float charge-constant current and voltage limiting switching requirements; An inversion module, configured to invert the actual operating temperature of the lithium battery in a low temperature environment by measuring the change in the battery internal resistance based on the differentiated lithium battery charge and discharge management parameters; The starting module is used to start the carbon fiber heating film self-heating system when the actual operating temperature of the lithium battery affects the power supply capacity of the UPS system, and adopt the PID temperature control algorithm to maintain the optimal working state of the battery.

[0007] In a third aspect, a device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments executes the above-mentioned method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments.

[0008] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, which, when executed on a computer, enables the computer to execute the above-mentioned method for intelligent management and temperature adaptive adjustment of lithium batteries in harsh environments.

[0009] The technical solution provided in this application establishes a database for monitoring composite harsh environments and simultaneously collects multi-dimensional environmental data such as high humidity, salt spray corrosion, and seasonal low temperatures in the cross-river tunnel environment of the subway UPS system. This overcomes the limitations of existing technologies that rely solely on monitoring a single environmental parameter and provides a comprehensive and accurate data basis for intelligent management decisions. The Kalman filter algorithm is used to integrate the influence of multiple environmental factors to calculate the environmental adaptability threat index of the UPS lithium battery, effectively solving the technical problem that traditional methods cannot accurately assess the comprehensive impact of composite harsh environments. The algorithm significantly improves the accuracy and stability of environmental threat assessment through a recursive process of state prediction and error correction. Based on the environmental adaptability threat index and the floating charge-constant current voltage limiting switching requirements of the UPS system, differentiated lithium battery charge and discharge management parameters are generated, breaking through the technical bottleneck of the existing technology that adopts a fixed charge and discharge strategy, and realizing dynamic matching of the battery management strategy with actual working conditions and environmental conditions. Based on the differentiated charge and discharge management parameters, the actual operating temperature of the lithium battery is inverted by measuring the change in the battery internal resistance, solving the technical problem that traditional surface temperature sensors cannot accurately reflect the internal thermodynamic state of the battery, and providing a more reliable temperature reference for temperature adaptive regulation. When the actual operating temperature of the lithium battery affects the power supply capacity of the UPS system, the carbon fiber heating film self-heating system is activated and the PID temperature control algorithm is used to maintain the optimal working state of the battery, effectively solving the technical defect of the existing technology that lacks active temperature regulation capability, and ensuring the continuous and reliable power supply of the UPS system in harsh environments.

[0010] In the specific application area of ​​subway UPS systems, the core algorithmic features of this application make a key contribution to the overall solution. The application of the Kalman filter algorithm in complex and harsh environment monitoring, through its inherent prediction-correction mechanism and noise suppression capabilities, effectively handles sensor measurement noise and random fluctuations in environmental parameters, providing high-precision data support for threat index calculation. The application of the fuzzy logic control algorithm in charge and discharge management strategy generation, leveraging its advantages in handling uncertainty and nonlinear relationships, successfully establishes an intelligent mapping relationship between environmental threat levels and battery management parameters, addressing the difficulty of traditional deterministic control methods in coping with complex environmental changes. The application of the Arrhenius equation in battery internal temperature inversion, based on its physical mechanism describing the relationship between chemical reaction rate and temperature, enables accurate inference from changes in battery internal resistance to internal temperature, providing a scientific and reliable temperature detection method for adaptive temperature regulation. The application of the PID temperature control algorithm in the self-heating system, through its proportional-integral-derivative control characteristics, achieves precise regulation of heating power and stable temperature control, effectively preventing temperature overshoot and oscillation, and ensuring stable operation of the battery within the optimal operating temperature range. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 This is a schematic diagram of an embodiment of a method for intelligent management and temperature adaptive adjustment of lithium batteries in harsh environments in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a lithium battery intelligent management and temperature adaptive adjustment system in a harsh environment in an embodiment of the present application; Figure 3 This is a schematic block diagram of the structure of a lithium battery intelligent management and temperature adaptive adjustment device in a harsh environment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a method and system for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0014] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments includes: Step S1: For the cross-river tunnel environment of the subway UPS system, high humidity, salt spray corrosion and seasonal low temperature environment data are collected simultaneously to establish a composite harsh environment monitoring database; Step S2: Based on the composite harsh environment monitoring database, the Kalman filter algorithm is used to integrate the influence of multiple environmental factors and calculate the environmental adaptability threat index of the UPS lithium battery; Step S3: Generate differentiated lithium battery charge and discharge management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system floating charge-constant current and voltage limiting switching requirements; Step S4: Based on the differentiated lithium battery charge and discharge management parameters, the actual operating temperature of the lithium battery in a low temperature environment is inverted by measuring the change in the battery internal resistance; Step S5: When the actual operating temperature of the lithium battery affects the power supply capacity of the UPS system, the carbon fiber heating film self-heating system is started and the PID temperature control algorithm is used to maintain the optimal working state of the battery.

[0015] It is understandable that the execution subject of this application can be a lithium battery intelligent management and temperature adaptive adjustment system in harsh environments, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0016] Specifically, the PT1000 platinum resistance temperature sensor array uses a three-wire connection system and measures temperature based on the linear relationship between platinum metal resistance and temperature. When the ambient temperature changes, the platinum resistance changes according to a specific temperature coefficient. This resistance change is converted into a voltage signal via a Wheatstone bridge circuit, and then acquired through an analog-to-digital converter to obtain digitized data on the internal temperature distribution of the battery. Meanwhile, the capacitive humidity sensor operates based on the principle of dielectric constant variation. When the ambient humidity changes, the polymer film within the sensor absorbs or releases moisture, causing the dielectric constant to change, which in turn changes the capacitance. This capacitance change is converted into a frequency signal via an oscillating circuit, ultimately providing humidity data. The conductivity sensor detects salt spray concentration by measuring the ionic conductivity of the solution. An AC voltage is applied between the sensor electrodes, and the conductivity value is calculated according to Ohm's law. Higher salt spray concentrations increase the conductivity, thereby providing data on the environmental corrosiveness. Data integration processing synchronizes and aligns the data from these three sensor types based on timestamps to establish a composite harsh environment monitoring database encompassing three environmental parameters: temperature, humidity, and corrosiveness.

[0017] The prediction phase calculates predicted environmental parameter values ​​based on state transition equations, which describe how environmental parameters change over time. A mathematical model established using historical data predicts the environmental state at the next moment. The error calculation process compares the predicted values ​​with the actual sensor values ​​to calculate the prediction error covariance matrix, which reflects the uncertainty of the predicted values. The environmental monitoring noise covariance matrix is ​​derived through statistical analysis of sensor measurement errors and includes the covariance information of temperature, humidity, and corrosion measurement noise. The dynamic adjustment process updates the Kalman gain based on the noise covariance matrix. The Kalman gain determines the weighting of the predicted and measured values ​​during the fusion process, with sensors with greater noise receiving smaller weights. The weighted fusion process linearly combines multiple environmental factor data according to fusion weight coefficients to generate the UPS lithium battery environmental adaptability threat index, which comprehensively reflects the impact of complex harsh environments on lithium battery performance.

[0018] A fuzzy logic controller is used to generate differentiated charge and discharge management parameters. The membership calculation process uses the environmental adaptability threat index and SOC status as input variables, and calculates the degree of belonging of each input variable to different fuzzy sets through a predefined membership function. The threat level membership value indicates the degree to which the current environmental state is a mild, moderate, or severe threat. The rule matching process searches for control rules that match the current membership value in the preset fuzzy rule base. The rule base contains charge and discharge strategy rules for different threat levels and SOC status combinations. Each rule defines the charging current limit and discharge cut-off voltage setting value under specific conditions. The fuzzy inference calculation performs inference operations based on the activated rule set, and merges the outputs of multiple rules through fuzzy operations to obtain a fuzzy control output. The center of gravity defuzzification process calculates the center of gravity position of the fuzzy output set, converts the fuzzy value into a numerical value, and obtains the specific charge and discharge current control parameters. The dual-condition adaptation processing adjusts parameters for the two working modes of the UPS system. In the floating charge working mode, a small current trickle charge is required to keep the battery fully charged. In the constant current and voltage limited discharge mode, a large current continuous discharge capability is required. The control parameters corresponding to the two working modes are automatically switched according to the mains status detection signal.

[0019] The operating temperature is inverted by the change in the battery's internal resistance. The charge and discharge current control process controls the current of the lithium battery according to differentiated management parameters, while monitoring the battery terminal voltage and current in real time, and uses Ohm's law to calculate the instantaneous internal resistance value to obtain the real-time internal resistance change data of the lithium battery. The lithium ion mobility and temperature relationship model is established based on the Arrhenius equation, which describes the exponential change relationship between ionic conductivity and temperature. The electrochemical parameter calculation process substitutes the internal resistance data into the model to calculate the internal resistance-temperature correlation coefficient that reflects the activity of the electrochemical reaction inside the battery. The temperature inversion calculation process of the Arrhenius equation uses logarithmic transformation and linear regression methods to infer the actual temperature inside the battery from the correlation coefficient. The temperature inversion value truly reflects the thermodynamic state inside the battery. The comparison and judgment process compares the inverted temperature value with the preset low temperature environment threshold. When the inverted temperature is lower than the threshold, it is determined that the battery is in a low temperature working state and temperature regulation measures need to be initiated.

[0020] The temperature impact judgment process compares the actual operating temperature of the lithium battery with the minimum power supply temperature requirement of the UPS system. When the battery temperature is too low and affects the ion mobility, the internal resistance of the battery increases sharply, resulting in a decrease in power supply capacity. At this time, a self-heating start trigger signal is generated. The carbon fiber heating film self-heating system starts working after receiving the trigger signal. The heating film uses the resistive heating characteristics of the carbon fiber material to control the heating power output through PWM modulation. The PID temperature controller adopts a proportional-integral-differential control algorithm. The proportional term responds to the current temperature deviation, the integral term eliminates the steady-state error, and the differential term predicts the temperature change trend. The three control effects are comprehensively calculated to obtain the PID adjustment control parameters. The dynamic adjustment process adjusts the heating power of the heating film in real time according to the PID control parameters. When the actual temperature is close to the target temperature, the heating power is reduced. When the temperature deviation is large, the heating power is increased. The battery is maintained within the optimal operating temperature range through closed-loop control.

[0021] In a specific embodiment, step S1 includes: The PT1000 platinum resistance temperature sensor array deployed inside the lithium battery module is used to collect and process temperature data to obtain the temperature distribution data inside the battery. The capacitive humidity sensor arranged on the surface of the lithium battery shell is used to monitor humidity and obtain environmental humidity change data; Conductivity sensors are deployed around the UPS system to detect salt spray concentration and obtain environmental corrosiveness data. The data integration and processing are performed based on the battery internal temperature distribution data, the environmental humidity change data and the environmental corrosive intensity data to obtain a composite harsh environment monitoring database.

[0022] Specifically, the temperature data acquisition and processing of the PT1000 platinum resistor temperature sensor array is based on the temperature characteristics of platinum metal resistors. The resistance of platinum metal is linearly related to temperature. At zero degrees Celsius, the resistance is 1000 ohms, and the resistance increases by approximately 3.85 ohms for every 1 degree Celsius increase in temperature. The sensor array uses a four-wire measurement method to eliminate the influence of lead resistance. A standard current is injected into the platinum resistor through a constant current source, and the voltage drop across the platinum resistor is measured. The resistance value is calculated according to Ohm's law, and then the resistance value is converted to the corresponding temperature value through a table lookup or linear interpolation method. Multiple sensors inside the lithium battery module operate simultaneously, each sensor is responsible for monitoring the temperature of a specific area. The analog-to-digital converter digitizes the voltage signal of each sensor at a fixed sampling frequency, generating a time series temperature data stream. After filtering to remove high-frequency noise, the internal battery temperature distribution data is formed. This data reflects the thermal state distribution at different locations within the battery.

[0023] The humidity monitoring process of a capacitive humidity sensor utilizes the hygroscopic properties of polymer films. The polymer dielectric layer inside the sensor absorbs or releases moisture under different humidity environments, causing the dielectric constant to change, and thus changing the capacitance value. The sensor circuit converts the capacitance change into a frequency change, and the oscillation frequency is measured by a frequency counter. There is a corresponding relationship between the frequency and the ambient humidity. The data processing unit converts the measured frequency value into a relative humidity percentage value based on a pre-calibrated frequency-humidity correspondence table. Multiple humidity sensors arranged on the surface of the lithium battery casing form a monitoring network. The measurement data of each sensor is transmitted to the central processing unit via the data bus. The processor averages the humidity data from different locations to eliminate the influence of local humidity fluctuations and generate ambient humidity change data representing the overall ambient humidity state.

[0024] The conductivity sensor's salt spray concentration detection process is based on the proportional relationship between solution conductivity and ion concentration. Chloride and sodium ions in salt spray increase the solution's conductivity, and higher conductivity values ​​indicate greater salt spray concentration. The sensor uses a four-electrode method: two current electrodes apply an AC excitation signal, and two voltage electrodes measure the voltage drop in the solution, minimizing the impact of electrode polarization on measurement accuracy. A signal conditioning circuit amplifies and filters the weak voltage signal, and an analog-to-digital converter converts the analog signal into a digital signal. A microprocessor calculates conductivity based on the measured voltage and current values. This conductivity value is then converted to a corresponding salt spray concentration using an empirical formula or calibration curve. Multiple conductivity sensors are deployed around the UPS system, covering different monitoring areas. The measurement results from each sensor are weighted averaged, with the weighting factor determined by the sensor's distance from the UPS equipment. Closer proximity results in a greater weighting, ultimately generating environmental corrosiveness data.

[0025] Data integration processing uses time synchronization and spatial interpolation methods to fuse the three types of sensor data. First, all sensor data is timestamp-aligned to ensure that different types of data at the same moment can be correctly matched. Temperature distribution data is generated using a spatial interpolation algorithm to generate a continuous temperature field distribution. Humidity change data and corrosive intensity data are filtered to eliminate sudden noise. The database structure adopts a relational design, with a main table storing timestamp information and subtables storing temperature, humidity, and corrosiveness data respectively. Foreign key associations establish logical relationships between the data. A data compression algorithm compresses and stores historical data, while an indexing mechanism is established to accelerate data retrieval, forming a composite harsh environment monitoring database that supports real-time queries and historical analysis.

[0026] In a specific embodiment, step S2 includes: The environmental parameters in the composite harsh environment monitoring database are input into the Kalman filter for state prediction processing to obtain the predicted values ​​of the environmental parameters; The error calculation is performed based on the predicted values ​​of environmental parameters and the measured environmental parameters to obtain the environmental monitoring noise covariance matrix; Dynamically adjust the environmental parameter weight coefficients according to the environmental monitoring noise covariance matrix to obtain the fusion weight coefficient matrix; The fusion weight coefficient matrix and multiple environmental factor data are weightedly fused and calculated to obtain the UPS lithium battery environmental adaptability threat index.

[0027] Specifically, the state prediction processing of the Kalman filter predicts environmental parameters based on a dynamic state space model. The filter contains two core components: the state transfer equation and the observation equation. The state transfer equation describes the evolution of environmental parameters over time, taking the temperature, humidity, and corrosive intensity of the previous moment as the state vector input, and calculating the predicted state at the next moment through the state transfer matrix. The elements of the state transfer matrix are determined according to the statistical laws of historical data, reflecting the mutual influence relationship and time evolution characteristics between various environmental parameters. The observation equation establishes a mapping relationship between the real environmental state and the sensor measurement value, taking into account the measurement noise and nonlinear characteristics of the sensor. The prediction process first uses the historical environmental parameters in the composite harsh environment monitoring database as the initial state input, and recursively calculates the predicted value of the environmental parameters at the current moment through the state transfer equation. The predicted value contains data from three dimensions: temperature prediction component, humidity prediction component, and corrosive intensity prediction component.

[0028] The error calculation process compares the predicted environmental parameter values ​​with the actual environmental parameters measured by the sensor one by one to calculate the prediction error vector. The temperature prediction error is equal to the predicted temperature value minus the measured temperature value, the humidity prediction error is equal to the predicted humidity value minus the measured humidity value, and the corrosivity prediction error is equal to the predicted corrosivity intensity value minus the measured corrosivity value. The outer product operation of the error vector generates an error covariance matrix. The diagonal elements of this matrix represent the variance of each environmental parameter prediction error, and the off-diagonal elements represent the covariance between the prediction errors of different environmental parameters. The environmental monitoring noise covariance matrix is ​​recursively updated using a sliding window method. New error data is added to the window while the oldest data is removed, maintaining a fixed number of statistical samples. The covariance matrix is ​​updated using a weighted average method, with more recent error data given a higher weight and the weight of long-term data gradually decreasing, ensuring that the noise covariance matrix can reflect the current measurement accuracy status.

[0029] The dynamic adjustment process calculates the Kalman gain based on the environmental monitoring noise covariance matrix. The Kalman gain determines the weighting of predicted and measured values ​​in state updates. When the measurement noise of a particular environmental parameter is high, the corresponding Kalman gain is low, indicating greater trust in the predicted value over the measured value. Conversely, when the measurement noise is low, the Kalman gain is high, indicating greater trust in the measured value. The dynamic adjustment of the weight coefficients is based on the optimal estimation principle in information theory, achieving optimal information fusion by minimizing the covariance of the estimation errors. The calculation process of the fusion weight coefficient matrix involves summing the prediction covariance matrix and the observation noise covariance matrix, then performing a matrix inversion to obtain the Kalman gain matrix. Finally, the Kalman gain matrix is ​​normalized to obtain the fusion weight coefficient matrix. The row vectors of the weight coefficient matrix correspond to different environmental parameters, and the column vectors correspond to different sensor channels. The matrix elements vary between zero and one, and the sum of all weight coefficients equals one.

[0030] The weighted fusion calculation process performs matrix multiplication on the fusion weight coefficient matrix and the multi-environmental factor data to obtain the fused environmental state estimate. The temperature fusion value is equal to the temperature weight coefficient multiplied by the temperature measurement value plus the temperature prediction weight coefficient multiplied by the temperature prediction value. The fusion calculation of humidity and corrosive parameters uses the same weighted summation method. The calculation of the UPS lithium battery environmental adaptability threat index is based on the fused environmental state estimate. The threat assessment function maps the multidimensional environmental parameters into a single threat level indicator. The threat assessment function uses a weighted linear combination form. The degree to which the temperature deviates from the optimal operating range is multiplied by the temperature threat coefficient, the degree to which the humidity exceeds the safety threshold is multiplied by the humidity threat coefficient, and the corrosive intensity level is multiplied by the corrosion threat coefficient. The sum of these three threat components yields the overall threat index. The threat coefficient is set based on the performance degradation pattern of UPS lithium batteries under different environmental conditions. The quantitative threat weight parameter is obtained by fitting experimental data.

[0031] In a specific embodiment, step S3 includes: The UPS lithium battery environmental adaptability threat index and the current SOC state of the lithium battery are input into the fuzzy logic controller for membership calculation and processing to obtain the threat level membership value; Based on the threat level membership value, the preset fuzzy rule base is matched to obtain the activation set of charge and discharge control rules; According to the charging and discharging control rule activation set, fuzzy reasoning calculation processing is performed to obtain the fuzzy output values ​​of the charging current limit and the discharge cut-off voltage; The fuzzy output value is defuzzified by the centroid method to obtain the charge and discharge current control parameters; Based on the UPS system's floating charge working mode during normal power supply and constant current and voltage-limited discharge mode during power outage, the charge and discharge current control parameters are adapted to dual working conditions to obtain differentiated lithium battery charge and discharge management parameters.

[0032] Specifically, the membership calculation process of the fuzzy logic controller first defines the fuzzy sets of the input variables. The UPS lithium battery environmental adaptability threat index is divided into three fuzzy sets: low threat, medium threat, and high threat. Each set corresponds to a trapezoidal or triangular membership function. The membership function describes the degree of belonging of the input value to each fuzzy set through a piecewise linear function. When the threat index is in the core area of ​​a set, the membership value is 1. When it is in the boundary area, the membership value changes linearly between 0 and 1. When it is outside the set, the membership value is 0. The current SOC state of the lithium battery is also divided into three fuzzy sets: low power, medium power, and high power, using a similar membership function definition method. The membership calculation process substitutes the specific values ​​of the threat index and SOC state into the corresponding membership function respectively, and obtains the membership value of each fuzzy set through piecewise function calculation. The threat level membership value is a vector containing six elements. The first three elements correspond to the three fuzzy set memberships of the threat index, and the last three elements correspond to the three fuzzy set memberships of the SOC state.

[0033] The rule matching process searches a preset fuzzy rule base for a control rule that matches the current membership value. The fuzzy rule base contains nine basic rules, each using an "if-then" conditional statement format. The rule antecedent is a fuzzy set combination of the threat level and the SOC state, while the rule consequent is a fuzzy set setting for the charge current limit and the discharge cutoff voltage. The rule activation level is determined by minimizing the membership values ​​of the antecedent fuzzy set. When multiple conditions in the antecedent are simultaneously met, the minimum membership value of each condition is used as the rule activation strength. The active set of charge and discharge control rules contains all rules with an activation strength greater than zero and their corresponding activation strength values, forming a mapping between rule numbers and activation strengths. The rule matching algorithm traverses the entire rule base, calculating the activation strength of each rule one by one. Rules with activation strengths greater than a preset threshold are added to the active set. The size of the active set depends on the degree of match between the current input state and the rule antecedent.

[0034] The fuzzy inference calculation process uses the Mamdani inference method, which performs parallel inference operations on each rule in the activation set. The consequent fuzzy set of each activated rule is pruned according to the activation strength of the rule. The height of the pruned fuzzy set is equal to the activation strength value, and the width maintains the original membership function shape. The fuzzy output of the charging current limit is obtained by merging the consequent fuzzy sets of all relevant rules. The merging operation adopts the maximum value operation, that is, taking the maximum value of the membership of each fuzzy set at each domain point. The fuzzy output of the discharge cutoff voltage uses the same merging method, and ultimately obtains two composite fuzzy sets as the fuzzy output value. The shape of the fuzzy output value reflects the weight distribution of different control strategies. The peak position corresponds to the optimal control parameter, and the peak height reflects the degree of certainty of the parameter.

[0035] The centroid defuzzification process converts fuzzy output values ​​into numerical control parameters, and the centroid calculation uses the area integral method. The defuzzification calculation of the charging current limit divides the domain of the fuzzy output set into several small intervals. The area of ​​each interval is equal to the interval width multiplied by the membership value of that point. The centroid position is equal to the sum of the product of the center position of each interval and the corresponding area divided by the total area. The defuzzification calculation of the discharge cutoff voltage uses the same centroid method to obtain the voltage value. Defuzzification converts the charge and discharge current control parameters from fuzzy language descriptions into specific engineering parameters. The accuracy of the control parameters depends on the discretization of the domain and the design accuracy of the membership function.

[0036] The dual-condition adaptation processing makes differential adjustments to the control parameters according to the two typical working modes of the UPS system. In the floating charge working mode, the UPS system supplies power normally, and the lithium battery needs to be kept fully charged when in standby mode. At this time, the charging current control parameter is adjusted to a small current trickle charge value to prevent overcharging from damaging the battery while compensating for self-discharge losses. In the constant current and voltage-limited discharge mode, the UPS system needs emergency power supply from the lithium battery when it is powered off. At this time, the discharge cut-off voltage control parameter is adjusted to a more conservative value to ensure stable power output even in harsh environments. Working condition identification is achieved by monitoring the mains status signal. When the mains power is normal, the floating charge working mode parameters are enabled, and when the mains power is abnormal, it automatically switches to the discharge mode parameters. The differentiated lithium battery charge and discharge management parameters include two complete sets of control parameters. Each set of parameters is optimized for specific working conditions. The switching process uses a smooth transition algorithm to avoid parameter mutations causing impact on the battery.

[0037] In a specific embodiment, the process of performing dual-condition adaptation processing on the charge and discharge current control parameters based on the floating charge working mode of the UPS system during normal power supply and the constant current and voltage-limited discharge mode during power outage may specifically include the following steps: Based on the floating charge working mode requirement of maintaining the battery fully charged when the UPS system is normally powered, the charge and discharge current control parameters are adapted to the low current trickle charge to obtain the floating charge mode current control parameters; According to the constant current and voltage-limited discharge mode requirements for powering the load when the UPS system is powered off, the charge and discharge current control parameters are adapted to large current continuous discharge to obtain the discharge mode current control parameters; The floating charge mode current control parameters and the discharge mode current control parameters are processed by the working mode switching logic design based on the mains status detection signal to obtain the floating charge-discharge mode automatic switching control strategy; Based on the floating charge-discharge mode automatic switching control strategy, the lithium battery charge and discharge parameters are dynamically matched in real time to obtain differentiated lithium battery charge and discharge management parameters.

[0038] Specifically, the low-current trickle charge adaptation process adjusts parameters based on the special requirements of the UPS system's float charge mode. The float charge mode refers to the UPS system's mode of operation in which the lithium battery is kept fully charged when the mains power is normally supplied. Trickle charging is a low-current density charging method, where the charging current is typically set to 5% to 10% of the battery capacity. The purpose is to compensate for the battery's self-discharge losses without causing overcharging. The adaptation process first obtains the charge and discharge current control parameters output by the fuzzy logic controller as the base value, and then performs a downward adjustment based on the current limit requirements of the float charge mode. The current reduction algorithm uses a proportional reduction method to multiply the base charging current by the float charge mode coefficient to obtain the adjusted current value. The float charge mode coefficient is typically between 0.1 and 0.3, and the specific value is determined by the battery type and environmental conditions. At the same time, the impact of harsh environments on the battery's self-discharge rate is taken into account. In high-temperature and high-humidity environments, the self-discharge rate increases, requiring a corresponding increase in the trickle charge current. In low-temperature environments, the self-discharge rate decreases, requiring a decrease in the trickle charge current. The float charge mode current control parameters also include the adjustment of the charge cut-off voltage. The cut-off voltage is set 0.1 to 0.2 volts lower than the standard charge voltage to avoid the risk of overcharging in the long-term float charge state.

[0039] High-current continuous discharge adaptation optimizes parameters to meet the emergency power requirements of the UPS system during power outages. Constant-current and voltage-limited discharge mode operates in this mode, where the lithium battery supplies power to the load at a constant current during a utility power outage while simultaneously monitoring the battery voltage to prevent over-discharge. High-current discharge capability directly impacts the UPS system's power supply time and load-carrying capacity. In harsh environments, the impact of temperature on discharge performance must be considered. Adaptation adjusts the discharge current and cutoff voltage based on the output parameters of the fuzzy logic controller. Discharge current adjustment utilizes an environmental compensation algorithm. When the ambient temperature falls below the optimal operating temperature, the discharge current limit is lowered based on the temperature coefficient to prevent voltage drops caused by increased internal resistance in low-temperature environments. Cutoff voltage adjustment accounts for the uncertainty of battery performance in harsh environments, setting the cutoff voltage 0.2 to 0.5 volts higher than the standard value to provide a safety margin and ensure stable UPS system power supply. Discharge mode current control parameters also include dynamic adjustment of the discharge rate, calculating the optimal discharge rate based on load demand and the current battery status to maximize discharge time while ensuring power supply requirements.

[0040] The operating mode switching logic is designed to establish an automatic switching mechanism between float charge and discharge modes. The mains status detection signal is the core input parameter of the switching logic. Mains status detection is based on three dimensions: voltage amplitude detection, frequency detection, and phase detection. When the mains voltage deviates from the rated value beyond the set range, the frequency fluctuation exceeds the allowable deviation, or a phase loss fault occurs, the detection circuit outputs a mains abnormality signal. The switching logic adopts a state machine design approach, defining four basic states: normal power supply, abnormality detection, switching, and fault. The state transition conditions are based on the mains detection signal and battery status information. The automatic floating charge-discharge mode switching control strategy includes switching timing control and parameter transition control. The switching timing control ensures timely response to mains status changes, while the parameter transition control prevents sudden parameter changes during the switching process from impacting the battery. The switching delay is set to prevent false triggering. The switching action is triggered only when the mains abnormality signal lasts longer than a preset threshold, avoiding erroneous switching caused by transient interference.

[0041] Real-time dynamic matching processing continuously adjusts the lithium battery charge and discharge parameters according to the automatic switching control strategy. The matching algorithm uses a combination of interpolation and filtering. Parameter interpolation processing generates intermediate transition parameters during the mode switching process to avoid parameter step changes. The interpolation function uses a cubic spline curve to ensure the smoothness of parameter changes. Filtering processing suppresses parameter fluctuations during the switching process, and a first-order low-pass filter is used to eliminate the impact of high-frequency noise on parameter stability. Differentiated lithium battery charge and discharge management parameters include data in four dimensions: current limit, voltage threshold, charge and discharge strategy, and protection parameters. Each dimension is adjusted in real time according to the current operating conditions and environmental status. The parameter update frequency is set to ten times per second to ensure that parameter adjustments can respond to environmental changes and operating condition switching in a timely manner, while avoiding overly frequent adjustments that affect system stability.

[0042] In a specific embodiment, step S4 includes: Based on differentiated lithium battery charge and discharge management parameters, the charge and discharge current of the lithium battery is controlled to obtain real-time internal resistance change data of the lithium battery; The real-time internal resistance change data of the lithium battery is input into the lithium ion mobility and temperature relationship model to calculate the electrochemical parameters and obtain the battery internal resistance-temperature correlation coefficient; Perform temperature inversion calculation on the Arrhenius equation based on the battery internal resistance-temperature correlation coefficient to obtain the battery internal temperature inversion value; The actual operating temperature of the lithium battery is obtained by comparing the inverted value of the battery internal temperature with the low temperature environment threshold.

[0043] Specifically, the charge and discharge current control process precisely regulates the lithium battery's current based on differentiated lithium battery charge and discharge management parameters. This control process utilizes PWM (pulse width modulation) technology to achieve precise current control. The PWM controller sets the duty cycle based on the current limit in the differentiated management parameters. The duty cycle is calculated based on the ratio of the target current to the maximum output current. When the target charge current is 30% of the maximum current, the PWM duty cycle is set to 30%. The current control circuit comprises a current sense resistor, an operational amplifier, and a power switch. The current sense resistor converts the flowing current into a voltage signal. The operational amplifier amplifies and conditions the voltage signal, and the power switch controls the actual charge and discharge current based on the PWM signal. Real-time internal resistance measurement utilizes the AC impedance method, which measures the AC response of the battery terminal voltage by superimposing a small-amplitude AC test signal on the DC charge and discharge current. The internal resistance is calculated using Ohm's law, dividing the AC voltage amplitude by the AC current amplitude to obtain the AC internal resistance. The AC internal resistance more accurately reflects the actual impedance characteristics of the battery. The real-time internal resistance change data of the lithium battery is obtained through continuous measurement and data acquisition. The sampling frequency is set to once per second, forming time series data of the internal resistance changing with time.

[0044] The electrochemical parameter calculation process inputs real-time internal resistance change data into a lithium-ion mobility-temperature relationship model for analysis. This model is based on the ionic conduction mechanism of the solid electrolyte interface membrane. Lithium-ion mobility refers to the ability of lithium ions to move within the electrolyte and directly affects the battery's internal resistance. Temperature is the primary factor affecting ion mobility. The relationship model uses an Arrhenius-type function to describe the exponential relationship between mobility and temperature. The mobility increases exponentially with increasing temperature and decays exponentially with decreasing temperature. The electrochemical parameter calculation process first substitutes the measured internal resistance value into the mobility equation and calculates the corresponding ion mobility value using an inverse function. Based on the known relationship between mobility and temperature, the sensitivity coefficient of mobility to temperature changes is calculated. This coefficient reflects the strength of the correlation between internal resistance and temperature changes. The battery internal resistance-temperature correlation coefficient is calculated using statistical analysis methods. A linear regression analysis is performed between the change in internal resistance over a period of time and the corresponding change in temperature. The regression coefficient is the internal resistance-temperature correlation coefficient. The value of the correlation coefficient reflects the sensitivity of the internal resistance to temperature changes; a larger coefficient indicates a greater sensitivity to temperature changes.

[0045] The temperature inversion calculation is based on the inverse of the Arrhenius equation, which describes the relationship between chemical reaction rate constants and temperature. In battery applications, it is used to describe the relationship between ionic conductivity and temperature. The basic form of the equation is an exponential function where the reaction rate constant is equal to the pre-exponential factor multiplied by the negative activation energy divided by the product of the gas constant and the absolute temperature. The activation energy is the energy barrier required for ion migration. The inversion calculation performs a logarithmic transformation on the Arrhenius equation, converting the exponential relationship into a linear relationship. The temperature value is then solved through algebraic operations. The calculation process first substitutes the internal resistance-temperature correlation coefficient into the transformed linear equation, then solves for the reciprocal of the absolute temperature. Finally, the reciprocal is converted to Celsius to obtain the inverted value of the battery's internal temperature. The inversion calculation takes into account the effects of battery aging and environmental factors on the parameters of the Arrhenius equation. The activation energy and pre-exponential factor parameters are regularly calibrated to ensure the accuracy of the inverted temperature. The inverted temperature value represents the true thermodynamic temperature inside the battery and better reflects the actual operating state of the battery than the value measured by a surface temperature sensor.

[0046] The comparison and judgment process compares the inverted battery internal temperature value with a preset low-temperature threshold to determine whether the battery is operating in a low-temperature state. The low-temperature threshold is set based on the performance characteristic curve of lithium batteries. When the battery temperature falls below this threshold, ion mobility decreases significantly, causing a sharp increase in internal resistance and significantly deteriorating the battery's charge and discharge performance. The threshold setting also takes into account the power supply reliability requirements of the UPS system, promptly initiating temperature adjustment measures when the battery temperature affects power supply capacity. The comparison and judgment is implemented using a digital comparator. When the inverted temperature value is less than the threshold, a low-temperature state signal is output; when the inverted temperature value is greater than or equal to the threshold, a normal temperature state signal is output. The judgment result also includes the calculation of the temperature deviation. The deviation value is equal to the threshold value minus the inverted temperature value. A larger deviation indicates a lower temperature and requires more heating power. The actual operating temperature of the lithium battery is determined by combining the inverted temperature value and the judgment result. When the battery is in a low-temperature state, the actual operating temperature is equal to the inverted temperature value. When the battery is in a normal state, the actual operating temperature is set to the threshold temperature. This processing method ensures a timely response to temperature adaptive adjustment.

[0047] In a specific embodiment, step S5 includes: The actual operating temperature of the lithium battery and the minimum temperature threshold of the UPS system power supply capacity are used to determine the temperature impact and obtain a self-heating start trigger signal; Based on the self-heating start trigger signal, the carbon fiber heating film self-heating system is powered by power control to obtain a preset heating power output; The actual operating temperature and target operating temperature of the lithium battery are input into the PID temperature controller to calculate the temperature deviation and obtain the PID adjustment control parameters; The heating power of the carbon fiber heating film is dynamically adjusted according to the PID adjustment control parameters to obtain the temperature control result of the optimal working state of the battery.

[0048] Specifically, the temperature impact determination process uses a digital comparator to compare the actual operating temperature of the lithium battery with the UPS system's minimum power supply capacity threshold in real time. This threshold is a critical temperature value determined based on the UPS system's power supply reliability requirements in harsh environments. The UPS system's minimum power supply capacity threshold is set based on the electrochemical performance degradation curve of lithium batteries in low-temperature environments. When the battery temperature falls below this threshold, the internal resistance increases, resulting in voltage drop and capacity decay, directly affecting the UPS system's power quality and duration. The determination process uses a hysteresis comparator to prevent frequent triggering when the temperature fluctuates near the threshold. The comparator sets upper and lower thresholds. When the actual temperature drops from the high temperature range to below the lower threshold, it outputs a start signal. When the actual temperature rises from the low temperature range to above the upper threshold, it stops outputting the signal. The self-heating start trigger signal is a digital logic signal. A high level indicates that the self-heating function is activated, while a low level indicates that heating is not required. This signal directly controls the operating state of the carbon fiber heating film self-heating system. The trigger signal generation also considers the battery SOC and UPS load demand. When the battery charge is sufficient and the load demand is high, the trigger threshold is increased accordingly to ensure sufficient power supply capacity.

[0049] The power control process sets the initial power for the carbon fiber heating film self-heating system based on the self-heating start trigger signal. The carbon fiber heating film is a flexible heating element that generates evenly distributed heat through the resistive heating properties of carbon fiber material. The heating film is driven by a low-voltage DC power supply with a rated operating voltage of 24V and a power density of 20 watts per square meter. The heating film is placed in close contact with the surface of the lithium battery module, and a thermally conductive silicone sheet is used to enhance heat conduction. The power control circuit includes a power switch, a current sense resistor, and a driver circuit. The power switch uses a MOSFET device with low on-resistance and fast switching characteristics. The preset heating power output is calculated based on the battery module's heat capacity and the target heating rate. The heat capacity is calculated by multiplying the battery mass by the specific heat capacity. The target heating rate is determined by the UPS system's response time requirements. The heating power calculation formula is power equal to heat capacity multiplied by the heating rate. For a battery mass of 50 kg, a specific heat capacity of 0.8 kilojoules per kilogram per degree Celsius, and a target heating rate of 2 degrees Celsius per minute, the required heating power is 1.33 kilowatts. The preset power output is achieved through PWM control, and the PWM duty cycle is calculated based on the ratio of the required power to the rated power.

[0050] The temperature deviation calculation process feeds the actual lithium battery operating temperature into a PID temperature controller to accurately quantify the deviation. The target operating temperature is the ideal temperature value determined based on the lithium battery's optimal electrochemical performance. The PID controller is a classic feedback control algorithm consisting of three control components: proportional, integral, and differential. The proportional term responds to the current temperature deviation, the integral term eliminates steady-state deviations, and the differential term predicts temperature trends and suppresses overshoot. The temperature deviation is equal to the target operating temperature minus the actual operating temperature. A positive deviation indicates a need for heating, while a negative deviation indicates a need to reduce heating power or stop heating. The proportional control parameter is calculated by multiplying the deviation by a proportional coefficient. The proportional coefficient is determined based on the thermal response characteristics of the heating element and the thermal inertia of the battery. The integral control parameter is calculated by accumulating historical deviations. The integral time constant is selected to balance response speed and stability requirements. The differential control parameter is calculated based on the rate of change of the deviation. The setting of the differential time constant takes into account the noise level of the temperature sensor and the dynamic response characteristics of the system. The PID control parameter is the weighted sum of the three control components, and the weight coefficient is optimized based on the specific application scenario.

[0051] Dynamic regulation adjusts the heating power of the carbon fiber heating film in real time based on PID control parameters. The regulation algorithm utilizes an incremental PID control method. This incremental PID method calculates the power increment of the current control cycle relative to the previous cycle, avoiding integral windup and output abrupt changes. The power increment is equal to the proportional increment plus the integral increment plus the differential increment. The proportional increment is equal to the proportional coefficient multiplied by the difference between the current and previous deviations; the integral increment is equal to the integral coefficient multiplied by the current deviation; and the differential increment is equal to the differential coefficient multiplied by the current deviation minus twice the previous deviation plus the combination of the previous two deviations. The actual output power is equal to the previous cycle power plus the current cycle power increment. Output power is ensured within the safe operating range of the heating film through upper and lower limit processing. Power regulation is achieved by adjusting the PWM duty cycle, with the PWM frequency set to 1 kHz to reduce electromagnetic interference and power loss. Optimal battery temperature control is achieved through closed-loop feedback control. When the actual temperature approaches the target temperature, the PID controller automatically reduces the heating power. When the temperature deviation is large, it increases the heating power, ultimately stabilizing the battery temperature within a small range around the target operating temperature.

[0052] The above describes the lithium battery intelligent management and temperature adaptive adjustment method under harsh environment in the embodiment of the present application. The following describes the lithium battery intelligent management and temperature adaptive adjustment system under harsh environment in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the lithium battery intelligent management and temperature adaptive adjustment system in harsh environments includes: The acquisition module is used to simultaneously collect data on high humidity, salt spray corrosion, and seasonal low temperature environments in the cross-river tunnel environment of the subway UPS system, and establish a database for monitoring composite harsh environments; A calculation module is used to calculate the environmental adaptability threat index of the UPS lithium battery based on the composite harsh environment monitoring database and by using a Kalman filter algorithm to fuse the influence of multiple environmental factors; A switching module is used to generate differentiated lithium battery charge and discharge management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system float charge-constant current and voltage limiting switching requirements; An inversion module, configured to invert the actual operating temperature of the lithium battery in a low temperature environment by measuring the change in the battery internal resistance based on the differentiated lithium battery charge and discharge management parameters; The starting module is used to start the carbon fiber heating film self-heating system when the actual operating temperature of the lithium battery affects the power supply capacity of the UPS system, and adopt the PID temperature control algorithm to maintain the optimal working state of the battery.

[0053] above Figure 2 From the perspective of modular functional entities, the lithium battery intelligent management and temperature adaptive adjustment system under harsh environments in an embodiment of the present invention is described in detail. The lithium battery intelligent management and temperature adaptive adjustment device under harsh environments in an embodiment of the present invention is described in detail from the perspective of hardware processing.

[0054] Reference Figure 3 In the embodiment of the present invention, a device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments is provided. The device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments can be a server, and its internal structure can be as follows: Figure 3 As shown. The device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments includes a processor, a memory, a display screen, an input device, a network interface and a database connected via a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments is used to store the corresponding data in this embodiment. The network interface of the device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0055] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the intelligent management and temperature adaptive adjustment device for lithium batteries in harsh environments to which the solution of the present invention is applied.

[0056] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the method for intelligent management and adaptive temperature adjustment of lithium batteries in harsh environments.

[0057] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0058] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a device for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0059] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments, characterized in that: The method comprises: Step S1: For the cross-river tunnel environment of the subway UPS system, high humidity, salt spray corrosion and seasonal low temperature environment data are collected simultaneously to establish a composite harsh environment monitoring database; Step S2: Based on the composite harsh environment monitoring database, a Kalman filter algorithm is used to fuse the influence of multiple environmental factors to calculate the UPS lithium battery environmental adaptability threat index; Step S3: generating differentiated lithium battery charge and discharge management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system floating charge-constant current and voltage limiting switching requirements; Step S4: Based on the differentiated lithium battery charge and discharge management parameters, inverting the actual operating temperature of the lithium battery in a low temperature environment by measuring the change in the battery internal resistance; Step S5: When the actual operating temperature of the lithium battery affects the power supply capacity of the UPS system, the carbon fiber heating film self-heating system is started, and the PID temperature control algorithm is used to maintain the optimal working state of the battery.

2. The method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to claim 1, characterized in that: The step S1 includes: The PT1000 platinum resistance temperature sensor array deployed inside the lithium battery module is used to collect and process temperature data to obtain the temperature distribution data inside the battery. The capacitive humidity sensor arranged on the surface of the lithium battery shell is used to monitor humidity and obtain environmental humidity change data; Conductivity sensors are deployed around the UPS system to detect salt spray concentration and obtain environmental corrosiveness data. The composite harsh environment monitoring database is obtained by performing data integration processing based on the battery internal temperature distribution data, the environmental humidity change data, and the environmental corrosiveness intensity data.

3. The method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to claim 1, characterized in that: The step S2 includes: Inputting the environmental parameters in the composite harsh environment monitoring database into a Kalman filter for state prediction processing to obtain predicted values ​​of the environmental parameters; Perform error calculation based on the predicted environmental parameter values ​​and the measured environmental parameters to obtain an environmental monitoring noise covariance matrix; Dynamically adjust the environmental parameter weight coefficients according to the environmental monitoring noise covariance matrix to obtain a fusion weight coefficient matrix; The fusion weight coefficient matrix is ​​subjected to weighted fusion calculation processing with multiple environmental factor data to obtain the UPS lithium battery environmental adaptability threat index.

4. The method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to claim 1, characterized in that: The step S3 includes: Inputting the UPS lithium battery environmental adaptability threat index and the current SOC state of the lithium battery into the fuzzy logic controller for membership calculation processing to obtain a threat level membership value; Performing rule matching processing on a preset fuzzy rule base based on the threat level membership value to obtain a charge and discharge control rule activation set; Performing fuzzy reasoning calculation processing according to the charge and discharge control rule activation set to obtain fuzzy output values ​​of the charge current limit and the discharge cut-off voltage; Defuzzifying the fuzzy output value by a centroid method to obtain a charge and discharge current control parameter; Based on the floating charge working mode of the UPS system during normal power supply and the constant current and voltage limiting discharge mode during power outage, the charge and discharge current control parameters are adapted to dual working conditions to obtain the differentiated lithium battery charge and discharge management parameters.

5. The method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to claim 4, characterized in that: The dual-condition adaptation processing of the charge and discharge current control parameters based on the floating charge working mode of the UPS system during normal power supply and the constant current and voltage-limited discharge mode during power outage is performed to obtain the differentiated lithium battery charge and discharge management parameters, including: Based on the floating charge working mode requirement of maintaining the battery in a fully charged state when the UPS system is normally powered, the charging and discharging current control parameters are adapted to a small current trickle charge to obtain a floating charge mode current control parameter; According to the constant current and voltage limiting discharge mode requirement for supplying power to the load when the UPS system is powered off, the charge and discharge current control parameters are adapted to a large current continuous discharge to obtain a discharge mode current control parameter; The floating charge mode current control parameter and the discharge mode current control parameter are subjected to a working mode switching logic design process based on a mains power state detection signal to obtain a floating charge-discharge mode automatic switching control strategy; Based on the floating charge-discharge mode automatic switching control strategy, real-time dynamic matching processing is performed on the lithium battery charge and discharge parameters to obtain the differentiated lithium battery charge and discharge management parameters.

6. The method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to claim 1, characterized in that: The step S4 comprises: Based on the differentiated lithium battery charge and discharge management parameters, the lithium battery is subjected to charge and discharge current control processing to obtain real-time internal resistance change data of the lithium battery; Inputting the real-time internal resistance change data of the lithium battery into the lithium ion mobility and temperature relationship model to perform electrochemical parameter calculation processing to obtain the battery internal resistance-temperature correlation coefficient; Performing temperature inversion calculation processing on the Arrhenius equation according to the battery internal resistance-temperature correlation coefficient to obtain an inversion value of the battery internal temperature; The actual operating temperature of the lithium battery is obtained based on a comparison and judgment process between the inverted value of the battery internal temperature and a low temperature environment threshold.

7. The method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to claim 1, characterized in that: The step S5 comprises: Performing temperature impact judgment processing on the actual operating temperature of the lithium battery and the minimum temperature threshold of the UPS system power supply capacity to obtain a self-heating start trigger signal; Performing power control processing on the carbon fiber heating film self-heating system based on the self-heating start trigger signal to obtain a preset heating power output; The actual operating temperature and the target operating temperature of the lithium battery are input into the PID temperature controller to calculate the temperature deviation and obtain the PID adjustment control parameters; The heating power of the carbon fiber heating film is dynamically adjusted according to the PID adjustment control parameters to obtain the temperature control result of the optimal working state of the battery.

8. A lithium battery intelligent management and temperature adaptive regulation system for harsh environments, characterized by: The method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to any one of claims 1 to 7 is used, and the system for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments comprises: The acquisition module is used to simultaneously collect data on high humidity, salt spray corrosion, and seasonal low temperature environments in the cross-river tunnel environment of the subway UPS system, and establish a database for monitoring composite harsh environments; A calculation module is used to calculate the environmental adaptability threat index of the UPS lithium battery based on the composite harsh environment monitoring database and by using a Kalman filter algorithm to fuse the influence of multiple environmental factors; A switching module is used to generate differentiated lithium battery charge and discharge management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system float charge-constant current and voltage limiting switching requirements; An inversion module, configured to invert the actual operating temperature of the lithium battery in a low temperature environment by measuring the change in the battery internal resistance based on the differentiated lithium battery charge and discharge management parameters; The starting module is used to start the carbon fiber heating film self-heating system when the actual operating temperature of the lithium battery affects the power supply capacity of the UPS system, and adopt the PID temperature control algorithm to maintain the optimal working state of the battery.

9. A lithium battery intelligent management and temperature adaptive adjustment device for harsh environments, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is enabled to execute the method for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments according to any one of claims 1 to 7.

Images