Intelligent management and temperature adaptive regulation method and system for lithium batteries in harsh environments

Abstract

This application relates to the field of battery management technology and discloses a method and system for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments. The method includes: simultaneously collecting multi-dimensional environmental data to establish a monitoring database for the harsh environment of a subway UPS system; calculating an environmental adaptability threat index using a Kalman filter algorithm; generating differentiated charge and discharge management parameters by combining the threat index and UPS switching requirements; inverting the actual battery operating temperature through internal resistance changes; and activating a carbon fiber heating film self-heating system when temperature affects power supply capacity, using a PID algorithm to maintain the battery in its optimal operating state. This application establishes a threat assessment system based on the fusion of multiple environmental factors and a differentiated charge and discharge management strategy, and significantly improves the power supply reliability and battery life of the UPS system in harsh environments such as subway tunnels crossing rivers through internal resistance temperature detection and PID self-heating control.

Description

Technical Field

[0001] This 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 under harsh environments. Background Technology

[0002] In existing technologies, subway UPS systems primarily use lead-acid batteries as backup power, coupled with simple battery management circuits for charge and discharge control. Traditional battery management methods are typically based 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 technologies mainly rely on passive heat dissipation designs or simple fan cooling, lacking effective active heating measures for seasonally low-temperature environments.

[0003] However, existing technologies have significant shortcomings in harsh environments such as subway tunnels spanning rivers. First, traditional lead-acid batteries are prone to electrolyte leakage and thermal runaway in high-humidity, salt spray, and corrosive environments, exhibiting severe performance degradation at low temperatures and high overall lifecycle costs. Second, current battery management methods lack real-time monitoring and data fusion capabilities for multiple environmental factors, making it impossible to accurately assess the comprehensive impact of combined harsh environments on battery performance. Furthermore, fixed charge / discharge management strategies cannot adapt to the specific operating conditions of UPS systems, such as float charging and constant current / voltage limiting switching, which can easily lead to rapid battery performance degradation in harsh environments. Summary of the Invention

[0004] This application provides a method and system for intelligent management and temperature adaptive regulation of lithium batteries in harsh environments, addressing the lack of such systems in existing technologies for intelligent management and temperature adaptive regulation of lithium batteries under complex and harsh conditions. 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 tunnels spanning rivers.

[0005] Firstly, this application provides a method for intelligent management and temperature adaptive regulation of lithium batteries under harsh environments, the method comprising:

[0006] Step S1: For the cross-river tunnel environment of the subway UPS system, collect data on high humidity, salt spray corrosion and seasonal low temperature environment simultaneously, and establish a composite harsh environment monitoring database;

[0007] Step S2: Based on the composite harsh environment monitoring database, the Kalman filter algorithm is used to fuse the influence of multiple environmental factors to calculate the UPS lithium battery environmental adaptability threat index;

[0008] Step S3: Based on the UPS lithium battery environmental adaptability threat index and combined with the UPS system's float charge-constant current voltage limiting switching requirements, generate differentiated lithium battery charging and discharging management parameters;

[0009] Step S4: Based on the differentiated lithium battery charge and discharge management parameters, the actual operating temperature of the lithium battery under low temperature conditions is determined by measuring the change in battery internal resistance.

[0010] 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 activated, and the PID temperature control algorithm is used to maintain the battery in the best working state.

[0011] Secondly, this application provides a lithium battery intelligent management and temperature adaptive regulation system for harsh environments, the lithium battery intelligent management and temperature adaptive regulation system for harsh environments comprising:

[0012] The data 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 to establish a composite harsh environment monitoring database.

[0013] The calculation module is used to calculate the environmental adaptability threat index of UPS lithium batteries based on the composite harsh environment monitoring database and by using the Kalman filter algorithm to fuse the influence of multiple environmental factors.

[0014] The switching module is used to generate differentiated lithium battery charging and discharging management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system float charge-constant current voltage limiting switching requirements.

[0015] The inversion module is used to invert the actual operating temperature of the lithium battery under low-temperature conditions by measuring the change in battery internal resistance based on the differentiated lithium battery charge and discharge management parameters.

[0016] The startup module is used to activate 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 to maintain the battery in the best working state using a PID temperature control algorithm.

[0017] Thirdly, a lithium battery intelligent management and temperature adaptive adjustment device for 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 to cause the lithium battery intelligent management and temperature adaptive adjustment device for harsh environments to execute the aforementioned lithium battery intelligent management and temperature adaptive adjustment method for harsh environments.

[0018] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the aforementioned intelligent management and temperature adaptive regulation method for lithium batteries under harsh environments.

[0019] The technical solution provided in this application establishes a comprehensive harsh environment monitoring database 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, providing a comprehensive and accurate data foundation for intelligent management decisions. The Kalman filter algorithm is used to fuse the influence of multiple environmental factors to calculate the UPS lithium battery environmental adaptability threat index, effectively solving the technical problem that traditional methods cannot accurately assess the comprehensive impact of complex harsh environments. This 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 UPS system's float charge-constant current voltage limiting switching requirements, differentiated lithium battery charge and discharge management parameters are generated, breaking through the technical bottleneck of existing technologies that use fixed charge and discharge strategies, and achieving dynamic matching of battery management strategies with actual operating 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 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 benchmark for temperature adaptive adjustment. 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 a PID temperature control algorithm is used to maintain the battery in its optimal operating state. This effectively solves the technical defect of existing technologies that lack active temperature regulation capabilities, and ensures the continuous and reliable power supply of the UPS system in harsh environments.

[0020] In specific subway UPS system applications, the core algorithmic features of this application have made crucial contributions to the overall solution. The application of the Kalman filter algorithm in monitoring complex harsh environments, 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 generating charge and discharge management strategies leverages its advantages in handling uncertainties and nonlinear relationships to successfully establish an intelligent mapping relationship between the degree of environmental threat and battery management parameters, solving the problem that traditional deterministic control methods struggle to cope 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 rates and temperature, achieves accurate calculation from changes in battery internal resistance to internal temperature, providing a scientifically reliable temperature detection method for adaptive temperature regulation. The application of the PID temperature control algorithm in self-heating systems, through its proportional-integral-derivative control characteristics, achieves precise adjustment of heating power and stable temperature control, effectively preventing temperature overshoot and oscillation phenomena, and ensuring stable battery operation within the optimal operating temperature range. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an embodiment of the intelligent management and temperature adaptive regulation method for lithium batteries under harsh environments in this application.

[0023] Figure 2 This is a schematic diagram of an embodiment of the intelligent management and temperature adaptive regulation system for lithium batteries in harsh environments, as described in this application.

[0024] Figure 3 This is a schematic block diagram of the intelligent management and temperature adaptive adjustment device for lithium batteries in harsh environments, as described in this embodiment of the invention. Detailed Implementation

[0025] This application provides 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 present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; 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 explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0026] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the intelligent management and temperature adaptive regulation method for lithium batteries under harsh environments in this application includes:

[0027] Step S1: For the cross-river tunnel environment of the subway UPS system, collect data on high humidity, salt spray corrosion and seasonal low temperature environment simultaneously, and establish a composite harsh environment monitoring database;

[0028] Step S2: Based on the composite harsh environment monitoring database, the Kalman filter algorithm is used to fuse the influence of multiple environmental factors to calculate the environmental adaptability threat index of UPS lithium batteries;

[0029] Step S3: Based on the UPS lithium battery environmental adaptability threat index and combined with the UPS system's float charge-constant current voltage limiting switching requirements, generate differentiated lithium battery charge and discharge management parameters;

[0030] Step S4: Based on the differentiated lithium battery charge and discharge management parameters, the actual operating temperature of the lithium battery under low temperature environment is inverted by measuring the change in battery internal resistance;

[0031] 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 activated, and the PID temperature control algorithm is used to maintain the battery in the best working state.

[0032] It is understood that the executing entity of this application can be a lithium battery intelligent management and temperature adaptive adjustment system under harsh environments, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiments use a server as an example for illustration.

[0033] Specifically, the PT1000 platinum resistance temperature sensor array employs a three-wire connection, utilizing the linear relationship between the resistance of platinum metal and temperature for temperature measurement. When the ambient temperature changes, the resistance of the platinum resistor changes according to a specific temperature coefficient. This resistance change is converted into a voltage signal by a Wheatstone bridge circuit, and then acquired by an analog-to-digital converter to obtain digitized data on the internal temperature distribution of the battery. Simultaneously, the capacitive humidity sensor operates based on the principle of dielectric constant change. When the ambient humidity changes, the polymer film inside the sensor absorbs or releases moisture, causing a change in the dielectric constant and thus the capacitance value. This capacitance change is converted into a frequency signal by an oscillation circuit, ultimately obtaining data on the change in ambient humidity. The conductivity sensor detects salt spray concentration by measuring the conductivity of ions in a 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 result in higher conductivity, thus obtaining data on the intensity of environmental corrosion. Data integration and processing synchronizes the data from the three types of sensors according to timestamps, establishing a composite harsh environment monitoring database containing three-dimensional environmental parameters of temperature, humidity, and corrosion.

[0034] The prediction phase calculates predicted environmental parameters based on the state transition equation, which describes the changes in environmental parameters over time. A mathematical model built from historical data predicts the environmental state at the next moment. Error calculation processing compares the predicted values ​​with the actual sensor measurements, calculating the prediction error covariance matrix, which reflects the degree of uncertainty in the predicted values. The environmental monitoring noise covariance matrix is ​​obtained through statistical analysis of sensor measurement errors, including the covariance information of temperature measurement noise, humidity measurement noise, and corrosion measurement noise. Dynamic adjustment processing updates the Kalman gain based on the noise covariance matrix. The Kalman gain determines the weighting of predicted and measured values ​​during the fusion process; sensors with higher noise levels have lower weights. Weighted fusion calculation linearly combines multiple environmental factor data according to fusion weight coefficients to obtain the UPS lithium battery environmental adaptability threat index. This index comprehensively reflects the combined impact of harsh environments on lithium battery performance.

[0035] A fuzzy logic controller is used to generate differentiated charge and discharge management parameters. Membership degree calculation takes the environmental adaptability threat index and SOC state as input variables. A predefined membership degree function calculates the degree to which each input variable belongs to different fuzzy sets. The threat level membership value indicates the degree to which the current environmental state belongs to a mild, moderate, or severe threat. Rule matching searches a preset fuzzy rule library for control rules that match the current membership degree value. The rule library contains charge and discharge strategy rules for different combinations of threat levels and SOC states. Each rule defines a charging current limit and a discharge cutoff voltage setpoint under specific conditions. Fuzzy inference calculation performs inference operations based on the activated rule set, merging the outputs of multiple rules through fuzzy operations to obtain a fuzzy control output. Centroid defuzzification calculates the centroid position of the fuzzy output set, converting the fuzzy values ​​into numerical values ​​to obtain the specific charge and discharge current control parameters. Dual-condition adaptation processing adjusts parameters for two operating modes of the UPS system. In float charging mode, the battery needs a small current trickle charge to maintain a full charge, while in constant current voltage limiting discharge mode, a large current continuous discharge capability is required. The control parameters corresponding to the two operating modes are automatically switched by detecting the mains power status signal.

[0036] Operating temperature is derived by inverting changes in battery internal resistance. The charge / discharge current control process manages the lithium battery current according to differentiated management parameters, while simultaneously monitoring the battery terminal voltage and current in real time. Ohm's law is used to calculate the instantaneous internal resistance value, obtaining real-time internal resistance change data. The lithium-ion mobility-temperature relationship model is established based on the Arrhenius equation, which describes the exponential change in ionic conductivity with temperature. The electrochemical parameter calculation process substitutes the internal resistance data into this model to calculate the internal resistance-temperature correlation coefficient, reflecting the activity of electrochemical reactions inside the battery. The temperature inversion calculation process based on the Arrhenius equation uses logarithmic transformation and linear regression to deduce the actual internal temperature of the battery from the correlation coefficient. This temperature inversion value truly reflects the thermodynamic state inside the battery. The comparison and judgment process compares the inverted temperature value with a preset low-temperature environment threshold. When the inverted temperature is lower than the threshold, the battery is determined to be in a low-temperature operating state, requiring the initiation of temperature regulation measures.

[0037] The temperature impact assessment compares the actual operating temperature of the lithium battery with the minimum power supply temperature required by the UPS system. When the battery temperature is too low, affecting ion mobility, the battery's internal resistance increases sharply, leading to a decrease in power supply capacity. At this point, a self-heating start-up trigger signal is generated. Upon receiving the trigger signal, the carbon fiber heating film self-heating system begins operation. The heating film utilizes the resistive heating characteristics of carbon fiber material, controlling the heating power output through PWM modulation. The PID temperature controller employs a proportional-integral-derivative (PI-DE) control algorithm. The proportional term responds to the current temperature deviation, the integral term eliminates steady-state error, and the derivative term predicts the temperature change trend. The combined control actions of these three terms yield the PID adjustment parameters. Dynamic adjustment adjusts the heating power of the heating film in real time based on the PID control parameters. When the actual temperature approaches the target temperature, the heating power is reduced; when the temperature deviation is large, the heating power is increased. Closed-loop control maintains the battery operating within its optimal operating temperature range.

[0038] In one specific embodiment, step S1 includes:

[0039] Temperature data is acquired and processed by a PT1000 platinum resistance temperature sensor array deployed inside the lithium battery module to obtain the internal temperature distribution data of the battery.

[0040] Humidity monitoring and processing are performed using a capacitive humidity sensor arranged on the surface of a lithium battery casing to obtain data on changes in ambient humidity.

[0041] Conductivity sensors are deployed around the UPS system to detect salt spray concentration and obtain data on environmental corrosion intensity.

[0042] Data is integrated and processed based on battery internal temperature distribution data, ambient humidity change data, and environmental corrosion intensity data to obtain a composite harsh environment monitoring database.

[0043] Specifically, the PT1000 platinum resistance temperature sensor array's temperature data acquisition and processing is based on the temperature characteristics of platinum metal resistance. 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 resistance through a constant current source, and the voltage drop across the platinum resistance is measured. The resistance value is calculated according to Ohm's law, and then converted into the corresponding temperature value through table lookup or linear interpolation. Multiple sensors inside the lithium battery module operate simultaneously, each responsible for monitoring the temperature of a specific area. An analog-to-digital converter digitizes the voltage signals from each sensor at a fixed sampling frequency, generating a time-series temperature data stream. After filtering to remove high-frequency noise, the resulting data forms the internal temperature distribution data of the battery, reflecting the thermal state distribution at different locations within the battery.

[0044] Capacitive humidity sensors utilize the hygroscopic properties of polymer films for humidity monitoring. The polymer dielectric layer inside the sensor absorbs or releases moisture under different humidity conditions, causing changes in the dielectric constant and thus altering the capacitance. The sensor circuit converts these capacitance changes into frequency changes, measuring the oscillation frequency using a frequency counter. The frequency corresponds to the ambient humidity. The data processing unit converts the measured frequency values ​​into relative humidity percentages 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. Measurement data from each sensor is transmitted to the central processing unit via a data bus. The processor averages the humidity data from different locations, eliminating the influence of local humidity fluctuations and generating ambient humidity change data representing the overall environmental humidity state.

[0045] The salt spray concentration detection and processing using conductivity sensors is based on the direct proportionality between solution conductivity and ion concentration. Chloride and sodium ions in the salt spray increase the solution's conductivity; a higher conductivity value indicates a greater salt spray concentration. The sensor employs a four-electrode method: two current electrodes apply AC excitation signals, and two voltage electrodes measure the voltage drop in the solution, avoiding the influence of electrode polarization effects 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. The microprocessor calculates the conductivity based on the measured voltage and current values, and then converts the conductivity value into the corresponding salt spray concentration value using empirical formulas or calibration curves. Multiple conductivity sensors deployed around the UPS system cover different monitoring areas. The measurement results from each sensor are weighted and averaged, with the weighting coefficient determined based on the distance between the sensor and the UPS equipment; closer sensors have higher weights. The final result is data on the intensity of environmental corrosion.

[0046] Data integration and processing employs time synchronization and spatial interpolation methods to fuse data from three types of sensors. First, all sensor data is timestamped to ensure correct matching of different data types at the same time. Temperature distribution data is used to generate a continuous temperature field distribution through spatial interpolation algorithms, while humidity variation data and corrosion intensity data undergo time filtering to eliminate abrupt noise. The database structure adopts a relational design, with the main table storing timestamp information and sub-tables storing temperature, humidity, and corrosion intensity data respectively. Logical relationships between the data are established through foreign keys. Data compression algorithms compress historical data for storage, while an indexing mechanism accelerates data retrieval, forming a comprehensive harsh environment monitoring database that supports real-time querying and historical analysis.

[0047] In one specific embodiment, step S2 includes:

[0048] The environmental parameters in the composite harsh environment monitoring database are input into a Kalman filter for state prediction processing to obtain the predicted values ​​of the environmental parameters.

[0049] Error calculation and processing are performed based on the predicted and measured environmental parameters to obtain the environmental monitoring noise covariance matrix;

[0050] The environmental parameter weighting coefficients are dynamically adjusted based on the environmental monitoring noise covariance matrix to obtain the fusion weighting coefficient matrix.

[0051] The UPS lithium battery environmental adaptability threat index is obtained by weighting and fusion calculation of the fusion weight coefficient matrix and multi-environmental factor data.

[0052] Specifically, the state prediction processing of the Kalman filter is based on a dynamic state-space model for predicting environmental parameters. This filter comprises two core components: a state transition equation and an observation equation. The state transition equation describes the evolution of environmental parameters over time, using the temperature, humidity, and corrosion intensity of the previous moment as state vector inputs, and calculating the predicted state for the next moment through a state transition matrix. The elements of the state transition matrix are determined based on the statistical regularities of historical data, reflecting the mutual influence relationships and temporal evolution characteristics of various environmental parameters. The observation equation establishes the mapping relationship between the actual environmental state and sensor measurements, considering the measurement noise and nonlinear characteristics of the sensors. The prediction process first uses historical environmental parameters from a composite harsh environment monitoring database as initial state inputs, and recursively calculates the predicted environmental parameters for the current moment through the state transition equation. The predicted values ​​include data in three dimensions: temperature prediction component, humidity prediction component, and corrosion intensity prediction component.

[0053] Error calculation involves comparing the predicted environmental parameters with the actual measured parameters from the sensors one by one to calculate the prediction error vector. The temperature prediction error equals the predicted temperature minus the measured temperature, the humidity prediction error equals the predicted humidity minus the measured humidity, and the corrosion prediction error equals the predicted corrosion intensity minus the measured corrosion intensity. The outer product of the error vectors generates an error covariance matrix. The diagonal elements of this matrix represent the variance of the prediction error for each environmental parameter, while 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 statistical sample size. The updated covariance matrix uses a weighted average method, assigning higher weights to recent error data and gradually decreasing the weights to older data, ensuring that the noise covariance matrix reflects the current measurement accuracy.

[0054] 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 certain environmental parameter is high, the corresponding Kalman gain is low, indicating greater trust in the predicted value rather than 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 error. The calculation process of the fusion weight coefficient matrix includes summing the prediction covariance matrix and the observation noise covariance matrix, then performing matrix inversion to obtain the Kalman gain matrix, and finally normalizing the Kalman gain matrix 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 element values ​​vary between zero and one, and the sum of all weight coefficients equals one.

[0055] The weighted fusion calculation process performs matrix multiplication on the fusion weight coefficient matrix and multi-environmental factor data to obtain the fused environmental state estimate. The fused temperature value equals the temperature weight coefficient multiplied by the measured temperature value, plus the temperature prediction weight coefficient multiplied by the predicted temperature value. The fusion calculation of humidity and corrosivity 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. A threat assessment function maps multi-dimensional environmental parameters into a single threat level index. The threat assessment function adopts a weighted linear combination form: the degree of temperature deviation from the optimal operating range is multiplied by the temperature threat coefficient; the degree of humidity exceeding the safety threshold is multiplied by the humidity threat coefficient; and the corrosion 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 law of UPS lithium batteries under different environmental conditions, and quantified threat weight parameters are obtained through fitting experimental data.

[0056] In one specific embodiment, step S3 includes:

[0057] The UPS lithium battery environmental adaptability threat index and the current SOC state of the lithium battery are input into the fuzzy logic controller to calculate the membership degree, and the threat level membership degree value is obtained.

[0058] Based on the threat level membership value, a pre-set fuzzy rule base is matched to obtain the charging and discharging control rule activation set.

[0059] Based on the charging and discharging control rules, the activation set is used for fuzzy reasoning calculation to obtain the fuzzy output values ​​of the charging current limit and the discharging cutoff voltage.

[0060] The fuzzy output values ​​are defuzzified using the centroid method to obtain the charging and discharging current control parameters.

[0061] Based on the UPS system's float charging mode during normal power supply and constant current voltage limiting discharge mode during power outage, the charging and discharging current control parameters are adapted to dual operating conditions to obtain differentiated lithium battery charging and discharging management parameters.

[0062] Specifically, the membership degree calculation process of the fuzzy logic controller first defines fuzzy sets of 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 located in the core region of a set, the membership value is 1; when it is located in the boundary region, the membership value changes linearly between 0 and 1; and when it is located outside the set, the membership value is 0. The current SOC state of the lithium battery is also divided into three fuzzy sets: low charge, medium charge, and high charge, using a similar membership function definition method. The membership degree calculation process substitutes the specific values ​​of the threat index and the SOC state into the corresponding membership functions, and calculates the membership value of each fuzzy set through a piecewise function. The threat level membership value is a vector containing six elements: the first three elements correspond to the three fuzzy set membership degrees of the threat index, and the last three elements correspond to the three fuzzy set membership degrees of the SOC state.

[0063] The rule matching process searches a preset fuzzy rule base for control rules that match the current membership value. The fuzzy rule base contains nine basic rules, each using an "if-then" conditional statement format. The antecedent of a rule is a fuzzy set combination of threat level and SOC state, while the consequent is a fuzzy set setting of charging current limit and discharge cutoff voltage. The rule activation level is determined by calculating the minimum membership degree of the antecedent fuzzy set. When multiple conditions in the antecedent are simultaneously true, the minimum membership degree of each condition is taken as the activation strength of the rule. The charge / discharge control rule activation set contains all rules with activation strength greater than zero and their corresponding activation strength values, forming a mapping relationship between rule number and activation strength. The rule matching algorithm traverses the entire rule base, calculating the activation strength of each rule one by one, and adding rules with activation strength greater than a preset threshold to the activation set. The size of the activation set depends on the matching degree between the current input state and the rule antecedent.

[0064] The fuzzy inference computation process employs the Mamdani inference method, performing parallel inference operations on each rule in the activation set. The consequent fuzzy set of each activation rule is pruned according to the activation strength of that rule; the height of the pruned fuzzy set equals the activation strength value, while the width retains 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 using a maximum value operation, i.e., taking the maximum value of the membership degree of each fuzzy set at each universe of discourse point. The fuzzy output of the discharge cutoff voltage uses the same merging method, ultimately yielding two composite fuzzy sets as the fuzzy output values. The shape of the fuzzy output values ​​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 determinism of that parameter.

[0065] The centroid method defuzzification converts fuzzy output values ​​into numerical control parameters, and the centroid calculation uses the area integration method. The defuzzification calculation of the charging current limit divides the universe of discourse 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, and the centroid position is equal to the sum of the products of the center positions of each interval and their corresponding areas divided by the total area. The defuzzification calculation of the discharge cutoff voltage uses the same centroid method to obtain the voltage value. The charging and discharging current control parameters are converted from fuzzy linguistic descriptions into specific engineering parameters through defuzzification. The accuracy of the control parameters depends on the degree of discretization of the universe of discourse and the design accuracy of the membership function.

[0066] Dual-condition adaptation processing differentiates control parameters based on two typical operating modes of the UPS system. In float charging mode, the UPS system provides normal power supply, and the lithium battery is in standby mode and needs to be maintained at a full charge. In this mode, the charging current control parameter is adjusted to a low-current trickle charging value to prevent overcharging and damage to the battery while compensating for self-discharge losses. In constant-current voltage-limited discharge mode, the UPS system experiences a power outage requiring emergency power from the lithium battery. In this mode, the discharge cut-off voltage control parameter is adjusted to a more conservative value to ensure stable power output even in harsh environments. Operating condition identification is achieved by monitoring the mains power status signal. When the mains power is normal, the float charging mode parameters are activated; when the mains power is abnormal, it automatically switches to the discharge mode parameters. Differentiated lithium battery charging and discharging management parameters include two complete sets of control parameters, each optimized for specific operating conditions. The switching process uses a smooth transition algorithm to avoid parameter abrupt changes impacting the battery.

[0067] In one specific embodiment, the process of performing dual-condition adaptation processing of the charging and discharging current control parameters based on the UPS system's float charging operating mode during normal power supply and constant current voltage limiting discharge mode during power outage can specifically include the following steps:

[0068] Based on the requirement of maintaining a fully charged battery during normal UPS system power supply, the charging and discharging current control parameters are adapted to the trickle charging mode to obtain the current control parameters for the float charging mode.

[0069] Based on the constant current and voltage-limited discharge mode requirement of the UPS system to supply power to the load when the power is off, the charging and discharging current control parameters are adapted for continuous discharge with high current to obtain the discharge mode current control parameters.

[0070] The floating charge mode current control parameters and the discharging mode current control parameters are processed by the mains power status detection signal to obtain the automatic switching control strategy for floating charge-discharge mode.

[0071] Based on the automatic switching control strategy of float charge-discharge mode, the charging and discharging parameters of lithium battery are dynamically matched in real time to obtain differentiated lithium battery charging and discharging management parameters.

[0072] Specifically, the low-current trickle charging adaptation process adjusts parameters based on the specific requirements of the UPS system's float charging mode. Float charging mode refers to the UPS system's operation mode where the lithium battery remains fully charged under normal mains power supply. Trickle charging is a low-current-density charging method, with the charging current typically set to 5% to 10% of the battery capacity to compensate for self-discharge losses and prevent overcharging. The adaptation process first obtains the charging and discharging current control parameters output by the fuzzy logic controller as the base value, and then adjusts them downwards according to the current limit requirements of the float charging mode. The current reduction algorithm uses a proportional reduction method, multiplying the base charging current by the float charging mode coefficient to obtain the adjusted current value. The float charging mode coefficient is typically between 0.1 and 0.3, with the specific value determined based on the battery type and environmental conditions. The impact of harsh environments on the battery's self-discharge rate is also considered. In high-temperature and high-humidity environments, the self-discharge rate increases, requiring a corresponding increase in the trickle charging current; in low-temperature environments, the self-discharge rate decreases, requiring a decrease in the trickle charging current. The current control parameters in float charging mode also include the adjustment of the charging cutoff voltage. The cutoff voltage is set 0.1 to 0.2 volts lower than the standard charging voltage to avoid the risk of overcharging during long-term float charging.

[0073] The high-current continuous discharge adaptation optimizes parameters to meet the emergency power supply needs during UPS system power outages. The constant-current, voltage-limited discharge mode refers to a working mode where the lithium battery supplies power to the load with a constant current when the mains power is interrupted, while simultaneously monitoring the battery voltage to prevent over-discharge. High-current discharge capability directly affects the UPS system's power supply time and load support capacity; in harsh environments, the impact of temperature on discharge performance must be considered. The adaptation adjusts the discharge current and cutoff voltage based on the output parameters of a fuzzy logic controller. The discharge current adjustment uses an environmental compensation algorithm; when the ambient temperature is below the optimal operating temperature, the discharge current limit is lowered according to the temperature coefficient to prevent voltage drops caused by increased internal resistance at low temperatures. The cutoff voltage adjustment considers 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 power supply to the UPS system. The 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 state to extend the discharge time as much as possible while ensuring power supply requirements.

[0074] The operating condition switching logic design establishes an automatic switching mechanism between float charging and discharging modes. The mains power status detection signal is the core input parameter of the switching logic. Mains power status detection comprehensively judges the situation through 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 power abnormality signal. The switching logic adopts a state machine design method, defining four basic states: normal power supply state, abnormal detection state, switching state, and fault state. State transition conditions are based on the mains power detection signal and battery status information. The automatic switching control strategy for float charging-discharging modes includes two aspects: switching timing control and parameter transition control. Switching timing control ensures timely response to changes in mains power status, while parameter transition control prevents parameter abrupt changes during switching from impacting the battery. The switching delay setting considers false trigger protection; the switching action is only triggered if the mains power abnormality signal lasts for more than a preset threshold, avoiding erroneous switching caused by instantaneous interference.

[0075] Real-time dynamic matching processing continuously adjusts lithium battery charging and discharging parameters according to the automatic switching control strategy. The matching algorithm uses a combination of interpolation and filtering. Parameter interpolation generates intermediate transition parameters during mode switching to avoid abrupt parameter changes. The interpolation function uses a cubic spline curve to ensure smooth parameter changes. Filtering suppresses parameter fluctuations during switching, using a first-order low-pass filter to eliminate the impact of high-frequency noise on parameter stability. Differentiated lithium battery charging and discharging management parameters include data in four dimensions: current limit, voltage threshold, charging and discharging 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 promptly to environmental changes and operating condition switching, while avoiding excessively frequent adjustments that could affect system stability.

[0076] In one specific embodiment, step S4 includes:

[0077] Based on the 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.

[0078] Real-time internal resistance change data of lithium battery is input into the lithium-ion mobility-temperature relationship model for electrochemical parameter calculation and processing to obtain the battery internal resistance-temperature correlation coefficient.

[0079] The Arrhenius equation is used to perform temperature inversion calculations based on the battery internal resistance-temperature correlation coefficient to obtain the battery internal temperature inversion value.

[0080] The actual operating temperature of the lithium battery is obtained by comparing and judging the internal temperature inversion value of the battery with the low temperature environment threshold.

[0081] Specifically, the charge / discharge current control process precisely regulates the current of the lithium battery based on differentiated lithium battery charge / discharge management parameters. The control process employs PWM (Pulse Width Modulation) technology to achieve precise current control. The PWM controller sets the duty cycle according to 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 charging current is 30% of the maximum current, the PWM duty cycle is set to 30%. The current control circuit includes a current sensing resistor, an operational amplifier, and a power switch. The current sensing 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 / discharge current according to the PWM signal. Real-time internal resistance measurement uses the AC impedance method, measuring the AC response of the battery terminal voltage by superimposing a small-amplitude AC test signal onto the DC charge / discharge current. Internal resistance calculation uses Ohm's law, dividing the AC voltage amplitude by the AC current amplitude to obtain the AC internal resistance value. The AC internal resistance more accurately reflects the actual impedance characteristics of the battery. Real-time internal resistance change data of lithium batteries are obtained through continuous measurement and data acquisition, with the sampling frequency set to once per second, forming time series data of internal resistance change over time.

[0082] Electrochemical parameter calculations input real-time internal resistance change data into a lithium-ion mobility versus temperature relationship model for analysis. This model is based on the ion conduction mechanism of the solid electrolyte interface membrane. Lithium-ion mobility refers to the ability of lithium ions to move in the electrolyte, directly affecting the battery's internal resistance. Temperature is the primary factor influencing ion mobility. The relationship model uses an Arrhenius function to describe the exponential relationship between mobility and temperature; mobility increases exponentially with increasing temperature and decreases exponentially with decreasing temperature. Electrochemical parameter calculations first substitute the measured internal resistance value into the mobility equation, calculating the corresponding ion mobility value through inverse function operations. Then, 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 changes and temperature changes. The battery internal resistance-temperature correlation coefficient is calculated using statistical analysis methods. Linear regression analysis is performed on the internal resistance change over a period of time and the corresponding temperature change; the regression coefficient is the internal resistance-temperature correlation coefficient. The magnitude of the correlation coefficient reflects the sensitivity of internal resistance to temperature changes; a larger coefficient indicates greater sensitivity.

[0083] Temperature inversion calculations are based on the Arrhenius equation, which describes the relationship between the chemical reaction rate constant and temperature. In battery applications, it describes the relationship between ionic conductivity and temperature. The basic form of the equation is that the reaction rate constant is an exponential function of the product of the pre-exponential factor, the negative activation energy, and the gas constant and absolute temperature, where the activation energy is the energy barrier that ions must overcome for migration. The inversion calculation performs a logarithmic transformation on the Arrhenius equation, converting the exponential relationship into a linear one, and then solves for the temperature value through algebraic operations. Specifically, the internal resistance-temperature correlation coefficient is first substituted into the transformed linear equation, then the reciprocal of the absolute temperature is solved, and finally, the reciprocal is taken and converted to Celsius to obtain the inverted value of the battery's internal temperature. The inversion calculation considers the influence of battery aging and environmental factors on the parameters of the Arrhenius equation, and the activation energy and pre-exponential factor parameters are periodically calibrated and corrected to ensure the accuracy of the inverted temperature. The temperature inversion value represents the true thermodynamic temperature inside the battery and reflects the actual operating state of the battery better than the measurement value of a surface temperature sensor.

[0084] The comparison and judgment process compares the battery's internal temperature inversion value with a preset low-temperature environment threshold to determine whether the battery is in a low-temperature operating state. The low-temperature environment threshold is set based on the lithium battery's performance characteristic curve. When the battery temperature is below this threshold, ion mobility decreases significantly, leading to a sharp increase in internal resistance and a significant deterioration in the battery's charge and discharge performance. The threshold setting also considers the power supply reliability requirements of the UPS system, promptly initiating temperature regulation measures when battery temperature affects power supply capacity. The comparison and judgment are implemented using a digital comparator. When the inversion temperature value is less than the threshold, a low-temperature state signal is output; when the inversion 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 temperature deviation, which is equal to the threshold minus the inversion temperature value. A larger deviation indicates a lower temperature and a higher required heating power. The determination of the actual operating temperature of the lithium battery comprehensively considers the inversion temperature value and the judgment result. When in a low-temperature state, the actual operating temperature equals the inversion temperature value; when in a normal state, the actual operating temperature is set to the threshold temperature. This processing method ensures timely response of temperature adaptive adjustment.

[0085] In one specific embodiment, step S5 includes:

[0086] The temperature influence between the actual operating temperature of the lithium battery and the minimum temperature threshold of the UPS system power supply capacity is judged to obtain the self-heating start trigger signal.

[0087] Based on the self-heating start-up trigger signal, the power control processing of the carbon fiber heating film self-heating system is performed to obtain the preset heating power output.

[0088] The actual operating temperature of the lithium battery and the target operating temperature are input into the PID temperature controller to calculate the temperature deviation and obtain the PID adjustment control parameters.

[0089] The heating power of the carbon fiber heating film is dynamically adjusted based on the PID control parameters to obtain the temperature control result for the optimal battery operating state.

[0090] Specifically, the temperature impact judgment process uses a digital comparator to compare the actual operating temperature of the lithium battery with the minimum temperature threshold for the UPS system's power supply capacity 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 minimum temperature threshold for the UPS system's power supply capacity is set based on the electrochemical performance degradation curve of the lithium battery in low-temperature environments. When the battery temperature is below this threshold, the increased internal resistance leads to voltage drops and capacity decay, directly affecting the UPS system's power supply quality and duration. The judgment process uses a hysteresis comparator to prevent frequent triggering when the temperature fluctuates around the threshold. The comparator has upper and lower thresholds. When the actual temperature drops from the high-temperature region to below the lower threshold, it outputs a start signal; when the actual temperature rises from the low-temperature region 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 needs to be activated, and 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 generation of the trigger signal also considers the battery's SOC state and UPS load requirements. When the battery has sufficient power and the load demand is high, the trigger threshold is increased accordingly to ensure sufficient power supply capacity.

[0091] The power control process sets the initial power of the carbon fiber heating film self-heating system based on the self-heating start-up trigger signal. The carbon fiber heating film is a flexible heating element that generates uniformly distributed heat through the resistive heating characteristics of carbon fiber material. The heating film uses low-voltage DC drive, with a rated operating voltage of 24V and a power density of 20 watts per square meter. The heating film is arranged in close contact with the surface of the lithium battery module, and the heat conduction effect is enhanced by thermally conductive silicone sheets. The power control circuit includes a power switch, a current sensing resistor, and a drive circuit. The power switch uses a MOSFET device, which has low on-resistance and fast switching characteristics. The calculation of the preset heating power output is based on the heat capacity of the battery module and the target heating rate. The heat capacity equals the battery mass multiplied by the specific heat capacity, and the target heating rate is determined according to the response time requirements of the UPS system. The heating power calculation formula is: power equals heat capacity multiplied by heating rate. When the battery mass is 50 kg, the specific heat capacity is 0.8 kJ / kg / degree Celsius, and the target heating rate is 2 degrees Celsius per minute, the required heating power is 1.33 kW. 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.

[0092] Temperature deviation calculation involves inputting the actual operating temperature of the lithium battery into a PID temperature controller to precisely quantify the deviation from the target operating temperature. The target operating temperature is an ideal temperature value determined based on the optimal electrochemical performance of the lithium battery. The PID controller is a classic feedback control algorithm, comprising three control components: proportional, integral, and derivative. The proportional component responds to the current temperature deviation, the integral component eliminates steady-state deviation, and the derivative component predicts temperature change trends and suppresses overshoot. The temperature deviation equals the target operating temperature minus the actual operating temperature; a positive deviation indicates the need for heating, while a negative deviation indicates the need to reduce heating power or stop heating altogether. The proportional control parameter is calculated based on the deviation value multiplied by a proportional coefficient, which is set according to the thermal response characteristics of the heating film and the thermal inertia of the battery. The integral control parameter is calculated by accumulating historical deviation values, and the integral time constant is chosen to balance response speed and stability requirements. The derivative control parameter is calculated based on the rate of change of deviation, and the derivative time constant is set considering the noise level of the temperature sensor and the dynamic response characteristics of the system. The PID adjustment control parameter is a weighted sum of the three control components, with the weighting coefficients optimized according to the specific application scenario.

[0093] The dynamic adjustment process adjusts the heating power of the carbon fiber heating film in real time based on PID control parameters. The adjustment algorithm adopts an incremental PID control method. The incremental PID calculates the power increment of the current control cycle relative to the previous cycle, avoiding integral saturation and sudden output changes. The power increment equals the proportional increment plus the integral increment plus the derivative increment, where the proportional increment equals the proportional coefficient multiplied by the difference between the current deviation and the previous deviation, the integral increment equals the integral coefficient multiplied by the current deviation, and the derivative increment equals the derivative coefficient multiplied by the current deviation minus twice the previous deviation plus the combination of the two previous deviations. The actual output power equals the power of the previous cycle plus the power increment of the current cycle. The output power is ensured to remain within the safe operating range of the heating film through upper and lower limit processing. Power adjustment is achieved by adjusting the PWM duty cycle, with the PWM frequency set to 1 kHz to reduce electromagnetic interference and power loss. Temperature control for optimal battery operating conditions 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, the heating power increases, ultimately stabilizing the battery temperature within a small range near the target operating temperature.

[0094] The above describes the intelligent management and temperature adaptive regulation method for lithium batteries under harsh environments in the embodiments of this application. The following describes the intelligent management and temperature adaptive regulation system for lithium batteries under harsh environments in the embodiments of this application. Please refer to [link / reference]. Figure 2 One embodiment of the intelligent management and temperature adaptive regulation system for lithium batteries under harsh environments in this application includes:

[0095] The data 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 to establish a composite harsh environment monitoring database.

[0096] The calculation module is used to calculate the environmental adaptability threat index of UPS lithium batteries based on the composite harsh environment monitoring database and by using the Kalman filter algorithm to fuse the influence of multiple environmental factors.

[0097] The switching module is used to generate differentiated lithium battery charging and discharging management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system float charge-constant current voltage limiting switching requirements.

[0098] The inversion module is used to invert the actual operating temperature of the lithium battery under low-temperature conditions by measuring the change in battery internal resistance based on the differentiated lithium battery charge and discharge management parameters.

[0099] The startup module is used to activate 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 to maintain the battery in the best working state using a PID temperature control algorithm.

[0100] above Figure 2 The intelligent management and temperature adaptive regulation system for lithium batteries in harsh environments in this embodiment of the invention is described in detail from the perspective of modular functional entities. The intelligent management and temperature adaptive regulation device for lithium batteries in harsh environments in this embodiment of the invention is described in detail from the perspective of hardware processing.

[0101] Reference Figure 3 This invention also provides a lithium battery intelligent management and temperature adaptive regulation device for harsh environments. This device can be a server, and its internal structure can be as follows: Figure 3 As shown. The lithium battery intelligent management and temperature adaptive regulation device for harsh environments includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor in this computer design provides computing and control capabilities. The memory of the lithium battery intelligent management and temperature adaptive regulation device for harsh environments includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the lithium battery intelligent management and temperature adaptive regulation device for harsh environments is used to store the data corresponding to this embodiment. The network interface of the lithium battery intelligent management and temperature adaptive regulation device for harsh environments is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0102] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the intelligent management and temperature adaptive regulation device for lithium batteries in harsh environments to which the present invention is applied.

[0103] 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, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the lithium battery intelligent management and temperature adaptive adjustment method under harsh environments.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0105] 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, in essence, or the part 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 to cause a lithium battery intelligent management and temperature adaptive adjustment device (which may be a personal computer, server, or network device, etc.) under harsh environments to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent management and temperature adaptive adjustment of lithium batteries under harsh environments, characterized in that, The method includes: Step S1: For the cross-river tunnel environment of the subway UPS system, collect data on high humidity, salt spray corrosion and seasonal low temperature environment simultaneously, and establish a composite harsh environment monitoring database; Step S2: Based on the composite harsh environment monitoring database, the 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: Based on the UPS lithium battery environmental adaptability threat index and combined with the UPS system's float charge-constant current voltage limiting switching requirements, generate differentiated lithium battery charging and discharging management parameters; Step S4: Based on the differentiated lithium battery charge and discharge management parameters, the actual operating temperature of the lithium battery under low temperature conditions is determined by measuring the change in 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 activated, and the PID temperature control algorithm is used to maintain the battery in the best working state.

2. The intelligent management and temperature adaptive adjustment method for lithium batteries under harsh environments according to claim 1, characterized in that, Step S1 includes: Temperature data is acquired and processed by a PT1000 platinum resistance temperature sensor array deployed inside the lithium battery module to obtain the internal temperature distribution data of the battery. Humidity monitoring and processing are performed using a capacitive humidity sensor arranged on the surface of a lithium battery casing to obtain data on changes in ambient humidity. Conductivity sensors are deployed around the UPS system to detect salt spray concentration and obtain data on environmental corrosion intensity. The composite harsh environment monitoring database is obtained by integrating and processing the battery internal temperature distribution data, the ambient humidity change data, and the ambient corrosion intensity data.

3. The intelligent management and temperature adaptive adjustment method for lithium batteries under harsh environments according to claim 1, characterized in that, Step S2 includes: The environmental parameters in the composite harsh environment monitoring database are input into a Kalman filter for state prediction processing to obtain predicted environmental parameter values. Based on the predicted environmental parameters and the measured environmental parameters, error calculation processing is performed to obtain the environmental monitoring noise covariance matrix. The environmental parameter weighting coefficients are dynamically adjusted based on the environmental monitoring noise covariance matrix to obtain the fusion weighting coefficient matrix. The fusion weight coefficient matrix is ​​weighted and fused with multiple environmental factor data to obtain the UPS lithium battery environmental adaptability threat index.

4. The intelligent management and temperature adaptive adjustment method for lithium batteries under harsh environments according to claim 1, characterized in that, 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 degree calculation to obtain the threat level membership degree value. Based on the threat level membership value, a rule matching process is performed on the preset fuzzy rule base to obtain the charging and discharging control rule activation set; Based on the charging and discharging control rules, the activation set is used for fuzzy reasoning calculation to obtain the fuzzy output values ​​of the charging current limit and the discharging cutoff voltage. The fuzzy output value is defuzzified using the centroid method to obtain the charging and discharging current control parameters. The charging and discharging current control parameters are adapted to the UPS system under two operating conditions: float charging mode during normal power supply and constant current voltage limiting discharge mode during power outage, to obtain the differentiated lithium battery charging and discharging management parameters.

5. The intelligent management and temperature adaptive adjustment method for lithium batteries under harsh environments according to claim 4, characterized in that, The charging and discharging current control parameters are adapted to both the float charging mode under normal power supply and the constant current voltage limiting discharge mode under power failure of the UPS system, resulting in differentiated lithium battery charging and discharging management parameters, including: Based on the requirement of maintaining a fully charged battery during normal UPS system power supply, the charging and discharging current control parameters are adapted to a trickle charging mode to obtain the current control parameters for the floating charging mode. Based on the constant current and voltage-limited discharge mode requirement of the UPS system to supply power to the load when the power is off, the charging and discharging current control parameters are adapted for continuous discharge with high current to obtain the discharge mode current control parameters. The floating charge mode current control parameters and the discharging mode current control parameters are processed by the working condition switching logic design based on the mains power status detection signal to obtain the automatic switching control strategy of floating charge-discharge mode. Based on the aforementioned automatic switching control strategy for float charge-discharge mode, the lithium battery charge and discharge parameters are dynamically matched in real time to obtain the differentiated lithium battery charge and discharge management parameters.

6. The intelligent management and temperature adaptive adjustment method for lithium batteries under harsh environments according to claim 1, characterized in that, Step S4 includes: Based on the aforementioned differentiated lithium battery charge and discharge management parameters, the lithium battery charge and discharge current 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-temperature relationship model for electrochemical parameter calculation and processing to obtain the battery internal resistance-temperature correlation coefficient. Based on the battery internal resistance-temperature correlation coefficient, the Arrhenius equation is used to perform temperature inversion calculation to obtain the battery internal temperature inversion value. The actual operating temperature of the lithium battery is obtained by comparing and judging the internal temperature inversion value of the battery with the low temperature environment threshold.

7. The intelligent management and temperature adaptive adjustment method for lithium batteries under harsh environments according to claim 1, characterized in that, Step S5 includes: The actual operating temperature of the lithium battery is compared with the minimum temperature threshold of the UPS system's power supply capacity to determine the temperature influence, and a self-heating start-up trigger signal is obtained. Based on the self-heating start-up trigger signal, the power control processing of the carbon fiber heating film self-heating system is performed to obtain the preset heating power output. The actual operating temperature of the lithium battery and the target operating temperature 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 control parameters to obtain the temperature control result of the battery in its optimal operating state.

8. A lithium battery intelligent management and temperature adaptive regulation system for harsh environments, characterized in that, A method for intelligent management and temperature adaptive regulation of lithium batteries under harsh environments as described in any one of claims 1-7, wherein the intelligent management and temperature adaptive regulation system for lithium batteries under harsh environments comprises: The data 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 to establish a composite harsh environment monitoring database. The calculation module is used to calculate the environmental adaptability threat index of UPS lithium batteries based on the composite harsh environment monitoring database and by using the Kalman filter algorithm to fuse the influence of multiple environmental factors. The switching module is used to generate differentiated lithium battery charging and discharging management parameters based on the UPS lithium battery environmental adaptability threat index and the UPS system float charge-constant current voltage limiting switching requirements. The inversion module is used to invert the actual operating temperature of the lithium battery under low-temperature conditions by measuring the change in battery internal resistance based on the differentiated lithium battery charge and discharge management parameters. The startup module is used to activate 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 to maintain the battery in the best working state using a PID temperature control algorithm.

9. A lithium battery intelligent management and temperature adaptive adjustment device for harsh environments, characterized in that, The method includes a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the intelligent management and temperature adaptive regulation method for lithium batteries under harsh environments as described in any one of claims 1 to 7.

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

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