Self-adaptive battery heat preservation system for plateau environment

Abstract

The invention discloses a self-adaptive battery heat preservation system for a plateau environment, and relates to the technical field of battery heat preservation systems. Comprising a temperature monitoring module, an intelligent control module, a heating module and a heat dissipation adjusting module. Wherein the temperature monitoring module comprises a sensor integration unit, and the sensor integration unit is used for detecting battery information in real time; the data processing unit is used for preprocessing the information acquired by the sensor integration unit; and the data transmission unit is used for realizing data transmission based on a data transmission channel constructed by the controller local area network bus. According to the invention, the sensing and identifying unit integrating specific plateau environment parameters such as air temperature, humidity and air pressure is combined with a battery state to dynamically predict a power demand change trend, so that active thermal management strategy adjustment is realized, and severe temperature fluctuation and low-pressure environment in a plateau area are effectively handled.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery thermal insulation systems, and in particular to a self-adaptive battery thermal insulation system for highland environments. BACKGROUND

[0002] With the transformation of global energy structure and the rapid development of renewable energy, lithium batteries as efficient energy storage devices are increasingly widely used in highland areas. However, the low air pressure, low temperature, large temperature difference and strong ultraviolet radiation in highland environments can significantly affect the performance and life of lithium batteries; and the traditional fixed-parameter thermal management system is difficult to adapt to the dynamic changing working condition requirements, resulting in the following problems:

[0003] In the low-temperature environment of highland areas, the lithium ion migration rate decreases significantly, and the electrolyte viscosity increases, causing the battery internal resistance to rise, the charging and discharging efficiency to decrease, and even causing polarization. In extreme cases, it may cause a sharp drop in capacity or failure to start the device. Existing solutions mostly use constant power heating, which can easily cause local overheating and energy waste, and lack precise regulation of the battery internal temperature field.

[0004] The power load in highland areas presents obvious fluctuation characteristics, but the control strategy based on static threshold of the conventional BMS cannot effectively predict the power demand change. When a large current discharge occurs, the instantaneous heat production exceeds the heat dissipation capacity, which may trigger a chain reaction of temperature rise; on the contrary, excessive heating in the light load state will shorten the battery cycle life.

[0005] Therefore, there is an urgent need for a self-adaptive battery thermal insulation system for highland environments to overcome the above problems. SUMMARY

[0006] The purpose of the present application is to overcome the shortcomings of the prior art and provide a self-adaptive battery thermal insulation system for highland environments.

[0007] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0008] A self-adaptive battery thermal insulation system for highland environments, comprising a temperature monitoring module, an intelligent control module, a heating module, and a heat dissipation adjustment module.

[0009] The temperature monitoring module comprises:

[0010] A sensor integration unit for real-time detection of battery information;

[0011] A data processing unit for preprocessing the information collected by the sensor integration unit;

[0012] A data transmission unit for realizing data transmission based on a data transmission channel constructed based on a controller area network bus.

[0013] The intelligent control module comprises:

[0014] The sensor integration unit is used for acquiring the temperature, humidity and air pressure of the external environment, and acquiring the meteorological conditions of the plateau region in real time.

[0015] The control strategy decision unit is used for dynamically adjusting the output according to the temperature deviation and the change rate thereof.

[0016] The man-machine interaction unit is used for setting the parameter threshold, checking the historical record and performing the manual intervention operation by the user based on the man-machine interaction.

[0017] The heating module comprises:

[0018] The heating element unit is used for implementing the heating work.

[0019] The heating control unit is used for sequentially starting the heating devices of the regions in a predetermined order, forming the gradient heating effect and realizing the overall uniform heating.

[0020] The safety and energy-saving management unit is used for monitoring the temperature rising trend in real time, predicting the potential risk and taking the cooling measure in advance.

[0021] Preferably, the heat dissipation adjustment module comprises:

[0022] The air-cooled heat dissipation unit is used for selecting a plurality of small direct-current brushless fans to form an array type air duct system and supplying air on demand.

[0023] The liquid-cooled circulation unit is used for driving the cooling liquid to flow in a closed pipeline by using a micro centrifugal pump, so as to realize the liquid cooling.

[0024] The auxiliary unit is used for monitoring the phase change process of the phase change material and judging whether the heat storage / heat release state is normal.

[0025] Preferably, the sensor integration unit selects different division numbers of thermocouples, which are distributed at the battery tab, the monomer connecting sheet and the surface center position, so as to capture the temperature fluctuation of the battery under different working conditions in real time; and a non-contact infrared thermometer is arranged to periodically scan and measure the temperature of the entire battery pack.

[0026] Preferably, the data processing unit adopts an analog-to-digital conversion chip to convert the analog temperature signal into a digital quantity; uses a digital filtering technology to eliminate the noise interference in the collection process; dynamically adjusts the filtering parameters for the frequent temperature mutation in the plateau environment; and adopts a differential encoding mode to compress and store the continuous temperature data.

[0027] The data transmission unit is based on a data transmission channel constructed by a controller area network bus, supports multi-node parallel communication, each sensor node acts as a slave station, and sends data packets to the master unit in order according to the preset priority, and adds accurate timestamp information to each temperature data packet; when a communication failure is encountered, the local cache unsuccessfully transmitted data is automatically continued after the connection is restored.

[0028] Preferably, the sensing identification unit accesses external temperature, humidity, and air pressure sensors to obtain real-time meteorological conditions in plateau regions; combines the current state parameters of the battery to comprehensively judge the severity of the working environment; analyzes and learns the historical power consumption patterns of the device load to establish a typical load curve library; predicts future power demand trends by matching the actual load with the preset pattern, and adjusts the battery management strategy in advance.

[0029] Preferably, the sensing identification unit analyzes the historical power consumption patterns of the device load and performs power prediction, specifically as follows:

[0030] S1: Data acquisition and preprocessing, recording daily / hourly load power data sequence {P1, P2, …, P n}, and performing normalization processing;

[0031] Using sliding window technology to extract short-term fluctuation characteristics, combined with Fourier transform to decompose periodic components;

[0032] Using exponential smoothing method to weaken random noise interference:

[0033] S t =α·X t +(1-α)·S t-1

[0034] Where S t is the smoothed value, X t is the original data point, and α ∈ [0, 1] is the smoothing coefficient;

[0035] S2: Construction of typical load curve library;

[0036] Based on the K-means clustering algorithm, the historical data is grouped to obtain several representative load patterns; each pattern is described by mean, standard deviation, and shape factor to describe its statistical characteristics;

[0037] S3: Matching degree calculation and trend prediction;

[0038] Define the similarity index of the current load and the kth mode:

[0039]

[0040] Where the denominator is the maximum possible distance to ensure Dk ∈[0,1], select the highest D k The corresponding mode as a reference benchmark;

[0041] According to the historical offset of the mode ΔP k+1 = P k+1 -P k , predict the next time power demand:

[0042]

[0043] β = f (SOC, Temp)

[0044] The coefficient β dynamic adjustment strategy: when the battery state of charge < 20%, β is reduced to 0.5, higher than the set temperature, β × temperature compensation factor.

[0045] Preferred: the control strategy decision unit, based on fuzzy PID controller, according to the temperature deviation and its rate of change dynamic adjustment output, when the temperature is close to the target value, switch to fuzzy control mode, reduce the overshoot; away from the target value, using PID control fast response, through online self-tuning function constantly optimize the proportional coefficient, integral time and differential link parameters; the establishment of battery thermodynamic model, predict the temperature trend in the future period of time; based on the rolling optimization principle, solve the optimal control sequence, make the battery temperature smooth transition to the set range, based on multi-objective optimization coordinator, using genetic algorithm to find the Pareto optimal solution, according to the actual situation give different target corresponding weight factor, realize the optimization of comprehensive performance.

[0046] Preferred: the control strategy decision unit, the establishment of battery thermodynamic model is as follows:

[0047]

[0048] Wherein, m is the battery quality; c p is the specific heat capacity; Q gen = I 2 R inner is the joule heat effect (I is the current, R inner is the internal resistance) ; UA is the comprehensive heat transfer coefficient; T ambient is the ambient temperature; Q heater is the external heating power input.

[0049] Preferred: the control strategy decision unit, based on multi-objective genetic algorithm optimization, as follows:

[0050] U = α1·L life + α2·η eff - α3·C cost

[0051] The sub-target quantization mode is as follows:

[0052] Lifetime index L life : According to the Arhennius law to estimate the cycle aging rate;

[0053] Efficiency index η eff : Effective output energy / total energy consumption;

[0054] Cost index C cost : Including electric heating loss electricity, equipment depreciation.

[0055] Preferably, the heating control unit, according to the structural characteristics and heat distribution law of the battery, is divided into multiple independent heating areas; for cylindrical lithium batteries, it is divided into top, middle and bottom three areas along the axial direction; for square lithium ion battery pack, it is divided into several small squares according to the row and column arrangement; each area controls the heating power and time independently; the heating devices of each area are started in turn according to the predetermined order, forming the effect of gradient heating; first slowly heat from the low temperature area, gradually advance to the high temperature area, adjust the heating rate and target temperature of each interval, realize the whole uniform heating; the actual temperature of each heating area is monitored in real time, compared with the set value, if the deviation exceeds the allowed range, immediately adjust the heating power of the corresponding area or close part of the heating wire, until the temperature returns to the normal range.

[0056] The beneficial effects of the present application are:

[0057] 1. The present application identifies the unit through the integration of high-altitude environment parameters such as temperature, humidity, air pressure, etc., combines the dynamic prediction of battery state power demand trend, realizes the active heat management strategy adjustment, effectively deals with the severe temperature fluctuation and low air pressure environment in plateau area.

[0058] 2. The present application adopts the composite heat dissipation scheme of gradient heating control and phase change material auxiliary, takes into account the rapid response and energy efficiency, and avoids the performance decay of traditional single mode under extreme conditions.

[0059] 3. The present application is based on the double control architecture of fuzzy PID and multi-objective genetic algorithm, which can quickly converge to the target temperature range, and can reduce the overshoot through rolling optimization; the battery thermodynamic model is established to make advance prediction, ensure smooth temperature transition and strong anti-interference ability. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A framework diagram of a self-adaptive battery heat preservation system for high-altitude environment is proposed. DETAILED DESCRIPTION

[0061] The technical solutions of the present application will be further described in detail in combination with specific embodiments.

[0062] Embodiment 1:

[0063] An adaptive battery heat preservation system for highland environment, comprising a temperature monitoring module, an intelligent control module, a heating module, and a heat dissipation adjustment module;

[0064] The temperature monitoring module comprises:

[0065] A sensor integration unit for real-time detection of battery information;

[0066] A data processing unit for preprocessing information collected by the sensor integration unit;

[0067] A data transmission unit for data transmission based on a data transmission channel constructed by a controller area network bus;

[0068] The intelligent control module comprises:

[0069] A sensing recognition unit for real-time acquisition of meteorological conditions in plateau areas by accessing external temperature, humidity, and air pressure sensors, and for predicting future power demand trends according to the matching degree of actual load and preset mode;

[0070] A control strategy decision unit for dynamically adjusting output according to temperature deviation and its change rate;

[0071] A human-computer interaction unit for users to set parameter thresholds, view historical records, and perform manual intervention operations based on human-computer interaction;

[0072] The heating module comprises:

[0073] A heating element unit for implementing heating work;

[0074] A heating control unit for sequentially starting heating devices in each region according to a predetermined order to form a gradient heating effect and achieve overall uniform heating;

[0075] A safety and energy-saving management unit for real-time monitoring of temperature rising trends, predicting potential risks, and taking cooling measures in advance;

[0076] The heat dissipation adjustment module comprises:

[0077] An air-cooled heat dissipation unit for selecting multiple small direct-current brushless fans to form an array type air duct system and supplying air on demand;

[0078] A liquid-cooled circulation unit for using a miniature centrifugal pump to drive the cooling liquid to circulate in a closed pipeline to achieve liquid cooling;

[0079] The auxiliary unit, based on a platinum resistance thermometer embedded inside the phase change material, monitors the phase change process in real time to determine whether the heat storage / heat release state is working properly.

[0080] The sensor integration unit uses thermocouples of different grades, distributed on the battery tabs, cell connecting pieces, and the center of the surface, to capture the temperature fluctuations of the battery under different operating conditions in real time; it is equipped with a non-contact infrared thermometer to periodically scan and measure the temperature of the entire battery pack.

[0081] The data processing unit uses an analog-to-digital converter chip to convert analog temperature signals into digital quantities; it employs digital filtering technology to eliminate noise interference during the acquisition process; it dynamically adjusts filtering parameters to address frequent temperature changes in high-altitude environments; and it uses differential coding to compress and store continuous temperature data.

[0082] The data transmission unit is based on a data transmission channel built on a controller local area network bus, which supports parallel communication of multiple nodes. Each sensor node acts as a slave station and sends data packets to the master control unit in an orderly manner according to a preset priority. Precise timestamp information is added to each temperature data packet. When a communication failure occurs, the data that was not successfully sent is cached locally and automatically resumed after the connection is restored.

[0083] The sensing and identification unit connects to external temperature, humidity, and air pressure sensors to obtain real-time meteorological conditions in high-altitude areas. Combined with the battery's current state parameters (SOC, voltage, and current), it comprehensively assesses the severity of the operating environment. For example, in low-temperature and low-pressure environments, it appropriately increases the battery's allowable operating temperature limit to prevent electrolyte solidification. By analyzing and learning from the historical power consumption patterns of the equipment load, a typical load curve library is established. Based on the matching degree between the actual load and the preset pattern, it predicts future power demand trends and adjusts battery management strategies in advance. For example, for loads with strong regularity, such as nighttime lighting equipment, a preheating program can be started before sunset. GPS positioning information is used to identify the specific geographical location (latitude, longitude, and altitude) of the equipment. Different regions have different solar radiation intensity and diurnal temperature differences; the initial settings of control parameters are optimized accordingly.

[0084] The sensing and identification unit performs historical power consumption pattern analysis and power prediction of the equipment load, as follows:

[0085] S1: Data acquisition and preprocessing, recording daily / hourly load power data sequences {P1, P2, ..., P...} n} and perform normalization processing;

[0086] Use the sliding window technique to extract short-term fluctuation characteristics (such as data from the most recent 24 hours), and combine it with Fourier transform to decompose periodic components (daily cycle, weekly cycle);

[0087] Exponential smoothing method is used to weaken random noise interference:

[0088] S t = α·X t + (1-α)·S t-1

[0089] Where S t is the smoothed value, X t is the original data point, and α∈[0,1] is the smoothing coefficient (0.3 is taken);

[0090] S2: Construction of typical load curve library;

[0091] Based on K-means clustering algorithm, historical data is grouped to obtain several representative load patterns (such as basic load, peak load, intermittent impact load); each pattern is described by mean, standard deviation and shape factor to describe its statistical characteristics;

[0092] S3: Matching degree calculation and trend prediction;

[0093] Define the similarity index of the current load and the kth mode:

[0094]

[0095] Where the denominator is the maximum possible distance, to ensure that D k ∈[0,1], select the highest D k corresponding mode as the reference benchmark;

[0096] According to the historical offset of the mode ΔP k+1 = P k+1 -P k , predict the power demand at the next moment:

[0097]

[0098] β = f(SOC, Temp)

[0099] Coefficient β dynamic adjustment strategy: when the battery state of charge is <20%, β is reduced to 0.5, and when the temperature is higher than the set temperature, β × temperature compensation factor (0.02 is reduced for every 1℃ rise).

[0100] The control strategy decision unit is based on a fuzzy PID controller, dynamically adjusts output according to temperature deviation and its change rate, switches to a fuzzy control mode when the temperature approaches a target value, reduces overshoot, adopts PID control for fast response when the temperature deviates from the target value, continuously optimizes proportional coefficient, integral time and differential link parameters through online self-tuning function, establishes a battery thermodynamic model, predicts temperature trend in a future period of time, solves an optimal control sequence based on a rolling optimization principle, makes the battery temperature smoothly transition to a set range, uses intelligent optimization methods such as a genetic algorithm to find a Pareto optimal solution based on a multi-objective optimization coordinator, and assigns different target weight factors according to actual conditions, and realizes optimization of comprehensive performance.

[0101] The control strategy decision unit establishes a battery thermodynamic model as follows:

[0102]

[0103] Wherein, m is the battery mass; c p is specific heat capacity; Q gen =I 2 R inner is the Joule-Thomson effect (I is current, R inner is internal resistance); UA is a comprehensive heat transfer coefficient; T ambient is ambient temperature; Q heater is external heating power input.

[0104] The control strategy decision unit is based on multi-objective genetic algorithm optimization, and is specifically as follows:

[0105] U = alpha1 * L life + alpha2 * eta eff - alpha3 * C cost

[0106] The sub-target quantization mode is as follows:

[0107] The life index L life : estimates the cycle aging rate according to the Arhennius law;

[0108] The efficiency index eta eff : effective output energy / total consumed energy;

[0109] The cost index C cost : including electric heating loss electricity fee, equipment depreciation fee.

[0110] The man-machine interaction unit provides an operation interface, displays real-time temperature curve, system state indication, alarm information content, and users set parameter threshold, view historical records, and perform manual intervention operation through a touch screen.

[0111] The heating element unit is made of a graphene material into a sheet-shaped electric heating film, is attached to the surface of the battery, and uses carbon fibers as a heating body and is woven into a net-shaped structure to wrap around the battery.

[0112] The heating control unit divides the battery into multiple independent heating areas according to the structural characteristics and heat distribution law of the battery. For example, for a cylindrical lithium battery, the battery is divided into three areas of top, middle and bottom along the axial direction; for a square lithium ion battery pack, the battery is divided into small squares according to the row and column arrangement; the heating power and time of each area are controlled independently; the heating devices of each area are started in a predetermined order to form a gradient heating effect. The low-temperature area is heated slowly first, and then gradually advances to the high-temperature area to avoid sudden high-power heating that causes excessive stress in the battery. The heating rate and target temperature of each area are adjusted to realize uniform heating of the whole battery; the actual temperature of each heating area is monitored in real time and compared with the set value. If the deviation exceeds the allowed range, the heating power of the corresponding area is adjusted or part of the heating wire is turned off until the temperature returns to the normal range.

[0113] The safety and energy-saving management unit is directly connected in series in the heating circuit through a built-in thermal switch to cut off the power supply immediately when an abnormally high temperature is detected; the temperature rising trend is monitored in real time to predict potential risks and take cooling measures in advance.

[0114] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. An adaptive battery insulation system for high-altitude environments, characterized in that, It includes a temperature monitoring module, an intelligent control module, a heating module, and a heat dissipation adjustment module; The temperature monitoring module includes: Sensor integration unit, used to detect battery information in real time; The data processing unit preprocesses the information collected by the sensor integration unit. The data transmission unit implements data transmission through a data transmission channel built on the controller local area network bus; The intelligent control module includes: The sensing and recognition unit connects to external temperature, humidity, and air pressure sensors to obtain real-time meteorological conditions in the plateau region. Based on the degree of matching between the actual load and the preset mode, it predicts the future trend of power demand changes. The control strategy decision unit dynamically adjusts the output based on the temperature deviation and its rate of change. The human-computer interaction unit allows users to set parameter thresholds, view historical records, and perform manual intervention operations based on human-computer interaction. The heating module includes: Heating element unit, used to perform heating operations; The heating control unit activates the heating devices in each area in a predetermined sequence to create a gradient heating effect and achieve uniform heating of the entire area. The safety and energy-saving management unit monitors the temperature rise trend in real time, anticipates potential risks, and takes cooling measures in advance.

2. The adaptive battery insulation system for high-altitude environments according to claim 1, characterized in that, The heat dissipation adjustment module includes: The air-cooled heat dissipation unit uses an array-type air duct system composed of multiple small DC brushless fans to supply air as needed. The liquid cooling circulation unit uses a miniature centrifugal pump to drive the coolant to circulate in a closed pipe to achieve liquid cooling. The auxiliary unit, based on a platinum resistance thermometer embedded inside the phase change material, monitors the phase change process in real time to determine whether the heat storage / heat release state is working properly.

3. The adaptive battery insulation system for high-altitude environments according to claim 1, characterized in that, The sensor integration unit uses thermocouples of different grades, distributed on the battery tabs, cell connecting pieces, and the center of the surface, to capture the temperature fluctuations of the battery under different operating conditions in real time; it is equipped with a non-contact infrared thermometer to periodically scan and measure the temperature of the entire battery pack.

4. The adaptive battery insulation system for high-altitude environments according to claim 1, characterized in that, The data processing unit uses an analog-to-digital converter chip to convert analog temperature signals into digital quantities; it employs digital filtering technology to eliminate noise interference during the acquisition process; it dynamically adjusts filtering parameters to address frequent temperature changes in high-altitude environments; and it uses differential coding to compress and store continuous temperature data. The data transmission unit is based on the data transmission channel built on the controller local area network bus, which supports parallel communication of multiple nodes. Each sensor node acts as a slave station and sends data packets to the master control unit in an orderly manner according to a preset priority. Precise timestamp information is added to each temperature data packet. When a communication failure occurs, the data that was not successfully sent is cached locally and automatically resumed after the connection is restored.

5. The adaptive battery insulation system for high-altitude environments according to claim 1, characterized in that, The sensing and recognition unit connects to external temperature, humidity, and air pressure sensors to obtain real-time meteorological conditions in the plateau region; combined with the current state parameters of the battery, it comprehensively judges the severity of the working environment; by analyzing and learning the historical power consumption patterns of the equipment load, it establishes a typical load curve library; based on the degree of matching between the actual load and the preset mode, it predicts the future power demand change trend and adjusts the battery management strategy in advance.

6. The adaptive battery insulation system for high-altitude environments according to claim 5, characterized in that, The sensing and identification unit performs historical power consumption pattern analysis and power prediction of the equipment load, as follows: S1: Data acquisition and preprocessing, recording daily / hourly load power data sequences {P1, P2, ..., P...} n } and perform normalization processing; The sliding window technique is used to extract short-term fluctuation characteristics, and Fourier transform is used to decompose periodic components. Exponential smoothing is used to reduce random noise interference. S t =α·X t +(1-α)·S t-1 Among them, S t X is the smoothed value. t The original data points are represented by α∈[0,1], which is the smoothing coefficient. S2: Construction of a typical load curve library; Historical data were grouped using the K-means clustering algorithm to obtain several representative load patterns; the statistical characteristics of each pattern were described by mean, standard deviation and shape factor. S3: Matching degree calculation and trend prediction; Define a similarity metric between the current load and the k-th type of pattern: Where the denominator is the maximum possible distance, ensuring D k ∈[0,1], select the highest D k The corresponding pattern serves as a reference benchmark; Based on the historical offset ΔP of this pattern k+1 =P k+1 -P k Predict the power demand at the next moment: β = f(SOC, Temp) Dynamic adjustment strategy for coefficient β: When the battery state of charge is <20%, β is reduced to 0.5; when the temperature is higher than the set temperature, β is multiplied by the temperature compensation factor.

7. The adaptive battery insulation system for high-altitude environments according to claim 1, characterized in that, The control strategy decision unit, based on a fuzzy PID controller, dynamically adjusts the output according to the temperature deviation and its rate of change. When the temperature approaches the target value, it switches to fuzzy control mode to reduce overshoot; when the temperature is far from the target value, it uses PID control for fast response and continuously optimizes the proportional coefficient, integral time, and derivative parameters through online self-tuning. It also establishes a battery thermodynamic model to predict the temperature trend over a period of time. Based on the principle of rolling optimization, the optimal control sequence is solved to ensure that the battery temperature smoothly transitions to the set range. Based on a multi-objective optimization coordinator, a genetic algorithm is used to find the Pareto optimal solution. According to the actual situation, different objectives are assigned corresponding weight factors to achieve the optimization of comprehensive performance.

8. The adaptive battery insulation system for high-altitude environments according to claim 7, characterized in that, The control strategy decision unit establishes the battery thermodynamic model as follows: Where m is the battery mass; c p Q is the specific heat capacity; gen =I 2 R inner This is due to the Joule heating effect (I is the electric current, R is the electric current). inner (Internal resistance); UA is the overall heat transfer coefficient; T ambient Q represents ambient temperature; heater For external heating power input.

9. The adaptive battery insulation system for high-altitude environments according to claim 7, characterized in that, The control strategy decision unit is optimized based on a multi-objective genetic algorithm, as follows: U=α1·L life +α2·η eff -α3·C cost The sub-target quantification method is as follows: Lifespan Index L life Estimate the cyclic aging rate based on Arhennius's law; Efficiency index η eff Effective output energy / Total energy consumed; Cost indicator C cost This includes electricity costs for heat loss and equipment depreciation.

10. An adaptive battery insulation system for high-altitude environments according to claim 1, characterized in that, The heating control unit divides the battery into multiple independent heating zones based on its structural characteristics and heat distribution patterns. For cylindrical lithium batteries, the heating zone is divided into three areas along the axial direction: top, middle, and bottom. For square lithium-ion battery packs, the heating zone is divided into several small squares arranged in rows and columns. The heating power and time of each zone are controlled independently. The heating devices of each zone are activated sequentially according to a predetermined order to create a gradient heating effect. The heating starts slowly from the low-temperature zone and gradually progresses to the high-temperature zone. By adjusting the heating rate and target temperature of each zone, uniform heating is achieved. The actual temperature of each heating zone is monitored in real time and compared with the set value. If the deviation exceeds the allowable range, the heating power of the corresponding zone is immediately adjusted or some heating wires are turned off until the temperature returns to the normal range.

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