Intelligent thermal management control system and control method of power battery

Abstract

The invention discloses an intelligent thermal management control system and control method for a power battery. The intelligent thermal management control system comprises a battery module, a temperature monitoring sensing module, an industrial personal computer and a dynamic cooling module, each battery module is provided with one sensing optical fiber, the sensing optical fibers are integrated with a plurality of optical fiber sensors arranged in an array mode, the optical fiber sensors in the same sensing optical fiber correspond to different wavelengths, and the industrial personal computer receives the temperature from the temperature monitoring sensing module. The trained temperature analysis model is used for predicting and analyzing the temperature state of the battery, a corresponding control decision is made according to preset logic, the optimal liquid cooling flow rate is obtained by adopting a genetic algorithm, and the dynamic cooling module is controlled to adjust the flow rate of cooling liquid to prevent the battery from being overheated.

Description

Technical Field

[0001] The present invention belongs to the field of power energy, and in particular relates to an intelligent thermal management control system and a control method for a power battery. Background Art

[0002] With the development of ship electrification, power batteries, as core components of marine power systems, are receiving increasing attention for their performance and safety. Marine lithium batteries work in high temperature and humid environments, and temperature fluctuations have a significant impact on the battery's charge and discharge efficiency, cycle life, and safety. At present, most marine lithium battery thermal management systems rely mainly on passive heat dissipation or traditional air cooling and liquid cooling technologies. These systems usually have problems such as uneven temperature control, delayed response, and high energy consumption. Especially when the ship is sailing at high speed or the load changes greatly, it is difficult to effectively deal with the heat generated by the battery. In addition, due to the limited space of the marine battery cabinet, traditional heat dissipation methods are difficult to achieve efficient heat dissipation in a compact environment, resulting in local overheating, increasing the risk of thermal runaway of the battery, and may cause safety accidents. Existing technologies fail to fully consider the environmental adaptability and energy efficiency optimization under special operating conditions of ships.

[0003] Existing power battery thermal management systems mostly use fixed cooling strategies and cannot be dynamically adjusted according to the ship's operating status or the actual temperature changes of the battery, resulting in low cooling efficiency and high energy consumption. At the same time, traditional thermal management systems have a slow response speed when the battery temperature is abnormal, lack intelligent safety protection mechanisms, and cannot effectively prevent the occurrence of thermal runaway.

[0004] CN 202310208836X discloses a battery module, a power battery pack and a cooling control method. When a battery experiences thermal runaway, a flow guide device of the module guides the thermal runaway gas to a specific area of ​​a gas collecting box and discharges the gas through an exhaust pipe, thereby effectively controlling the flow of the thermal runaway gas of the battery and avoiding accidents caused by gas accumulation. At the same time, heat can also be dissipated to reduce the battery temperature.

[0005] At present, fiber optic sensors are used for distributed temperature monitoring. Fiber optic sensors are used to monitor battery temperature, and FBG (based on wavelength variation) technology is used. This technology can set multiple gratings in one optical fiber to achieve multi-point, distributed temperature monitoring. For this reason, an intelligent thermal management control system for marine lithium batteries is urgently needed. The system should have temperature monitoring sensors, dynamic adjustment cooling strategies, energy efficiency optimization and safety protection mechanisms to ensure the safe and efficient operation of batteries in various working environments. Summary of the invention

[0006] The purpose of the present invention is to provide a marine power battery intelligent thermal management control system and control method. By introducing intelligent temperature monitoring and dynamic adjustment mechanisms, the system can monitor the operating temperature of the battery in real time, automatically adjust the cooling system according to the ship's operating status, and ensure that each battery unit is always within the optimal temperature range. In addition, the present invention also aims to improve the energy efficiency utilization of the system, reduce unnecessary energy consumption by optimizing the cooling strategy, and take safety protection measures in time when the battery temperature is abnormal, thereby preventing thermal runaway, ensuring the safe operation of the battery in a complex marine environment, and improving the overall performance and reliability of the ship's power system.

[0007] The first aspect of the present invention is to provide a power battery intelligent thermal management control system, including a battery module, a temperature monitoring sensor module, an industrial computer and a dynamic cooling module;

[0008] The thermal management control system is applied to a multi-layer battery cabinet, each layer of which is provided with a battery module, and the battery module includes a plurality of battery cells arranged in an array;

[0009] The temperature monitoring sensor module is used to detect the wavelength light information of each battery cell in real time to obtain the temperature change inside the battery unit, and convert the obtained reflection spectrum into electrical signals and digital signals in turn, and then amplify and filter them; the temperature monitoring sensor module includes a light source, an optical fiber splitter, multiple sensing optical fibers, a spectrometer, a photoelectric detector, and a data acquisition and preprocessing unit connected in turn; each battery module is provided with a sensing optical fiber, and the sensing optical fiber integrates a plurality of optical fiber sensors arranged in an array, and each optical fiber sensor in the same sensing optical fiber corresponds to a different wavelength; the number of the optical fiber sensors corresponds to the number of battery cells in the battery module, and the optical fiber sensor is located inside the battery cell; the data acquisition and preprocessing unit collects the electrical signal of the photoelectric detector, and after amplifying and filtering the electrical signal, digitizes the analog signal and transmits it to the industrial computer;

[0010] The industrial computer receives temperature data from the temperature monitoring sensor module, as well as data transmitted from the temperature sensor and the flow sensor, predicts and analyzes the temperature state of the battery using the trained temperature analysis model, and makes corresponding control decisions according to the preset logic; wherein the temperature sensor is arranged at the liquid inlet and the liquid outlet in the liquid cooling plate, and the flow sensor is located at the liquid inlet;

[0011] The dynamic cooling module is used to adjust the cooling pump according to the instructions of the industrial computer so as to adjust the flow rate of the coolant in the liquid cooling plate; the dynamic cooling module includes a cooling pump, a control valve, a liquid cooling plate and a radiator connected in sequence; the top and bottom surfaces of the liquid cooling plate can both be in contact with adjacent battery cells; the liquid cooling plate has an S-shaped liquid cooling pipe inside, the liquid cooling pipe contains coolant, and the liquid inlet of the liquid cooling pipe is connected to the cooling pump through the control valve, and the liquid outlet is connected to the radiator; the coolant circulates through the cooling pump to bring heat from the battery module to the radiator.

[0012] Furthermore, a heat insulation plate is provided between the side walls of the adjacent battery cells in the battery module.

[0013] Furthermore, the light source is a superfluorescent light source, the optical fiber splitter is used to distribute the optical signal of the light source to each of the sensing optical fibers; the spectrometer is used to detect the reflection spectrum of the optical fiber sensor; the photoelectric detector is used to read the spectral information and convert the optical signal into an electrical signal which is then received by the data acquisition and preprocessing unit; the data acquisition and preprocessing unit includes a data acquisition card, an amplifier and a filter.

[0014] Furthermore, the industrial computer includes a demodulation submodule, a historical temperature storage submodule, and a temperature prediction and analysis submodule. The demodulation submodule receives a digital signal from a data acquisition and preprocessing unit, runs a demodulation algorithm according to the signal of the reflection spectrum, and calculates the temperature value detected by each optical fiber sensor; the historical temperature storage submodule stores the temperature data obtained from the demodulation submodule as historical temperature data, and forms a temperature curve based on a timestamp; the temperature prediction and analysis submodule controls the dynamic cooling module to adjust the coolant flow rate according to the temperature change trend obtained by the historical temperature storage submodule and the data transmitted by the temperature sensor and the flow sensor.

[0015] Furthermore, the top wall of the liquid cooling plate is in close contact with the bottom surface of the battery cell, and the bottom wall of the liquid cooling plate 4 is in close contact with the tab of the battery cell;

[0016] Furthermore, the industrial computer can also adjust the speed of the fan of the radiator according to the prediction and analysis results.

[0017] Furthermore, the cooling pump is adjusted by controlling the rotation speed of the cooling pump or adjusting the opening of the control valve.

[0018] Furthermore, the power battery intelligent thermal management control system also includes a safety protection module, which is used for the industrial computer to automatically reduce the battery load and limit the current output when the battery cell temperature or voltage continues to exceed the preset safety range; if the temperature still rises and reaches a dangerous level, the industrial computer will quickly cut off the battery power supply to prevent thermal runaway or battery damage.

[0019] A second aspect of the present invention is to provide a control method for the power battery intelligent thermal management control system, comprising:

[0020] Step 1: The temperature monitoring sensor module emits a light source, the light is reflected in the optical fiber sensor in the sensing optical fiber, and the spectrometer detects the reflection spectrum of the optical fiber sensor. Then, it is converted into an electrical signal, amplified, filtered, and then converted into a digital signal and transmitted to the industrial computer for data processing and analysis through a photoelectric detector and a data acquisition and preprocessing unit;

[0021] Step 2: Run a demodulation algorithm based on the digital signal of the received reflection spectrum to calculate the temperature value detected by each optical fiber sensor, and form a battery temperature change curve with the obtained temperature value;

[0022] Step 3: Determine whether the battery cell is in a charging and discharging state. If so, input the temperature change curve, battery state and battery cell temperature difference into the trained convolutional neural network temperature analysis model for prediction and analysis. When it is determined that the dynamic cooling module needs to be started, execute step 4; wherein the temperature analysis model is trained to obtain the charging safety temperature threshold, discharging safety temperature threshold, discharging warning temperature threshold and limit temperature of the battery cell;

[0023] Step 4: When step 3 determines that the dynamic cooling module needs to be started, according to the battery charging and discharging status and the corresponding temperature threshold determined in step 3, a dynamic adjustment cooling strategy based on a genetic algorithm is used to obtain the optimal liquid cooling flow rate, and the dynamic adjustment cooling strategy is applied to adjust the dynamic cooling module to keep the battery temperature within a reasonable range.

[0024] Furthermore, the step three specifically includes:

[0025] S31: First, determine whether the battery cell is in a charging or discharging state; if so, execute S32; otherwise, consider that the battery cell is in a standby or low-load state and continue monitoring;

[0026] S32: when it is determined that the battery module is in a charging state, and the charging temperature of any of the battery cells is within the charging safety temperature range determined by the temperature prediction and analysis submodule, the coolant flow rate is maintained unchanged; when the charging temperature of any of the battery cells exceeds the above safety temperature range, step 4 is executed;

[0027] When it is determined that the battery module is in a discharge state, and the discharge temperature of any of the battery cells is within the discharge safety temperature range determined by the temperature prediction and analysis submodule, step 4 is executed to reduce the coolant flow rate; when the discharge temperature of any of the battery cells reaches the discharge warning temperature threshold, an early warning is issued to the user to remind the operator to pay attention to the temperature change; when the discharge temperature reaches the set limit temperature, the system will automatically cut off the power supply to protect the battery;

[0028] When the battery BMS detects that the battery cell discharge rate is 1C or above, execute step 4.

[0029] Furthermore, the dynamic adjustment of the cooling strategy in step 4 adopts a genetic algorithm, models the problem as a multi-objective optimization problem, and encodes the coolant flow rate as a binary string or a real number vector; initializes a population, and each individual corresponds to a different cooling strategy combination; comprehensively considers the three aspects of the safe temperature range, energy consumption and response speed obtained in step 2, and adopts a tournament selection method to select individuals with high fitness as the parents of the next generation, and iterates to output the optimal cooling strategy;

[0030] When it is determined in step 3 that the battery module is in a discharge state and the discharge temperature of any of the battery cells is within a discharge safety temperature range, the coolant flow rate is reduced to an optimal liquid cooling flow rate obtained by the genetic algorithm;

[0031] When step 3 determines that the battery module is in a charging state and exceeds the above-mentioned safe temperature range, a genetic algorithm is used to obtain the optimal liquid cooling flow rate, and the cooling pump is adjusted in real time according to the temperature change to increase the coolant flow rate;

[0032] When step three determines that the discharge rate of the battery module is 1C or above, the dynamic cooling module is started in advance within 10 seconds after the load is discharged, the cooling pump is adjusted to increase the coolant flow rate, and a genetic algorithm is used to obtain the optimal liquid cooling flow rate to prevent the battery from overheating.

[0033] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0034] An intelligent thermal management control system for a power battery of the present invention effectively prevents battery overheating and reduces the risk of thermal runaway by real-time monitoring of battery temperature and dynamically adjusting a dynamic cooling module, thereby ensuring that the battery pack is always within a safe operating temperature range during navigation.

[0035] Second, the intelligent thermal management control system of the power battery of the present invention adopts an intelligent optimization algorithm to dynamically adjust the cooling strategy according to the actual working state and environmental conditions of the battery, significantly reducing energy consumption. This optimization not only extends the service life of the battery, but also reduces the operating costs of the ship.

[0036] Third, the intelligent thermal management control system of a power battery of the present invention integrates multiple safety protection mechanisms. When abnormal temperature or fault is detected, the system can promptly issue an alarm and take corresponding measures. This design enhances the reliability of the system and reduces the potential risk of equipment damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1This is an overall structural diagram of a battery cabinet of a power battery intelligent thermal management control system of the present invention;

[0038] Figure 2 is a top view of the dynamic cooling module;

[0039] Figure 3 is a working principle diagram of the battery thermal management system;

[0040] Figure 4 It is a flow chart of the control method of the power battery intelligent thermal management control system. In the figure: 1: Tab; 2: Battery cell; 3: Heat shield; 4: Liquid cooling plate; 5: Liquid inlet; 6: Fiber optic sensor; 7: Liquid cooling pipe; 8: Liquid outlet; 9: Amplifier; 10: Filter; 11: Industrial computer; 12: Cooling pump; 13: Radiator; 14: Control valve. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution, beneficial effects and significant progress of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention is clearly and completely described below in conjunction with the drawings provided in the examples of the present invention. Obviously, all the described embodiments are only partial embodiments of the present invention, rather than all embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] like Figure 3 As shown, a power battery intelligent thermal management control system includes a battery module, a temperature monitoring sensor module, an industrial computer 11, a dynamic cooling module and a safety protection module.

[0043] The thermal management control system is applied to Figure 1 The battery cabinet shown is a 5-layer battery cabinet, each layer is provided with a battery module, the battery module includes 9 battery cells 2 arranged in an array, and a heat insulation plate 3 is provided between the side walls of adjacent battery cells 2.

[0044] The temperature monitoring sensor module is used to collect the temperature data of each battery cell in real time. The FBG optical fiber sensor therein continuously detects the temperature of the battery at a preset sampling frequency. By measuring the temperature change inside the battery, the temperature value is converted into an electrical signal and sent to the industrial computer 11. In this embodiment, the temperature monitoring sensor module includes a light source, an optical fiber splitter, 5 sensing optical fibers, a spectrometer / interferometer, a photodetector, and a data acquisition and preprocessing unit connected in sequence. The light source is connected to the optical fiber splitter, and the optical fiber splitter is respectively connected to one end of the sensing optical fiber, the other end of the sensing optical fiber is connected to the spectrometer, the other end of the spectrometer is connected to the photodetector, the photodetector is connected to the data acquisition and preprocessing unit, and the data acquisition and preprocessing unit is electrically connected to the preprocessing module. The light source is a super fluorescent light source (SLED) that emits a continuous spectrum. The optical fiber splitter is used to distribute the optical signal of the light source to different optical fibers, and can reduce the number of light sources in the system, thereby reducing costs and simplifying the system architecture. According to actual needs, the optical fiber splitter can be replaced by an optical switch. When the superfluorescent light source passes through the sensing optical fiber, the FBG optical fiber sensor 6 will reflect a specific wavelength of light that matches its Bragg wavelength. The reflection spectrum of the FBG optical fiber sensor 6 is detected by the spectrometer. The photodetector is used to read the spectral information and convert the optical signal into an electrical signal, which is then received by the data acquisition and preprocessing unit. The data acquisition and preprocessing unit includes a data acquisition card, an amplifier 9 and a filter 10. The data acquisition card uses an ADC (analog-to-digital converter) with a resolution of 12 bits or higher. The amplified analog signal is converted into a digital signal through an analog-to-digital converter (ADC) to ensure accurate digitization of the signal; and it needs to have a multi-channel function, which can collect multiple signals at the same time to meet the simultaneous monitoring of multiple FBG sensors. The digital signal can be directly processed by the central control unit (industrial computer) and used for real-time analysis. This step is crucial to the real-time performance of the system, ensuring that the data accurately reflects the current temperature state of the battery. The data acquisition card collects the electrical signal of the photoelectric detector, and amplifies the weak electrical signal from the optical fiber sensor 6 through the amplifier 9 to ensure that the sensor data will not experience signal attenuation during long-distance transmission; then the high-frequency or low-frequency interference noise is removed through the filter, especially in the complex electrical environment of the ship, the filtering function can significantly improve the reliability of the signal; then the amplified and filtered analog signal is digitized and transmitted to the industrial computer 11.

[0045] Each sensing optical fiber integrates 9 high-precision FBG (Fiber Bragg Grating) optical fiber sensors arranged in an array, and each FBG optical fiber sensor 6 corresponds to a different Bragg wavelength. An FBG optical fiber sensor 6 is provided inside each battery cell 2 (close to the inside of the battery cell or the active material area) to monitor the overall and local temperature of the battery module. The FBG optical fiber sensor 6 of each layer is integrated into a sensing optical fiber.

[0046] The industrial computer 11 is the core component of the intelligent thermal management control system for marine lithium batteries, which is responsible for receiving data from the temperature monitoring sensor module, analyzing the temperature state of the battery, and making corresponding control decisions according to the preset logic. The industrial computer 11 is connected to the dynamic cooling module and the temperature monitoring sensor module respectively, including a historical temperature storage submodule, a demodulation submodule, and a temperature prediction and analysis submodule; the demodulation submodule receives the digital signal from the data acquisition and preprocessing unit, runs the demodulation algorithm of the FBG signal according to the signal of the reflection spectrum, and accurately calculates the temperature value detected by each FBG optical fiber sensor; the historical temperature storage submodule stores the temperature data obtained from the demodulation submodule as historical temperature data, and the collected signal changes with time to form a temperature curve, thereby reflecting the actual thermal condition of the lithium battery during operation. The temperature prediction and analysis submodule executes the control algorithm according to the temperature change trend obtained by the historical temperature storage submodule and the data transmitted by the temperature sensor and the flow sensor. The control algorithm is optimized by a convolutional neural network (CNN) and a deep learning model is used to predict and analyze the temperature data. At the same time, the temperature data of the battery cells 2 corresponding to each FBG optical fiber sensor is displayed in real time on the display screen connected to the industrial computer 11, and the industrial computer 11 can transmit the data to the remote server through the network, which is convenient for remote monitoring and maintenance. The industrial computer is electrically connected to the temperature sensor and the flow sensor, and can transmit the temperature and flow rate of the coolant in the dynamic cooling module in real time.

[0047] The dynamic cooling module is used to adjust the working frequency of the water pump and the flow rate of the coolant according to the instructions of the industrial computer (the industrial computer adjusts the feedback based on the temperature sensor and the flow sensor), and includes a cooling pump 12, a control valve 14, a liquid cooling plate 4 and a radiator 13 connected in sequence.

[0048] A horizontally arranged liquid cooling plate 4 is arranged between the battery modules of adjacent layers and below the battery modules of the lowest layer, and a total of 5 layers of horizontally arranged liquid cooling plates 4 are provided. The distance between adjacent liquid cooling plates 4 is equal to the height of the power battery, so that the top wall of the liquid cooling plate 4 is closely attached to the bottom surface of the battery cell 2, and the bottom wall of the liquid cooling plate 4 is closely attached to the pole ear 1 of the battery cell 2. Figure 2 As shown, the liquid cooling plate 4 is a hollow structure, which includes a liquid cooling pipe 7, a liquid inlet 5 and a liquid outlet 8, and the liquid inlet 5 and the liquid outlet 8 are respectively located at the two ends of the liquid cooling pipe. The liquid cooling pipe 7 is laid in an S shape inside the liquid cooling plate 4, and the internal working fluid is water; the bottom of the liquid cooling plate 4 has a groove adapted to the outer contour of the pole ear and the electrical connector. When the liquid cooling plate 4 is buckled on the pole ear and the electrical connector as a cover plate of the pole ear 1 and the electrical connector, the liquid cooling plate 4 can fully contact the pole ear and the electrical connector located on the top of the battery cell 2, which is convenient for cooling and heat dissipation of the pole ear.

[0049] The liquid inlet of the liquid cooling pipe 7 is connected to the cooling pump 12 through the control valve 14, and the liquid outlet is connected to the radiator 13. The coolant in the cooling pipe 7 circulates through the cooling pump 12, bringing heat from the battery module to the radiator 13. The heat of the coolant in the radiator 13 is transferred to the surface of the radiator through the heat conducting sheet or pipe. In addition, the temperature sensor is installed at the coolant inlet and outlet to monitor the coolant temperature difference to understand the temperature change of the coolant flowing under the battery after heat exchange; the flow sensor is located at the coolant inlet to monitor the actual flow of the coolant entering the liquid cooling plate; the radiator 13 transfers the heat in the coolant to the air, and the radiator can be cooled by air cooling or water cooling. The industrial computer 11 dynamically adjusts the radiator fan speed or the external cooling medium flow rate according to the battery temperature and the coolant temperature.

[0050] The speed of the fan of the radiator 13 is controlled by a PWM signal (pulse width modulation). The industrial computer 11 adjusts the speed of the fan motor through the PWM signal according to the real-time temperature sensor feedback. The common fan speed adjustment range is 0-100%. The flow rate of the cooling pump 12 is adjusted by the frequency converter to control the speed of the cooling pump 12 or adjust the opening of the valve. The industrial computer adjusts the working frequency of the water pump according to the feedback of the temperature sensor and the flow sensor to adjust the flow rate of the coolant.

[0051] The safety protection module monitors the working status of the battery module (such as temperature, voltage, current, etc.) through the temperature monitoring sensor module, detects potential safety risks, and triggers the safety protection mechanism by the industrial computer when an abnormality is detected. Its purpose is to ensure that the battery operates within the normal working range, avoid abnormal conditions such as overheating, overcharging, over-discharging, and prevent safety accidents. In the case that the temperature of the battery cell or the BMS detection voltage inside the battery pack continues to exceed the preset safety range, the industrial computer automatically reduces the battery load and limits the current output to reduce heat generation; if the temperature still rises and reaches a dangerous level, the industrial computer will quickly cut off the battery power supply to prevent thermal runaway or battery damage, and alarm the user at the same time. The alarm can be sent to the main control panel through the BMS system through sound, light, etc. to remind the user.

[0052] like Figure 4 As shown, the working process of the power battery intelligent thermal management control system is as follows:

[0053] Step 1: Initially, the control valve of the dynamic cooling module is opened, and the coolant flow rate can be slightly slow or even zero when the temperature does not rise sharply;

[0054] The light source of the temperature monitoring sensor module emits SLED, and the light is reflected in the optical fiber sensor 6 in the sensing optical fiber. The spectrometer detects the reflection spectrum of the FBG optical fiber sensor 6, and then is converted into an electrical signal through a photoelectric detector and a data acquisition and preprocessing unit, amplified, and filtered to eliminate errors caused by sensor deviation, environmental noise, etc., and to remove short-term abnormal noise or transient temperature spikes to prevent data instability from affecting subsequent decisions; then it is converted into a digital signal through an analog-to-digital converter; then it is transmitted to the industrial computer 11 for data processing and analysis; and the industrial computer 11 can perform time synchronization processing on the data of multiple sensors to ensure that the data reflects the temperature status of each part of the battery pack at the same time, which is convenient for subsequent comprehensive analysis.

[0055] Step 2: After receiving the digital signal of the reflection spectrum, the industrial computer runs the demodulation algorithm of the FBG signal to accurately calculate the temperature value detected by each FBG optical fiber sensor;

[0056] Based on the basic principle of fiber Bragg grating, the corresponding relationship between temperature and fiber Bragg grating reflection spectrum is determined, that is, temperature change leads to change of Bragg wavelength. Specifically, the periodic refractive index change in the optical fiber leads to reflection of light of a specific wavelength. As the temperature changes, the wavelength of the reflected light will also change, so the temperature can be calculated by measuring the change in wavelength. The relationship between temperature change and Bragg wavelength change can be expressed by the formula Δλ B =α·λ B (T0) · ΔT; Δλ B is the Bragg wavelength change, α is the temperature sensitivity coefficient (usually in nm / ℃), which is determined by the fiber material and grating characteristics and is usually a known constant, λ B (T0) is the Bragg wavelength at the reference temperature T0, and ΔT is the temperature change. Using the above formula, the temperature can be calculated from the change in the Bragg wavelength: T0 is a known reference temperature.

[0057] The obtained temperature values ​​form a temperature curve, and the collected current temperature changes are input into the convolutional neural network temperature analysis model for predictive analysis.

[0058] Step 3: The trained and verified convolutional neural network can be deployed in the industrial computer 11 to process the temperature data of the FBG optical fiber sensor in real time and analyze the battery temperature change trend, battery status and battery cell temperature difference.

[0059] The convolutional neural network model needs to be trained before temperature analysis. First, the historical temperature data of the FBG fiber optic sensor 6 needs to be collected and prepared, including the temperature value and timestamp of each FBG fiber optic sensor, the time series recording the temperature change, and the external environment data, such as ambient temperature and humidity. Data preprocessing requires normalizing the temperature data and converting the time series temperature data into a format suitable for convolutional neural network processing, usually a three-dimensional tensor, i.e., the number of samples, time steps, and number of features. The collected temperature data is divided into a training set and a test set according to a certain ratio, 80% training set and 20% test set, to ensure that the model has enough data for learning and verification.

[0060] A convolutional neural network (CNN) model is designed to extract features from time series and is used to predict the trend of battery temperature. The input data of the input layer is in the format of a three-dimensional tensor; the convolution layer uses multiple convolution kernels to perform sliding convolution operations on the input time series data to extract the pattern and trend of temperature data over time. A nonlinear activation function (such as ReLU) is used after the convolution layer to introduce nonlinear characteristics and enhance the expressiveness of the model; the pooling layer is used to reduce the size of the convolution layer output, thereby reducing the amount of calculation and model complexity; after the convolution and pooling layers extract features, the data is usually flattened and input into the fully connected layer. The fully connected layer integrates the extracted features and predicts the temperature trend; the output layer is a single-value output, which represents the battery temperature at a certain point in the future, which helps to warn of battery overheating.

[0061] Model training defines a loss function. In order to minimize the loss function, a gradient descent optimization algorithm is used to adjust the network weights. The Adam optimizer converges quickly and is suitable for processing large-scale time series data. The preprocessed temperature data is input into the CNN model and trained through multiple rounds of iterations (epochs). In each iteration, the model extracts time series features through convolution operations and adjusts weights to minimize the loss function. After each training, the model is verified using a test set to evaluate the generalization ability and prediction accuracy of the model.

[0062] After the model training is completed, the test set is used for final evaluation. The mean square error (MSE) is used to measure the difference between the predicted value and the actual value; R 2 The value measures how well the model fits the data, with 1 being the best and 0 indicating that the model has no explanatory power.

[0063] According to the temperature prediction and analysis submodule, the charging safety temperature range of each battery cell in the battery module is 0°C to 45°C, the discharging safety temperature is -20°C to 45°C, the discharging warning temperature is 45°C to 60°C, and the limit temperature is 70°C.

[0064] S31: First, determine whether the battery cell is in a charging and discharging state; the current charging and discharging state of the battery cell is in the battery management system (BMS), and the terminal voltage of the battery is monitored by a voltage sensor to estimate the SOC of the battery; by monitoring the terminal voltage of the battery and combining it with the internal resistance of the battery, the size of the load of the battery cell 2 is estimated. The BMS can use the pulse current method or the AC impedance method to regularly measure the internal resistance of the battery. All sensor data (such as voltage, current, temperature) is periodically collected through the industrial computer of the battery management system. The collection frequency is once per second, and the data collection accuracy of each sensor is guaranteed to be within 0.1%. The system dynamically adjusts the collection cycle and accuracy according to demand to meet the real-time requirements of different application scenarios. Combined with the actual working state of the battery, such as charging, discharging, and static, select a suitable cooling strategy.

[0065] When the battery is judged to be in a static state, indicating that the battery cell is in standby or low load or dormant state, the dynamic cooling module will automatically enter energy saving mode to reduce unnecessary energy consumption. The low load or dormant state referred to in this article means that the battery module supplies power to low-power devices, such as the navigation system, communication equipment, ship monitoring equipment, etc. on the ship, which have low power requirements for the battery.

[0066] S32: When it is determined that the battery cell is in the charge and discharge state, if the temperature value of any battery cell at the current timestamp exceeds the preset threshold, the trained temperature prediction and analysis submodule is used to analyze the temperature change trend formed at multiple time points according to the severity, rising speed, and load conditions of the temperature, and to determine whether the current temperature trend of each battery cell is steadily rising or falling. The dynamic cooling module is started in advance or shut down later, and the flow rate or flow of the coolant is adjusted to remove the internal heat of the lithium battery through the circulating coolant, thereby achieving a more efficient thermal management effect. The dynamic cooling module is usually started when the battery load is large or the ambient temperature is too high.

[0067] Charging state: When it is determined that the battery module is in a charging state, and the charging temperature of any of the battery cells is within the charging safety temperature range of 0°C to 45°C, it is considered that the lithium battery can be safely charged, and the charging efficiency is the highest. The analysis of the temperature prediction and analysis submodules should be considered to maintain the coolant flow rate unchanged; when the charging temperature of any battery cell exceeds the above-mentioned safe temperature range, execute step 4 to start the dynamic cooling module to prevent the accumulation of heat generated by charging;

[0068] Discharge state: When the battery module is judged to be in the discharge state and the discharge temperature is within the discharge safety temperature range of -20°C to 45°C, the lithium battery can be discharged safely, but the discharge performance will be affected by the temperature, and the capacity and discharge efficiency may be reduced at low temperatures; at this time, execute step 4 to start the dynamic cooling module to reduce the flow rate;

[0069] When the discharge temperature reaches the discharge warning temperature range (45°C to 60°C), the system will trigger the warning mechanism to remind the operator to pay attention to temperature changes and take appropriate measures; when the discharge temperature reaches the set limit temperature of 70°C, the system will automatically cut off the power supply to protect the battery.

[0070] When the battery BMS detects that the battery cell is discharging under a large load (i.e., the discharge rate is 1C and above), the battery temperature rises faster, and step four (i.e., running the dynamic cooling module) is executed to adjust the flow control valve to regulate the flow rate of the coolant to avoid overheating; and reduce the load of the battery and limit the current output to reduce heat generation; if the temperature still rises and reaches a dangerous level, the system will quickly cut off the battery power supply to prevent thermal runaway or battery damage.

[0071] Battery idle state: When the battery is in low load or dormant state, the cooling system will automatically enter energy saving mode to reduce unnecessary energy consumption.

[0072] Step 4: When it is determined in step 3 that the dynamic cooling module needs to be started, a genetic algorithm (GA) is used to design a control algorithm for the dynamic cooling module of the battery module. The optimization goal of the dynamic cooling module is based on the working state (charging, discharging, and static) and temperature change of the battery obtained in step 3. The temperature sensor and the flow sensor transmit the temperature and flow value of the coolant in the dynamic cooling module to the industrial computer in real time;

[0073] Dynamically adjust the cooling strategy to keep the battery temperature within a reasonable range while optimizing energy consumption and cooling efficiency.

[0074] The problem is modeled as a multi-objective optimization problem: keep the battery temperature within a safe range (20℃-40℃); minimize the energy consumption of the cooling system; respond quickly to temperature changes to avoid overheating or overcooling. Encode the coolant flow rate as a binary string or real vector: Liquid cooling flow rate (0-100%): control the flow rate of the coolant; Cooling start temperature threshold: when the battery temperature exceeds the threshold obtained by the temperature analysis model in step 2, the dynamic cooling module is started; Energy-saving mode temperature threshold: when the temperature is lower than the preset energy-saving temperature threshold, the cooling system enters the energy-saving mode, that is, adjusts the flow rate to 1L / min.

[0075] Initialize a population, each individual corresponds to a different cooling strategy combination. Individuals in the population are randomly generated to cover a large search space to ensure that the genetic algorithm can effectively search for the global optimal solution. Define a fitness function to evaluate the pros and cons of the cooling strategy, taking into account the temperature maintenance range, energy consumption and response speed. According to the fitness value, use the tournament selection method to select individuals with high fitness as the parents of the next generation. Generate new offspring individuals through single-point crossover, and randomly select some parameters (such as temperature) from the offspring individuals for mutation operations. Mutation can prevent the algorithm from falling into the local optimum and maintain the diversity of the population. Replace the old population with the new offspring, and repeat the above steps until the algorithm converges or reaches the maximum number of iterations. When the fitness of the population is no longer significantly improved or the set number of iterations is reached, the genetic algorithm stops and outputs the optimal cooling strategy.

[0076] After the genetic algorithm generates the optimal cooling strategy, the industrial computer can dynamically apply these strategies according to the working state of the battery (charging, discharging, and static). When step S31 detects that the battery is charging and is at the warning threshold, the genetic algorithm is used to obtain the optimal liquid cooling flow rate and adjust it in real time according to the temperature change; when step S31 detects that the battery is discharging and is in the safe discharge temperature range, the genetic algorithm is used to obtain the optimal liquid cooling flow rate and reduce the coolant flow rate; when the battery is detected to be discharging and discharging under a large load, the dynamic cooling module starts in advance within 10 seconds after the load is discharged, and uses an optimized cooling strategy to prevent the battery from overheating; in a static or low-load state, the cooling system will operate according to the optimized energy-saving strategy to avoid unnecessary energy consumption.

[0077] According to the control method of the present invention, when the temperature of the battery cell is greater than 60°C and the rising speed exceeds 1°C / minute, especially when the temperature is close to 50°C or above and the discharge current of the battery exceeds 1C, it means that the battery is overheating too fast. Then, the frequency of the cooling pump 12 is quickly adjusted according to the optimal liquid cooling flow rate obtained by the genetic algorithm, and the flow rate of the coolant is adjusted to 5-8L / min. When the battery cell is in the temperature range of 45-60°C, it is not very serious. According to the optimal liquid cooling flow rate obtained by the genetic algorithm, the flow rate of the dynamic cooling module is increased to 2-4L / min to enhance the heat dissipation effect; when the battery module is in the charging state and the temperature is in the safe temperature range (0-45°C), the flow rate of the dynamic cooling module is kept unchanged at 1-2L / min.

[0078] The present invention can increase or decrease the working intensity of the cooling pump 12 and the coolant flow rate according to the heat generation and temperature changes of the battery to achieve precise control. For example, when the temperature rises rapidly, the system will increase the coolant flow rate to improve the heat dissipation efficiency; and when the temperature drops, the coolant flow rate will slow down accordingly. The industrial computer will predict the temperature changes of the battery system in real time, and dynamically adjust the cooling strategy or trigger an alarm according to the model prediction results to ensure the safe operation of the power battery system.

[0079] Furthermore, since the coolant in the system flows through the radiator after passing through the battery module, the dynamic liquid cooling module can further enhance the cooling effect by controlling the radiator fan speed or the flow rate of the external cooling medium.

[0080] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents, and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention. The non-essential improvements, adjustments or replacements made by those skilled in the art based on the contents of this specification are all within the scope of protection required by the present invention.

Claims

1. A power battery intelligent thermal management control system, characterized in that It includes a battery module, a temperature monitoring sensor module, an industrial computer (11) and a dynamic cooling module; The thermal management control system is applied to a multi-layer battery cabinet, each layer being provided with a battery module, the battery module comprising a plurality of battery cells (2) arranged in an array; The temperature monitoring sensor module is used to detect the wavelength light information of each battery cell in real time to obtain the temperature change inside the battery unit, and convert the obtained reflection spectrum into electrical signals and digital signals in turn, and then amplify and filter them; the temperature monitoring sensor module includes a light source, an optical fiber splitter, multiple sensing optical fibers, a spectrometer, a photoelectric detector, and a data acquisition and preprocessing unit connected in turn; each battery module is provided with a sensing optical fiber, and the sensing optical fiber integrates a plurality of optical fiber sensors arranged in an array, and each optical fiber sensor in the same sensing optical fiber corresponds to a different wavelength; The number of the optical fiber sensors corresponds to the number of battery cells in the battery module, and the optical fiber sensors are located inside the battery cells; the data acquisition and preprocessing unit acquires the electrical signal of the photoelectric detector, amplifies and filters the electrical signal, digitizes the analog signal, and transmits it to the industrial control computer (11); The industrial computer (11) receives temperature data from the temperature monitoring sensor module and data transmitted from the temperature sensor and the flow sensor, predicts and analyzes the temperature state of the battery using a trained temperature analysis model, and makes corresponding control decisions according to a preset logic; wherein the temperature sensor is arranged at the liquid inlet and the liquid outlet in the liquid cooling plate, and the flow sensor is located at the liquid inlet; The dynamic cooling module is used to adjust the cooling pump (12) according to the instructions of the industrial computer, thereby adjusting the flow rate of the cooling liquid in the liquid cooling plate (4); the dynamic cooling module comprises a cooling pump (12), a control valve (14), a liquid cooling plate (4) and a radiator (13) connected in sequence; the top surface and the bottom surface of the liquid cooling plate can both be in contact with adjacent battery cells; the liquid cooling plate (4) has an S-shaped liquid cooling pipe (7) inside, the liquid cooling pipe (7) contains cooling liquid, and the liquid inlet of the liquid cooling pipe (7) is connected to the cooling pump (12) through the control valve (14), and the liquid outlet is connected to the radiator (13); the cooling liquid circulates through the cooling pump (12) to bring heat from the battery module to the radiator (13).

2. The power battery intelligent thermal management control system according to claim 1, characterized in that: A heat insulation board (3) is arranged between the side walls of the adjacent battery cells (2) in the battery module.

3. The power battery intelligent thermal management control system according to claim 1, characterized in that: The light source is a superfluorescent light source; the optical fiber splitter is used to distribute the optical signal of the light source to each of the sensing optical fibers; the spectrometer is used to detect the reflection spectrum of the optical fiber sensor (6); the photoelectric detector is used to read the spectrum information and convert the optical signal into an electrical signal which is then received by the data acquisition and preprocessing unit; the data acquisition and preprocessing unit comprises a data acquisition card, an amplifier (9) and a filter (10).

4. The power battery intelligent thermal management control system according to claim 1, characterized in that: The industrial computer (11) comprises a demodulation submodule, a historical temperature storage submodule, and a temperature prediction and analysis submodule, wherein the demodulation submodule receives a digital signal from a data acquisition and preprocessing unit, runs a demodulation algorithm according to a signal of a reflection spectrum, and calculates a temperature value detected by each optical fiber sensor; The historical temperature storage submodule stores the temperature data obtained from the demodulation submodule as historical temperature data and forms a temperature curve based on a timestamp; the temperature prediction and analysis submodule controls the dynamic cooling module to adjust the coolant flow rate according to the temperature change trend obtained by the historical temperature storage submodule and the data transmitted by the temperature sensor and the flow sensor.

5. The power battery intelligent thermal management control system according to claim 4, characterized in that: The industrial computer (11) can also adjust the speed of the fan of the radiator 13 according to the prediction and analysis results.

6. The power battery intelligent thermal management control system according to claim 1, characterized in that: The cooling pump (12) is adjusted by controlling the rotation speed of the cooling pump (12) or regulating the opening of a control valve.

7. The power battery intelligent thermal management control system according to claim 1, characterized in that: The power battery intelligent thermal management control system also includes a safety protection module, which is used to reduce the battery load and limit the current output by the industrial computer when the battery cell temperature or voltage continues to exceed the preset safety range; if the temperature continues to rise and reaches the preset limit temperature, the industrial computer will quickly cut off the battery power supply.

8. The control method of the power battery intelligent thermal management control system according to claim 1, comprising: Step 1: The temperature monitoring sensor module emits a light source, and the light is reflected in the optical fiber sensor (6) in the sensing optical fiber. The spectrometer detects the reflection spectrum of the optical fiber sensor (6), and then the light is converted into an electrical signal, amplified, filtered, and then converted into a digital signal and transmitted to the industrial computer (11) for data processing and analysis through a photoelectric detector and a data acquisition and preprocessing unit. Step 2: Run a demodulation algorithm based on the digital signal of the received reflection spectrum to calculate the temperature value detected by each optical fiber sensor, and form a battery temperature change curve with the obtained temperature value; Step 3: Determine whether the battery module is in a charging and discharging state. If so, input the temperature change curve, battery state and battery cell temperature difference into the trained convolutional neural network temperature analysis model for prediction and analysis. When it is determined that the dynamic cooling module needs to be started, execute step 4; wherein the temperature analysis model is trained to obtain the charging safety temperature threshold, discharging safety temperature threshold, discharging warning temperature threshold and limit temperature of the battery cell; Step 4: When step 3 determines that the dynamic cooling module needs to be started, according to the battery charging and discharging status and the corresponding temperature threshold determined in step 3, a dynamic adjustment cooling strategy based on a genetic algorithm is used to obtain the optimal liquid cooling flow rate, and the dynamic adjustment cooling strategy is applied to adjust the dynamic cooling module to keep the battery temperature within a reasonable range.

9. The control method according to claim 8, characterized in that The step three specifically includes: S31: First, determine whether the battery cell is in a charging or discharging state; if so, execute S32; otherwise, consider that the battery cell is in a standby or low-load state and continue monitoring; S32: when it is determined that the battery module is in a charging state, and the charging temperature of any of the battery cells is within the charging safety temperature range determined by the temperature prediction and analysis submodule, the coolant flow rate is maintained unchanged; when the charging temperature of any of the battery cells exceeds the above safety temperature range, step 4 is executed; When it is determined that the battery module is in a discharge state, and the discharge temperature of any of the battery cells is within the discharge safety temperature range determined by the temperature prediction and analysis submodule, executing step 4 to reduce the flow rate of the coolant; when the discharge temperature of any of the battery cells reaches the discharge warning temperature threshold, an early warning is issued to the user to remind the operator to pay attention to the temperature change; when the discharge temperature reaches the set limit temperature, the power supply is cut off to protect the battery; When the battery BMS detects that the battery cell discharge rate is 1C or above, execute step 4.

10. The control method according to claim 8, characterized in that The step 4 specifically includes: the dynamic adjustment of the cooling strategy in the step 4 adopts a genetic algorithm, models the problem as a multi-objective optimization problem, and encodes the coolant flow rate as a binary string or a real number vector; comprehensively considers the three aspects of the safe temperature range, energy consumption and response speed obtained in step 2, adopts a tournament selection method to select individuals with high fitness as the parents of the next generation, and iterates to output the optimal cooling strategy; When it is determined in step 3 that the battery module is in a discharge state and the discharge temperature of any of the battery cells is within a discharge safety temperature range, the coolant flow rate is reduced to an optimal liquid cooling flow rate obtained by the genetic algorithm; When it is determined in step 3 that the battery module is in a charging state and exceeds the above-mentioned safe temperature range, a genetic algorithm is used to obtain an optimal liquid cooling flow rate, and the cooling pump (12) is adjusted in real time according to the temperature change to increase the cooling liquid flow rate; When step three determines that the discharge rate of the battery module is 1C or above, the dynamic cooling module is started in advance within 10 seconds after the load is discharged, the cooling pump (12) is adjusted to increase the coolant flow rate, and a genetic algorithm is used to obtain the optimal liquid cooling flow rate to prevent the battery from overheating.

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