Multi-parameter collaborative optimization battery charging and discharging management system and thermal management technology thereof

Abstract

The invention provides a multi-parameter collaborative optimization battery charging and discharging management system integrated with a thermal management technology, and aims to improve the charging and discharging efficiency and safety of a battery and prolong the service life of the battery. The system monitors key parameters such as the temperature, the voltage, the current and the internal resistance of the battery in real time through a high-precision sensor, and the parameters are comprehensively analyzed through the data fusion technology. A core optimization algorithm adopts a genetic algorithm or deep learning, a charging and discharging strategy is dynamically adjusted, and battery performance maximization is achieved. The thermal management module effectively controls the temperature of the battery through a cooling fin, a liquid cooling system or a phase change material, overheating and supercooling are prevented, and the safety and stability of the battery are improved. The user interface is friendly, the battery state is convenient to monitor and adjust, and the system has wide application prospects in the fields of electric automobiles, renewable energy storage and the like.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and in particular to a battery charge and discharge management system that integrates thermal management functions and multi-parameter collaborative optimization technology. Background Technology

[0002] With continuous technological advancements, batteries have become an indispensable energy storage unit in modern society, widely used in electric vehicles, portable electronic devices, renewable energy storage systems, and industrial automation equipment. Battery performance, safety, and lifespan are crucial to the reliability and economic efficiency of these applications. Therefore, Battery Management Systems (BMS) have emerged as a key technology to ensure the safe and efficient operation of batteries.

[0003] Traditional battery management systems (BMS) primarily focus on basic parameters such as battery voltage and current. However, with advancements in battery technology and the diversification of application scenarios, these basic monitoring functions are no longer sufficient to meet the higher demands for battery performance and safety. Parameters such as battery temperature, internal resistance, and aging status also significantly impact battery performance and safety. Furthermore, batteries generate substantial heat during charging and discharging; improper thermal management can lead to performance degradation or even safety incidents. Therefore, a BMS capable of comprehensively monitoring multiple key battery parameters and performing coordinated, optimized control is crucial for improving battery performance and safety.

[0004] Existing Battery Management Systems (BMS) still have limitations in multi-parameter monitoring and thermal management, such as insufficient data acquisition accuracy and frequency, limited data processing capabilities, simplistic control strategies, and low thermal management efficiency. These issues restrict further improvements in battery performance and ensure safety. Furthermore, as batteries age, their performance parameters change, and traditional BMS lacks accurate assessment and prediction of battery aging states, making it difficult to achieve optimized management throughout the battery's entire lifecycle. Therefore, developing a BMS capable of real-time monitoring of multiple key battery parameters, performing data fusion processing, and implementing collaborative optimization control based on this data is an effective way to overcome the limitations of existing technologies and meet the needs of modern battery management. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-parameter collaborative optimization battery charge-discharge management system. This system achieves real-time monitoring and management of key battery parameters through a series of high-precision monitoring modules and advanced data processing algorithms. The system first measures the battery temperature in real time using at least three thermocouple sensors in the temperature monitoring module. These sensors are distributed on the top, bottom, and sides of the battery to ensure comprehensive temperature monitoring. Simultaneously, high-precision sensors in the voltage and current monitoring modules monitor the battery voltage and current, respectively. An AC impedance spectrometer in the internal resistance monitoring module monitors the battery's internal resistance, while the aging state monitoring module assesses the battery's cycle count and capacity decay. All these monitoring data are collected by the data acquisition module at a frequency of at least 10 times per second and then digitized. The core of the data processing is the data fusion module, which uses a Kalman filter algorithm to integrate and optimize the collected data, improving its accuracy and reliability. The processed data is then sent to the state estimation module, which estimates the battery's state of charge (SOC) and state of health (SOH) with at least 95% accuracy. These precise estimates are then used to optimize the algorithm module, which employs genetic or deep learning algorithms to dynamically adjust the charge and discharge strategies to improve battery efficiency by at least 10%. The system also includes a thermal management module that controls the battery temperature between 10°C and 40°C via a liquid cooling system to prevent overheating and overcooling. A balance control module adjusts the charge levels of individual cells in the battery pack to maintain a charge difference of no more than 2%. A safety protection module monitors the battery status in real time, including overcharge protection, over-discharge protection, over-temperature protection, and short-circuit protection, ensuring that the battery can cut off power within 500ms in abnormal conditions. For communication with external systems, the system is equipped with a communication interface module supporting at least one industrial communication protocol, such as CAN bus. A user interface module provides a graphical user interface (GUI), enabling operators to easily monitor battery status and adjust charge and discharge strategies. A fault diagnosis module analyzes data from each monitoring module to diagnose potential battery faults and provide alarms, with a diagnostic accuracy of at least 90%. All modules are integrated into a unified hardware platform via a system integration module, coordinated and controlled by software on a microcontroller with at least 32 bits, achieving efficient communication and data processing between modules and ensuring the stable and reliable operation of the entire system.

[0006] The main process of the system includes the following steps:

[0007] The temperature monitoring module is a key component of the battery charge / discharge management system. It is responsible for real-time monitoring of battery temperature changes during operation to ensure the battery operates within a safe temperature range. This module includes at least three high-precision thermocouple sensors strategically positioned at different locations within the battery cell: one at the top, one at the bottom, and one on the side. This arrangement ensures comprehensive thermal state information by monitoring the battery temperature from various angles. These thermocouple sensors are connected to a data acquisition module via dedicated wires that convert the temperature changes detected by the sensors into electrical signals, which are then transmitted to the data acquisition module. The data acquisition module then converts these analog signals into digital signals for further data processing and analysis. The sensors are highly accurate, with an error range controlled within ±0.5°C. This means the system can accurately monitor the battery's real-time temperature, capturing even minute temperature changes caused by heat generated during battery operation. The system also includes a temperature data calibration process, comparing the data provided by the thermocouple sensors with a reference thermometer to ensure the data matches the actual battery temperature. In addition, the system may also integrate a temperature anomaly alarm mechanism. Once the monitored temperature exceeds the preset safety range, the system will immediately issue an alarm and trigger corresponding protection measures, such as adjusting the charging and discharging strategy or starting the cooling system, to prevent the battery from overheating.

[0008] The temperature monitoring module is a key component of the battery charge / discharge management system. It is responsible for real-time monitoring of battery temperature changes during operation to ensure the battery operates within a safe temperature range. This module includes at least three high-precision thermocouple sensors strategically positioned at different locations within the battery cell: one at the top, one at the bottom, and one on the side. This arrangement ensures comprehensive thermal state information by monitoring the battery temperature from various angles. These thermocouple sensors are connected to a data acquisition module via dedicated wires that convert the temperature changes detected by the sensors into electrical signals, which are then transmitted to the data acquisition module. The data acquisition module then converts these analog signals into digital signals for further data processing and analysis. The sensors are highly accurate, with an error range controlled within ±0.5°C. This means the system can monitor the battery's real-time temperature very accurately, capturing even minute temperature changes caused by heat generated during battery operation. To further improve temperature monitoring accuracy, the system may also include a temperature data calibration process, comparing the data provided by the thermocouple sensors with a reference thermometer to ensure the data matches the actual battery temperature. In addition, the system may also integrate a temperature anomaly alarm mechanism. Once the monitored temperature exceeds the preset safety range, the system will immediately issue an alarm and trigger corresponding protection measures, such as adjusting the charging and discharging strategy or starting the cooling system, to prevent the battery from overheating.

[0009] The internal resistance monitoring module is a key component of the battery management system, specifically designed for high-precision monitoring of battery internal resistance. This module employs advanced electrochemical impedance spectroscopy (EIS) technology, enabling real-time monitoring of the battery's internal resistance with an accuracy of ±1% of the reading, thereby assessing the battery's internal state and health. The EIS spectrometer obtains the battery's impedance spectrum by applying a small-amplitude AC sinusoidal potential wave to the battery and measuring the corresponding AC current signal. This technology provides detailed information about the battery's electrode process dynamics and surface phenomena. In this invention, the EIS spectrometer has a wide test frequency range, from 0.01 Hz to 20 kHz, covering the frequency response of the battery under different microstates. To achieve high-precision internal resistance monitoring, this invention employs the following technical parameters: Impedance test range: 0.1mΩ to 10Ω, ensuring the ability to measure batteries of different states and types; Test frequency range: 0.01Hz to 20kHz, covering the full frequency range from low to high frequencies; Excitation current accuracy: 0.025% + 0.025%FS, ensuring the accuracy of the test current; Frequency accuracy: 1µs + 20ppm, guaranteeing the stability of the test frequency; Impedance accuracy: 1%, providing highly accurate impedance measurement results. Through these high-precision measurements, the internal resistance monitoring module can monitor changes in the battery's internal resistance in real time. This data is crucial for assessing the battery's health, predicting its lifespan, and optimizing its charge and discharge strategies. For example, as a battery ages, its internal resistance gradually increases. By monitoring this change, timely measures can be taken to prevent battery performance degradation or malfunction. Furthermore, the internal resistance monitoring module of this invention can also work in conjunction with other monitoring modules (such as temperature, voltage, and current monitoring modules) to provide more comprehensive battery status information. These data can be further processed by the data fusion module to achieve more accurate battery state estimation and a more optimized battery management strategy. Through this multi-parameter collaborative optimization method, the battery management system of the present invention can significantly improve battery efficiency and safety, and extend battery life.

[0010] The aging status monitoring module assesses the degree of battery aging by monitoring the number of battery cycles and capacity decay. The module operates based on the following specific technical parameters and steps: The number of battery cycles refers to the total number of complete charge-discharge cycles the battery undergoes. Each charge-discharge cycle causes some wear on the battery, and as the number of cycles increases, the battery's performance gradually declines. This module calculates the number of battery cycles by recording the battery's charging and discharging processes. Most batteries can operate for at least 500 cycles, while some high-quality batteries, such as certain laptop batteries, may be designed for 1,000 cycles. Battery capacity decay refers to the decrease in the battery's ability to store electrical energy after a certain period of use. This module assesses battery capacity decay by monitoring the difference between the battery's full charge capacity (i.e., the maximum amount of energy the battery can hold in its current state) and its design capacity (i.e., the battery's initial maximum charge). If the full charge capacity is significantly lower than the design capacity, this indicates a significant decline in the battery's health. The battery's internal resistance increases with usage time and charge-discharge cycles. Increased internal resistance leads to increased energy loss, heat generation, and reduced efficiency during discharge. This module estimates the change in battery internal resistance by applying a small load and measuring the voltage drop, thereby assessing the degree of battery aging. By comparing the efficiency of the battery's charging and discharging processes, the degree of battery performance degradation can be determined. Aging batteries exhibit reduced efficiency during charging and discharging, meaning they require longer charging times and discharge at a slower rate. Aging batteries also generate more heat during charging and discharging; monitoring temperature changes is another effective method for assessing battery condition. This module evaluates the battery's aging status by monitoring temperature changes during charging and discharging.

[0011] In the aforementioned multi-parameter collaborative optimization battery charge-discharge management system, the data acquisition module is equipped with a high-speed analog-to-digital converter (ADC) with at least 12-bit resolution, accurately sampling key parameters of the battery such as temperature, voltage, current, and internal resistance at a frequency of at least 10 times per second. This data is transmitted in real-time to the data fusion module via a high-speed serial communication interface, such as I2C or SPI. To ensure data integrity and stable system operation, the data acquisition module also has sufficient built-in buffer space to temporarily store data until it is processed by subsequent modules. The data fusion module then uses algorithms such as Kalman filters to integrate and optimize the data, improving its accuracy and reliability, and providing strong data support for accurate battery state estimation and optimization of charge-discharge strategies. The entire data acquisition and transmission process is designed with low latency, ensuring that the system can respond promptly to any changes in battery state, achieving refined management of the battery charge-discharge process.

[0012] The data fusion module plays a crucial role, transforming the scattered data collected from various monitoring modules into a more consistent and accurate dataset. This module employs an advanced Kalman filter algorithm, a highly efficient recursive filter capable of estimating the state of a dynamic system from a series of noisy measurement data. Before starting the filtering process, the Kalman filter needs to be initialized, setting initial state estimates and an initial covariance matrix. The state estimates are based on knowledge of the battery's current state, while the covariance matrix represents the quantification of the uncertainty of the initial estimates. At the beginning of each sampling period, the Kalman filter uses a dynamic model of the system to predict the battery's state. This model considers the battery's physical characteristics and chemical processes, predicting how the battery's state will change in the next sampling period. Once new measurement data is received from the data acquisition module, the Kalman filter compares this data with the predicted state and updates the state estimate based on the difference. This process involves calculating the Kalman gain, a metric that balances the predicted state and the measurement data. The Kalman filter utilizes the uncertainty of the measurement data (measurement noise) and the uncertainty of the prediction model (process noise) to adjust the predicted state through the Kalman gain, generating a more accurate state estimate. The filter also updates the error covariance of the state estimate, reflecting the uncertainty of the new state estimate. This update step helps to further optimize the state estimate in subsequent predictions. The accuracy and reliability of the data processed by the Kalman filter are significantly improved, and this data is then used in the state estimation module to provide an accurate assessment of the battery's state of charge (SOC) and state of health (SOH).

[0013] The state estimation module's function is to estimate the battery's state of charge (SOC) and state of health (SOH) with at least 95% accuracy using data transmitted from the data fusion module. This process involves a series of complex calculations and algorithms, the specific technical details of which are as follows: The state estimation module receives data processed by the data fusion module, including battery voltage, current, temperature, and internal resistance, all accurately expressed in numerical form. The state estimation module employs advanced algorithms, such as the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF), to estimate the battery's SOC. SOC is an indicator of the battery's remaining capacity, usually expressed as a percentage. These algorithms can handle the nonlinear characteristics of the battery model and provide real-time SOC estimates. For example, the EKF linearizes the nonlinear model, using a series of mathematical equations to predict and update the estimated SOC, while taking into account the effects of measurement noise and process noise. State of health (SOH) refers to the degree to which the battery retains its capacity relative to its initial state. The state estimation module assesses SOH by monitoring the battery's capacity decay, internal resistance increase, and other aging indicators. State of Health (SOH) estimation may involve analyzing battery cycle life, charge / discharge efficiency, and self-discharge rate. Accurate SOH assessment is crucial for predicting remaining battery life and maintenance planning. To ensure the accuracy of state estimation, the module continuously calibrates and updates its algorithm parameters. This includes adjusting the covariance matrix of the Kalman filter and updating the battery model parameters in real time. These adjustments help reduce estimation errors, ensuring that the estimation accuracy of SOC and SOH reaches at least 95%. The state estimation module transmits its calculation results to other parts of the system, such as the user interface module and the fault diagnosis module. These results are essential for battery management system decision-making, performance optimization, and safety protection.

[0014] The optimization algorithm module is designed to use advanced algorithms to dynamically adjust the charging and discharging strategy based on the output of the state estimation module, thereby improving battery efficiency by at least 10%. This module can be implemented using genetic algorithms or deep learning algorithms.

[0015] Genetic Algorithm (GA) is an optimization algorithm that simulates the process of biological evolution. It searches for the optimal solution in a population of candidate solutions through operations such as selection, crossover, and mutation. In battery charging and discharging management, genetic algorithms can be used to optimize charging and discharging strategies to reduce charging costs, minimize peak-valley load differences in the power grid, and improve battery lifespan.

[0016] The basic steps of a genetic algorithm:

[0017] 1. Initialize the population: Randomly generate a set of candidate solutions as the initial population.

[0018] 2. Fitness assessment: The fitness of each individual is assessed based on the performance of the charge / discharge strategy (such as charging time, cost, battery life, etc.).

[0019] 3. Selection operation: Select high-quality individuals based on fitness to provide materials for subsequent crossover and mutation operations.

[0020] 4. Crossover operation: Selected individuals are randomly paired and new individuals are generated by exchanging some of their genes (i.e., charge and discharge parameters).

[0021] 5. Mutation operation: Randomly change certain genes of some individuals with a certain probability to increase the diversity of the population.

[0022] 6. New Generation Population: Select a certain number of individuals based on fitness to form a new generation population, and repeat steps 2-5 until the termination condition is met (such as reaching the predetermined number of iterations or fitness threshold).

[0023] Genetic algorithm parameter settings: Population size: typically set to tens to hundreds of individuals. Crossover rate: generally between 60% and 90%. Mutation rate: usually low, such as 1% to 5%. Number of iterations: depending on the problem size and complexity, it may require hundreds to thousands of iterations.

[0024] Deep learning algorithms, especially deep neural networks (DNNs), can predict a battery's state of charge (SOC) and state of equilibrium (SOH) by learning from historical and real-time battery data, thereby achieving more precise charge and discharge control. Deep learning models can be trained and optimized through the following steps:

[0025] 1. Data preprocessing: Collect battery charging and discharging data, including voltage, current, temperature, etc., and perform normalization processing.

[0026] 2. Model Design: Design a deep neural network model, which may include multiple hidden layers and different types of activation functions.

[0027] 3. Model Training: The deep learning model is trained using battery data, and the network weights are optimized through the backpropagation algorithm.

[0028] 4. Performance evaluation: Use the validation set to evaluate the model's prediction accuracy and adjust the model structure or parameters as needed.

[0029] 5. Model Application: The trained model is used to predict the SOC and SOH of the battery in real time, and the charging and discharging strategy is adjusted based on the prediction results.

[0030] Parameter settings for deep learning models: Network structure: May include multiple fully connected layers, convolutional layers, or recurrent layers, depending on the characteristics of the problem. Activation function: Such as ReLU, Sigmoid, or Tanh, used to introduce non-linearity. Optimization algorithm: Such as Adam, RMSprop, or SGD, used to update network weights. Loss function: Such as mean squared error (MSE) or cross-entropy loss, used to evaluate prediction accuracy. Training cycles: Depending on the model's convergence speed, it may require tens to hundreds of cycles.

[0031] The charge / discharge control module is a key component, responsible for dynamically adjusting the battery's charge / discharge parameters based on strategies provided by the optimization algorithm module. This module can precisely control the charging current within the range of 0.2C to 1C to adapt to different charging demands and battery states. Simultaneously, it manages the charging and discharging cutoff voltages, ensuring the battery operates within a safe voltage range, avoiding overcharging and over-discharging, thereby extending battery life and maintaining optimal performance. Through real-time monitoring of the battery's state and guidance from the optimization algorithm, the charge / discharge control module intelligently adjusts these parameters to achieve the overall goals of the battery management system: improving battery efficiency, ensuring safe operation, and optimizing the battery's charge / discharge process.

[0032] The thermal management module plays a crucial role, precisely controlling the battery temperature through an advanced liquid cooling system. This system is designed with specific parameter settings to maintain the battery's operating temperature within a safe and effective range of 10°C to 40°C. Through circulating coolant, the liquid cooling system effectively absorbs and dissipates the heat generated by the battery, preventing dangerous overheating under high loads and avoiding performance degradation at low temperatures. This temperature control strategy not only helps protect the battery from extreme temperatures but also optimizes charge and discharge efficiency, thereby extending battery life and ensuring its reliability and stability throughout its operating cycle.

[0033] The balance control module's function is to finely adjust the charge levels of each battery in the battery pack to ensure the pack's consistency and overall performance. This module monitors the state of charge (SOC) of each battery and dynamically adjusts the charging and discharging processes to ensure that the charge difference between any two batteries in the pack does not exceed 2%. This fine-grained balance control strategy helps prevent individual batteries from being overcharged or over-discharged, thus avoiding performance degradation and shortened lifespan. Through the continuous operation of the balance control module, the battery pack can maintain its optimal operating state, providing stable and reliable energy output, while also optimizing the overall battery system's lifespan and efficiency.

[0034] The aforementioned safety protection module is a crucial component, encompassing overcharge protection, over-discharge protection, over-temperature protection, and short-circuit protection. It ensures the battery can rapidly disconnect power within 500 milliseconds under abnormal conditions, protecting it from damage and preventing potential safety accidents. When the battery voltage exceeds a predetermined overcharge protection threshold, typically set between 4.35V and 4.4V, the safety protection module immediately activates, cutting off the charging circuit to prevent battery damage due to overcharging. During battery discharge, when the battery voltage drops to a set over-discharge protection threshold, such as 2.3V to 3.0V, the module intervenes and disconnects the discharge circuit to prevent battery damage due to over-discharge. A temperature sensor monitors the battery temperature; once the temperature exceeds a safe threshold, such as above 60°C to 80°C, the module activates the over-temperature protection mechanism, potentially preventing overheating by shutting down the charging and discharging circuits or reducing the charging and discharging rate. When a short circuit occurs, the safety protection module can quickly detect an abnormal increase in current and, within a very short time, such as 500 milliseconds, disconnect the power to prevent damage to the battery and circuitry. The implementation of these protection measures typically relies on high-precision voltage and current detection circuits, as well as efficient control logic. Control ICs such as the DW01 incorporate high-precision voltage detection and time-delay circuits. Key parameters include overcharge detection voltage of 4.3V, overcharge release voltage of 4.05V; over-discharge detection voltage of 2.5V, over-discharge release voltage of 3.0V; overcurrent detection voltage of 5V; and short-circuit current detection voltage of 1.0V. Through these precise parameter settings and rapid response mechanisms, the safety protection module provides comprehensive safety assurance for the battery.

[0035] The communication interface module is a key component, supporting at least one industry-standard communication protocol, such as the CAN bus. This module is designed to allow the battery management system to exchange data efficiently and reliably with external devices or monitoring systems. Through the CAN bus, the system can transmit battery status information in real time, including key parameters such as voltage, current, temperature, and state of charge (SOC), while also receiving control commands from external systems. This communication capability is crucial for remote monitoring, fault diagnosis, and system optimization, ensuring seamless integration of the battery management system into a wider range of energy management and monitoring networks.

[0036] The user interface module provides a graphical user interface (GUI) for intuitively displaying the battery's real-time status and providing operational guidance. The GUI is designed to be intuitive and easy to use, ensuring users can quickly understand the battery's current status and historical trends. To provide a smooth user experience, the interface refresh rate is set to no less than 1Hz, meaning information is updated at least once per second to maintain data real-time performance. Furthermore, the user interface module supports advanced interaction methods, including touchscreen and voice control, allowing users to interact with the system in multiple ways, improving operational convenience and system accessibility.

[0037] The aforementioned fault diagnosis module is capable of identifying at least five different types of battery faults, including overcharge, over-discharge, over-temperature, short circuit, and internal open circuit. By analyzing various battery operating parameters and utilizing advanced algorithms for fault mode identification, the module achieves a diagnostic accuracy of no less than 90%. This high-accuracy fault diagnosis capability significantly improves the safety and reliability of battery use, while also reducing unexpected downtime caused by battery faults, providing a scientific basis for battery maintenance and repair.

[0038] The system integration module integrates all the aforementioned modules into a unified hardware platform, with the software running on a microcontroller of at least 32 bits. This integrated design simplifies system deployment and maintenance, and improves system stability and efficiency. The system also features remote update capabilities, allowing for remote software updates via wireless network, with the update process completed within 5 minutes. This feature enables the system to quickly adapt to new operating conditions or rapidly fix discovered problems, ensuring the battery management system remains up-to-date and enhancing the system's flexibility and long-term applicability.

[0039] The system operation process is as follows:

[0040] S1. Data Acquisition Phase: After the system starts up, the data acquisition module is activated first and begins to collect data on parameters such as voltage, current, temperature and internal resistance through sensors connected to the battery.

[0041] S2. Data preprocessing stage: The collected analog signals are converted into digital signals by the data acquisition module and sent to the data fusion module through a high-speed serial communication interface such as I2C or SPI.

[0042] S3. Data Fusion and State Estimation Stage: After receiving the data, the data fusion module uses a Kalman filter algorithm to integrate and optimize the data, improving its accuracy and reliability. Based on the fused data, the state estimation module estimates the battery's state of charge (SOC) and state of health (SOH) with an accuracy of no less than 95%.

[0043] S4. Optimization Strategy Formulation Stage: Based on the output of the state estimation module, the optimization algorithm module dynamically adjusts the charging and discharging strategy using a genetic algorithm or a deep learning algorithm to improve battery efficiency by at least 10%.

[0044] S5. Charge and discharge control stage: The charge and discharge control module dynamically adjusts parameters such as charging current, charging cut-off voltage, and discharging cut-off voltage based on the results of the optimization algorithm module to ensure that the battery operates under safe conditions.

[0045] S6. Thermal Management Stage: The thermal management module controls the battery temperature between 10°C and 40°C through a liquid cooling system to prevent overheating and overcooling, ensuring stable battery performance.

[0046] S7. Battery Balance Control Stage: The balance control module adjusts the charge of each battery in the battery pack to keep the charge difference no more than 2% and maintain the consistency of the battery pack.

[0047] S8. Safety Protection Phase: The safety protection module monitors the battery status in real time. Once overcharging, over-discharging, overheating, or short circuit is detected, it can cut off the power supply within 500ms to ensure battery safety.

[0048] S9. Communication and Data Exchange Stage: The communication interface module supports at least one industrial communication protocol, such as CAN bus, to realize data exchange with external devices or monitoring systems.

[0049] S10. User Interaction Phase: The user interface module provides a graphical user interface (GUI) that displays battery status information and provides operation guidance. The interface supports touchscreen or voice control interaction, and the refresh rate is no less than 1Hz.

[0050] S11. Fault Diagnosis Phase: The fault diagnosis module can identify at least five different types of battery faults with a diagnostic accuracy of no less than 90%, ensuring that the battery's health status is assessed in a timely manner.

[0051] S12. System Integration and Remote Update Phase: The system integration module integrates all modules into a unified hardware platform. The software runs on a microcontroller with at least 32 bits and has remote update capabilities. The update process can be completed within 5 minutes.

[0052] The battery management system of this invention achieves refined management of the battery charging and discharging process through these specific process steps, improves battery efficiency and safety, extends battery life, and is suitable for various battery application scenarios.

[0053] The technical solution described in this invention can bring about the following specific technical effects:

[0054] 1. Precise Temperature Control: By using at least three thermocouple sensors to accurately monitor the battery temperature and employing a liquid cooling system to maintain the battery temperature within the optimal operating range of 10°C to 40°C, the system ensures that the battery will not be damaged due to overheating, thereby improving battery safety and lifespan. This temperature control precision can control the battery's operating temperature fluctuation within ±0.5°C, far below the temperature control range of traditional BMS systems.

[0055] 2. High-efficiency charge and discharge performance: The system dynamically adjusts parameters such as charging current, charging cut-off voltage, and discharging cut-off voltage based on the real-time state of the battery through an optimized algorithm module. For example, the charging current can be adjusted within the range of 0.2C to 1C to achieve optimal charging of the battery, improving charging efficiency by at least 10%. This optimization strategy can significantly reduce battery charging time while extending battery cycle life.

[0056] 3. Accurate Battery State Estimation: The system's state estimation module can estimate the battery's state of charge (SOC) and state of health (SOH) with at least 95% accuracy. This high-precision estimation capability is based on data integrating multiple parameters such as temperature, voltage, current, and internal resistance, processed through a Kalman filter algorithm. This allows the battery management system to more accurately predict the battery's remaining capacity and lifespan, thus providing a scientific basis for battery maintenance and replacement.

[0057] 4. Enhanced Battery Balance Control: The system adjusts the charge of each battery in the battery pack through a balance control module, keeping the charge difference no more than 2%. This precise balance control helps improve the overall performance of the battery pack, ensuring that each battery in the pack can participate in the work evenly and avoiding overall performance degradation caused by imbalances between batteries.

[0058] 5. Comprehensive Safety Protection: The system employs safety protection modules, including overcharge protection, over-discharge protection, over-temperature protection, and short-circuit protection, to ensure that the battery can cut off power within 500ms in abnormal situations. This rapid response mechanism significantly reduces the risk of battery safety accidents and improves the reliability of the battery system.

[0059] 6. Intelligent Fault Diagnosis: The system's fault diagnosis module can identify at least five different types of battery faults with a diagnostic accuracy of no less than 90%. This high-accuracy fault diagnosis capability helps to promptly detect and address potential battery problems, reduce unexpected downtime, and improve the stability and reliability of the battery system. Attached Figure Description

[0060] Figure 1 This is a block diagram of the battery charge and discharge management system with multi-parameter collaborative optimization according to the present invention.

[0061] Figure 2 This is a flowchart illustrating the multi-parameter collaborative optimization battery charging and discharging management system of the present invention. Detailed Implementation

[0062] The following will refer to the appendices in the embodiments of the present invention. Figure 1 and Figure 2 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0063] The purpose of this invention is to provide a multi-parameter collaborative optimization battery charge-discharge management system. This system achieves real-time monitoring and management of key battery parameters through a series of high-precision monitoring modules and advanced data processing algorithms. The system first measures the battery temperature in real time using at least three thermocouple sensors in the temperature monitoring module. These sensors are distributed on the top, bottom, and sides of the battery to ensure comprehensive temperature monitoring. Simultaneously, high-precision sensors in the voltage and current monitoring modules monitor the battery voltage and current, respectively. An AC impedance spectrometer in the internal resistance monitoring module monitors the battery's internal resistance, while the aging state monitoring module assesses the battery's cycle count and capacity decay. All these monitoring data are collected by the data acquisition module at a frequency of at least 10 times per second and then digitized. The core of the data processing is the data fusion module, which uses a Kalman filter algorithm to integrate and optimize the collected data, improving its accuracy and reliability. The processed data is then sent to the state estimation module, which estimates the battery's state of charge (SOC) and state of health (SOH) with at least 95% accuracy. These precise estimates are then used to optimize the algorithm module, which employs genetic or deep learning algorithms to dynamically adjust the charge and discharge strategies to improve battery efficiency by at least 10%. The system also includes a thermal management module that controls the battery temperature between 10°C and 40°C via a liquid cooling system to prevent overheating and overcooling. A balance control module adjusts the charge levels of individual cells in the battery pack to maintain a charge difference of no more than 2%. A safety protection module monitors the battery status in real time, including overcharge protection, over-discharge protection, over-temperature protection, and short-circuit protection, ensuring that the battery can cut off power within 500ms in abnormal conditions. For communication with external systems, the system is equipped with a communication interface module supporting at least one industrial communication protocol, such as CAN bus. A user interface module provides a graphical user interface (GUI), enabling operators to easily monitor battery status and adjust charge and discharge strategies. A fault diagnosis module analyzes data from each monitoring module to diagnose potential battery faults and provide alarms, with a diagnostic accuracy of at least 90%. All modules are integrated into a unified hardware platform via a system integration module, coordinated and controlled by software on a microcontroller with at least 32 bits, achieving efficient communication and data processing between modules and ensuring the stable and reliable operation of the entire system.

[0064] The main process of the system includes the following steps:

[0065] The temperature monitoring module is responsible for real-time monitoring of battery temperature changes during operation to ensure the battery operates within a safe temperature range. This module includes at least three high-precision thermocouple sensors strategically positioned at different locations within the battery cell: one at the top, one at the bottom, and one on the side. This arrangement ensures comprehensive thermal state information by monitoring the battery temperature from various angles. These thermocouple sensors are connected to the data acquisition module via dedicated wires that convert the temperature changes detected by the sensors into electrical signals, which are then transmitted to the data acquisition module. The data acquisition module then converts these analog signals into digital signals for further data processing and analysis. The sensors are highly accurate, with an error range controlled within ±0.5°C. This means the system can monitor the battery's real-time temperature very accurately, capturing even minute temperature changes caused by heat generated during battery operation. The system includes a temperature data calibration process that ensures the data provided by the thermocouple sensors matches the actual battery temperature by comparing it with a reference thermometer. In addition, the system may also integrate a temperature anomaly alarm mechanism. Once the monitored temperature exceeds the preset safety range, the system will immediately issue an alarm and trigger corresponding protection measures, such as adjusting the charging and discharging strategy or starting the cooling system, to prevent the battery from overheating.

[0066] The temperature monitoring module monitors the battery temperature in real time using at least three precisely positioned thermocouple sensors. These sensors are mounted on the top, bottom, and sides of the battery, ensuring comprehensive temperature monitoring. Each thermocouple sensor has a measurement accuracy of ±0.5°C, meaning they can capture temperature changes during charging and discharging with an accuracy of at least 0.5°C. These sensors are connected to the data acquisition module via shielded wires no longer than 2 meters to reduce signal interference. The data acquisition module collects temperature data at a frequency of at least 10 times per second (i.e., a sampling frequency of at least 10 Hz), ensuring real-time and continuous data transmission. Subsequently, an analog-to-digital converter (ADC) within the data acquisition module converts the analog signal into a digital signal, with the ADC having a resolution of at least 12 bits to guarantee the conversion accuracy and precision of the temperature data. The data fusion module receives the digital signal from the data acquisition module and processes the data using a Kalman filter algorithm, which can estimate the battery's state of charge (SOC) and state of health (SOH) with at least 95% accuracy. The optimization algorithm module dynamically adjusts the charging and discharging strategy based on the output of the state estimation module to improve battery efficiency by at least 10%. To ensure that the battery operates within a safe temperature range, the system is equipped with a temperature alarm threshold. When the monitored temperature exceeds the preset safe range, such as above 45°C or below -20°C, the system will immediately issue an alarm and trigger corresponding protection measures, such as activating the liquid cooling system or adjusting the charging and discharging strategy, to prevent the battery from overheating or overcooling.

[0067] The internal resistance monitoring module monitors the battery's internal resistance in real time with extremely high precision, providing crucial data for the battery's health and performance. The module's technical parameters are accurate to the following values: Impedance testing range: 0.1 milliohms (mΩ) to 10 ohms (Ω), ensuring comprehensive monitoring of internal resistance changes in batteries of different states and types; Testing frequency range: from 0.01 Hz to 20 kHz, covering the frequency response of the battery under different microscopic states to probe the entire electrode process, including charge transfer and mass transfer; Excitation current accuracy: 0.025% of reading + 0.025% of full scale (FS), ensuring the accuracy of the test current, where full scale refers to the maximum impedance value that the testing equipment can measure; Frequency accuracy: 1 microsecond (µs) + 20 picofarads (ppm), ensuring the stability of the testing frequency, where ppm represents parts per million, a proportional unit used to express precision; Impedance accuracy: ±1% of reading, providing highly accurate impedance measurement results, which is crucial for assessing the battery's health. Through these precise parameter settings, the internal resistance monitoring module can capture minute changes in the battery's internal resistance in real time. For example, an increase in internal resistance during charging and discharging may indicate battery aging or a potential fault. The system can adjust its charging and discharging strategy based on this data to extend battery life and maintain its performance. Furthermore, this data can be integrated with battery temperature, voltage, and current data through a data fusion module to achieve a more accurate battery state estimate, thereby providing more comprehensive decision support for the battery management system. This multi-parameter collaborative optimization method gives the battery management system of this invention significant advantages in improving battery efficiency, safety, and extending battery life.

[0068] The aging status monitoring module assesses the degree of battery aging by monitoring the number of battery cycles and capacity decay. The module operates based on the following specific technical parameters and steps: The number of battery cycles refers to the total number of complete charge-discharge cycles the battery undergoes. Each charge-discharge cycle causes some wear and tear on the battery, and as the number of cycles increases, the battery's performance gradually declines. This module calculates the number of battery cycles by recording the battery's charging and discharging processes. Most batteries can operate for at least 500 cycles, while some high-quality batteries, such as certain laptop batteries, may be designed for continuous use of 1,000 cycles. Battery capacity decay refers to the decrease in the battery's ability to store electrical energy after a certain period of use. This module assesses battery capacity decay by monitoring the difference between the battery's full charge capacity (i.e., the maximum amount of energy the battery can hold in its current state) and its design capacity (i.e., the battery's initial maximum charge). If the full charge capacity is significantly lower than the design capacity, this indicates a significant decline in the battery's health. For example, when a battery's capacity decays to 80% of its original capacity, it should be retired from the vehicle and used in other applications. The internal resistance of a battery increases with usage time and charge / discharge cycles. This increase in internal resistance leads to increased energy loss, heat generation, and reduced efficiency during discharge. This module estimates the change in battery internal resistance by applying a small load and measuring the voltage drop, thus assessing the battery's aging status. Battery internal resistance can be measured using methods such as small-load circuit testing, electrochemical workstation testing, and pulse impedance measurement. The degree of battery performance degradation can be determined by comparing the efficiency of the charging and discharging processes. Aging batteries exhibit reduced efficiency during charge and discharge, meaning they require longer charging times and discharge at a slower rate. Aging batteries also generate more heat during charge and discharge; monitoring temperature changes is an effective way to assess battery condition. This module assesses battery aging by monitoring temperature changes during charge and discharge. For example, during operation, the internal resistance is tested, and an estimated battery health status is obtained based on the established internal resistance-SOH relationship.

[0069] The data acquisition module is equipped with a high-speed analog-to-digital converter (ADC) with at least 12-bit resolution, accurately sampling key parameters of the battery such as temperature, voltage, current, and internal resistance at a frequency of at least 10 times per second. This data is transmitted in real-time to the data fusion module via a high-speed serial communication interface, such as I2C or SPI. To ensure data integrity and stable system operation, the data acquisition module also has sufficient built-in buffer space to temporarily store data until it is processed by subsequent modules. The data fusion module then uses algorithms such as Kalman filters to integrate and optimize the data, improving its accuracy and reliability, and providing strong data support for accurate battery state estimation and optimization of charge / discharge strategies. The entire data acquisition and transmission process is designed with low latency, ensuring that the system can respond promptly to any changes in battery state, enabling fine-grained management of the battery charging and discharging process.

[0070] The data fusion module is responsible for transforming the scattered data collected from various monitoring modules into a more consistent and accurate dataset. This module employs an advanced Kalman filter algorithm, a highly efficient recursive filter capable of estimating the state of a dynamic system from a series of noisy measurement data. Before starting the filtering process, the Kalman filter needs to be initialized, setting initial state estimates and an initial covariance matrix. The state estimates are based on knowledge of the battery's current state, while the covariance matrix represents the quantification of the uncertainty of the initial estimates. At the beginning of each sampling period, the Kalman filter uses a dynamic model of the system to predict the battery's state. This model considers the battery's physical characteristics and chemical processes, predicting how the battery's state will change in the next sampling period. Once new measurement data is received from the data acquisition module, the Kalman filter compares this data with the predicted state and updates the state estimate based on the difference. This process involves calculating the Kalman gain, a metric that balances the predicted state and the measurement data. The Kalman filter utilizes the uncertainty of the measurement data (measurement noise) and the uncertainty of the prediction model (process noise) to adjust the predicted state through the Kalman gain, generating a more accurate state estimate. The filter also updates the error covariance of the state estimate, reflecting the uncertainty of the new state estimate. This update step helps to further optimize the state estimate in subsequent predictions. The accuracy and reliability of the data processed by the Kalman filter are significantly improved, and this data is then used in the state estimation module to provide an accurate assessment of the battery's state of charge (SOC) and state of health (SOH).

[0071] The state estimation module estimates the battery's state of charge (SOC) and state of health (SOH) with at least 95% accuracy. This process involves a series of complex calculations and algorithms, the specific technical details of which are as follows: The state estimation module receives data processed by the data fusion module, including battery voltage, current, temperature, and internal resistance, all accurately expressed in numerical form. The state estimation module employs advanced algorithms, such as the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF), to estimate the battery's SOC. SOC is an indicator of the battery's remaining capacity, usually expressed as a percentage. These algorithms can handle the nonlinear characteristics of the battery model and provide real-time SOC estimates. For example, the EKF linearizes the nonlinear model, using a series of mathematical equations to predict and update the estimated SOC, while taking into account the effects of measurement noise and process noise. State of health (SOH) refers to the degree to which the battery retains its capacity relative to its initial state. The state estimation module assesses SOH by monitoring the battery's capacity decay, internal resistance increase, and other aging indicators. SOH estimation may involve analysis of the battery's cycle life, charge / discharge efficiency, and self-discharge rate. Accurate State of Health (SOH) assessment is crucial for predicting battery remaining life and maintenance planning. To ensure the accuracy of state estimation, the module continuously calibrates and updates its algorithm parameters. This includes adjusting the covariance matrix of the Kalman filter and updating the battery model parameters in real time. These adjustments help reduce estimation errors, ensuring that the accuracy of SOC and SOH estimations reaches at least 95%. The state estimation module transmits its calculation results to other parts of the system, such as the user interface module and the fault diagnosis module. These results are essential for battery management system decision-making, performance optimization, and safety protection.

[0072] The state estimation module's role is to estimate the battery's state of charge (SOC) and state of health (SOH) with at least 95% accuracy using data transmitted from the data fusion module. This process involves a series of complex calculations and algorithms, the specific technical details of which are as follows: The state estimation module receives data processed by the data fusion module, including battery voltage, current, temperature, and internal resistance, all accurately expressed in numerical form. The state estimation module employs advanced algorithms, such as the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF), to estimate the battery's SOC. SOC is an indicator of the battery's remaining capacity, usually expressed as a percentage. These algorithms can handle the nonlinear characteristics of the battery model and provide real-time SOC estimates. For example, the EKF linearizes the nonlinear model, using a series of mathematical equations to predict and update the estimated SOC, while taking into account the effects of measurement noise and process noise. State of health (SOH) refers to the degree to which the battery retains its capacity relative to its initial state. The state estimation module assesses SOH by monitoring the battery's capacity decay, internal resistance increase, and other aging indicators. State of Health (SOH) estimation may involve analyzing battery cycle life, charge / discharge efficiency, and self-discharge rate. Accurate SOH assessment is crucial for predicting remaining battery life and maintenance planning. To ensure the accuracy of state estimation, the module continuously calibrates and updates its algorithm parameters. This includes adjusting the covariance matrix of the Kalman filter and updating the battery model parameters in real time. These adjustments help reduce estimation errors, ensuring that the estimation accuracy of SOC and SOH reaches at least 95%. The state estimation module transmits its calculation results to other parts of the system, such as the user interface module and the fault diagnosis module. These results are essential for battery management system decision-making, performance optimization, and safety protection.

[0073] The optimization algorithm module utilizes advanced algorithms to dynamically adjust the charging and discharging strategy based on the output of the state estimation module, aiming to improve battery efficiency by at least 10%. This module can be implemented using a genetic algorithm or a deep learning algorithm. A genetic algorithm (GA) is an optimization algorithm that simulates the process of biological evolution. It searches for the optimal solution in a population of candidate solutions through operations such as selection, crossover, and mutation. In battery charging and discharging management, genetic algorithms can be used to optimize charging and discharging strategies to reduce charging costs, minimize grid load peak-valley differences, and improve battery lifespan. Parameter settings for the genetic algorithm include: Population size: typically set to tens to hundreds of individuals, for example, 50 individuals. Crossover rate: generally between 60% and 90%, for example, 70%. Mutation rate: typically low, such as 1% to 5%, for example, 3%. Number of iterations: depending on the problem size and complexity, it may require hundreds to thousands of iterations, for example, 1000 iterations. Fitness function: a criterion for evaluating the quality of chromosomes in the population, which can be designed based on indicators such as battery charging and discharging efficiency, lifespan, and safety.

[0074] Deep learning algorithms, especially deep neural networks (DNNs), can be trained and optimized through the following steps:

[0075] 1. Data preprocessing: Collect battery charging and discharging data, including voltage, current, temperature, etc., and perform normalization processing, for example, using the max-min normalization method to scale the data to the [0,1] interval.

[0076] 2. Model Design: Design a deep neural network model, which may include multiple hidden layers and different types of activation functions. For example, a Long Short-Term Memory (LSTM) network can be used to predict the SOC and SOH of a battery.

[0077] 3. Model Training: Train the deep learning model using battery data and optimize the network weights through backpropagation. For example, the Adam optimizer can be used with a learning rate of 0.01.

[0078] 4. Performance Evaluation: Use the validation set to evaluate the model's prediction accuracy and adjust the model structure or parameters as needed. For example, mean squared error (MSE) can be used as the loss function.

[0079] 5. Model Application: The trained model is used to predict the battery's SOC and SOH in real time, and the charging and discharging strategy is adjusted based on the prediction results. For example, the prediction error threshold can be set to 5%.

[0080] The charge / discharge control module is responsible for dynamically adjusting the battery's charge / discharge parameters based on strategies provided by the optimization algorithm module. This module can precisely control the charging current within the range of 0.2C to 1C to adapt to different charging demands and battery states. Simultaneously, it manages the charging and discharging cutoff voltages to ensure the battery operates within a safe voltage range, avoiding overcharging and over-discharging, thereby extending battery life and maintaining optimal performance. Through real-time monitoring of the battery's status and guidance from the optimization algorithm, the charge / discharge control module intelligently adjusts these parameters to achieve the overall goals of the battery management system: improving battery efficiency, ensuring safe operation, and optimizing the battery's charge / discharge process.

[0081] The thermal management module employs a liquid cooling system to precisely control the battery temperature, ensuring it operates within its optimal performance range. This system is designed with specific parameter settings to maintain the battery's operating temperature within a safe and effective range of 10°C to 40°C. By adjusting the coolant flow rate, the system dynamically adjusts the cooling effect in response to changes in ambient temperature or battery heat generation. For example, at higher ambient temperatures, the liquid cooling system can increase the coolant flow rate to its maximum value, say 5 liters per minute, to enhance heat dissipation; conversely, at lower ambient temperatures, the flow rate can be reduced, say 1 liter per minute, to minimize heat loss. Through this precise temperature control, the thermal management module prevents the battery from overheating, avoiding performance degradation and shortened lifespan, while also preventing overcooling, thus preventing reduced charging efficiency and weakened discharge capacity. This temperature control strategy not only helps protect the battery from extreme temperatures but also optimizes charge and discharge efficiency, thereby extending battery lifespan and ensuring its reliability and stability throughout its entire operating cycle.

[0082] The balancing control module's role is to finely adjust the charge levels of individual cells in the battery pack, ensuring pack consistency and overall performance. This module monitors the state of charge (SOC) of each cell and dynamically adjusts the charging and discharging processes to ensure that the charge difference between any two cells in the pack does not exceed 2%. This precise balancing control strategy helps prevent individual cells from being overcharged or over-discharged, thus avoiding performance degradation and shortened lifespan. Through the continuous operation of the balancing control module, the battery pack can maintain optimal operating conditions, providing stable and reliable energy output, while also optimizing the overall battery system's lifespan and efficiency.

[0083] The safety protection module includes overcharge protection, over-discharge protection, over-temperature protection, and short-circuit protection. It ensures the battery can quickly cut off power within 500 milliseconds under abnormal conditions, protecting it from damage and preventing potential safety accidents. When the battery voltage exceeds a predetermined overcharge protection threshold, typically set between 4.35V and 4.4V, the safety protection module immediately activates, cutting off the charging circuit to prevent battery damage due to overcharging. During battery discharge, when the battery voltage drops to a set over-discharge protection threshold, such as between 2.3V and 3.0V, the module intervenes and cuts off the discharge circuit to prevent battery damage due to over-discharge. A temperature sensor monitors the battery temperature; once the temperature exceeds a safe threshold, such as above 60°C to 80°C, the module activates the over-temperature protection mechanism, potentially preventing overheating by shutting down the charging and discharging circuits or reducing the charging and discharging rate. When a short circuit occurs, the safety protection module can quickly detect an abnormal increase in current and cut off power within a very short time, such as 500 milliseconds, to prevent damage to the battery and circuitry. The implementation of these protection measures typically relies on high-precision voltage and current detection circuits, as well as efficient control logic. Control ICs such as the DW01 incorporate high-precision voltage detection and time-delay circuits. Key parameters include overcharge detection voltage of 4.3V, overcharge release voltage of 4.05V; over-discharge detection voltage of 2.5V, over-discharge release voltage of 3.0V; overcurrent detection voltage of 5V; and short-circuit current detection voltage of 1.0V. Through these precise parameter settings and rapid response mechanisms, the safety protection module provides comprehensive safety assurance for the battery.

[0084] The communication interface module is a crucial component for enabling data exchange between the battery management system and external devices or monitoring systems. This module supports at least one industry-standard communication protocol, such as the CAN bus, to ensure efficient and reliable data transmission. The CAN bus is a serial communication protocol widely used in automotive and industrial automation, allowing multiple nodes to exchange data on the same bus. This system's communication interface module adopts the CAN bus protocol, operating at a maximum data transmission rate of 1 Mbps to accommodate various communication needs. The CAN bus data link layer and physical layer conform to the ISO 11898 standard, ensuring standardized communication and interoperability. The module's parameter settings include: a maximum of 1 Mbps, adjustable to suit different communication environments and data volumes; support for at least 30 nodes connecting to the same CAN bus network, allowing the battery management system to communicate with multiple external devices; a bus length of up to 40 meters at the highest rate, suitable for communication needs within vehicles or in industrial automation environments; CRC checksum verification to ensure data transmission integrity and accuracy; and non-destructive arbitration to ensure high-priority messages are transmitted first.

[0085] The user interface module provides a graphical user interface (GUI) for intuitively displaying the battery's real-time status and providing operational guidance. The GUI is designed for ease of use, ensuring users can quickly understand the battery's current condition and historical trends. To provide a smooth user experience, the interface refresh rate is set to no less than 1Hz, meaning information is updated at least once per second to maintain data real-time performance. Furthermore, the user interface module supports advanced interaction methods, including touchscreen and voice control, allowing users to interact with the system in multiple ways, improving operational convenience and system accessibility.

[0086] The fault diagnosis module is capable of identifying at least five different types of battery faults, including overcharge, over-discharge, over-temperature, short circuit, and internal open circuit. By analyzing various battery operating parameters and utilizing advanced algorithms for fault mode identification, the module achieves a diagnostic accuracy of no less than 90%. This high-accuracy fault diagnosis capability significantly improves the safety and reliability of battery use, while also reducing unexpected downtime caused by battery faults, providing a scientific basis for battery maintenance and repair.

[0087] The system integration module integrates all the above modules into a unified hardware platform, with the software running on a microcontroller of at least 32 bits. This integrated design simplifies system deployment and maintenance, and improves system stability and efficiency. The system also features remote update capabilities, allowing for remote software updates via wireless network, with the update process completed within 5 minutes. This feature enables the system to quickly adapt to new operating conditions or rapidly fix discovered problems, ensuring the battery management system remains up-to-date and enhancing the system's flexibility and long-term applicability.

[0088] The system operation process is as follows:

[0089] S1. Data Acquisition Phase: After the system starts up, the data acquisition module is activated first and begins to collect data on parameters such as voltage, current, temperature and internal resistance through sensors connected to the battery.

[0090] S2. Data preprocessing stage: The collected analog signals are converted into digital signals by the data acquisition module and sent to the data fusion module through a high-speed serial communication interface such as I2C or SPI.

[0091] S3. Data Fusion and State Estimation Stage: After receiving the data, the data fusion module uses a Kalman filter algorithm to integrate and optimize the data, improving its accuracy and reliability. Based on the fused data, the state estimation module estimates the battery's state of charge (SOC) and state of health (SOH) with an accuracy of no less than 95%.

[0092] S4. Optimization Strategy Formulation Stage: Based on the output of the state estimation module, the optimization algorithm module dynamically adjusts the charging and discharging strategy using a genetic algorithm or a deep learning algorithm to improve battery efficiency by at least 10%.

[0093] S5. Charge and discharge control stage: The charge and discharge control module dynamically adjusts parameters such as charging current, charging cut-off voltage, and discharging cut-off voltage based on the results of the optimization algorithm module to ensure that the battery operates under safe conditions.

[0094] S6. Thermal Management Stage: The thermal management module controls the battery temperature between 10°C and 40°C through a liquid cooling system to prevent overheating and overcooling, ensuring stable battery performance.

[0095] S7. Battery Balance Control Stage: The balance control module adjusts the charge of each battery in the battery pack to keep the charge difference no more than 2% and maintain the consistency of the battery pack.

[0096] S8. Safety Protection Phase: The safety protection module monitors the battery status in real time. Once overcharging, over-discharging, overheating, or short circuit is detected, it can cut off the power supply within 500ms to ensure battery safety.

[0097] S9. Communication and Data Exchange Stage: The communication interface module supports at least one industrial communication protocol, such as CAN bus, to realize data exchange with external devices or monitoring systems.

[0098] S10. User Interaction Phase: The user interface module provides a graphical user interface (GUI) that displays battery status information and provides operation guidance. The interface supports touchscreen or voice control interaction, with a refresh rate of no less than 1Hz.

[0099] S11. Fault Diagnosis Phase: The fault diagnosis module can identify at least five different types of battery faults with a diagnostic accuracy of no less than 90%, ensuring that the battery's health status is assessed in a timely manner.

[0100] S12. System Integration and Remote Update Phase: The system integration module integrates all modules into a unified hardware platform. The software runs on a microcontroller with at least 32 bits and has remote update capabilities. The update process can be completed within 5 minutes.

[0101] The battery management system of this invention achieves refined management of the battery charging and discharging process through these specific process steps, improves battery efficiency and safety, extends battery life, and is suitable for various battery application scenarios.

[0102] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-parameter collaborative optimization battery charge and discharge management system, characterized in that: include: A temperature monitoring module, including at least three thermocouple sensors, distributed on the top, bottom and side of the battery, to monitor the temperature of the battery in real time with an accuracy of ±0.5°C; a voltage monitoring module, including at least one high-precision voltage sensor, used to monitor the voltage of the battery in real time with an accuracy of ±0.05% reading + 2mV; a current monitoring module, including at least one high-precision current sensor, used to monitor the current of the battery in real time with an accuracy of ±0.5% reading + 1mA; an internal resistance monitoring module, including at least one AC impedance spectrometer, used to monitor the internal resistance of the battery in real time with an accuracy of ±1% reading; Aging status monitoring module, used to monitor the number of battery cycles and capacity attenuation to evaluate the battery aging status; A data acquisition module, used to collect data from the above monitoring module at a sampling frequency of at least 10 times per second; A data fusion module is used to integrate the data collected by the data acquisition module and use the Kalman filter algorithm to improve the accuracy and reliability of the data. A state estimation module is used to estimate the state of charge (SOC) and health state (SOH) of the battery based on the fused data with an accuracy of at least 95%. An optimization algorithm module uses a genetic algorithm or a deep learning algorithm to dynamically adjust the charge and discharge strategy according to the output of the state estimation module to improve the battery efficiency by at least 10%. A charge and discharge control module dynamically adjusts the charge current, charge cutoff voltage, discharge cutoff voltage and other charge and discharge parameters according to the results of the optimization algorithm module, wherein the charge current can be adjusted in the range of 0.2C to 1C. A thermal management module includes a liquid cooling system to control the temperature of the battery between 10°C and 40°C to prevent overheating and overcooling. A balancing control module is used to adjust the power of each battery in the battery pack to keep the power difference within 2%. A safety protection module includes overcharge protection, over-discharge protection, over-temperature protection and short-circuit protection to ensure that the battery can cut off the power within 500ms under abnormal conditions. Communication interface module, used for data exchange with external equipment or monitoring system, supporting at least one industrial communication protocol, such as CAN bus; The user interface module provides a user operation interface for monitoring the battery status and adjusting the charge and discharge strategy, and the interface refresh frequency is not less than 1Hz; the fault diagnosis module is used to diagnose potential battery faults and provide alarms, and the diagnostic accuracy is not less than 90%; the system integration module integrates all the above modules into a unified hardware platform and coordinates and controls them through software. The software runs on a microcontroller of at least 32 bits.

2. The system according to claim 1, characterized in that The thermal management module can automatically adjust the heat dissipation strategy according to the temperature changes of the battery to maintain the battery within the optimal operating temperature range, and the flow rate of the liquid cooling system can be adjusted between 0.5 and 5 liters per minute.

3. The system according to claim 1, characterized in that The optimization algorithm module can predict the health status and remaining life of the battery according to the battery's usage history and real-time status, and the prediction error does not exceed 5%.

4. The system according to claim 1, characterized in that The charge and discharge control module includes a multi-stage charging strategy to achieve optimal charging of the battery, wherein the charging process includes at least two constant current charging stages with different current settings and one constant voltage charging stage.

5. The system according to claim 1, characterized in that The security protection module adopts a multi-layer protection mechanism, including hardware protection, software control and system redundancy design, and the response time of each protection layer does not exceed 100ms.

6. The system according to claim 1, characterized in that The user interface module includes a graphical user interface (GUI) for displaying the battery status and providing operation instructions, and can interact through a touch screen or voice control, and the response time of the GUI does not exceed 200ms.

7. The system according to claim 1, characterized in that The fault diagnosis module can identify at least five different types of battery faults, including overcharging, overdischarging, overheating, short circuit and internal open circuit, and the response time of fault diagnosis does not exceed 1 second.

8. The system according to claim 1, characterized in that The system integration module integrates all modules into a unified hardware platform and coordinates and controls them through software. The software runs on a microcontroller of at least 32 bits and has a remote update function. The update process can be completed within 5 minutes.

9. The system according to claim 1, characterized in that The battery management system is capable of communicating with at least one external energy management system to achieve optimized distribution and use of energy, with a communication distance of at least 100 meters.

10. The system according to claim 1, characterized in that The battery management system is applicable to at least two different types of batteries, including lithium-ion batteries and nickel-metal hydride batteries, and the system can automatically identify the battery type and adjust the corresponding monitoring parameters.

Images