A Solid-State Battery Multimodal Charge Management System

Abstract

The present invention discloses a solid-state battery multi-modal charge management system, including an endogenous intelligent sensing module, a quantum computing prediction module, a multi-modal environment interaction module, and a multi-objective optimization module. By embedding micro-sensors inside the battery to monitor the internal chemical state of the battery in real time and combining quantum computing to optimize the charge and discharge strategies, the battery usage efficiency and lifespan are effectively improved, especially suitable for the environment of high-frequency charge and discharge of electric vehicles. Compared with the prior art, the present invention significantly reduces the problems of response lag and insufficient optimization of traditional systems. By combining a multi-modal environment perception system, this technology can dynamically adjust the battery management strategy according to the external environment (such as humidity, vibration, etc.), ensure the stable operation of the battery in a complex environment, reduce the impact of the environment on the battery performance, and significantly improve the reliability and safety of the battery system. It can not only achieve precise monitoring, but also adapt to environmental changes, improving the intelligent level of battery usage.

Description

Technical Field

[0001] The present invention relates to the technical field of solid-state battery management, and in particular to a multi-modal charge management system for solid-state batteries. Background Art

[0002] As a new battery technology, solid-state batteries have higher energy density, better safety, and longer service life, and are expected to be widely used in fields such as electric vehicles and consumer electronics. However, at present, solid-state batteries are still in the research and development stage and have not been widely popularized. The current battery management system is mainly based on traditional liquid battery technology, relying on external sensors to monitor parameters such as battery voltage, current, and temperature, and performing charge and discharge management based on these data. Although these technologies have achieved certain success in traditional battery management, the existing battery management systems still face some limitations. For example: 1. The battery management system usually relies on external sensors to monitor the battery state, and cannot accurately and real-time capture the chemical changes inside the battery (such as electrolyte concentration, ion migration speed, etc.), resulting in certain delays and insufficient accuracy in the monitoring results. 2. Most existing systems adopt traditional optimization algorithms, which can improve the operating efficiency of the battery to a certain extent, but cannot process multi-dimensional and multi-mode data, and are difficult to cope with complex application scenarios (such as high-frequency charge and discharge of electric vehicles, environmental changes, etc.). 3. Most current battery management systems do not consider the impact of the external environment on battery performance. For example, factors such as temperature, humidity, vibration, and pollutant concentration have not been effectively alleviated, which is likely to lead to a decline in battery performance or premature degradation.

[0003] To overcome these problems, the industry usually tries to make up for the lack of internal state monitoring by strengthening the arrangement of external sensors, but this approach still cannot capture the minute changes inside the battery in real time and there is a data processing delay, or tries to introduce more complex optimization algorithms to handle multi-dimensional data. However, due to the large amount of calculation, traditional algorithms still have bottlenecks in processing speed and accuracy, and it is difficult to achieve the ideal optimization effect. In terms of environmental adaptability, some systems try to reduce the environmental impact through measures such as temperature and humidity control, but it is still difficult to achieve comprehensive adjustment and intelligent response in a complex environment. Summary of the Invention

[0004] The technical problem solved by the present invention is to provide a multi-modal charge management system for solid-state batteries to address the deficiencies in the existing battery management system in monitoring the internal chemical state of the battery, coping with multi-dimensional data processing, and the impact of external environmental changes on battery performance, as described in the above background art.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is as follows: A multi-modal charge management system for a solid-state battery, characterized in that it includes an in-battery intelligent sensing module, a quantum computing prediction module, a multi-modal environment interaction module, and a multi-objective optimization module, wherein: The in-battery intelligent sensing module includes a micro-sensor group embedded inside the solid-state battery and an adaptive algorithm unit; The micro-sensor group is used to collect the chemical state parameters inside the solid-state battery in real time, and the chemical state parameters include electrolyte concentration and ion migration speed; The adaptive algorithm unit calculates according to the chemical state parameters through the following calculation formula: , where, represents the th electrolyte concentration at time value, is the initial electrolyte concentration, is the th ion migration speed at time value, is the weight coefficient of the electrolyte concentration obtained according to experimental data, is the weight coefficient related to the ion migration speed; The in-battery intelligent sensing module transmits the battery health state index to the multi-objective optimization module in real time;

[0006] The quantum computing prediction module is used to process the multi-dimensional operation data of the solid-state battery based on the quantum computing architecture, and the multi-dimensional operation data includes voltage, current, temperature, and the battery health state index; The quantum computing prediction module calculates the battery aging trend and generates a dynamic charge and discharge strategy through the characteristics of quantum state superposition and entanglement, specifically through the following calculation formula: , where, is the th multi-dimensional operation data at time real-time value, The weight coefficients obtained through optimization analysis for quantum computing; the quantum computing prediction module outputs the predicted health value and the dynamic charge and discharge strategy to the multi-objective optimization module; the multi-modal environment interaction module includes an environment perception unit and an environment adjustment unit; the environment perception unit includes a vibration sensor, a humidity sensor, and a pollutant sensor, and is used to collect external environment parameters in real time, and the external environment parameters include environmental vibration frequency, humidity value, and pollutant concentration; the environment adjustment unit adjusts the operation mode of the solid-state battery according to the external environment parameters; the multi-objective optimization module receives the battery health state index, the predicted health value, the dynamic charge and discharge strategy, and the adjusted operation mode, and generates a comprehensive optimization strategy through a multi-objective optimization algorithm; the multi-objective optimization algorithm uses a genetic algorithm, and the specific steps include: initializing the population, setting performance optimization goals and lifespan extension goals according to the battery health state index and the predicted health value; calculating the fitness of each individual, and iteratively optimizing through crossover and mutation operations until the convergence condition is met to obtain a comprehensive optimization strategy; the comprehensive optimization strategy includes a charging current adjustment value, a discharge depth threshold, and an operation mode switching parameter, and is used to control the charge and discharge process of the solid-state battery.

[0007] As a further solution of the present invention, the battery endogenous intelligent perception module further includes a module for feature extraction based on the battery internal data collected in real time, and the feature extraction module uses an adaptive algorithm to automatically adjust the acquisition frequency and data processing method when the battery health state changes, so as to accurately evaluate the battery health state.

[0008] As a further solution of the present invention, the quantum computing prediction module uses a quantum regression algorithm to predict the aging trend of the battery based on real-time battery operation data and health state indicators, and outputs a predicted health value and a charge and discharge strategy, wherein the predicted health value is obtained by optimizing the weighted average of historical operation data and real-time data through quantum computing.

[0009] As a further solution of the present invention, the multi-modal environment interaction module further includes an environment interference detection unit, which is used to monitor the impact of external environment changes on the solid-state battery in real time. When the monitoring result shows that the environment interference exceeds a preset threshold, the environment adjustment unit is used to adjust the operation mode of the battery to protect the battery from the adverse environment.

[0010] As a further solution of the present invention, the multi-objective optimization module uses a genetic algorithm to optimize the balance between the battery health state index and the predicted health value, so as to optimize the charging current adjustment value and the discharge depth threshold of the battery, and optimize the operation mode according to the balance to extend the service life of the battery.

[0011] As a further solution of the present invention, the quantum computing prediction module further includes a historical data storage unit for storing the operating data of the battery. The historical data storage unit ensures that the quantum computing module can perform calculations based on the most relevant and up-to-date data by periodically cleaning up obsolete data, thereby improving the prediction accuracy.

[0012] As a further solution of the present invention, the battery endogenous intelligent sensing module further includes a feedback mechanism unit for self-learning. This unit automatically updates and optimizes the adaptive algorithm by regularly evaluating the difference between the battery health state and the actual performance to improve the evaluation accuracy of the battery health state.

[0013] As a further solution of the present invention, the humidity sensor and the vibration sensor in the multi-modal environment interaction module transmit the collected environmental data to the multi-objective optimization module through a wireless communication protocol. The multi-objective optimization module adjusts the charging strategy of the battery according to this data, thereby reducing the battery performance degradation caused by environmental factors.

[0014] As a further solution of the present invention, the multi-objective optimization module generates a comprehensive optimization strategy through the following calculation formula: , where is the weighted data of the th optimization objective (such as battery health state, depth of discharge, etc.), is the weight coefficient dynamically adjusted for each optimization objective through quantum computing. The comprehensive optimization strategy is used to determine multiple parameters during the charging and discharging process of the battery to achieve the best performance.

[0015] As a further solution of the present invention, the battery endogenous intelligent sensing module further includes a fault prediction unit based on a deep learning model. This unit obtains the fault mode through historical data training and performs real-time prediction based on the current data, and timely provides a potential fault risk alarm to the multi-objective optimization module.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: By introducing an endogenous intelligent perception module and quantum computing, precise real-time monitoring and dynamic adjustment are achieved in the battery management system. Through embedded micro-sensors, the chemical states inside the battery (such as electrolyte concentration and ion migration speed) can be collected in real time and transmitted to the battery management system, thus avoiding the problems of delay and insufficient accuracy of external sensors. Quantum computing helps process and analyze multi-dimensional data of the battery under different environmental conditions, enabling real-time optimization of the charge and discharge strategies and maximizing the service life and efficiency of the battery. Especially in the application scenario of high-frequency charging and discharging of electric vehicles, this intelligent management system can effectively cope with frequent charging and discharging processes, ensuring that the battery remains stable and efficient in a rapidly changing usage environment. The system also combines multi-modal environmental perception modules (such as vibration, humidity, pollutant monitoring, etc.), enabling the battery to not only adjust according to its internal state but also adapt to changes in the external environment in real time, such as high humidity, high temperature, or polluted environments, thereby reducing the negative impact of the environment on the battery health. This intelligent perception and adaptive adjustment mechanism significantly improves the safety and reliability of the battery, is particularly suitable for consumer electronics with high-performance requirements, can maintain stable operation for a long time in complex environments, and avoids the decline in battery performance caused by changes in the external environment, significantly extending the service life of the battery and enhancing the user experience of the device. It not only solves the problems of response lag and insufficient efficiency in traditional methods but also demonstrates stronger intelligence and adaptability in practical applications through refined management. The application prospect of this solution is broad, especially in rapidly developing fields such as electric vehicles and consumer electronics, providing consumers with longer-lasting, stable, and safe battery performance, and thus promoting the wide application of battery technology in various complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic diagram of the system architecture of the multi-modal charge management system for the solid-state battery of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Please refer to Figure 1 , in the embodiments of the present invention, a multi-modal charge management system for a solid-state battery, characterized in that it includes a battery-internal intelligent sensing module, a quantum computing prediction module, a multi-modal environment interaction module, and a multi-objective optimization module, wherein: the battery-internal intelligent sensing module includes a micro-sensor group embedded inside the solid-state battery and an adaptive algorithm unit; the micro-sensor group is used to collect the chemical state parameters inside the solid-state battery in real time, and the chemical state parameters include electrolyte concentration and ion migration speed; the adaptive algorithm unit calculates according to the chemical state parameters through the following calculation formula: , where represents the value of the th electrolyte concentration at time , is the initial electrolyte concentration, is the value of the th ion migration speed at time , is the weight coefficient of the electrolyte concentration obtained according to experimental data, is the weight coefficient related to the ion migration speed; the battery-internal intelligent sensing module transmits the battery health state index to the multi-objective optimization module in real time; the quantum computing prediction module is used to process the multi-dimensional operation data of the solid-state battery based on the quantum computing architecture, and the multi-dimensional operation data includes voltage, current, temperature, and the battery health state index; the quantum computing prediction module calculates the battery aging trend and generates a dynamic charge and discharge strategy through the quantum state superposition and entanglement characteristics, specifically through the following calculation formula: , where is the real-time value of the th multi-dimensional operation data at time , is the weight coefficient obtained by the quantum computing through optimization analysis; the quantum computing prediction module outputs the predicted health value and the dynamic charge and discharge strategy to the multi-objective optimization module;

[0021] The multi-modal environment interaction module includes an environment perception unit and an environment regulation unit; the environment perception unit includes a vibration sensor, a humidity sensor, and a pollutant sensor, which are used to collect external environment parameters in real time. The external environment parameters include environmental vibration frequency, humidity value, and pollutant concentration; the environment regulation unit adjusts the operating mode of the solid-state battery according to the external environment parameters; the multi-objective optimization module receives the battery health status index, the predicted health value, the dynamic charge and discharge strategy, and the adjusted operating mode, and generates a comprehensive optimization strategy through a multi-objective optimization algorithm; the multi-objective optimization algorithm uses a genetic algorithm, and the specific steps include: initializing the population, setting performance optimization goals and life extension goals according to the battery health status index and the predicted health value; calculating the fitness of each individual, and iteratively optimizing through crossover and mutation operations until the convergence condition is met to obtain a comprehensive optimization strategy; the comprehensive optimization strategy includes a charging current adjustment value, a discharge depth threshold, and an operating mode switching parameter, which are used to control the charge and discharge process of the solid-state battery. The battery endogenous intelligent perception module further includes a module for feature extraction based on real-time collected battery internal data. The feature extraction module uses an adaptive algorithm to automatically adjust the acquisition frequency and data processing method when the battery health status changes, so as to accurately evaluate the battery health status. The quantum computing prediction module uses a quantum regression algorithm to predict the aging trend of the battery based on real-time battery operation data and health status indicators, and outputs a predicted health value and a charge and discharge strategy. Among them, the predicted health value is obtained by quantum computing to optimize the weighted average of historical operation data and real-time data. The multi-modal environment interaction module further includes an environment interference detection unit, which is used to monitor the impact of external environment changes on the solid-state battery in real time. When the monitoring result shows that the environmental interference exceeds a preset threshold, the environment regulation unit is used to adjust the operating mode of the battery to protect the battery from adverse environmental effects. The multi-objective optimization module uses a genetic algorithm to optimize the balance between the battery health status index and the predicted health value, so as to optimize the charging current adjustment value and the discharge depth threshold of the battery, and optimize the operating mode according to the balance to extend the service life of the battery. The quantum computing prediction module also includes a historical data storage unit, which is used to store the operation data of the battery. The historical data storage unit ensures that the quantum computing module can perform calculations based on the most relevant and latest data by periodically cleaning up outdated data, thereby improving the prediction accuracy. The battery endogenous intelligent perception module also includes a feedback mechanism unit for self-learning. This unit automatically updates and optimizes the adaptive algorithm by regularly evaluating the difference between the battery health status and the actual performance, so as to improve the accuracy of the battery health status assessment. The humidity sensor and the vibration sensor in the multi-modal environment interaction module transmit the collected environmental data to the multi-objective optimization module through a wireless communication protocol. The multi-objective optimization module adjusts the charging strategy of the battery according to these data, thereby reducing the battery performance degradation caused by environmental factors.

[0022] As a further solution of the present invention, the multi-objective optimization module generates a comprehensive optimization strategy through the following calculation formula: , where is the weighted data of the th optimization objective (such as state of health of the battery, depth of discharge, etc.), is the weight coefficient dynamically adjusted for each optimization objective through quantum computing. The comprehensive optimization strategy is used to determine multiple parameters during the charge and discharge process of the battery to achieve the best performance. The battery endogenous intelligent perception module further includes a fault prediction unit based on a deep learning model. This unit obtains the fault mode through historical data training and makes real-time predictions based on current data, and timely provides a potential fault risk alarm to the multi-objective optimization module.

[0023] Embodiment 1: This embodiment specifically describes each module. The core task of the battery endogenous intelligent perception module is to continuously monitor the chemical state inside the solid-state battery in real time, including electrolyte concentration and ion migration speed. For this purpose, this module continuously monitors the battery state through a micro sensor group. The sensor group can accurately collect the chemical state parameters inside the battery, such as electrolyte concentration and ion migration speed. These data are transmitted to the adaptive algorithm unit in real time for processing. The adaptive algorithm unit calculates the state of health index of the battery using the following formula: , where: represents the value of the th electrolyte concentration at time ; is the initial electrolyte concentration; is the value of the th ion migration speed at time ; is the weight coefficient of the electrolyte concentration obtained from experimental data; is the weight coefficient related to the ion migration speed; and are obtained in real time by the sensors embedded inside the battery and updated by the adaptive algorithm unit according to the real-time operating state of the battery; the weight coefficients and are obtained through experimental data to optimize the accuracy of the battery state of health assessment; whenever the state of health index calculation is completed, the module transmits it to the multi-objective optimization module as the input for optimization decision-making.

[0024] The core function of the quantum computing prediction module is to predict the aging trend of the battery based on the battery's operating data and health status indicators, and generate a dynamic charge and discharge strategy according to the prediction results. This module uses quantum computing technology to process multi-dimensional operating data (such as voltage, current, temperature, and battery health status indicators). Through the quantum computing architecture, this module can quickly process a large amount of real-time data, and combine the characteristics of quantum state superposition and entanglement to predict the aging trend of the battery. The dynamic charge and discharge strategy is calculated by the following formula: , where: is the real-time value of the th multi-dimensional operating data at time , is the weight coefficient obtained by quantum computing through optimization analysis. are the battery operating parameters (such as voltage, current, temperature, etc.) obtained in real time through the battery management system; the weight coefficient is obtained through the quantum computing optimization algorithm, weighted based on historical data and real-time data to improve the accuracy of prediction; the quantum computing prediction module transmits the health value and charge and discharge strategy calculated based on this formula to the multi-objective optimization module for further optimizing the charge and discharge process of the battery. The multi-modal environment interaction module works together through the environment perception unit and the environment regulation unit to collect and adjust the impact of the external environment on the battery in real time. The environment perception unit monitors the external environment parameters in real time through vibration sensors, humidity sensors, and pollutant sensors, including environmental vibration frequency, humidity value, and pollutant concentration. The sensor data is transmitted to the multi-objective optimization module through wireless communication; the environment regulation unit: according to the external environment parameters, the environment regulation unit adjusts the operating mode of the battery to ensure the stable operation of the battery in a complex environment. The specific implementation of the regulation strategy is through the interactive adjustment of the environmental impact parameters and the internal state of the battery to avoid the performance fluctuation of the battery caused by environmental changes. The core function of the multi-objective optimization module is to generate a comprehensive optimization strategy by integrating the battery health status, predicted health value, charge and discharge strategy, and environment regulation data through an optimization algorithm. This module uses a genetic algorithm for optimization, and the specific steps are as follows: initialize the population: set the optimization goal according to the battery health status indicators and predicted health value, and initialize the optimization population. Calculate the fitness: calculate the fitness of each individual through the real-time data and health status evaluation of the battery. Crossover and mutation operations: continuously optimize the population through crossover and mutation operations until the convergence condition is met, and generate the final comprehensive optimization strategy. The optimization strategy includes the charging current adjustment value, discharge depth threshold, and operating mode switching parameters, which will be used to control the charge and discharge process of the battery to ensure the maximization of the battery's usage efficiency and lifespan.

[0025] Embodiment 2: In this embodiment, one of the core modules of the system is the battery-internal intelligent sensing module. This module continuously monitors the chemical state inside the battery through a micro-sensor group embedded in the solid-state battery. Specifically, the sensors collect key parameters such as electrolyte concentration and ion migration speed in real time to evaluate the health status of the battery. To improve the accuracy and real-time performance of the data collected by the sensors, an adaptive algorithm is used to dynamically adjust the data collection frequency and processing method. Especially when the health status of the battery changes, it can automatically optimize the adjustment. Whenever new data is collected by the sensors, the adaptive algorithm unit calculates the health status index of the battery in real time based on the chemical changes inside the battery, combined with the initial value of the known electrolyte concentration and ion migration speed data. This process is weighted based on real-time data and historical data through the algorithm to ensure that the evaluation result accurately reflects the current condition of the battery. The calculation method of this health status index includes the weighted summation of the electrolyte concentration change and the ion migration speed to ensure that the comprehensive reflection of the impact of various state changes inside the battery on the battery health is reflected.

[0026] The quantum computing prediction module performs predictive analysis on the multi-dimensional operation data of the battery through the quantum computing architecture, including voltage, current, temperature, and health status index, etc. Through the parallel processing ability of quantum computing, these data can achieve efficient data analysis. Through the characteristics of quantum state superposition and entanglement, this module can accurately predict the battery aging trend and generate a dynamic charge and discharge strategy based on this. Specifically, the working process of the quantum computing prediction module includes two parts: First, based on the real-time battery operation data, combined with the health status index of the battery, the quantum computing module predicts the battery aging trend; Second, the module generates a charge and discharge strategy adapted to different environmental conditions by analyzing the prediction results. These strategies will be dynamically adjusted according to the predicted health value of the battery to ensure that during the use of the battery, the service life of the battery is maximally extended and the operation efficiency is improved.

[0027] To better cope with the impact of the external environment on battery performance, the system also introduces a multi-modal environment interaction module. This module includes an environment perception unit and an environment regulation unit, which can monitor external environment changes in real time and dynamically adjust the battery's operating mode according to the collected environmental data (such as humidity, vibration frequency, pollutant concentration, etc.). The environment perception unit uses devices such as vibration sensors and humidity sensors to collect external environment information in real time and transmits the data to the multi-objective optimization module in the system through a wireless communication protocol. The environment regulation unit then adjusts the battery's charge and discharge mode based on the transmitted environmental data and the changes in the battery's internal health status indicators. For example, when it is detected that the external environment humidity is too high, the system will adjust the battery's discharge depth to avoid the negative impact of environmental humidity on battery performance, thus ensuring that the battery is always in the most suitable operating state. The core technology of this module is the interactive optimization based on environmental parameters and the battery's internal state, ensuring that the battery's operating efficiency is not affected by external interference. The multi-objective optimization module is the key decision-making module in this system. By comprehensively considering the battery health status, environmental factors, and predicted health values, it generates a comprehensive optimization strategy. This optimization strategy is based on a genetic algorithm and goes through steps such as initializing the population, calculating fitness, crossover, and mutation operations, and finally generates the optimal charge and discharge strategy through iterative optimization. Specifically, this module optimizes the battery's charging current, discharge depth, and operating mode by setting an objective function and comprehensively considering the battery health status, environmental regulation results, and predicted health values.

[0028] Example 3: This example shows specific experimental data comparisons. For example, in the experimental design and setup, comparative tests are carried out under two sets of conditions. The experimental group: uses the solid-state battery multi-modal charge management system of the present invention, including an in-battery endogenous intelligent perception module, a quantum computing prediction module, a multi-modal environment interaction module, and a multi-objective optimization module. The control group: uses an existing battery management system on the market, which mainly relies on external sensors to monitor the battery status and manages the charge and discharge strategy through traditional optimization algorithms. All experiments use the same model of solid-state battery with a battery capacity of 50 Ah. The standard values of the electrolyte concentration and ion migration speed inside the battery are known, and the experimental environmental conditions are controlled at a temperature of 25°C and a humidity of 45%.

[0029] Experiment 1: Comparison of the accuracy of battery state of health assessment. The experiment first verified the accuracy of the endogenous intelligent sensing module of the present invention in battery state of health assessment. By real-time monitoring of the electrolyte concentration and ion migration speed, the system can quickly capture the minute changes inside the battery. The test results show that: the change error of the electrolyte concentration in the experimental group (using the technology of the present invention) is 2.1%, while that in the control group is 4.3%; the change error of the ion migration speed in the experimental group is 1.8%, while that in the control group is 5.6%; these data indicate that the experimental group is significantly superior to the control group in the monitoring accuracy of the electrolyte concentration change and ion migration speed change, and the errors are reduced by approximately 50% and 70% respectively.

[0030] Experiment 2: Comparison of the optimization effect of charge and discharge strategies. This experiment evaluated the effect of the quantum computing prediction module of the present invention in optimizing charge and discharge strategies. By simulating the use of the battery under high-frequency charge and discharge conditions, the charging efficiency and discharge depth of the two systems under different load conditions were compared: under high-load charging conditions, the charging efficiency of the experimental group is 94.6%, while that of the control group is 86.3%. The discharge depth of the experimental group is 55.2%, and that of the control group is 62.1%; under low-load charging conditions, the charging efficiency of the experimental group is 98.1%, and that of the control group is 90.2%. The discharge depth of the experimental group is 52.3%, and that of the control group is 57.8%; under high-frequency discharge conditions, the charging efficiency of the experimental group is 93.2%, and that of the control group is 81.4%. The discharge depth of the experimental group is 48.4%, and that of the control group is 60.7%; in the context of high-load charging and frequent discharging, the charging efficiency of the experimental group is increased by approximately 8 - 10%, and after the discharge depth is optimized, the battery shows higher stability in the experimental group and can maintain a higher power level for a longer time.

[0031] Experiment 3: Effect of environmental adaptability regulation. To verify the response effect of the multi-modal environmental interaction module to external environmental changes, this experiment simulated three different environmental impacts of high humidity, high temperature, and strong vibration, and tested the battery performance responses of the two systems: in a high-humidity environment, the performance decline of the experimental group is 4.3%, while that of the control group is 12.5%; in a high-temperature environment, the performance decline of the experimental group is 3.2%, while that of the control group is 10.9%; in a strong-vibration environment, the performance decline of the experimental group is 5.1%, while that of the control group is 14.3%. In high-humidity and high-temperature environments, the battery performance decline of the experimental group is significantly lower than that of the control group, proving that the multi-modal environmental perception and regulation module of the present invention effectively mitigates the impact of environmental factors on battery performance.

[0032] Example 4: In this example, the core task of the solid-state battery multimodal charge management system is to handle complex application scenarios, especially environments such as high-frequency charging and discharging of electric vehicles, and provide a more efficient, safe, and long-lasting battery management solution. The following specifically describes the working principle and application scenarios of this system. First, the battery endogenous intelligent sensing module monitors the chemical state of the battery in real time through micro-sensors embedded inside the solid-state battery, especially the electrolyte concentration and ion migration speed. These sensors continuously collect data inside the battery and transmit it to the adaptive algorithm unit for real-time processing. For example, when the battery is in a high-load or high-frequency charging and discharging state, changes in the electrolyte concentration will directly affect the battery's performance. Therefore, the micro-sensors can accurately capture these changes and calculate the battery health state index based on real-time data. This index not only reflects the current health status of the battery but also provides basic data for subsequent optimization strategies.

[0033] In a typical electric vehicle usage scenario, when the vehicle frequently accelerates and brakes on urban roads, the load on the battery changes rapidly, and the endogenous intelligent sensing module can monitor these changes in real time and react quickly. For example, if the electrolyte concentration inside the battery changes too quickly or the ion migration speed is abnormal, this module will enhance the monitoring accuracy by adjusting the data collection frequency to ensure real-time updates of the health status. The adaptive algorithm will comprehensively evaluate the health status of the battery using a formula to ensure the continuous and stable operation of the battery under high load. Secondly, the quantum computing prediction module uses the advantages of quantum computing to process multi-dimensional operation data, including the battery's voltage, current, temperature, and health status index, etc., and accurately predicts the aging trend of the battery. For example, when there are slight voltage fluctuations during high-frequency charging and discharging of the battery, the quantum computing module can predict the battery's aging speed through the quantum regression algorithm, based on the weighted average of real-time and historical data. This prediction not only helps optimize the current charging and discharging strategy but also effectively avoids premature degradation and extends the battery's service life. For example, in a low-temperature environment in winter, the chemical reaction rate of the battery usually slows down, resulting in a decrease in battery performance. In this situation, the quantum computing module will adjust the charging strategy according to the environmental temperature and the real-time operating state of the battery to prevent the battery from being damaged due to excessive charging current. This dynamic, quantum-computing-based strategy adjustment can ensure the efficient operation of the battery under different environmental conditions.

[0034] In the multi-modal environment interaction module, the environmental perception unit collects external environmental data in real time through vibration sensors, humidity sensors, and pollutant sensors. These environmental data will be transmitted to the multi-objective optimization module for further analysis. For example, when the vehicle passes through a section with large vibrations, the vibration sensor will detect abnormal vibration frequencies and then adjust the charging and discharging mode of the battery to reduce the impact of external vibrations on the battery. The humidity sensor can monitor the environmental humidity and adjust the depth of discharge of the battery when the humidity is too high, thus avoiding the decline of battery performance caused by moisture. The interaction between such environmental data and the internal state of the battery provides a more intelligent response mechanism for battery management, ensuring that the battery can still maintain the best performance in a complex external environment. For example, the battery may be threatened by short circuits or unstable electrochemical reactions in a high-humidity environment, and the environmental regulation unit will reduce these risks by reducing the depth of discharge and adjusting the charging current. Finally, the multi-objective optimization module generates a comprehensive optimization strategy based on all input data (including battery health status, environmental parameters, and predicted health values, etc.) through a genetic algorithm. The core of the genetic algorithm lies in optimizing the charging and discharging strategy of the battery by simulating the process of natural selection. Specifically, the genetic algorithm sets the optimization goal as improving the performance and extending the life of the battery, uses crossover and mutation operations, continuously adjusts various parameters, and finally determines the optimal charging current adjustment value, depth of discharge threshold, and operation mode switching parameters. These strategies not only optimize the operating efficiency of the battery but also extend the service life of the battery.

[0035] Example 5: The core function of the battery endogenous intelligent perception module is to monitor the chemical state inside the solid-state battery in real time, especially key parameters such as electrolyte concentration and ion migration speed. In this example, the embedded micro-sensor group collects the electrolyte concentration and ion migration speed inside the battery in real time and transmits them to the adaptive algorithm unit for processing. The adaptive algorithm calculates the battery health status index in real time based on the collected data. The specific process is as follows: Battery health status assessment: By performing a weighted calculation on the electrolyte concentration and ion migration speed, the battery health status index is obtained. The weighting coefficients are obtained based on experimental data and are dynamically adjusted according to real-time data in the algorithm. This calculation method can effectively reflect the chemical changes inside the battery and ensure accurate assessment of the battery during use. Data transmission and processing: The adaptive algorithm unit transmits the calculated battery health status index to the subsequent module as the basis for further optimization decisions. At this time, the algorithm not only pays attention to the change in electrolyte concentration but also considers the influence of ion migration speed. By performing weighted processing on these variables, a comprehensive assessment of the battery state is ensured.

[0036] The quantum computing prediction module is mainly used to predict the aging trend of the battery based on multi-dimensional operating data (such as voltage, current, temperature, etc.) and generate dynamic charge and discharge strategies. In this embodiment, the quantum computing architecture can process large-scale real-time data in parallel, and through the characteristics of quantum state superposition and entanglement, achieve efficient prediction of the battery aging process. Data input and analysis: The quantum computing module combines the health status data from the battery's built-in intelligent sensing module with the real-time battery operating data for multi-dimensional analysis. By predicting the battery aging trend, the quantum computing module can output corresponding charge and discharge strategies to optimize the battery usage method. Strategy generation and feedback: Based on the health values and prediction data obtained by quantum computing, the charge and discharge strategies are dynamically adjusted to ensure that the battery maintains optimal performance in different operating environments. This strategy is continuously optimized through real-time data feedback to form a closed-loop control.

[0037] The core of the multi-modal environment interaction module is to continuously monitor external environmental changes through the environmental perception unit and adjust the operating mode of the solid-state battery through the environmental regulation unit. The environmental perception unit includes a humidity sensor, a vibration sensor, and a pollutant monitor for collecting environmental data. Environmental parameter collection and analysis: The environmental perception unit continuously monitors environmental humidity, vibration frequency, and pollutant concentration and transmits this data to the multi-objective optimization module. The environmental regulation unit adjusts the operating mode of the battery based on this data and in combination with the health status indicators inside the battery to mitigate the impact of environmental changes on the battery. Operating mode adjustment: For example, when it is detected that the environmental humidity is too high, the environmental regulation unit automatically adjusts the depth of discharge of the battery to reduce the battery performance degradation caused by moisture. This adjustment process fully relies on the interaction between real-time sensor data and the battery health status to ensure the stable operation of the battery in complex environments.

[0038] The multi-objective optimization module comprehensively receives inputs from the battery health state, predicted health value, dynamic charge and discharge strategy, and environmental regulation data, and optimizes the charge and discharge strategy and battery health management through a genetic algorithm. The specific optimization process is as follows: Initialization and fitness evaluation: First, set the optimization objectives according to the battery health state indicators and predicted health value, and initialize the optimization population. Each individual in the population represents a charge and discharge strategy or operating mode. By calculating the fitness of each individual, evaluate its effect in the current state. Crossover and mutation operations: Through the crossover and mutation operations in the genetic algorithm, continuously generate new optimization strategies. Each iteration optimizes the population according to the fitness until the convergence condition is met to obtain the optimal comprehensive optimization strategy. This strategy covers key control parameters such as the charging current adjustment value, discharge depth threshold, and operating mode switching parameters. Final strategy output and execution: The finally generated comprehensive optimization strategy is used to control the charge and discharge process of the battery to ensure that the operating efficiency and lifespan of the battery are maximized. The specific implementation steps are as follows: Hardware deployment: Embed micro sensors in the solid-state battery to ensure that the internal chemical state data of the battery can be collected in real time. The environmental perception unit and the sensor are connected through a wireless communication protocol to ensure that the data can be transmitted to the central processing module in real time; Data collection and processing: Collect the internal chemical state data and external environmental data of the battery and input them into the adaptive algorithm and quantum computing module for processing. According to the processing results, the battery health state and charge and discharge strategy are adjusted in real time to optimize battery management; System feedback and optimization: The multi-objective optimization module optimizes the charge and discharge strategy according to the real-time data, optimizes the operating mode through the genetic algorithm, and outputs the optimization strategy to guide the charge and discharge process of the battery to ensure that the battery maintains the best state in various working environments.

[0039] In the present invention, unless otherwise clearly defined and limited, terms such as "installation", "setting", "connection", "fixation", "swivel connection" and the like shall be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components or the interaction relationship between two components. Unless otherwise clearly defined, for those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention.

Claims

1. A solid-state battery multi-modal charge management system, characterized in that, It includes an in - battery intelligent sensing module, a quantum computing prediction module, a multi - modal environment interaction module, and a multi - objective optimization module. Among them: The in - battery intelligent sensing module includes a micro - sensor group embedded inside the solid - state battery and an adaptive algorithm unit; The micro - sensor group is used to collect the chemical state parameters inside the solid - state battery in real time, and the chemical state parameters include electrolyte concentration and ion migration speed; The adaptive algorithm unit calculates according to the chemical state parameters through the following calculation formula: , where represents the value of the - th electrolyte concentration at time , is the initial electrolyte concentration, is the value of the - th ion migration speed at time , is the weight coefficient of the electrolyte concentration obtained according to experimental data, is the weight coefficient related to the ion migration speed; The in - battery intelligent sensing module transmits the battery health state index to the multi - objective optimization module in real time; The quantum computing prediction module is used to process multi-dimensional operation data of the solid-state battery based on the quantum computing architecture. The multi-dimensional operation data includes voltage, current, temperature, and the battery health state indicator. The quantum computing prediction module calculates the battery aging trend and generates a dynamic charge-discharge strategy through the characteristics of quantum state superposition and entanglement, specifically through the following calculation formula: , where is the real-time value of the -th multi-dimensional operation data at time , is the weight coefficient obtained by the quantum computing through optimization analysis. The quantum computing prediction module outputs the predicted health value and the dynamic charge-discharge strategy to the multi-objective optimization module; The multi-modal environment interaction module includes an environment perception unit and an environment regulation unit; the environment perception unit includes a vibration sensor, a humidity sensor, and a pollutant sensor, which are used to collect external environment parameters in real time, and the external environment parameters include environmental vibration frequency, humidity value, and pollutant concentration; the environment regulation unit adjusts the operating mode of the solid-state battery according to the external environment parameters; The multi-objective optimization module receives the battery health state index, the predicted health value, the dynamic charge and discharge strategy, and the adjusted operating mode, and generates a comprehensive optimization strategy through a multi-objective optimization algorithm; the multi-objective optimization algorithm uses a genetic algorithm, and the specific steps include: initializing the population, setting performance optimization goals and life extension goals according to the battery health state index and the predicted health value; calculating the fitness of each individual, and iteratively optimizing through crossover and mutation operations until the convergence condition is met to obtain a comprehensive optimization strategy; the comprehensive optimization strategy includes a charging current adjustment value, a discharge depth threshold, and an operating mode switching parameter, which are used to control the charge and discharge process of the solid-state battery.

2. The multimodal charge management system for a solid-state battery according to claim 1, characterized in that The battery endogenous intelligent perception module further includes a module for feature extraction based on real-time collected battery internal data, and the feature extraction module uses an adaptive algorithm to automatically adjust the acquisition frequency and data processing method when the battery health state changes.

3. The multimodal charge management system for a solid-state battery according to claim 1, characterized in that The quantum computing prediction module uses a quantum regression algorithm to predict the aging trend of the battery based on real-time battery operation data and health state indicators, and outputs a predicted health value and a charge and discharge strategy, where the predicted health value is obtained by optimizing the weighted average of historical operation data and real-time data through quantum computing.

4. A solid-state battery multi-modal charge management system according to claim 1, wherein The multi-modal environment interaction module further includes an environment interference detection unit, which is used to monitor the impact of external environment changes on the solid-state battery in real time. When the monitoring result shows that the environment interference exceeds a preset threshold, the environment regulation unit is used to adjust the operating mode of the battery.

5. A multimodal charge management system for a solid-state battery according to claim 1, characterized in that, The multi-objective optimization module uses a genetic algorithm to optimize the balance between the battery health state index and the predicted health value, so as to optimize the charging current adjustment value and the discharge depth threshold of the battery, and optimize the operating mode according to the balance.

6. The multimodal charge management system for a solid-state battery according to claim 1, characterized in that The quantum computing prediction module also includes a historical data storage unit, which is used to store the operation data of the battery. The historical data storage unit ensures that the quantum computing module can perform calculations based on the most relevant and up-to-date data by periodically clearing obsolete data.

7. The multimodal charge management system for a solid-state battery according to claim 1, characterized in that The battery endogenous intelligent perception module also includes a feedback mechanism unit for self-learning, which automatically updates and optimizes the adaptive algorithm by periodically evaluating the difference between the battery health state and the actual performance.

8. A multimodal charge management system for a solid-state battery according to claim 1, wherein The humidity sensor and the vibration sensor in the multi-modal environment interaction module transmit the collected environmental data to the multi-objective optimization module through a wireless communication protocol.

9. A solid-state battery multi-modal charge management system according to claim 1, wherein, The multi-objective optimization module generates a comprehensive optimization strategy through the following calculation formula: , where is the weighted data of the th optimization objective, is the weight coefficient for dynamically adjusting each optimization objective through quantum computing.

10. A multimodal charge management system for a solid-state battery according to claim 1, characterized in that, The battery endogenous intelligent perception module also includes a fault prediction unit based on a deep learning model, which obtains fault modes through historical data training and makes real-time predictions based on current data, and timely provides potential fault risk alerts to the multi-objective optimization module.

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