Chaotic characteristic driven intelligent charging and discharging dynamic regulation and control system for energy storage equipment

Abstract

The invention discloses an intelligent charging and discharging dynamic regulation and control system for energy storage equipment driven by chaotic characteristics, and relates to the technical field of energy storage equipment control, and the system comprises a data sensing and collecting module which collects operation and external environment data of the energy storage equipment in real time by using multiple types of sensors, and transmits the data after analog-to-digital conversion and preprocessing; the chaotic characteristic analysis module adopts a correlation dimension algorithm to analyze data and identify chaotic characteristics; the chaos model construction module constructs a model according to the data, and simulates and optimizes charging and discharging behaviors; the fuzzy execution regulation and control module generates an execution signal and controls charging and discharging, and the dynamic optimization adaptation module adjusts parameters, detects faults and feeds back the faults; according to the energy storage equipment charging and discharging dynamic regulation and control system, chaotic analysis and a fuzzy control algorithm are fused, and full-process closed-loop management is achieved through multi-module cooperation; the system accurately captures data characteristics, adaptively adjusts a control strategy, effectively improves the charging and discharging stability and adaptability, reduces the energy consumption, prolongs the service life of equipment, and guarantees the efficient and safe operation.

Description

Technical Field

[0001] This invention relates to the field of energy storage device control technology, specifically to an intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics. Background Technology

[0002] With the rapid development of the new energy industry, energy storage devices, as core components of energy storage and dispatch, are increasingly widely used in various fields, including new energy power generation supporting storage, smart grid peak shaving and frequency regulation, power supply for portable electronic devices, and electric vehicle power storage. In actual operation, the charging and discharging performance of energy storage devices directly affects energy utilization efficiency and equipment lifespan. The operating status of the equipment is easily affected by various factors, including internal voltage, current, and temperature changes, as well as external light intensity and ambient temperature fluctuations. To achieve efficient and stable operation of energy storage devices, dynamic control of their charging and discharging processes is required. This involves real-time sensing of operating data, analysis of data characteristics, and formulation of appropriate control strategies to cope with dynamic changes under complex operating conditions and meet the performance requirements of energy storage devices in different application scenarios. This demand has driven the continuous development and optimization of energy storage device charging and discharging control technology.

[0003] Traditional energy storage device charging and discharging control technologies often employ fixed threshold control or simple linear control methods, which struggle to accurately capture the complex nonlinear characteristics and dynamic changes during device operation. These control methods are typically based on preset fixed parameters and single control logic, failing to effectively adapt to varying load conditions and external environmental fluctuations. This leads to issues such as poor voltage stability, large current fluctuations, and high energy loss during charging and discharging. Furthermore, traditional technologies have limited monitoring dimensions for device operation, lacking the ability to fuse and analyze multi-dimensional data and identify chaotic characteristics. This makes it difficult to predict abnormal device operation in advance, resulting in insufficient timeliness in fault detection and response. This not only affects the operating efficiency and lifespan of energy storage devices but may also pose safety risks in extreme situations, failing to meet the current demand for refined and adaptive control in energy storage devices. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics. The system acquires real-time data on device operation and external environment through a data sensing and acquisition module. After chaotic characteristic analysis and model construction, the charging and discharging behavior is simulated and optimized. The system utilizes a fuzzy execution control module to generate execution signals according to preset rules and membership functions to precisely control the charging and discharging process. At the same time, a dynamic optimization and adaptation module integrates environmental and operational data, dynamically adjusts model parameters, detects faults in a timely manner, and formulates countermeasures to ensure the safe and efficient operation of the system.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a dynamic control system for intelligent charging and discharging of energy storage devices driven by chaotic characteristics, the system comprising: Data sensing and acquisition module: Voltage sensor, current sensor, temperature sensor, and light intensity sensor are selected and placed at corresponding positions on the energy storage device to collect the voltage, current, temperature, light intensity and external environmental data of the device in real time. The analog signals are converted into digital signals through an analog-to-digital converter, and the digital signals are preprocessed and stored. Chaotic characteristics analysis module: Receives preprocessed data output from the data sensing and acquisition module, analyzes the data using a correlation dimension calculation algorithm, and identifies the chaotic characteristics in the data; Chaos Model Construction Module: Based on the identified chaotic characteristics, a chaotic system model is constructed. By adjusting the model parameters, the model is adapted to different operating conditions, simulating the charging and discharging behavior of energy storage devices and optimizing control strategies, and generating dynamic charging and discharging control signals. Fuzzy execution control module: Receives control signals generated by the chaotic model construction module, detects the current state of the energy storage device, presets a fuzzy rule base and membership function, calculates the deviation and deviation change rate through fuzzy control algorithm, and generates specific execution signals through fuzzy inference and defuzzification to control the charging and discharging current and voltage; Dynamic optimization and adaptation module: Collects equipment operating parameters and external environment data through sensor network, defines evaluation indicators to quantify equipment operating status, integrates environmental and operating data, dynamically adjusts chaotic model parameters, compares real-time data with historical data and preset thresholds to detect faults, formulates countermeasures and provides feedback for adjustment.

[0006] Furthermore, in the data sensing and acquisition module, the sensors are specifically arranged as follows: a voltage sensor is connected in parallel to the positive and negative terminals of the energy storage device; a current sensor is connected in series in the main charging and discharging circuit; a temperature sensor is attached to the surface of the energy storage device's battery cell; and a light intensity sensor is arranged in an unobstructed area outside the energy storage device. Each sensor is connected to the data acquisition terminal via a wired cable, and the data transmission rate is not less than 1 Mbps.

[0007] Furthermore, the analog-to-digital conversion process of the data sensing and acquisition module is as follows: a 16-bit successive approximation analog-to-digital converter chip is used, which has a built-in high-precision reference voltage source; the analog signal output by the sensor is first conditioned to the chip input range of [0V, 5V] by an instrumentation amplifier, and then filtered out high-frequency interference by a low-pass filter; the sampling frequency is adapted according to the sensor type, wherein the sampling frequency of the voltage sensor and the current sensor is 100Hz, and the sampling frequency of the temperature sensor and the light intensity sensor is 10Hz; the conditioned signal is discretely sampled and quantized into a 16-bit binary digital quantity, and transmitted to the microcontroller through the SPI interface; parity check is performed during the transmission to verify data integrity, and if the check fails, the data is retransmitted.

[0008] Furthermore, the specific process by which the chaotic characteristic analysis module identifies chaotic characteristics using the correlation dimension calculation algorithm is as follows: Let the received real-time running data sequence be... Where n is the number of data points; the correlation function is defined as: ,in, This is an association function used to describe the distance between two points in a data sequence that is less than or equal to... The probability, and It is the index of a data point in the data sequence, used to traverse all combinations of data points. For the Heaviside function, when hour The value is 1 if it is not 0 otherwise; retrieve the data sequence. maximum value and minimum value , It is a distance threshold used to determine whether the distance between data points meets a certain condition. const is a scaling factor set based on the volatility of the data sequence. It is a data sequence The maximum value in, It is a data sequence The minimum value in; calculate the correlation dimension, the formula is: , It is the correlation dimension, used to determine whether the data has chaotic characteristics. When the value is a non-integer and has a finite value, the data is determined to have chaotic characteristics.

[0009] Furthermore, the specific method by which the chaos model building module constructs a chaotic system model is as follows: defining system state variables. It represents the normalized state of charge (SOC) of the energy storage device; defines the model parameter set. Establish a nonlinear dynamic system as a chaotic model: ,in, For time; These are parameters used to adjust the weights of various components in the model; and These are the angular frequency and initial phase of the periodic perturbation term, respectively; To use real-time charging and discharging current Terminal voltage and equipment temperature The control function for the input variables; by adjusting the set of model parameters. and the control function The internal parameters enable the chaotic model to simulate the dynamic charging and discharging behavior of energy storage devices under various operating conditions, including rated load, light load, and overload.

[0010] Furthermore, the specific steps of the chaos model construction module in optimizing the control strategy are as follows: First, set charging and discharging efficiency, energy loss, and equipment lifespan as optimization objectives; second, establish a weighted summation objective function containing the multiple objectives. The chaos model construction module contains a weighted summation objective function containing multiple objectives, with the weight coefficients for charging and discharging efficiency preset to 0.4, energy loss preset to 0.3, and equipment lifespan preset to 0.3. The sum of all weight coefficients is 1. The weights are based on the energy storage device's priority of high efficiency and energy saving, and the synergy between energy storage and longevity. The core requirements were determined using the Analytic Hierarchy Process (AHP). Specifically, a hierarchical structure was established, with the optimization of the charging and discharging control strategy as the target layer and the three major optimization objectives as the criterion layer. A judgment matrix was constructed using the 1-9 scaling method and normalized after a consistency check (maximum eigenvalue 3.0, consistency index 0, consistency ratio 0 less than 0.1). This matrix supports scenario-based adjustments, and the weights remain 1 after adjustment. The weight coefficients for charging and discharging efficiency are adjusted from 0.35 to 0.5, energy loss from 0.25 to 0.35, and equipment lifespan from 0.15 to 0.4. The objective function expression is as follows: ;in , , These are the preset weight coefficients for the three major optimization objectives. , , These are the normalized values ​​for charge / discharge efficiency, energy loss, and equipment lifespan, respectively; then, a traversal search algorithm is used to analyze the control function. The control parameters are optimized within a preset parameter range; wherein, the control function Represented as: ,in , , These are control parameters, and their preset parameter ranges are as follows: Finally, the optimal control strategy is determined based on the optimization results, and the corresponding dynamic charging and discharging control signal is generated.

[0011] Furthermore, in the fuzzy execution control module, the preset fuzzy rule base contains 25 rules, which are based on deviations. and rate of change of deviation The language value combination is used for construction; where the deviation The language value takes the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and the rate of change of deviation is... The linguistic values ​​take the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. Each rule corresponds to a unique output linguistic value, which also takes the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. The membership function uses a triangular membership function, whose mathematical expression is: ;in, Input variables For fuzzy sets membership degree The central value of the membership function. This is the width parameter of the membership function. Input variables or The specific value; for deviation The center values ​​of the membership function of the triangle are -5, -3, -1, 0, 1, 3, and 5, respectively, and the width parameter... All are 1; for the rate of change of deviation The center values ​​of the membership function of the triangle are -2, -1.2, -0.4, 0, 0.4, 1.2, and 2, respectively, and the width parameter... All are 0.4; for the output, the center values ​​of its triangle membership function are 0, 2, 4, 6, 8, 10, and 12 respectively, and the width parameter... Both are 1.5.

[0012] Furthermore, in the dynamic optimization and adaptation module, the defuzzification of the fuzzy execution control module adopts the centroid method. The specific calculation process is as follows: Let the membership function of the output fuzzy set be... If y is an element of the universe of discourse of the output variable, then the defuzzified output value is: , It is the output value after defuzzification. The calculation result is retained to two decimal places and is output as the final control signal to the charging and discharging actuator.

[0013] Furthermore, the evaluation indicators defined in the dynamic optimization and adaptation module include: charging and discharging efficiency, voltage stability, current fluctuation amplitude, device temperature change, and SOC change rate; wherein, the quantitative scoring range for charging and discharging efficiency is 0 to 100, the quantitative scoring range for voltage stability is 0 to 100, the quantitative scoring range for current fluctuation amplitude is 0 to 5, the quantitative scoring range for device temperature change is 0 to 20, and the quantitative scoring range for SOC change rate is 0 to 10; when fusing environmental and operational data, the light intensity and ambient temperature data are weighted and fused with the device's operating voltage, current, and temperature data according to preset weighting coefficients; the preset weighting coefficients for each data source include: the weighting coefficient for device operating voltage. The weighting coefficients are as follows: equipment operating current is 0.3, equipment temperature is 0.2, ambient temperature is 0.1, and light intensity is 0.1. The sum of all weighting coefficients is 1, and the weighting is determined based on orthogonal experimental design combined with variance analysis. The weighting ratio is finally determined by testing the contribution rate of each data source to the charge and discharge regulation effect under different operating conditions. The preset thresholds are: lower limit threshold of charge and discharge efficiency 60, lower limit threshold of voltage stability 70, upper limit threshold of current fluctuation amplitude 3, upper limit threshold of equipment temperature change 15, upper limit threshold of SOC change rate 8, effective range of light intensity 100 to 10000 lux, and effective range of ambient temperature -20℃ to 60℃.

[0014] Furthermore, the fault response measures of the dynamic optimization and adaptation module include: pausing charging and discharging operations, adjusting the charging and discharging current or voltage to a safe range, and activating the backup cooling system. The triggering conditions for the response measures are: the real-time data of any of the evaluation indicators continuously exceeds its corresponding threshold range for 5 seconds or more, or the trend of change is determined to be abnormal for 3 seconds or more through time series analysis. During the feedback adjustment process, the adjustment step size of the model parameters in the chaos model construction module is set to 5% of the current value of each parameter. After each adjustment, the device operating status is continuously monitored for 30 seconds, and the next round of adjustment is determined based on the status feedback results.

[0015] Compared with existing technologies, this intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics has the following beneficial effects: I. This invention achieves refined dynamic control of charging and discharging of energy storage devices through a multi-module collaborative architecture. Relying on the data sensing and acquisition module, it comprehensively captures key information about device operation and the external environment. Through chaotic characteristic analysis, it accurately identifies complex dynamic features in the data. Combined with the construction and optimization of a chaotic model, it forms a control strategy adapted to different operating conditions. The fuzzy execution control module achieves precise conversion of control signals based on preset rules and membership functions. The dynamic optimization and adaptation module continuously integrates multi-dimensional data and dynamically adjusts model parameters, monitors the operating status in real time, and responds quickly to anomalies. The entire system breaks through the limitations of traditional control methods, realizing closed-loop management of the entire process from data acquisition and feature analysis to control execution and dynamic optimization. It effectively improves the stability and adaptability of the charging and discharging process, reduces energy loss, extends the service life of the equipment, and adapts to the operating needs of diverse application scenarios.

[0016] Second, this invention integrates chaotic characteristic analysis and fuzzy control algorithms, fully leveraging the simulation advantages of chaotic models for complex nonlinear systems and the robustness of fuzzy control. This enables precise prediction and dynamic control of the charging and discharging behavior of energy storage devices. The dynamic optimization and adaptation module comprehensively considers key operating parameters such as charging and discharging efficiency and voltage stability through a quantitative evaluation index system. By combining historical and real-time data comparison and analysis, it promptly detects faults and triggers targeted countermeasures. The system does not rely on a fixed control mode and can adaptively adjust the control strategy according to environmental changes and equipment operating status, effectively addressing the impact of different load conditions and environmental fluctuations, improving energy utilization efficiency, ensuring equipment operating safety, and providing reliable technical support for the efficient and stable operation of energy storage devices.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart of an intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics; Figure 2 A schematic diagram illustrating data transmission between various steps in an intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example 1: Charging and discharging control scenarios for residential distributed photovoltaic energy storage systems.

[0022] Voltage sensors are connected in parallel across the positive and negative terminals of the energy storage device in a residential distributed photovoltaic energy storage system. A current sensor is connected in series in the main charging and discharging circuit. A temperature sensor is attached to the surface of the energy storage cell. A light intensity sensor is installed on an unobstructed area outside the device. All sensors are connected to a data acquisition terminal via wired cables, ensuring a data transmission rate of at least 1 Mbps for rapid transmission of equipment operation and environmental data. The sensors collect real-time data on voltage, current, temperature, light intensity, and ambient temperature, comprehensively capturing key information affecting the charging and discharging of the energy storage device. The collected analog signals are first conditioned to the appropriate range by an instrumentation amplifier and then filtered by a low-pass filter to remove high-frequency interference, preventing irrelevant signals from affecting data accuracy. Corresponding sampling frequencies are set for different sensors: 100Hz for voltage and current sensors, and 10Hz for temperature and light intensity sensors, to accurately acquire data tailored to their varying characteristics. Subsequently, a 16-bit successive approximation analog-to-digital converter chip is used to discretely sample and quantize the conditioned signal into digital quantities, which are then transmitted to the microcontroller via an SPI interface. Parity checking is performed during transmission to ensure no data loss or errors occur. If the check fails, data retransmission is triggered. The preprocessed data is then stored for later use, providing a reliable foundation for subsequent analysis and calculations in various modules. Figure 1 As shown.

[0023] The chaotic characteristic analysis module receives preprocessed data output from the data sensing and acquisition module, including a series of data such as voltage, current, temperature, light intensity, and ambient temperature. This data comprehensively reflects the operating status of the home photovoltaic energy storage device and changes in the external environment. A correlation dimension calculation algorithm is used to analyze this data. Let the received real-time operating data sequence be... Where n is the number of data points; the correlation function is defined as: ,in, This is an association function used to describe the distance between two points in a data sequence that is less than or equal to... The probability, and It is the index of a data point in the data sequence, used to traverse all combinations of data points. For the Heaviside function, when hour The value is 1 if it is not 0 otherwise; retrieve the data sequence. maximum value and minimum value , It is a distance threshold used to determine whether the distance between data points meets a certain condition. const is a scaling factor set based on the volatility of the data sequence. It is a data sequence The maximum value in, It is a data sequence The minimum value in; calculate the correlation dimension, the formula is: , It is the correlation dimension, used to determine whether the data has chaotic characteristics. When the values ​​are non-integer and have finite values, the data is determined to have chaotic characteristics. This clarifies the chaotic behavior of equipment operation data in the context of home photovoltaic energy storage, providing accurate characteristic support for the construction of subsequent chaotic models.

[0024] A chaotic system model is constructed based on the identified chaotic characteristics. The normalized state of charge of the energy storage device is used as the system state variable, and a selection is made. The model parameter set is composed to establish a nonlinear dynamic system containing periodic disturbance terms and control functions as a chaotic model: ,in, For time; These are parameters used to adjust the weights of various components in the model; and These are the angular frequency and initial phase of the periodic perturbation term, respectively; To use real-time charging and discharging current Terminal voltage and equipment temperature The control function for the input variables; by adjusting the set of model parameters. and the control function The internal parameters of the model enable the chaotic model to simulate the dynamic charging and discharging behavior of energy storage devices under various operating conditions, including rated load, light load, and overload. Charging and discharging efficiency, energy loss, and device lifespan are set as optimization objectives, aligning with the core needs of residential users for efficient, energy-saving, and long-lasting energy storage devices. A multi-objective weighted summation objective function is established, with charging and discharging efficiency, energy loss, and device lifespan as the core optimization dimensions. This objective function is constructed using a weighted summation method, assigning positive weight coefficients to charging and discharging efficiency, negative weight coefficients to energy loss, and positive weight coefficients to device lifespan. Each weight coefficient is rationally allocated according to the priority of residential users' needs for efficiency, energy saving, and longevity, and the sum of the weights is 1. The final result is a weighted expression: "Objective function = Charging and discharging efficiency × its positive weight coefficient + Energy loss × its negative weight coefficient + Device lifespan × its positive weight coefficient." This achieves comprehensive consideration and coordinated optimization of multiple optimization objectives for residential energy storage devices. Then, a traversal search algorithm is used to optimize the control function. The control parameters are optimized within a preset parameter range; wherein, the control function Represented as: ,in , , These are control parameters, and their preset parameter ranges are as follows: Finally, the optimal control strategy is determined based on the optimization results, and the corresponding dynamic charging and discharging control signal is generated. According to the usage requirements of the home energy storage system, the model parameter set and the internal parameters of the control function are adjusted to enable the model to adapt to various operating conditions such as rated load, light load, and overload. This accurately simulates the dynamic charging and discharging behavior of energy storage devices under different operating conditions, optimizes the control strategy, and generates the corresponding dynamic charging and discharging control signal, providing instruction support for the precise regulation of the equipment.

[0025] The fuzzy execution control module receives control signals generated by the chaotic model construction module and simultaneously monitors the current operating status of the energy storage device in real time, including current SOC, actual charging and discharging current, and voltage, ensuring precise matching between control commands and the actual device status. It then calls upon a pre-set fuzzy rule library containing 25 rules, with the rules based on deviations... and rate of change of deviation The language value combination is used for construction; where the deviation The language value takes the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and the rate of change of deviation is... The linguistic values ​​take the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. Each rule corresponds to a unique output linguistic value, which also takes the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. The membership function uses a triangular membership function, whose mathematical expression is: ;in, Input variables For fuzzy sets membership degree The central value of the membership function. This is the width parameter of the membership function. Input variables or The specific value; for deviation The center values ​​of the membership function of the triangle are -5, -3, -1, 0, 1, 3, and 5, respectively, and the width parameter... All are 1; for the rate of change of deviation The center values ​​of the membership function of the triangle are -2, -1.2, -0.4, 0, 0.4, 1.2, and 2, respectively, and the width parameter... All are 0.4; for the output, the center values ​​of its triangle membership function are 0, 2, 4, 6, 8, 10, and 12 respectively, and the width parameter... Both are 1.5. After fuzzy inference, the centroid method is used for defuzzification. Let the membership function of the output fuzzy set be... If y is an element of the universe of discourse of the output variable, then the defuzzified output value is: , The output value after defuzzification is calculated and retained to two decimal places. It is then used as the final control signal output to the charging and discharging actuator to precisely control the charging and discharging current and voltage of the energy storage device. This ensures stable operation of the device in household power scenarios and avoids problems such as overcharging, over-discharging, or abnormal temperature.

[0026] By continuously collecting equipment operating parameters and external environmental data through a sensor network, charging and discharging efficiency, voltage stability, current fluctuation amplitude, equipment temperature change, and SOC change rate are defined as evaluation indicators. Each indicator is quantified and scored according to a preset range, comprehensively and accurately reflecting the equipment's operating status. Light intensity, ambient temperature data, and equipment operating data are weighted and fused with preset weighting coefficients, allowing model adjustments to comprehensively consider the influence of internal and external factors and dynamically adjust the parameters of the chaotic model. The adjustment step size is set to 5% of the current value of each parameter, ensuring smooth and accurate parameter adjustments. Real-time data is compared with historical data, and fault detection is performed using preset thresholds to promptly identify abnormalities in equipment operation. When the real-time data of any evaluation indicator continuously exceeds the threshold range for 5 seconds or more, or when the change trend is determined to be abnormal for 3 seconds or more through time series analysis, corresponding fault response measures are triggered to quickly avoid the risk of equipment damage. After each parameter adjustment, the equipment operating status is continuously monitored for 30 seconds. Based on the status feedback, a decision is made on whether to proceed with the next round of adjustments, ensuring that the system adapts to the dynamic changes in light intensity and electricity load in residential photovoltaic energy storage scenarios, and always keeps the equipment operating in optimal condition.

[0027] In summary, this embodiment addresses the practical needs of a home-based distributed photovoltaic energy storage system. It employs a multi-location, precisely deployed array of sensors to acquire comprehensive and reliable operational and environmental data through a standardized acquisition process. By analyzing the chaotic characteristics of the data using a correlation dimension calculation algorithm, a chaotic model adapted to the home scenario is constructed, and control strategies are optimized. This, combined with a fuzzy rule library containing 25 rules, enables precise control of charging and discharging. A dynamic optimization and adaptation module continuously integrates internal and external data, responding promptly to faults based on preset thresholds and anomaly detection logic. Through stable parameter adjustments to adapt to changes in sunlight and electricity load, the system ensures efficient, stable, and safe operation of the home energy storage device throughout the entire process, fully meeting the core demands of home users for energy storage systems.

[0028] Example 2: Charging and discharging control scenarios for energy storage power stations in industrial and commercial parks.

[0029] For large-capacity energy storage equipment in industrial and commercial park energy storage power stations, voltage sensors are connected in parallel across the positive and negative terminals of each energy storage unit. Current sensors are connected in series in the main charging and discharging circuit of the power station and the branch circuits of each unit. Temperature sensors are uniformly attached to the surface of each energy storage cell and the heat dissipation components of the equipment. Multiple light intensity sensors are deployed in an open, unobstructed area outdoors to obtain average light intensity data, comprehensively capturing the operating data and environmental information of all parts of the power station. All sensors are connected to a centralized data acquisition terminal via industrial-grade wired cables, ensuring a data transmission rate of no less than 1Mbps to meet the massive data transmission needs of large-capacity energy storage power stations. The sensors collect in real time the voltage, current, temperature, average light intensity, and external ambient temperature data of each energy storage unit and the overall system, providing comprehensive data support for the overall regulation and unit equalization control of the power station. The collected analog signals are conditioned to the appropriate range by an instrumentation amplifier and then filtered by a low-pass filter to remove high-frequency interference signals in the industrial environment, ensuring the accuracy of the data in complex industrial environments. The sampling frequency is set according to the sensor type: voltage and current sensors use a 100Hz sampling frequency, while temperature and light intensity sensors use a 10Hz sampling frequency, aligning with the varying data patterns in industrial scenarios. A 16-bit successive approximation analog-to-digital converter chip is used to discretely sample and quantize the conditioned signal into a digital quantity, which is then transmitted to the main controller via an SPI interface. Parity checking is performed during transmission to verify data integrity; if the check fails, a data retransmission process is initiated. All preprocessed data is categorized and stored in the power plant data center, providing a stable and reliable data source for subsequent power plant analysis, model building, and control. Figure 2 As shown.

[0030] The chaotic characteristic analysis module retrieves pre-processed system operation data from the power station data center, covering voltage, current, and temperature data of each energy storage unit, as well as comprehensive system operation data and external environmental data. This data comprehensively reflects the complex operating status of the industrial and commercial park energy storage power station. A correlation dimension calculation algorithm is used to analyze this large-scale data. First, a correlation function is defined, which can accurately describe the probability that the distance between any two points in the data sequence is less than or equal to a set distance threshold. The distance threshold is determined based on the difference between the maximum and minimum values ​​of the data sequence and a preset constant adapted to the industrial and commercial scenario, ensuring that the threshold setting meets the requirements of industrial-grade applications. The Heaviside function is used to determine whether the distance between data points meets the calculation conditions of the correlation function, laying the foundation for accurate calculation of the correlation dimension. Then, the correlation dimension is solved using the correlation dimension calculation formula. Based on the characteristic that the correlation dimension is non-integer and has a finite value, the chaotic characteristics in the complex operating data of the industrial and commercial park energy storage power station are accurately identified, providing a precise characteristic basis for the subsequent construction of a chaotic model that fits the industrial scenario.

[0031] Based on the analyzed chaotic characteristics, a chaotic system model adapted to large-capacity energy storage equipment in industrial and commercial parks is constructed. The overall normalized state of charge (SOC) of the entire energy storage power station and the SOC of each unit are used as system state variables. A set of model parameters is constructed, and a nonlinear dynamic system containing periodic disturbance terms and multi-input control functions is established as a chaotic model, enabling the model to accurately adapt to the operational characteristics of large-capacity, multi-unit energy storage power stations. The control functions are constructed using real-time charging and discharging currents, terminal voltages, cell temperatures, and overall system operating parameters of each energy storage unit as input variables, employing a linear combination form to ensure the model can quickly respond to changes in the operating state of various parts of the power station. Charging and discharging efficiency, energy loss, equipment lifespan, and grid-side voltage stability are set as optimization objectives, aligning with the core demands of industrial and commercial parks for efficient operation, cost reduction, and grid stability of energy storage power stations. A multi-objective weighted summation objective function is established, and an traversal search algorithm is used to optimize the control parameters within a preset parameter range, ensuring the scientific validity and optimality of the control strategy. Considering the characteristics of large electricity load, significant peak-valley electricity price differences, and frequent fluctuations in charging and discharging power in industrial and commercial parks, the model parameter set and internal parameters of the control function are adjusted to enable the model to adapt to various operating conditions such as rated load, light load, and overload. This allows for accurate simulation of the dynamic charging and discharging behavior of energy storage power stations under different operating conditions, optimization of control strategies, and generation of dynamic charging and discharging control signals adapted to large-scale energy storage systems. This provides precise instructions for the overall regulation of the power station and the balanced control of each unit.

[0032] The fuzzy execution control module receives control signals generated by the chaotic model construction module and monitors the overall operation of the energy storage power station and the operation of each energy storage unit in real time, including current SOC, charging and discharging power, and voltage and current balance of each unit. This ensures that control commands can take into account both the overall operation of the power station and the balance of each unit. A preset fuzzy rule base containing 25 rules is activated. This rule base is constructed based on seven linguistic value combinations of deviation and deviation change rate. Each rule corresponds to a unique output linguistic value, covering the complex and variable operating scenarios of energy storage power stations in industrial and commercial parks. A triangular membership function is used to fuzzify the deviation and deviation change rate, improving the accuracy and efficiency of fuzzy data processing in industrial scenarios. The fuzzy control algorithm accurately calculates the deviation and deviation change rate. After fuzzy inference, the centroid method is used for defuzzification. The calculation results are retained to two decimal places, generating specific execution signals for the overall system and each unit. This achieves precise control of the charging and discharging current, voltage, and balance of each unit in the energy storage power station, meeting the high requirements of industrial and commercial parks for energy storage system response speed, operational stability, and power supply reliability.

[0033] By collecting real-time operating parameters and external environmental data of the energy storage power station through a sensor network, core evaluation indicators are defined as charging and discharging efficiency, voltage stability, current fluctuation amplitude, equipment temperature change, and SOC change rate. These indicators are quantified and scored within preset ranges to comprehensively and accurately assess the overall operation of the power station and its individual units. Light intensity and ambient temperature data are weighted and fused with power station operating data using preset weighting coefficients. This allows the model adjustment to comprehensively consider the influence of complex internal and external factors, dynamically adjusting the parameters of the chaotic model. The adjustment step size is set to 5% of the current value of each parameter, ensuring that parameter adjustments are both accurate and adaptable to the stability requirements of industrial scenarios. By comparing real-time operating data with historical data and preset thresholds, potential faults and anomalies in power station operation can be detected promptly. When the real-time data of any evaluation indicator continuously exceeds the threshold range for 5 seconds or more, or when time series analysis indicates that its change trend is consistently abnormal for 3 seconds or more, fault response measures are immediately triggered to quickly reduce the impact of the fault on power station operation and park power supply. After each parameter adjustment, the power station's operating status is continuously monitored for 30 seconds. Based on the status feedback results of each unit and the overall system, a decision is made on whether to carry out the next round of parameter optimization and adjustment. This ensures that the energy storage power station can dynamically adapt to fluctuations in the electricity load of industrial and commercial parks, changes in grid voltage, and changes in external environmental conditions, thereby guaranteeing the long-term stable and efficient operation of the power station and providing reliable energy support for the park's production and operation.

[0034] In summary, this embodiment addresses the characteristics of large-capacity, multi-unit, and complex operating conditions of energy storage power stations in industrial and commercial parks by constructing a comprehensive data acquisition system to ensure the accurate transmission and storage of massive amounts of operational and environmental data. By leveraging correlation dimension calculation algorithms to uncover chaotic characteristics in complex data, a chaotic model adapted to industrial scenarios is established, generating optimal control strategies guided by multi-objective optimization. Fuzzy execution control achieves precise coordinated control of the power station as a whole and its individual units. Dynamic optimization and adaptation modules dynamically adjust parameters based on internal and external factors, rapidly responding to faults and changes in operating conditions. This effectively adapts to fluctuations in park electricity load and grid demand, ensuring the long-term stable and efficient operation of the energy storage power station and providing reliable energy support for the park's production and operations.

[0035] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics, characterized in that, The system includes: Data sensing and acquisition module: Voltage sensor, current sensor, temperature sensor, and light intensity sensor are selected and placed at corresponding positions on the energy storage device to collect the voltage, current, temperature, light intensity and external environmental data of the device in real time. The analog signals are converted into digital signals through an analog-to-digital converter, and the digital signals are preprocessed and stored. Chaotic Feature Analysis Module: Receives preprocessed data from the data sensing and acquisition module, analyzes the data using a correlation dimension calculation algorithm, and identifies chaotic features within the data. Let the received real-time running data sequence be... Where n is the number of data points; the correlation function is defined as: ,in, This is an association function used to describe the distance between two points in a data sequence that is less than or equal to... The probability, and It is the index of a data point in the data sequence, used to traverse all combinations of data points. For the Heaviside function, when hour The value is 1 if it is not 0 otherwise; retrieve the data sequence. maximum value and minimum value , It is a distance threshold used to determine whether the distance between data points meets a certain condition. const is a scaling factor set based on the volatility of the data sequence. It is a data sequence The maximum value in, It is a data sequence The minimum value in; calculate the correlation dimension, the formula is: , It is the correlation dimension, used to determine whether the data has chaotic characteristics. When the integer is non-integer and has a finite value, the data is determined to have chaotic characteristics; Chaos Model Construction Module: Based on the identified chaotic characteristics, a chaotic system model is constructed. By adjusting the model parameters, the model is adapted to different operating conditions, simulating the charging and discharging behavior of energy storage devices and optimizing control strategies, and generating dynamic charging and discharging control signals. Fuzzy execution control module: Receives control signals generated by the chaotic model construction module, detects the current state of the energy storage device, presets a fuzzy rule base and membership function, calculates the deviation and deviation change rate through fuzzy control algorithm, and generates specific execution signals through fuzzy inference and defuzzification to control the charging and discharging current and voltage; Dynamic optimization and adaptation module: Collects equipment operating parameters and external environment data through sensor network, defines evaluation indicators to quantify equipment operating status, integrates environmental and operating data, dynamically adjusts chaotic model parameters, compares real-time data with historical data and preset thresholds to detect faults, formulates countermeasures and provides feedback for adjustment.

2. The intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics according to claim 1, characterized in that, In the data sensing and acquisition module, the sensors are arranged as follows: voltage sensors are connected in parallel to the positive and negative terminals of the energy storage device; current sensors are connected in series in the main charging and discharging circuit; temperature sensors are attached to the surface of the energy storage device's battery cells; and light intensity sensors are arranged in an unobstructed area outside the energy storage device. Each sensor is connected to the data acquisition terminal via a wired cable, and the data transmission rate is not less than 1 Mbps.

3. The intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics according to claim 1, characterized in that, The analog-to-digital conversion process of the data sensing and acquisition module is as follows: a 16-bit successive approximation analog-to-digital converter chip is used, which has a built-in high-precision reference voltage source; the analog signal output by the sensor is first conditioned to the chip input range of [0V, 5V] by an instrumentation amplifier, and then filtered to remove high-frequency interference by a low-pass filter; the sampling frequency is adapted according to the sensor type, wherein the sampling frequency of voltage and current sensors is 100Hz, and the sampling frequency of temperature and light intensity sensors is 10Hz; the conditioned signal is discretely sampled and quantized into a 16-bit binary digital quantity, and transmitted to the microcontroller through the SPI interface; parity check is performed during the transmission to verify data integrity, and if the check fails, the data is retransmitted.

4. The intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics according to claim 1, characterized in that, The specific method by which the chaos model construction module constructs a chaos system model is as follows: defining system state variables. It represents the normalized state of charge (SOC) of the energy storage device; defines the model parameter set. Establish a nonlinear dynamic system as a chaotic model: ,in, For time; These are parameters used to adjust the weights of various components in the model; and These are the angular frequency and initial phase of the periodic perturbation term, respectively; To use real-time charging and discharging current Terminal voltage and equipment temperature The control function for the input variables; by adjusting the set of model parameters. and the control function The internal parameters enable the chaotic model to simulate the dynamic charging and discharging behavior of energy storage devices under various operating conditions, including rated load, light load, and overload.

5. The intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics according to claim 1, characterized in that, The specific steps of the chaotic model construction module to optimize the control strategy are as follows: First, set charging and discharging efficiency, energy loss, and equipment lifespan as optimization objectives; second, establish a weighted summation objective function that includes the multiple objectives; then, use a traversal search algorithm to optimize the control function. The control parameters are optimized within a preset parameter range; wherein, the control function Represented as: ,in , , These are control parameters, and their preset parameter ranges are as follows: Finally, the optimal control strategy is determined based on the optimization results, and the corresponding dynamic charging and discharging control signal is generated.

6. The intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics according to claim 1, characterized in that, The fuzzy execution control module has a preset fuzzy rule base containing 25 rules, which are based on deviations. and rate of change of deviation The language value combination is used for construction; where the deviation The language value takes the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, and the rate of change of deviation is... The linguistic values ​​take the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. Each rule corresponds to a unique output linguistic value, which also takes the values ​​{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. The membership function uses a triangular membership function, whose mathematical expression is: ;in, Input variables For fuzzy sets membership degree The central value of the membership function. This is the width parameter of the membership function. Input variables or The specific value; for deviation The center values ​​of the membership function of the triangle are -5, -3, -1, 0, 1, 3, and 5, respectively, and the width parameter... All are 1; for the rate of change of deviation The center values ​​of the membership function of the triangle are -2, -1.2, -0.4, 0, 0.4, 1.2, and 2, respectively, and the width parameter... All are 0.4; for the output, the center values ​​of its triangle membership function are 0, 2, 4, 6, 8, 10, and 12 respectively, and the width parameter... Both are 1.

5.

7. The intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics according to claim 1, characterized in that, In the dynamic optimization and adaptation module, the defuzzification of the fuzzy execution control module adopts the centroid method. The specific calculation process is as follows: Let the membership function of the output fuzzy set be... If y is an element of the universe of discourse of the output variable, then the defuzzified output value is: , It is the output value after defuzzification. The calculation result is retained to two decimal places and is output as the final control signal to the charging and discharging actuator.

8. The intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics according to claim 1, characterized in that, The dynamic optimization and adaptation module defines the following evaluation indicators: charge / discharge efficiency, voltage stability, current fluctuation amplitude, device temperature change, and SOC change rate. The quantitative scoring range for charge / discharge efficiency is 0 to 100, for voltage stability it is 0 to 100, for current fluctuation amplitude it is 0 to 5, for device temperature change it is 0 to 20, and for SOC change rate it is 0 to 10. When fusing environmental and operational data, the light intensity and ambient temperature data are weighted and fused with the device's operating voltage, current, and temperature data according to preset weighting coefficients. The preset thresholds are: a lower limit of 60 for charge / discharge efficiency, a lower limit of 70 for voltage stability, an upper limit of 3 for current fluctuation amplitude, an upper limit of 15 for device temperature change, an upper limit of 8 for SOC change rate, an effective range of 100 to 10000 lux for light intensity, and an effective range of -20℃ to 60℃ for ambient temperature.

9. The intelligent charging and discharging dynamic control system for energy storage devices driven by chaotic characteristics according to claim 1, characterized in that, The fault response measures of the dynamic optimization and adaptation module include: pausing charging and discharging operations, adjusting the charging and discharging current or voltage to a safe range, and activating the backup cooling system. The triggering conditions for the response measures are: the real-time data of any of the evaluation indicators continuously exceeds its corresponding threshold range for 5 seconds or more, or the trend of change is determined to be abnormal for 3 seconds or more through time series analysis. During the feedback adjustment process, the adjustment step size of the model parameters in the chaos model construction module is set to 5% of the current value of each parameter. After each adjustment, the device operating status is continuously monitored for 30 seconds, and the next round of adjustment is determined based on the status feedback results.

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