Charging and discharging strategy dynamic adjustment control method and system based on microcontroller

Abstract

The invention relates to the technical field of batteries, in particular to a charging and discharging strategy dynamic adjustment control method and system based on a microcontroller, and an acquisition module in the system is used for acquiring current working state parameters of a battery; the communication module is connected with the acquisition module and the decision-making module and is used for uploading the current working state parameters to the decision-making module; the decision module generates a charging control parameter of the battery according to a preset deep learning method based on the current working state parameter, and outputs the charging control parameter of the battery to the control module; and the control module is used for determining a target charging mode of the battery according to the battery charging control parameters and executing dynamic programmable constant-current (or constant-voltage) charging (discharging) control on the battery according to the target charging mode. According to the technical scheme, dynamic self-adaptive adjustment of the charging and discharging strategy of the battery is achieved, the problem that the charging efficiency is too low under the extreme temperature condition in the related technology is effectively solved, and therefore the charging efficiency and the discharging operation reliability of the battery are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a method and system for dynamic adjustment control of charging and discharging strategies based on a microcontroller. Background Technology

[0002] Secondary batteries, as energy storage devices, are widely used in consumer electronics, electric vehicles, and energy storage systems.

[0003] In related technologies, battery charging management typically employs constant current charging or constant voltage charging, i.e., first charging with a constant current and then charging with a constant voltage until fully charged, which is achieved by hardware charging ICs or simple feedback control (such as PID regulation).

[0004] However, parameter settings are generally fixed and do not dynamically adjust according to the battery's state. For new and aging batteries, low-temperature and normal-temperature environments, and batteries with different discharge histories, a uniform fixed charging curve cannot balance the fastest charging speed with optimal lifespan maintenance, which urgently needs to be addressed. Summary of the Invention

[0005] This invention provides a microcontroller-based dynamic adjustment control method and system for charging and discharging strategies, which enables dynamic adaptive adjustment of battery charging and discharging strategies, solves the problem of low charging efficiency under extreme temperature conditions, and improves battery charging efficiency and discharge operation reliability.

[0006] To achieve the above objectives, a first aspect of the present invention provides a microcontroller-based dynamic adjustment control system for charging and discharging strategies, comprising a data acquisition module, a communication module, a control module, and a decision module. The data acquisition module is used to acquire current operating state parameters of the battery. The communication module is connected to the data acquisition module and the decision module, and is used to upload the current operating state parameters to the decision module. The decision module is used to generate charging control parameters for the battery based on the current operating state parameters using a preset deep learning method, and output the battery charging control parameters to the control module. The control module is used to determine the target charging mode of the battery based on the battery charging control parameters, and perform constant current charging or constant voltage charging on the battery according to the target charging mode.

[0007] Furthermore, in some embodiments, the acquisition module includes: an analog signal acquisition unit for acquiring analog signals of the battery via a microcontroller, wherein the analog signals of the battery include at least one of battery voltage, battery current, battery ambient temperature, battery ambient humidity, and battery operating temperature; and a digital status signal acquisition unit for acquiring digital status signals of the protection circuit corresponding to the battery via a microcontroller.

[0008] Furthermore, in some embodiments, the communication module includes: a signal processing unit, configured to format the current battery operating status parameters acquired by the acquisition module based on a preset signal processing method to obtain formatted operating status parameters; and a communication unit, configured to send the formatted operating status parameters to the decision module based on serial communication and / or wireless communication.

[0009] Further, in some embodiments, the control module includes: a first power supply module, configured to provide a first DC voltage to the battery when the target charging mode is a constant voltage charging mode, wherein the first DC voltage is within a first voltage range; and a first control unit, configured to determine the target duty cycle of the buck converter based on the PWM signal output by the microcontroller, and control the buck converter according to the target duty cycle.

[0010] Furthermore, in some embodiments, the control module further includes: a second power supply module, used to provide a second DC voltage to the battery when the target charging mode is a constant current charging mode, wherein the second DC voltage is in a second voltage range; and a second control unit, used to determine the target current of the voltage-controlled constant current source circuit based on the control signal output by the microcontroller, and to control the voltage-controlled constant current source circuit according to the target current.

[0011] Furthermore, in some embodiments, the second DC voltage range is included within the first DC voltage range.

[0012] Furthermore, in some embodiments, the system further includes: a protection module, configured to generate a charging fault signal and send the charging fault signal to the control module when it is determined that the battery has a charging fault based on the current operating parameters, so as to stop charging the battery through the control module; and an alarm module, configured to generate alarm information based on the charging fault signal, so as to perform acoustic alarm and / or optical alarm according to the alarm information.

[0013] Furthermore, in some embodiments, the system further includes: a display module for displaying at least one of battery voltage, battery current, battery ambient temperature, battery ambient humidity, battery operating temperature, and the alarm information; and a power supply module for supplying power to the acquisition module, the communication module, the control module, and the decision module, respectively.

[0014] According to the embodiment of the present invention, a microcontroller-based dynamic adjustment control system for charging and discharging strategies comprises a data acquisition module that first acquires the current operating state parameters of the battery, and a communication module that connects the data acquisition module and the decision module and uploads the parameters to the decision module. Based on the received parameters, the decision module generates battery charging control parameters through a preset deep learning method and outputs them to the control module. The control module then determines the target charging mode of the battery based on the charging control parameters, and performs constant current or constant voltage charging on the battery, forming a complete intelligent charging control link for the battery. This realizes the dynamic adaptive adjustment of the battery charging and discharging strategy, effectively solves the problem of low charging efficiency under extreme temperature conditions in related technologies, and thus significantly improves the charging efficiency and discharge operation reliability of the battery.

[0015] To achieve the above objectives, a second aspect of the present invention provides a microcontroller-based dynamic adjustment control method for charge and discharge strategies. The method is applied to the microcontroller-based dynamic adjustment control system for charge and discharge strategies as described in any of the preceding claims, and includes the following steps: collecting current operating state parameters of the battery; uploading the current operating state parameters of the battery to the decision module via the communication module; generating charging control parameters of the battery based on the current operating state parameters using a preset deep learning method, and outputting the battery charging control parameters to the control module; determining the target charging mode of the battery based on the battery charging control parameters, and performing constant current charging or constant voltage charging on the battery according to the target charging mode.

[0016] Furthermore, in some embodiments, the acquisition of the current operating state parameters of the battery includes: acquiring analog signals of the battery through a microcontroller, wherein the analog signals of the battery include at least one of battery voltage, battery current, battery ambient temperature, battery ambient humidity, and battery operating temperature; and acquiring digital status signals of the protection circuit corresponding to the battery through a microcontroller.

[0017] According to the embodiment of the present invention, the microcontroller-based dynamic adjustment control method for charging and discharging strategies first acquires the current operating state parameters of the battery. The communication module connects the acquisition module and the decision module, and uploads the above parameters to the decision module. Based on the received parameters, the decision module generates battery charging control parameters through a preset deep learning method and outputs them to the control module. The control module then determines the target charging mode of the battery based on the charging control parameters, and then performs constant current or constant voltage charging on the battery, forming a complete intelligent charging control link for the battery. This realizes the dynamic adaptive adjustment of the battery charging and discharging strategy, effectively solves the problem of low charging efficiency under extreme temperature conditions in related technologies, and thus significantly improves the charging efficiency and discharge operation reliability of the battery.

[0018] Therefore, the present invention has the following beneficial effects: (1) This invention utilizes a deep learning model to dynamically adjust the charging current, enabling the battery to charge at a near-optimal rate at each stage, which significantly shortens the charging time compared to a fixed constant current and constant voltage strategy. It increases the input power per unit time without compromising safety and lifespan, rapidly restoring the battery's usable capacity.

[0019] (2) This invention avoids overcharging current and excessive heat generation through intelligent control, and the system effectively slows down the capacity decay rate of lithium batteries. The optimized current curve output by the deep learning model ensures that the battery always operates under relatively mild conditions, reducing internal side reactions and material aging of lithium-ion batteries. It can extend the battery cycle life while meeting the charging rate. The optimized charging strategy can reduce the peak temperature rise of the battery and avoid overpressure stress, thereby delaying the battery life decay.

[0020] (3) This invention monitors the battery voltage, current and temperature in real time. Once an abnormality occurs, it can take measures to terminate charging or discharging and alarm, providing multiple safety protections.

[0021] (4) The hardware and software architecture proposed in this invention can be flexibly ported and applied to different models of secondary battery management systems, and has good versatility. Attached Figure Description

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A block diagram of a microcontroller-based dynamic adjustment control system for charging and discharging strategies provided according to an embodiment of the present invention; Figure 2 A schematic diagram of a Wheatstone bridge circuit with a PT1000 resistor for temperature acquisition according to a specific embodiment of the present invention is provided. Figure 3 A schematic diagram of a positive and negative dual power supply conversion module for powering an instrument amplifier according to a specific embodiment of the present invention; Figure 4 A schematic diagram of the hardware structure of a battery charge and discharge monitoring and intelligent control system according to a specific embodiment of the present invention; Figure 5 This is a schematic diagram showing an experimental comparison between a dynamic control charging strategy provided by a specific embodiment of the present invention and a traditional charging strategy; Figure 6 This is a schematic diagram of a charge / discharge switching and charging current monitoring circuit according to a specific embodiment of the present invention; Figure 7 A schematic diagram of experimental data for dynamically adjusting battery charging and discharging strategy for high temperature early warning according to a specific embodiment of the present invention; Figure 8This is a schematic diagram of experimental results for deep learning prediction of battery state of health (SOH) and battery remaining life (RUL) according to a specific embodiment of the present invention; Figure 9 A flowchart illustrating an intelligent charge and discharge control strategy according to a specific embodiment of the present invention; Figure 10 This is a flowchart illustrating a microcontroller-based dynamic adjustment control method for charging and discharging strategies provided in an embodiment of the present invention. Detailed Implementation

[0023] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0024] The following describes a microcontroller-based dynamic adjustment control method and system for charging and discharging strategies according to an embodiment of the present invention, with reference to the accompanying drawings. First, the microcontroller-based dynamic adjustment control system for charging and discharging strategies according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0025] Figure 1 This is a block diagram of a microcontroller-based dynamic adjustment control system for charging and discharging strategies provided according to an embodiment of the present invention.

[0026] like Figure 1 As shown, the microcontroller-based dynamic adjustment control system 10 for charging and discharging strategies includes: a data acquisition module 100, a communication module 200, a control module 300, and a decision module 400.

[0027] The acquisition module 100 is used to acquire the current operating status parameters of the battery; the communication module 200 is connected to the acquisition module 100 and the decision module 400, and is used to upload the current operating status parameters to the decision module 400; the decision module 400 is used to generate battery charging control parameters based on the current operating status parameters and according to a preset deep learning method, and output the battery charging control parameters to the control module 300; the control module 300 is used to determine the target charging mode of the battery according to the battery charging control parameters, and perform constant current charging or constant voltage charging on the battery according to the target charging mode.

[0028] In some embodiments, the acquisition module 100 includes: an analog signal acquisition unit 101, used to acquire analog signals of the battery through a microcontroller, wherein the analog signals of the battery include at least one of battery voltage, battery current, battery ambient temperature, battery ambient humidity and battery operating temperature; and a digital status signal acquisition unit 102, used to acquire digital status signals of the protection circuit corresponding to the battery through a microcontroller.

[0029] As one possible approach, the microcontroller acquires analog signals such as battery voltage, current, ambient temperature and humidity, and battery operating temperature in real time through the ADC interface of the analog signal acquisition unit 101. The battery operating temperature can be acquired using a high-precision PT1000 platinum resistance detector. Figure 2 The diagram illustrates a Wheatstone bridge circuit with a PT1000 resistor for temperature acquisition according to a specific embodiment of the present invention. The sensor can be attached to the surface of the battery casing or embedded in the battery cell during battery manufacturing. Combined with the Wheatstone bridge and an instrumentation amplifier, the signal is input to the microcontroller via an ADC channel for precise measurement. The microcontroller acquires digital status signals through the battery protection circuit (e.g., DW01 or other battery management IC) of the digital status signal acquisition unit 102. The data acquisition frequency can be set according to application requirements, for example, it can be set to 1 Hz or a higher frequency to capture instantaneous changes in battery status.

[0030] Furthermore, in some embodiments, the communication module 200 includes: a signal processing unit 201, used to format the current working state parameters of the battery collected by the acquisition module 100 based on a preset signal processing method to obtain formatted working state parameters; and a communication unit 202, used to send the formatted working state parameters to the decision module 400 based on serial communication and / or wireless communication.

[0031] As one possible approach, all collected battery current operating status parameters are filtered and formatted at the microcontroller end, and then transmitted to the host computer (i.e., decision module 400) via serial port (USART) or WiFi wireless communication. Serial communication can be connected to the PC via UART to USB. In this embodiment of the invention, data can be transmitted using text frames (such as "U=3.75V\n") or custom binary frames (including frame header, data segment, and check bit) to improve reliability and communication efficiency.

[0032] Furthermore, the decision module 400 in this embodiment of the invention adopts a deep learning network structure such as LSTM (Long Short-Term Memory) or Transformer to mine the complex nonlinear relationship between historical battery data and the optimal charging strategy. The model input includes the multi-dimensional time series of the battery collected such as voltage, current, and temperature, as well as the current state parameters of the battery (such as SOC, temperature trend, etc.). The output is dynamically optimized charging control parameters (such as current / voltage setpoints or incremental adjustment instructions). The system can make a comprehensive judgment based on temperature status, decay trend, self-discharge rate, etc., and give a larger current to improve efficiency in the early stage of charging, and reduce the current to extend life when the voltage is close to the upper limit or the temperature rises.

[0033] It should be noted that before model inference, the present invention requires outlier removal, trend smoothing and unit standardization of the data to enhance the robustness of the model. Compared with traditional threshold or PID control methods, this data-driven strategy has stronger adaptive and predictive capabilities.

[0034] Furthermore, in some embodiments, the control module 300 includes: a first power supply module 301, used to provide a first DC voltage to the battery when the target charging mode is a constant voltage charging mode, wherein the first DC voltage is in a first voltage range; and a first control unit 302, used to determine the target duty cycle of the buck converter based on the PWM signal output by the microcontroller, and control the buck converter according to the target duty cycle.

[0035] As one possible approach, when the target charging mode is constant voltage charging mode, the power supply module 800 can output an adjustable DC voltage (i.e., the first DC voltage) within the range of 0–24V (i.e., the first voltage range), wherein the effective voltage range for battery charging is 0–4.4V. The microcontroller controls the duty cycle of the buck converter by outputting a PWM signal, thereby adjusting the output voltage to achieve constant voltage charging.

[0036] Furthermore, in some embodiments, the control module 300 further includes: a second power supply module 303, used to provide a second DC voltage to the battery when the target charging mode is a constant current charging mode, wherein the second DC voltage is in a second voltage range; and a second control unit 304, used to determine the target current of the voltage-controlled constant current source circuit based on the control signal output by the microcontroller, and to control the voltage-controlled constant current source circuit according to the target current.

[0037] As another possible approach, when the target charging mode is constant current charging mode, the microcontroller uses its DAC (digital-to-analog converter) function to output a control voltage of 0–1.8V to drive the voltage-controlled constant current source circuit to adjust the charging current and keep it within the set value (0–3A) range, thereby achieving constant current charging control.

[0038] It should be noted that the embodiments of the present invention dynamically determine and switch the working mode according to the current charging state of the battery. For example, when the battery voltage is close to the target upper limit (such as 4.2V), it automatically switches from constant current mode to constant voltage mode to ensure that the battery will not be overcharged. The power supply module 800 is equipped with voltage and current sampling circuits. The voltage sampling is fed back to the ADC channel of the microcontroller through a resistor voltage divider, and the current sampling is amplified by a series low-resistance sampling resistor and combined with an instrumentation amplifier circuit before being sent to the microcontroller for ADC acquisition, so as to realize real-time closed-loop control.

[0039] In some embodiments, the second DC voltage range is included within the first DC voltage range.

[0040] Furthermore, in some embodiments, the system further includes: a protection module 500, configured to generate a charging fault signal and send the charging fault signal to a control module when a charging fault is determined to exist in the battery based on the current operating parameters, so as to stop charging the battery through the control module; and an alarm module 600, configured to generate alarm information based on the charging fault signal, so as to perform acoustic alarm and / or optical alarm based on the alarm information.

[0041] As one possible approach, to prevent sudden current surges from impacting the battery, the microcontroller employs a ramp-type or linear approximation algorithm to smoothly transition the current setpoint. During charging, the microcontroller continuously monitors voltage, current, and temperature. Upon detecting overheating (e.g., temperature greater than 60 degrees Celsius), overvoltage, overcurrent, or a fault signal from the protection chip, charging is immediately stopped and an alarm is sent. The alarm can be displayed via a buzzer, LED, or host computer interface. The platform can also be configured with relays or MOS switches to disconnect the battery from the power supply to prevent the fault from escalating. During discharging, if the voltage is detected to be lower than the set cutoff threshold (e.g., 2.5V) or the discharge current is abnormal, the system also activates the protection mechanism.

[0042] Furthermore, in some embodiments, it further includes: a display module 700, which is used to display at least one of battery voltage, battery current, battery ambient temperature, battery ambient humidity, battery operating temperature and alarm information; and a power supply module 800, which is used to supply power to the acquisition module 100, communication module 200, control module 300 and decision module 400 respectively.

[0043] As one possible way to achieve this, Figure 3 A schematic diagram of a positive and negative dual power supply conversion module for powering an instrument amplifier according to a specific embodiment of the present invention is shown below. Figure 3 As shown, the power supply module 800 is connected to a +5V DC power supply via a Type-C interface, uses a buck regulator circuit to generate a stable +3.3V voltage, and outputs it through a charge pump inverter chip. The 3.3V voltage, after being stabilized by the filter capacitor, provides a stable ±3.3V voltage to the instrumentation amplifier, ensuring signal acquisition accuracy and system stability.

[0044] To enable those skilled in the art to better understand the microcontroller-based dynamic adjustment control system for charging and discharging strategies according to embodiments of the present invention, the following explanation will be provided in conjunction with specific embodiments.

[0045] Specifically Figure 4 This is a schematic diagram of the hardware structure of a battery charge / discharge monitoring and intelligent control system according to a specific embodiment of the present invention, as shown below. Figure 4 As shown, the battery management and testing system of this invention can use various microcontrollers as the core control unit, and combine them with a host computer Python program to build a complete hardware and software experimental platform. The microcontroller collects the voltage, current and temperature sensor signals of the battery in real time through its ADC module, and executes the charge and discharge control algorithm simultaneously. The host computer communicates with the microcontroller through a UART serial port (e.g., 115200bps) to realize real-time monitoring of the battery status and the issuance of control commands. The experimental object is a LIR2032 model rechargeable lithium-ion button battery. Table 1 shows the battery management and testing system provided according to a specific embodiment of this invention. The specifications of the rechargeable lithium-ion battery (LIR2032) are shown in Table 1. The main parameters are as follows: The charging control module consists of a voltage-controlled constant current source and a PWM-modulated constant voltage source, supporting both constant current and constant voltage charging modes. The microcontroller automatically switches the charging strategy based on battery voltage and temperature feedback. The discharge circuit integrates a programmable electronic load, which is controlled by the microcontroller to achieve precise discharge. The Python program on the host computer is responsible for serial communication and data parsing, as well as real-time visualization of key parameters such as voltage, current, and capacity. In the cycle life prediction experiment, it calls a pre-trained deep learning model for intelligent inference.

[0046] Table 1 Commercially available rechargeable lithium-ion batteries (LIR2032)

[0047] Furthermore, the microcontroller firmware is designed with a serial communication protocol to periodically send measurement data to the host computer in frame format and receive instructions from the host computer. For example, a data frame is sent once per second, containing a timestamp, ambient temperature / humidity, battery voltage, current, temperature, and current operating mode identifier. The host computer's Python program reads the data frames through the serial interface, parses them, stores them in a buffer, and displays the curve changes in real time. When the host computer detects that a key parameter has reached a threshold (such as the voltage approaching 3.6V or reaching the temperature threshold), it can send a command to notify the microcontroller to switch the charging control strategy or perform an emergency shutdown.

[0048] For example, in this embodiment of the invention, the host computer first initializes the serial port connection and sends a start command. After the microcontroller confirms, it starts the corresponding experimental process. During this process, the microcontroller continuously collects and sends sensor data. The host computer receives the data and performs preprocessing such as filtering and calibration, and plots curves in real time for monitoring. For instance, in a fast charging experiment, the host computer determines when to send a command to switch the microcontroller to constant voltage charging mode based on the voltage rise trend. In a high-temperature experiment, the host computer monitors the temperature data and sends a safety protection command when the temperature exceeds a threshold. It should be noted that all experimental data is stored on the host computer in CSV format to provide input for subsequent analysis and deep learning model prediction.

[0049] further, Figure 5 This is a schematic diagram illustrating an experimental comparison between a dynamic control charging strategy provided by a specific embodiment of the present invention and a traditional charging strategy. First, before the test, the battery is discharged at a constant current to 2.7V to simulate a near-completely discharged state. During the charging process, the system collects voltage and current data in real time and feeds them back to the host computer. This data is transmitted via the microcontroller's serial port, and a deep learning model is used to dynamically evaluate the charging state and adjust the strategy. Figure 5 As shown, in this embodiment of the invention, based on the model output, the power supply is periodically charged with a high-current pulse at a 2C rate (approximately 80mA), charging at 80mA every two minutes, interspersed with a 1-minute 40mA buffer to reduce polarization and heat generation. This strategy effectively improves the initial charging rate, and the battery voltage rapidly rises from 2.7V to 3.6V within 60 minutes, reaching the constant voltage conversion threshold set by the platform. At this point, the system detects that the voltage meets the requirement of the battery reaching 3.6V, and the microcontroller switches the optocoupler to use constant voltage mode to stably maintain the output voltage at 3.6V, and monitors it in real time. The system monitors current changes; subsequently, during the constant voltage phase, the charging current naturally decays over time until it drops below 5mA, at which point charging is terminated. The control group uses a traditional CC-CV strategy, which initially charges at a constant current of 1C (40mA) to 3.6V before switching to constant voltage charging. The traditional method exhibits a significantly lower voltage rise rate than the fast strategy, and the current decrease during the constant voltage phase is relatively slow. Ultimately, the traditional charging termination time is approximately 5022 seconds, while the system of this invention, based on predictive control, completes the entire charging process in 3909 seconds, reducing the total charging time by approximately 22%. Figure 6 This is a schematic diagram of a charge / discharge switching and charging current monitoring circuit according to a specific embodiment of the present invention.

[0050] This verifies that the present invention achieves dynamic identification and autonomous switching of charging modes, which not only ensures that the battery voltage and current are within a safe range, but also improves the overall charging efficiency. In addition to having fast charging capabilities, it can also intelligently adjust the charging strategy through continuous learning and judgment of the battery status, which has a significantly better effect than traditional fixed logic control.

[0051] Furthermore, this invention also experimentally verifies the dynamic adjustment of battery charging and discharging strategies under high-temperature warning. The battery charging and discharging conditions are set to constant current 1C charging below 40 degrees Celsius, and to switch to constant voltage charging above 40 degrees Celsius. When the temperature reaches 55 degrees Celsius, charging is stopped and the battery power supply is cut off. At the start of the experiment, the LIR2032 battery was placed in a 25°C environment and charged in a conventional manner, while a heating platform was used to heat the battery at a rate of 0.07°C / s. Figure 7 This is a schematic diagram of experimental data for dynamically adjusting battery charging and discharging strategies for high-temperature early warning according to a specific embodiment of the present invention, wherein... Figure 7 (a) is a schematic diagram of the experimental data of the charge and discharge strategy in the cross-current operating mode. Figure 7 (b) is a schematic diagram of the experimental data for the charging and discharging strategy in the transverse voltage operating mode, as shown in the figure. Figure 7 As shown in (a), the battery temperature (solid line) gradually rises from 25°C to about 40°C in the first 240 seconds. During this stage, the system charges in normal constant current 1C mode (the dashed current curve remains constant at about 40mA from 0 to 240 seconds, "Normal CC Mode"). When the temperature rises to the threshold of 40°C, the microcontroller detects the over-temperature signal and automatically switches the charging control strategy to constant voltage mode.

[0052] Specifically, the system no longer maintains a high-current constant charging, but instead maintains the battery voltage at the current level, with the charging current gradually decreasing from 40mA. This constant voltage and current limiting control prevents the battery from becoming dangerous due to continuous high-current charging at high temperatures. When the temperature continues to rise and reaches 55°C, the system triggers a charging termination and alarm protection mechanism. The microcontroller immediately cuts off the charging circuit and simultaneously issues an alarm signal through the buzzer and communication interface to indicate an abnormal temperature. This experiment demonstrates that the system of this invention has significant technical effects in temperature monitoring and safety control: when the ambient temperature exceeds 40°C, it automatically reduces the charging current to suppress further temperature rise; when the temperature reaches 55°C, it forcibly stops charging and discharging and issues an alarm to prevent thermal runaway or damage to the battery.

[0053] Furthermore, this invention also verifies the prediction of battery state of health (SOH) and remaining battery life (RUL) using deep learning experiments. The experiment uses a commercially available LIR2032 lithium-ion battery, and the battery RUL failure value is set to 80% of the average capacity of the battery in the first three cycles. The SOH capacity of the battery is set to 100% of the average capacity of the first three cycles. The experiment is based on a microcontroller for full-cycle charge and discharge control, real-time acquisition of voltage, current and capacity information, and continuous transmission of data to the host computer via serial communication. The system first performs three complete charge and discharge cycles, discharging to 2.7V at 0.5C, then charging at a constant current of 0.5C and switching to constant voltage mode (3.6V, cutoff current of 5mA) to simulate early aging behavior under normal use conditions. During the charge and discharge process, the microcontroller acquires key operating parameters at fixed time intervals and sends the capacity information and operating status of each cycle to the host computer in real time via serial port as continuous input to the deep learning model. The deep learning model deployed on the host computer is a time series prediction framework based on a long short-term memory network (LSTM), which has been pre-trained offline on a large amount of similar battery data. After receiving the data from the initial three cycles, the model immediately performs real-time predictions, outputting: (1) the SOH trend curve for the next few cycles; and (2) the number of cycles required to reach the 80% capacity threshold, i.e., the RUL estimate. This inference process requires no human intervention and is completed entirely automatically by the system. It also has the ability to continuously accept new data and autonomously update the prediction results. Figure 8 This is a schematic diagram illustrating the experimental results of deep learning prediction of SOH and RUL according to a specific embodiment of the present invention, as shown below. Figure 8 As shown, the battery capacity decays to 80% of its initial capacity around the 295th cycle, reaching the RUL (Relative Limiting Utility). At this point, the SOH (State of Health) is also 80%. The dashed line represents the SOH decreasing trend predicted by the model based on the data from the first three cycles, demonstrating good fitting and foresight. The model predicts that the battery will reach 80% capacity on the 278th cycle, with an error of only 17 cycles. When using only the data from the first three cycles for prediction, the RUL value is only 5.7%. In practical applications, the system can automatically trigger the host computer algorithm to update the SOH / RUL prediction for each new charge / discharge cycle, achieving a closed-loop feedback between intelligent prediction and battery state management.

[0054] Therefore, the platform of this invention uses a deep learning model to dynamically model the input data, without relying on static rules or fixed curve fitting. The system can continuously receive new data during battery operation and optimize internal state judgment and strategy reasoning in real time, exhibiting strong online adaptability and generalization ability. This invention not only achieves high-precision prediction of battery SOH / RUL, but also provides a decision-making basis for subsequent dynamic adjustment of charging and discharging strategies, optimization of operating temperature, current distribution and usage frequency, significantly improving the intelligence and automation level of battery management.

[0055] To enable those skilled in the art to better understand the microcontroller-based dynamic adjustment control system for charging and discharging strategies according to embodiments of the present invention, the following explanation is provided in conjunction with specific embodiments.

[0056] Figure 9 This is a flowchart illustrating an intelligent charge / discharge control strategy according to a specific embodiment of the present invention, as shown below. Figure 9 As shown, the battery's operating data is first collected by sensors and transmitted to the microcontroller. The microcontroller then uploads the collected data to the computer and receives instructions from the computer. After analyzing the data, the computer generates a decision through algorithmic reasoning and feeds it back to the microcontroller. Finally, the microcontroller controls and adjusts the battery's charging and discharging strategy based on the decision, forming a closed-loop process of "data acquisition - upload and analysis - decision issuance - strategy control".

[0057] According to the embodiment of the present invention, a microcontroller-based dynamic adjustment control system for charging and discharging strategies comprises a data acquisition module that first acquires the current operating state parameters of the battery, and a communication module that connects the data acquisition module and the decision module and uploads the parameters to the decision module. Based on the received parameters, the decision module generates battery charging control parameters through a preset deep learning method and outputs them to the control module. The control module then determines the target charging mode of the battery based on the charging control parameters, and performs constant current or constant voltage charging control on the battery, forming a complete intelligent charging control link for the battery. This realizes the dynamic adaptive adjustment of the battery charging and discharging strategy, effectively solves the problem of low charging efficiency under extreme temperature conditions in related technologies, and thus significantly improves the charging efficiency and discharge operation reliability of the battery.

[0058] Next, referring to the accompanying drawings, a microcontroller-based dynamic adjustment control method for charging and discharging strategies according to an embodiment of the present invention is described.

[0059] Figure 10 This is a flowchart illustrating a microcontroller-based dynamic adjustment control method for charging and discharging strategies provided in an embodiment of the present invention.

[0060] like Figure 10 As shown, the microcontroller-based dynamic adjustment control method for charging and discharging strategies includes the following steps: In step S1001, the current operating status parameters of the battery are collected.

[0061] In step S1002, the current operating status parameters of the battery are uploaded to the decision module through the communication module.

[0062] In step S1003, based on the current working state parameters, the battery charging control parameters are generated according to a preset deep learning method, and the battery charging control parameters are output to the control module.

[0063] In step S1004, the target charging mode of the battery is determined according to the battery charging control parameters, and the battery is charged with constant current or constant voltage according to the target charging mode.

[0064] Furthermore, in some embodiments, acquiring the current operating state parameters of the battery includes: acquiring analog signals of the battery through a microcontroller, wherein the analog signals of the battery include at least one of battery voltage, battery current, battery ambient temperature, battery ambient humidity, and battery operating temperature; and acquiring digital status signals of the protection circuit corresponding to the battery through a microcontroller.

[0065] It should be noted that the foregoing explanation of the embodiment of the microcontroller-based dynamic adjustment control system for charging and discharging strategies also applies to the microcontroller-based dynamic adjustment control method for charging and discharging strategies in this embodiment, and will not be repeated here.

[0066] According to the embodiment of the present invention, the microcontroller-based dynamic adjustment control method for charging and discharging strategies first acquires the current operating state parameters of the battery. The communication module connects the acquisition module and the decision module, and uploads the above parameters to the decision module. Based on the received parameters, the decision module generates battery charging control parameters through a preset deep learning method and outputs them to the control module. The control module then determines the target charging mode of the battery based on the charging control parameters, and then performs constant current or constant voltage charging on the battery, forming a complete intelligent charging control link for the battery. This realizes the dynamic adaptive adjustment of the battery charging and discharging strategy, effectively solves the problem of low charging efficiency under extreme temperature conditions in related technologies, and thus significantly improves the charging efficiency and discharge operation reliability of the battery.

[0067] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0068] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0069] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A microcontroller-based dynamic adjustment control system for charging and discharging strategies, characterized in that, It includes a data acquisition module, a communication module, a control module, and a decision-making module, among which, The acquisition module is used to acquire the current operating status parameters of the battery; The communication module is connected to the acquisition module and the decision module, and is used to upload the current working status parameters to the acquisition module of the decision module. The decision module is used to generate the battery charging control parameters based on the current working state parameters and according to a preset deep learning method, and output the battery charging control parameters to the control module. The control module is used to determine the target charging mode of the battery according to the battery charging control parameters, and to perform constant current charging or constant voltage charging on the battery according to the target charging mode.

2. The microcontroller-based dynamic adjustment control system for charging and discharging strategies according to claim 1, characterized in that, The acquisition module includes: An analog signal acquisition unit is used to acquire analog signals of the battery through a microcontroller, wherein the analog signals of the battery include at least one of battery voltage, battery current, battery operating ambient temperature, battery operating ambient humidity, and battery operating temperature. The digital status signal acquisition unit is used to acquire the digital status signal of the protection circuit corresponding to the battery through a microcontroller.

3. The microcontroller-based dynamic adjustment control system for charging and discharging strategies according to claim 1, characterized in that, The communication module includes: The signal processing unit is used to format the current working state parameters of the battery acquired by the acquisition module based on a preset signal processing method to obtain formatted working state parameters. A communication unit is used to send the formatted working status parameters to the decision module based on serial communication and / or wireless communication.

4. The microcontroller-based dynamic adjustment control system for charging and discharging strategies according to claim 1, characterized in that, The control module includes: The first power supply module is used to provide a first DC voltage to the battery when the target charging mode is a constant voltage charging mode, wherein the first DC voltage is within a first voltage range; The first control unit is used to determine the target duty cycle of the buck converter based on the PWM (pulse width modulation) signal output by the microcontroller, and to control the buck converter according to the target duty cycle.

5. The microcontroller-based dynamic adjustment control system for charging and discharging strategies according to claim 4, characterized in that, The control module further includes: The second power supply module is used to provide a second DC voltage to the battery when the target charging mode is constant current charging mode, wherein the second DC voltage is in a second voltage range; The second control unit is used to determine the target current of the voltage-controlled constant current source circuit based on the control signal output by the microcontroller, and to control the voltage-controlled constant current source circuit according to the target current.

6. The microcontroller-based dynamic adjustment control system for charging and discharging strategies according to claim 5, characterized in that, The second DC voltage range is included in the first DC voltage range.

7. The microcontroller-based dynamic adjustment control system for charging and discharging strategies according to claim 1, characterized in that, Also includes: The protection module is used to generate a charging fault signal and send the charging fault signal to the control module when it is determined that the battery has a charging fault based on the current operating parameters, so that the control module can stop charging the battery. An alarm module is used to generate alarm information based on the charging fault signal, so as to perform acoustic and / or optical alarms according to the alarm information.

8. The microcontroller-based dynamic adjustment control system for charging and discharging strategies according to claim 7, characterized in that, Also includes: The display module is used to display at least one of the following: battery voltage, battery current, battery ambient temperature, battery ambient humidity, battery operating temperature, and alarm information; A power supply module is provided to supply power to the acquisition module, the communication module, the control module, and the decision module, respectively.

9. A method for dynamically adjusting and controlling a charging and discharging strategy based on a microcontroller, characterized in that, The method is applied to a microcontroller-based dynamic adjustment control system for charging and discharging strategies as described in any one of claims 1-8, wherein the method includes the following steps: Collect the current operating status parameters of the battery; The current operating status parameters of the battery are uploaded to the decision module through the communication module. Based on the current working state parameters, the charging control parameters of the battery are generated according to a preset deep learning method, and the battery charging control parameters are output to the control module. The target charging mode of the battery is determined based on the battery charging control parameters, and the battery is controlled to charge under constant current or constant voltage according to the target charging mode.

10. The method according to claim 9, characterized in that, The collection of the current operating status parameters of the battery includes: The analog signals of the battery are acquired by a microcontroller, wherein the analog signals of the battery include at least one of battery voltage, battery current, battery ambient temperature, battery ambient humidity and battery operating temperature; The digital status signal of the protection circuit corresponding to the battery is acquired by the microcontroller.

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