Lead-modified lithium battery charging system with intermittent charging current control

Abstract

The invention relates to the technical field of automobile ignition starting batteries, in particular to a lead-modified lithium battery charging system with intermittent charging current control, which comprises a core unit, a protection unit, a measurement unit and a control unit, an MOS (Metal Oxide Semiconductor) tube of the core unit controls the on-off of the charging current, a manganese-copper alloy chip resistor monitors the current, and the control unit controls the on-off of the charging current. The protection unit adopts a multi-redundancy protection framework, processes and automatically recovers charging according to the fault degree, and performs cooperative protection with an engine management system, the measurement unit collects charging parameters and transmits the charging parameters to the control unit, and the control unit adjusts the on-off time ratio of an MOS tube by applying a self-adaptive intelligent control algorithm and processes abnormal fluctuation in combination with a dynamic real-time adjustment mechanism. A battery charging behavior model is established by using a machine learning algorithm to optimize control parameters and predict the change of the charging state, in addition, the system also considers the influence of the battery aging degree on the control parameters, the charging current is effectively controlled, the performance and safety of the charging system are improved, and the service life of the battery is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive ignition start batteries, and more specifically, to a lead-to-lithium battery charging system with intermittent charging current control. Background Art

[0002] Automotive ignition start batteries are an important technology. With the development of lithium battery technology, lithium batteries have gradually become the main choice to replace traditional lead-acid batteries due to their advantages such as light weight, high energy density, long life, fast charging speed, improved fuel-saving power, and good environmental performance. In contrast, lead-acid batteries have disadvantages such as short life, large weight, and environmental unfriendliness. In recent years, with the rapid decline in the cost of lithium batteries, their prices have approached those of lead-acid batteries, so there has been an iterative trend of lithium batteries replacing lead-acid batteries.

[0003] However, when applying lithium batteries to the field of automotive ignition start batteries, there is a fatal defect, that is, it violates the "small charge and large discharge" usage rule of lithium batteries. According to the charging and discharging characteristics of lithium batteries, their charging current should usually be 1 / 2 or even smaller than the discharge current. In the automotive application scenario, after ignition, when there is a large pressure difference in the battery, the generator will generate a large charging current, and the generator itself cannot effectively control this charging current. This large current charging state will not only lead to the risk of damage to lithium batteries or battery protection boards and generators, but also seriously affect the service life and safety of lithium batteries. To solve this technical problem, we provide a lead-to-lithium battery charging system with intermittent charging current control. Summary of the Invention

[0004] The purpose of the present invention is to provide a lead-to-lithium battery charging system with intermittent charging current control to solve the problems raised in the above background art.

[0005] To achieve the above purpose, a lead-to-lithium battery charging system with intermittent charging current control is provided, including a core unit, a protection unit, a measurement unit, and a control unit; The core unit includes a MOS transistor and a manganese copper alloy patch resistor. The MOS transistor uses intelligent control technology to achieve the conduction and disconnection of the circuit, controlling the on-off of the charging current. The manganese copper alloy patch resistor is used to monitor the charging current in real time and convert the current signal into a voltage signal for output; The protection unit adopts a multiple redundant protection architecture, takes different treatment measures according to the severity of the fault, automatically resumes charging after the fault is eliminated, and establishes a collaborative protection mechanism with the vehicle's engine management system. When the charging system is abnormal, it sends a signal to collaboratively adjust the vehicle's operating state; The measurement unit collects the current and voltage parameters during the charging process in real time through sensors and transmits the collected data to the control unit; The control unit uses a micro-control module to receive the data transmitted by the core unit and the measurement unit, and applies an adaptive intelligent control algorithm to dynamically adjust the on-off time ratio of the MOS transistor by comprehensively considering the state of charge of the battery, the temperature, and the charging duration. Then, through a dynamic real-time adjustment mechanism, it collects and analyzes data in real time, adjusts the on-off strategy of the MOS transistor when the charging parameters fluctuate abnormally, and establishes a battery charging behavior model through a machine learning algorithm. It optimizes the control parameters according to historical data and predicts the future change of the battery charging state, and adjusts the charging strategy.

[0006] As a further improvement of this technical solution, the multiple redundant protection architecture of the protection unit includes three protection sub-modules: overcurrent protection, overvoltage protection, and undervoltage protection. Each protection sub-module adopts a dual-circuit backup design. When one of the circuits fails, the other circuit takes over the work.

[0007] As a further improvement of this technical solution, the specific method for the protection unit to take different treatment measures according to the severity of the fault is as follows: When the detected current exceeds the rated value by 10%-20%, it is defined as a slight overcurrent and the charging current is reduced by 20%. When the detected current exceeds the rated value by 20%-50%, it is defined as a medium overcurrent and the charging is paused for 5 seconds and then resumed at 50% of the original charging current. When the detected current exceeds the rated value by more than 50%, it is defined as a severe overcurrent and the charging circuit is immediately cut off, and an emergency signal is sent to the vehicle engine management system.

[0008] As a further improvement of this technical solution, when the protection unit establishes a collaborative protection mechanism with the vehicle's engine management system, a fuzzy control algorithm is used to determine the strategy for collaborative adjustment of the vehicle's operating state, which is specifically as follows: This algorithm takes the degree of abnormality of the charging system and the current operating parameters of the vehicle as inputs. The degree of abnormality includes the deviation degree of current and voltage, and the operating parameters include vehicle speed and engine speed. Through fuzzy inference rules, control instructions for adjusting the vehicle engine power and vehicle speed operating state are output.

[0009] As a further improvement of this technical solution, in the adaptive intelligent control algorithm of the control unit, the specific formula for dynamically adjusting the on-off time ratio of the MOS transistor by comprehensively considering the state of charge of the battery, the temperature, and the charging duration is: On-off time ratio ; where is the state of charge of the battery, is the battery temperature, is the charging duration, is the weight coefficient determined according to the battery characteristics and historical data, and .

[0010] As a further improvement of this technical solution, in the dynamic real-time adjustment mechanism of the control unit, the method of adjusting the MOS transistor on-off strategy when the charging parameters fluctuate abnormally is as follows: The Kalman filter algorithm is used to filter the collected current and voltage data to remove noise interference and obtain the charging parameters. When the deviation between the filtered data and the preset normal parameter range exceeds 15%, the adjustment strategy is started, and the on-off time ratio of the MOS transistor is adjusted through a proportional-integral-derivative controller to restore the charging parameters to the normal range.

[0011] As a further improvement of this technical solution, when the control unit establishes a battery charging behavior model through a machine learning algorithm, the long short-term memory network algorithm is used. This algorithm takes the historical charging current, voltage, temperature, and state of charge of the battery as inputs. After training by the long short-term memory network, it outputs the predicted value of the battery charging state in the next period of time. According to the deviation between the predicted value and the target value, the charging current, voltage, and charging duration are adjusted.

[0012] As a further improvement of this technical solution, the method for the control unit to optimize the control parameters according to historical data is as follows: The genetic algorithm is used to optimize the weight coefficients in the adaptive intelligent control algorithm. Taking the battery charging efficiency and the battery life extension index as the fitness function, the weight coefficients are continuously iteratively updated through the selection, crossover, and mutation operations of the genetic algorithm to make the control parameters reach the optimal value.

[0013] As a further improvement of this technical solution, during the process of the protection unit automatically resuming charging after troubleshooting, a method of gradually increasing the charging current is adopted. The initial charging current is 30% of the normal charging current, and it increases by 10% every 2 minutes until the normal charging current is reached.

[0014] As a further improvement of this technical solution, when the control unit applies the adaptive intelligent control algorithm, it considers the influence of the battery aging degree on the control parameters, evaluates the battery aging degree by monitoring the change of the battery internal resistance, and dynamically adjusts the value of the weight coefficient according to the aging degree.

[0015] Compared with the prior art, the beneficial effects of the present invention are: In a lead-to-lithium battery charging system with intermittent charging current control, a control unit uses an adaptive intelligent control algorithm to dynamically adjust the on-off time ratio of the MOS tube by comprehensively considering the battery charge state, temperature and charging time, and can accurately control the charging current to avoid damage to the lithium battery, battery protection board and generator due to high current charging, which complies with the "small charge and large discharge" usage rule of lithium batteries. The protection unit adopts a multiple redundant protection architecture, including three protection submodules of overcurrent protection, overvoltage protection and undervoltage protection with dual circuit backup design, with high reliability, and different processing measures are taken according to the severity of the fault. It can flexibly respond to various abnormal situations, automatically resume charging after the fault is eliminated, and adopt a method of gradually increasing the charging current to avoid instantaneous large current from causing impact on the battery. When using the adaptive intelligent control algorithm, the control unit evaluates the battery aging degree by monitoring the change of the battery's internal resistance, and dynamically adjusts the value of the weight coefficient according to the aging degree, so that the charging control is more in line with the actual situation of the battery, further prolongs the battery service life, and reduces the probability of circuit damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is an overall block diagram of the present invention; Figure 2 It is a schematic diagram of the workflow of the present invention.

[0017] The meaning of each number in the figure is: 1. Core unit; 2. Protection unit; 21. Overcurrent protection submodule; 22. Overvoltage protection submodule; 23. Undervoltage protection submodule; 3. Measurement unit; 4. Control unit; 41. Microcontroller module. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The present invention provides a lead-to-lithium battery charging system with intermittent charging current control. Figure 1 - Figure 2 As shown, it includes a core unit 1, a protection unit 2, a measurement unit 3 and a control unit 4; The core unit 1 includes a MOS tube and a manganese-copper alloy chip resistor. The MOS tube uses intelligent control technology to realize the conduction and disconnection of the circuit and control the on-off of the charging current. The manganese-copper alloy chip resistor is used to monitor the charging current in real time and convert the current signal into a voltage signal for output; Protection unit 2 adopts a multi-redundant protection architecture, takes different processing measures according to the severity of the fault, automatically resumes charging after the fault is eliminated, and establishes a collaborative protection mechanism with the vehicle's engine management system. When the charging system is abnormal, it sends a signal to coordinate and adjust the vehicle's operating status; In order to ensure the reliability and stability of protection unit 2, a multiple redundant protection architecture is used to avoid the failure of the protection function due to a single circuit failure. Overcurrent, overvoltage and undervoltage protection are common and important protection types in the battery charging process. The dual circuit backup design can further enhance the fault tolerance of the system. The multiple redundant protection architecture of protection unit 2 includes three protection sub-modules: overcurrent protection, overvoltage protection and undervoltage protection. Each protection sub-module adopts a dual circuit backup design. When one of the circuits fails, the other circuit takes over the work. The details are as follows: Overcurrent protection submodule 21: Design two independent overcurrent detection circuits, namely circuit A and circuit B. Each circuit includes a current sensor and a comparator. The current sensor monitors the charging current in real time. , and convert it into a voltage signal , the comparator will With the preset overcurrent threshold voltage Make comparisons; Overvoltage protection submodule 22: Two independent overvoltage detection circuits are also designed, circuit C and circuit D, using a voltage sensor to monitor the charging voltage in real time , and convert it into the corresponding voltage signal , with the preset overvoltage threshold voltage Compare; Undervoltage protection submodule 23: Two independent undervoltage detection circuits, circuit E and circuit F, are set to monitor the charging voltage The converted voltage signal , with the preset undervoltage threshold voltage In comparison, the reliability and stability of the protection unit are improved, and the risk of protection function failure due to circuit failure is reduced.

[0020] Different treatment measures are taken according to the severity of the fault, which can minimize the impact on the charging process under the premise of ensuring battery safety. The specific method for the protection unit 2 to take different treatment measures according to the severity of the fault is as follows: When the detected current exceeds the rated value by 10% - 20%, it is defined as a minor overcurrent and the charging current is reduced by 20%. When the detected current exceeds the rated value by 20% - 50%, it is defined as a medium overcurrent and the charging is paused for 5 seconds and then resumed at 50% of the original charging current. When the detected current exceeds the rated value by more than 50%, it is defined as a severe overcurrent and the charging circuit is immediately cut off, and an emergency signal is sent to the vehicle engine management system. This not only ensures the safety of the battery but also minimizes the impact on the charging efficiency, improves the safety and efficiency of the charging system, avoids unnecessary charging interruptions, and can take timely measures to protect the battery and the vehicle in case of serious failures.

[0021] The fuzzy control algorithm can handle complex and uncertain systems. When the charging system and the vehicle engine management system cooperate for protection, the degree of abnormality of the charging system and the operating parameters of the vehicle have a certain degree of fuzziness. When the protection unit 2 establishes a cooperative protection mechanism with the vehicle's engine management system, the fuzzy control algorithm is used to determine the strategy for coordinately adjusting the vehicle's operating state, as follows: This algorithm takes the degree of abnormality of the charging system and the current operating parameters of the vehicle as inputs. The degree of abnormality includes current deviation and voltage deviation , and the operating parameters include vehicle speed and engine speed . Fuzzy sets are defined for each input variable, a series of fuzzy inference rules are formulated, and based on the membership degrees of the input variables, inferences are made using the fuzzy inference rules to obtain the membership degrees of the output variable's fuzzy set. The centroid method is used to convert the membership degrees of the output variable's fuzzy set into specific control instruction values, making the cooperative protection between the charging system and the vehicle engine management system more intelligent and effective, and better ensuring the safety of the battery and the vehicle.

[0022] The measurement unit 3 collects the current and voltage parameters during the charging process in real time through sensors and transmits the collected data to the control unit 4; The control unit 4 uses the micro - control module 41 to receive the data transmitted by the core unit 1 and the measurement unit 3, and uses the adaptive intelligent control algorithm to dynamically adjust the on - off time ratio of the MOS transistor by comprehensively considering the state of charge of the battery, temperature, and charging duration. Then, through the dynamic real - time adjustment mechanism, data is collected and analyzed in real time. When the charging parameters fluctuate abnormally, the on - off strategy of the MOS transistor is adjusted, and a battery charging behavior model is established through machine learning algorithms. The control parameters are optimized based on historical data and the future charging state changes of the battery are predicted to adjust the charging strategy; By comprehensively considering the battery state of charge, temperature, and charging duration to dynamically adjust the on-off time ratio of the MOS transistor, the charging process can better meet the actual needs of the battery, improve charging efficiency and battery life. In the adaptive intelligent control algorithm of control unit 4, the specific formula for dynamically adjusting the on-off time ratio of the MOS transistor by comprehensively considering the battery state of charge, temperature, and charging duration is as follows: Obtain the battery state of charge in real time , the battery temperature , and the charging duration , and calculate the on-off time ratio of the MOS transistor according to the formula , where , is the weight coefficient determined according to the battery characteristics and historical data, and , making the adjustment of the on-off time ratio of the MOS transistor more scientific and reasonable, and improving charging efficiency and battery life; In the dynamic real-time adjustment mechanism of control unit 4, the method for adjusting the on-off strategy of the MOS transistor when the charging parameters fluctuate abnormally is as follows: There may be noise interference during the charging parameter acquisition process. The Kalman filter algorithm can be used to remove the noise and obtain accurate charging parameters. The Kalman filter algorithm is used to filter the collected current and voltage data to remove noise interference and obtain the charging parameters. Let the true value of the charging parameter be , the measured value be , and the steps of the Kalman filter algorithm are as follows: Prediction step: , ; Update step: , , , where is the system matrix, is the process noise covariance, is the measurement noise covariance, is the Kalman gain. When the deviation of the filtered data from the preset normal parameter range exceeds 15%, the proportional-integral-derivative controller calculates the control quantity according to the deviation ; where are the proportional, integral, and derivative coefficients. By adjusting the on-off time ratio of the MOS transistor through the control quantity , the damage to the battery caused by parameter fluctuations is reduced, and the reliability of the charging system is improved.

[0023] The long short-term memory network algorithm can process sequential data. The battery charging process has the characteristics of time series. By establishing a battery charging behavior model using the long short-term memory network algorithm, the future charging state of the battery can be accurately predicted, so as to adjust the charging strategy in advance, collect the historical charging data of the battery, including the charging current , voltage , temperature and state of charge . Organize them into time series data, construct a long short-term memory network including an input layer, a long short-term memory network layer and an output layer. The input layer receives historical charging data. The long short-term memory network layer contains multiple long short-term memory network units for processing sequential data. The output layer outputs the predicted value of the battery in the future for a period of time . Use the training data to train the long short-term memory network, and adjust the weights and biases of the network through the backpropagation algorithm to minimize the error between the predicted value and the actual value. According to the deviation between the predicted value and the target value, adjust the charging current, voltage and charging duration, avoiding problems such as overcharging and over-discharging, extending the service life of the battery and improving the charging efficiency.

[0024] The method for the charging current control unit 4 to optimize the control parameters according to historical data is as follows: The genetic algorithm is an optimization algorithm that searches for the optimal solution by simulating the biological evolution process. Using the genetic algorithm to optimize the weight coefficients in the adaptive intelligent control algorithm can make the control parameters reach the optimal value and further improve the performance of the charging system. Encode the weight coefficients as chromosomes, randomly generate a certain number of chromosomes as the initial population, use the battery charging efficiency and battery life extension index as the fitness function, select excellent chromosomes to enter the next generation according to the fitness function value, perform crossover operations on the selected chromosomes to generate new chromosomes, perform mutation operations on the newly generated chromosomes to increase the diversity of the population, and repeat the selection, crossover and mutation operations until the fitness function value no longer improves, further optimizing the charging strategy, improving the charging efficiency and battery life, and making the charging system more intelligent and efficient.

[0025] During the process of the protection unit 2 automatically resuming charging after troubleshooting, by adopting the method of gradually increasing the charging current, it can avoid causing too much impact on the battery and protect the performance and life of the battery. After troubleshooting, the control unit 4 sets the charging current to , increase the charging current once every 2 minutes, and the increase amount is , that is . When the charging current reaches , stop increasing and keep normal charging, reducing the risk of battery damage due to sudden change of charging current, extending the service life of the battery and improving the safety of the charging system.

[0026] When the control unit 4 applies the adaptive intelligent control algorithm, it considers the influence of the battery aging degree on the control parameters, evaluates the battery aging degree by monitoring the change of the battery internal resistance, and dynamically adjusts the weight coefficient value; The aging degree of the battery will affect its charging characteristics. Considering the influence of the battery aging degree on the control parameters can make the charging strategy more adaptable to the charging requirements of the aging battery, extend the service life of the battery, and evaluate the battery aging degree by monitoring the change of the battery internal resistance Let the initial internal resistance of the battery be , and the current internal resistance be , then the aging degree index ; According to the aging degree index dynamically adjust the weight coefficient value, making the charging strategy more adaptable to the charging requirements of the aging battery, extending the service life of the aging battery, and improving the utilization rate of the battery.

[0027] In the present invention, the MOS transistor of the core unit 1 controls the on-off of the charging current, the manganin alloy patch resistor monitors the current, the protection unit 2 adopts a multiple redundant protection architecture, processes and automatically resumes charging according to the degree of the fault, and also cooperates with the engine management system for protection. The measurement unit 3 collects the charging parameters and transmits them to the control unit 4. The control unit 4 uses the adaptive intelligent control algorithm to adjust the on-off time ratio of the MOS transistor, combines the dynamic real-time adjustment mechanism to handle abnormal fluctuations, uses the machine learning algorithm to establish a battery charging behavior model to optimize the control parameters and predict the change of the charging state. In addition, the system also considers the influence of the battery aging degree on the control parameters, effectively controls the charging current, improves the performance and safety of the charging system, and extends the service life of the battery.

[0028] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only the preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A lead-modified lithium battery charging system with intermittent charging current control, characterized in that It includes a core unit (1), a protection unit (2), a measurement unit (3), and a control unit (4); The core unit (1) contains MOS transistors and manganese copper alloy chip resistors. The MOS transistors use intelligent control technology to achieve the conduction and disconnection of the circuit, control the on-off of the charging current, and the manganese copper alloy chip resistors are used to monitor the charging current in real time and convert the current signal into a voltage signal for output; The protection unit (2) adopts a multi-redundancy protection architecture, takes different treatment measures according to the severity of the fault, automatically resumes charging after the fault is eliminated, and establishes a cooperative protection mechanism with the vehicle's engine management system. When the charging system is abnormal, it sends a signal to cooperatively adjust the vehicle's operating state; The measurement unit (3) uses sensors to collect the current and voltage parameters during the charging process in real time and transmits the collected data to the control unit (4); The control unit (4) uses a micro-control module (41) to receive the data transmitted by the core unit (1) and the measurement unit (3), and uses an adaptive intelligent control algorithm to dynamically adjust the on-off time ratio of the MOS transistors comprehensively considering the state of charge of the battery, temperature, and charging duration. Then, through a dynamic real-time adjustment mechanism, it collects and analyzes data in real time, adjusts the on-off strategy of the MOS transistors when the charging parameters fluctuate abnormally, and establishes a battery charging behavior model through machine learning algorithms, optimizes the control parameters according to historical data, predicts the future change of the battery charging state, and adjusts the charging strategy.

2. The lead-lithium battery charging system with intermittent charging current control according to claim 1, wherein, The multi-redundancy protection architecture of the protection unit (2) includes three protection sub-modules: overcurrent protection, overvoltage protection, and undervoltage protection. Each protection sub-module adopts a dual-circuit backup design. When one circuit fails, the other circuit takes over the work.

3. The lead-lithium battery charging system with intermittent charging current control according to claim 2, wherein, The specific method for the protection unit (2) to take different treatment measures according to the severity of the fault is as follows: When the detected current exceeds the rated value by 10%-20%, it is defined as a minor overcurrent and the charging current is reduced by 20%. When the detected current exceeds the rated value by 20%-50%, it is defined as a medium overcurrent and the charging is paused for 5 seconds and then resumed at 50% of the original charging current. When the detected current exceeds the rated value by more than 50%, it is defined as a severe overcurrent and the charging circuit is immediately cut off, and an emergency signal is sent to the vehicle engine management system.

4. A lead-lithium battery charging system with intermittent charging current control according to claim 1, wherein, When the protection unit (2) establishes a cooperative protection mechanism with the vehicle's engine management system, a fuzzy control algorithm is used to determine the strategy for cooperatively adjusting the vehicle's operating state, specifically as follows: This algorithm takes the abnormal degree of the charging system and the current operating parameters of the vehicle as inputs. The abnormal degree includes the deviation degree of current and voltage, and the operating parameters include vehicle speed and engine speed, and outputs control instructions for adjusting the vehicle engine power and vehicle speed operating state through fuzzy inference rules.

5. A lead-lithium battery charging system with intermittent charging current control according to claim 1, characterized in that, In the adaptive intelligent control algorithm of the control unit (4), the specific formula for dynamically adjusting the on-off time ratio of the MOS transistors comprehensively considering the state of charge of the battery, temperature, and charging duration is: On-off time ratio ; where is the state of charge of the battery, is the battery temperature, is the charging duration, is the weighting coefficient determined according to the battery characteristics and historical data, and .

6. The lead-lithium battery charging system with intermittent charging current control according to claim 1, wherein In the dynamic real-time adjustment mechanism of the control unit (4), the method for adjusting the on-off strategy of the MOS transistors when the charging parameters fluctuate abnormally is: The Kalman filter algorithm is used to filter the collected current and voltage data to remove noise interference and obtain the charging parameters. When the deviation of the filtered data from the preset normal parameter range exceeds 15%, the adjustment strategy is activated, and the on-off time ratio of the MOS transistor is adjusted through a proportional-integral-derivative controller to restore the charging parameters to the normal range.

7. A lead-lithium battery charging system with intermittent charging current control according to claim 3, characterized in that, When the control unit (4) establishes a battery charging behavior model through a machine learning algorithm, the long short-term memory network algorithm is adopted. This algorithm takes the historical charging current, voltage, temperature, and state of charge of the battery as inputs. After training by the long short-term memory network, it outputs the predicted value of the battery's charging state in the next period of time. According to the deviation between the predicted value and the target value, the charging current, voltage, and charging duration are adjusted.

8. A lead-lithium battery charging system with intermittent charging current control according to claim 7, characterized in that, The method for the control unit (4) to optimize the control parameters according to historical data is as follows: The weight coefficients in the adaptive intelligent control algorithm are optimized using a genetic algorithm The battery charging efficiency and the battery life extension index are used as fitness functions, and the weight coefficients are continuously iteratively updated through the selection, crossover, and mutation operations of the genetic algorithm to make the control parameters reach the optimal values.

9. A lead-lithium battery charging system with intermittent charging current control according to claim 1, characterized in that, During the process of automatically resuming charging after troubleshooting by the protection unit (2), the charging current is increased step by step. The initial charging current is 30% of the normal charging current, and it increases by 10% every 2 minutes until the normal charging current is reached.

10. A lead-lithium battery charging system with intermittent charging current control according to claim 1, characterized in that, When applying the adaptive intelligent control algorithm, the control unit (4) considers the influence of the battery aging degree on the control parameters, evaluates the battery aging degree by monitoring the change in the battery internal resistance, and dynamically adjusts the value of the weight coefficient according to the aging degree.

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