Lead-acid battery repairing equipment and repairing method

Abstract

The invention discloses lead-acid battery repairing equipment and a repairing method, which realize automation, intelligence and standardization of a lead-acid battery repairing process by constructing a one-key automatic repairing process consisting of a state machine engine, a knowledge base and multiple modules. Through high-precision data acquisition, a restoration strategy can be configured and a uniform threshold system is adopted, so that diagnosis, charging and discharging, pulse restoration and effect verification have consistency and repeatability; the safety and the stability of long-time high-frequency work are improved through double safety protection of hardware and software and an optimized heat dissipation structure; through a model library and a parameter configurable mechanism, the adaptability of the equipment to batteries with different specifications is enhanced; through full-process data recording and multi-dimensional verification of capacity, internal resistance and the like, quantitative evaluation and result traceability of a repair effect are realized.

Description

Technical Field

[0001] This invention relates to the field of lead-acid batteries, specifically to a lead-acid battery repair device and repair method. Background Technology

[0002] Existing lead-acid battery maintenance and testing equipment generally adopts a decentralized structure, with its core hardware typically existing as independent units, lacking effective integration and coordinated control capabilities between modules. Current equipment generally only features basic voltage and current detection circuits, with low sampling accuracy (usually below ±1%), failing to achieve high-precision quantitative analysis of battery status. Furthermore, external instruments or manual methods are required to detect battery operating status, making it difficult to meet the real-time monitoring needs under complex operating conditions.

[0003] Meanwhile, most existing equipment uses general-purpose power devices and is not optimized for high-frequency pulse application scenarios, which makes the output pulse waveform prone to distortion, affecting the stability and effectiveness of the repair process.

[0004] Furthermore, existing equipment generally lacks a unified knowledge base system and has no standardized criteria for judgment thresholds (such as full charge voltage and lamp switching current), heavily relying on operator experience. This results in poor consistency between devices and uncontrollable repair effects. Current methods for evaluating repair effectiveness are also relatively simplistic, typically relying on a single discharge test for a rough assessment. A closed-loop verification system for repair, charging, and discharging has not been established, making it difficult to quantify and systematically evaluate repair effectiveness. Summary of the Invention

[0005] To address the above-mentioned shortcomings, the present invention provides a lead-acid battery repair device that solves the problems of low automation, inconsistent results, poor safety, and weak adaptability of existing lead-acid battery repair devices, thereby achieving automated, intelligent, and standardized technical effects in the repair process.

[0006] The first aspect of this invention discloses a lead-acid battery repair device. The device is used to detect and output detection data of the voltage, current, internal resistance, and temperature of the lead-acid battery to be repaired; to perform charging, discharging, and pulse repair operations on the lead-acid battery; and to receive operation commands and display the device's operating status and result information. It includes a state machine engine, a device knowledge base module, a repair control module, and a data log and effect verification module.

[0007] The state machine engine is used to perform state-based control of the charging, discharging, pulse repair, and safety protection processes of the device based on the repair instructions and the detection data.

[0008] The device knowledge base module is used to store parameter data corresponding to different battery models. The parameter data includes at least: rated capacity, capacity judgment threshold (including first threshold capacity and second threshold capacity), charging termination threshold, discharging termination threshold, deep discharge threshold, pulse parameters of standard repair strategy and deep repair strategy, and repair effect evaluation criteria.

[0009] The repair control module is used to retrieve the corresponding pulse repair parameters from the device knowledge base module and issue control commands when the state machine engine determines that the battery is in a standard repair state or a deep repair state, so as to complete the multi-stage composite pulse repair.

[0010] The data log and effect verification module is used to record data throughout the entire repair process. This data includes at least voltage, current, internal resistance, temperature, pre-repair capacity C1, and post-repair capacity C2. Based on the repair effect evaluation criteria, the module judges and grades the repair effect and generates a repair result report. The state machine engine is configured to: perform an initial safety diagnosis of the battery at the start of the repair process; and determine whether the battery has a short circuit, overheating, or failure risk based on the detected voltage, internal resistance, and temperature; if the diagnosis fails, output fault information.

[0011] When the diagnosis is successful, perform a preset charging and discharging test to obtain the discharge duration and discharge current and calculate the capacity C1 before repair.

[0012] The pre-repair capacity C1 is compared with the capacity judgment threshold of the corresponding battery model in the device knowledge base module, and the battery is determined to be in one of the following states: healthy state, standard repair state, or deep repair state. Specifically: when C1 ≥ first threshold capacity, the battery is judged to be healthy, the process ends and a health report is output; when second threshold capacity ≤ C1 < first threshold capacity, the battery enters the standard repair state; when C1 < second threshold capacity, the battery enters the deep repair state.

[0013] In standard repair or deep repair mode, the repair control module is invoked to implement the corresponding pulse repair strategy, and supplementary charging is performed after repair.

[0014] After recharging, a capacity retest is performed to obtain the repaired capacity C2. C2 is then compared with the repair effect evaluation criteria to automatically determine whether the repair is successful or the repair effect is poor, and a repair result report is output.

[0015] This is to achieve automated repair control of lead-acid batteries and closed-loop verification of repair effects.

[0016] According to one embodiment of the present invention, the device knowledge base module stores the rated capacity, factory standard internal resistance, charging termination threshold, discharging termination threshold, and deep discharge threshold for each battery model. The state machine engine performs normalized calculations of C1 and C2 based on the rated capacity of the model in both the capacity evaluation state and the effect verification state, thereby unifying the repair process and effect judgment standards for different battery models.

[0017] According to one embodiment of the present invention, the device knowledge base module further supports updating the threshold, pulse parameters and battery model data by importing or exporting configuration files without modifying the program code of the state machine engine and the repair control module, thereby enabling configurable and adaptive optimization of the repair strategy.

[0018] According to one embodiment of the present invention, the repair strategy corresponding to the standard repair state is as follows: without performing deep discharge, a multi-stage composite pulse is output with a first preset frequency range and a first preset duty cycle range to perform a repair on the battery for a continuous first preset duration.

[0019] The repair strategy corresponding to the deep repair state is as follows: first, perform deep discharge with a small current to the deep discharge threshold stored in the device knowledge base module, and then output multi-stage composite pulses with a second preset frequency range and a second preset duty cycle range to perform repair on the battery for a continuous second preset duration, wherein the second preset duration is greater than the first preset duration.

[0020] According to an embodiment of the present invention, in the deep repair state, the repair control module divides the composite pulse repair process into two stages, the first stage uses pulse parameters of a first frequency range, the second stage uses pulse parameters of a second frequency range, and the second frequency range is higher than the first frequency range. The pulse parameters of the two stages are combined as a deep repair strategy for the battery model and written into the device knowledge base module.

[0021] According to an embodiment of the present invention, the state machine engine presets entry conditions, execution logic and exit conditions for each state, wherein the exit condition for the charging state includes at least: the voltage reaches the full charge threshold of the corresponding battery model, and the current is less than the corresponding turn-on current threshold and lasts for a third preset time.

[0022] The exit conditions for the effect verification state include at least the following: the discharge capacity C2 has been calculated; or an over-temperature / over-current abnormality is detected midway; when an over-temperature / over-current abnormality occurs, the state is terminated and a fault report is output.

[0023] According to an embodiment of the present invention, when determining the repair effect, the data log and effect verification module, in addition to comparing C2 with the rated capacity and C1, also calculates the improvement ratio of charging acceptance capability based on the change in the duration of the constant current charging stage before and after the repair. When the improvement ratio exceeds the preset charging acceptance capability threshold in the device knowledge base module, the improvement ratio is used as auxiliary evidence to improve the confidence of the repair success determination.

[0024] According to one embodiment of the present invention, the repair control module embeds polarization elimination logic in the charging and discharging process or the pulse repair process. The polarization elimination logic includes: continuously charging to a fourth preset time; outputting a short-time negative pulse; resting for a fifth preset time; and cyclically controlling in the above order.

[0025] According to one embodiment of the present invention, at the end of the repair process, the data log and effect verification module generates a repair report containing a comparison of key parameters before and after repair and the repair effect level based on the recorded C1, C2, internal resistance changes, charge acceptance capability changes and the temperature curve of the repair process, and outputs it in the form of display, printing or data export.

[0026] According to one embodiment of the present invention, the state machine engine collects open-circuit voltage, internal resistance and temperature during the initial safety diagnosis phase. When it detects that the voltage is lower than the repairable voltage threshold of the corresponding model in the device knowledge base module and / or the internal resistance is higher than the repairable internal resistance threshold of the corresponding model and / or the temperature exceeds the safety limit, it is directly determined to be in an unrepairable state, all charging, discharging or repair operations are stopped, and a suggestion to replace the battery is given.

[0027] A second aspect of this invention discloses a method for repairing lead-acid batteries, comprising the following steps:

[0028] Step S1: Connect the lead-acid battery to be repaired to the device and issue a repair command.

[0029] Step S2: The state machine engine performs an initial safety diagnosis. If the safety diagnosis conditions are not met, the process is terminated and fault information is output.

[0030] Step S3: When the safety diagnosis is passed, perform charging and enter the capacity assessment state, and calculate the capacity C1 before repair by discharging test.

[0031] Step S4: Compare C1 with the capacity judgment threshold for the corresponding battery model in the device knowledge base module, and automatically select one of the three paths: health judgment, standard repair, and deep repair.

[0032] Step S5: When standard repair or deep repair is selected, the repair control module calls the corresponding pulse repair strategy from the device knowledge base module, performs multi-stage composite pulse repair, and performs supplementary charging after the repair is completed.

[0033] Step S6: After the supplementary charging is completed, perform the final capacity test discharge, calculate the repaired capacity C2, and compare C2 with the rated capacity, C1 and internal resistance change by the data log and effect verification module.

[0034] Step S7: Based on the repair effect evaluation criteria in the equipment knowledge base module, automatically determine whether the repair is successful or unsatisfactory, generate a repair report and output it.

[0035] According to one embodiment of the present invention, in step S4, the capacity determination threshold includes: when C1 is greater than or equal to a first threshold, the battery is determined to be a healthy battery, and a health report is directly output. When C1 is between the first threshold and a second threshold, a standard repair path is executed. When C1 is less than the second threshold, a deep repair path is executed.

[0036] According to an embodiment of the present invention, in step S6, the data log and effect verification module further calculates the charging acceptance capability improvement ratio based on the change in the duration of the constant current charging stage before and after the repair, and compares the ratio with the charging acceptance capability evaluation threshold in the knowledge base, using it as an auxiliary criterion for evaluating the capacity and internal resistance results.

[0037] One or more technical solutions proposed in this application have at least the following technical effects:

[0038] The beneficial effects achieved by this invention are as follows: First, this invention solidifies the steps of charging, discharging, deep discharging, pulse repair, supplementary charging and effect verification into a repair process in the form of a state machine, and drives the automatic jump of each state by real-time data acquisition, without the need for manual intervention; it avoids the defects of traditional equipment that require manual switching of processes and manual judgment, realizes the automated control of the repair process, and improves the operating efficiency and process repeatability.

[0039] Secondly, by employing an architecture combining a state machine engine and a device knowledge base, the system can automatically select the health assessment, standard repair, or deep repair path based on real-time battery voltage, current, internal resistance, temperature, and capacity parameters, and dynamically invoke appropriate pulse repair strategies. This solution enables the device to perform adaptive repair for different types and degrees of battery aging, improving the targeting and accuracy of the repair process.

[0040] By storing standardized charging thresholds, discharge termination thresholds, deep discharge thresholds, and effectiveness evaluation criteria in a knowledge base, all devices execute the repair process according to the same parameter system. This design effectively avoids the inconsistencies in results caused by reliance on human experience in traditional repair operations, achieving standardization and consistency in repair parameters and repair quality.

[0041] This invention employs a voltage / current sampling circuit with an accuracy of ±0.5%, a 16-bit ADC, and an automatic internal resistance measurement module, which can provide high-precision detection data in real time. This high-precision acquisition effectively improves the reliability of capacity detection, internal resistance estimation, and effect judgment, providing an accurate basis for repair strategy selection and state transitions.

[0042] This invention constructs a configurable battery model database, and realizes dynamic updates of repair parameters, thresholds and models through import / export methods, enabling the device to adapt to lead-acid batteries of different brands, capacities and usage conditions, and further expands the scope of application by adjusting pulse frequency, duty cycle, charging current and deep discharge current.

[0043] The data log module can record voltage, current, temperature, internal resistance, and capacity data throughout the entire process, forming a closed-loop repair mechanism in conjunction with the effect verification module. By comprehensively comparing the changes in capacity C1 before and after repair, the improvement in internal resistance, and the increase in charging acceptance, the repair effect can be automatically determined and a report generated, achieving quantification, traceability, and verifiability of the repair effect.

[0044] In summary, this invention achieves automation, intelligence, and standardization in the lead-acid battery repair process by constructing a one-click automated repair workflow composed of a state machine engine, a knowledge base, and multiple collaborative modules. Through high-precision data acquisition, configurable repair strategies, and a unified threshold system, consistency and repeatability are ensured in diagnosis, charging / discharging, pulse repair, and effect verification. Dual hardware and software safety protection and optimized heat dissipation structure improve the safety and stability of long-term, high-frequency operation. A model library and configurable parameter mechanism enhance the device's adaptability to different battery specifications. Full-process data recording and multi-dimensional verification of capacity, internal resistance, and other parameters enable quantitative evaluation of repair effects and traceability of results. Attached Figure Description

[0045] Figure 1 This is a block diagram of the lead-acid battery repair equipment system disclosed in an embodiment of the present invention;

[0046] Figure 2 This is a flowchart of the lead-acid battery repair method disclosed in an embodiment of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0048] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0049] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0050] This invention proposes a lead-acid battery repair device that solves the problems of low automation, inconsistent results, poor safety, and weak adaptability of existing lead-acid battery repair devices, and realizes the automation, intelligence and standardization of the repair process.

[0051] First, the previously fragmented charging, discharging, deep discharging, repair, and effect verification processes are linked together into a single "one-click repair" process through an intelligent decision-making core, eliminating the need for manual intervention. Second, a high-precision data acquisition module (voltage / current / internal resistance / temperature), a high-frequency pulse-optimized power execution module, and a highly reliable heat dissipation and safety protection module are integrated to solidify the foundation for automation. Third, a core state machine engine, a configurable knowledge base system, and a full lifecycle data log system are built to achieve closed-loop control from detection to decision-making, execution, and verification. Finally, parameter thresholds (such as full charge voltage, lamp switching current, and deep discharge threshold), triggering conditions, and effect evaluation criteria are standardized across all stages to ensure consistent repair performance.

[0052] This invention relates to an intelligent repair device for lead-acid batteries, comprising a hardware system and a software system. The two systems work together through a pre-defined interface to achieve fully automated one-click repair. The hardware system acts as the "execution carrier," responsible for data acquisition and command execution; the software system acts as the "intelligent brain," responsible for decision control and effect evaluation.

[0053] The hardware components include: a data acquisition unit, a power execution unit, a safety protection and heat dissipation unit, and an interaction unit. All units are connected via a PCB board bus.

[0054] The data acquisition unit includes a voltage sampling circuit, a current sampling circuit, an internal resistance measurement module, and a temperature sensor (NTC type). It collects real-time data on the battery's open-circuit voltage, charging / discharging current, internal resistance, and temperature, providing accurate input for software decision-making.

[0055] The voltage / current sampling circuit accuracy has been improved to ±0.5%, and a 16-bit ADC chip has been adopted with a sampling rate of ≥10Hz to ensure data real-time performance and accuracy.

[0056] The built-in internal resistance measurement circuit automatically calculates the battery's internal resistance by applying a tiny 1kHz excitation signal, without the need for external devices.

[0057] The temperature sensor is attached to the battery interface, with a measurement range of -20℃ to 85℃ and an accuracy of ±0.3℃, providing a basis for over-temperature protection.

[0058] The power execution unit includes a charging circuit, a discharging circuit, a pulse repair circuit (independently designed), MOSFET power devices, and a driver chip. It receives instructions from the main control chip and performs operations such as constant current / constant voltage charging, low current discharging, and high-frequency pulse repair.

[0059] The three circuits are designed independently and switched by relays to avoid mutual interference; the MOSFETs are selected to adapt to high-frequency pulse conditions and have a peak current withstand capability of ≥3 times the rated current to ensure that the pulse waveform is not distorted.

[0060] The charging circuit supports constant current charging (0.1C~0.2C), constant voltage charging (voltage can be dynamically adjusted by software), and 13.8V trickle charging mode.

[0061] The discharge circuit supports low-current discharge of 0.05C, and the termination voltage can be set via software (normal discharge of 10.5V / 12V batteries, deep discharge of 9.0V / 12V batteries).

[0062] The pulse repair circuit supports adjustable frequency (8kHz~12kHz), adjustable duty cycle (30%~50%), and periodic voltage fluctuation (12V~15V) to achieve multi-stage composite pulse output.

[0063] According to one embodiment of the present invention, such as Figure 2 As shown in the diagram. This hardware unit may also include a safety protection and heat dissipation unit, which includes an over-temperature protection switch, an overcurrent / short-circuit detection circuit, an electromagnetic shielding cover, a heat sink, and a silent fan. These are used to ensure safe operation of the equipment and prevent overheating and electromagnetic interference.

[0064] It adopts dual protection of hardware and software. At the hardware level, the detection circuit monitors the current / temperature in real time and directly cuts off the power circuit when the threshold is exceeded. At the software level, it links with the data acquisition unit to provide early warning and trigger protection.

[0065] The electromagnetic shielding cover covers the pulse repair circuit and data acquisition unit, and is sealed with conductive foam to reduce the interference of high-frequency pulses on sampling accuracy.

[0066] The heat dissipation system is designed for long-term, high-frequency operation: the heat sink area is ≥100cm², and the fan speed is adjustable (automatically switching gears according to the temperature) to ensure that the temperature of power devices is ≤85℃.

[0067] The interactive unit includes a touchscreen display (≥5 inches), an audible and visual alarm module (LED light + buzzer), and a print / export interface. It provides operation input and status feedback, and supports repair report output.

[0068] The touchscreen highlights the "One-Click Repair" core button and displays the real-time progress of the process (diagnosing, charging, repairing, verifying, completed) and key parameters (current voltage / current / internal resistance / remaining time).

[0069] The audible and visual alarm module distinguishes different fault types (a continuous red light indicates that the battery is unrepairable; a short yellow light indicates an overheat warning), and the screen displays the cause of the fault simultaneously.

[0070] like Figure 1 As shown, the software unit includes: a state machine engine, a device knowledge base module, a repair control module, and a data log and effect verification module. These modules interact via a data bus. Specifically, they are represented as follows:

[0071] graph LR

[0072] A [Core State Machine Engine] --> B [Device Knowledge Base Module]

[0073] A --> C [Data Log Module]

[0074] A --> D [Repair Control Module]

[0075] A --> E [Effect Verification Module]

[0076] F [High-precision data acquisition unit] --> A

[0077] D --> G [High-performance power execution unit]

[0078] E --> H [Human-Computer Interaction Unit]

[0079] Among them, the state machine engine serves as the core of the entire process control, enabling closed-loop transitions from detection, decision-making, execution, and verification, and accurately managing the state of each process.

[0080] The equipment is used to detect and output the detection data of the voltage, current, internal resistance, and temperature of the lead-acid battery to be repaired. It performs charging, discharging, and pulse repair operations on the lead-acid battery, receives operation commands, and displays the equipment's operating status and results. It includes a state machine engine, an equipment knowledge base module, a repair control module, and a data log and effect verification module.

[0081] The state machine engine is used to perform state-based control of the charging, discharging, pulse repair, and safety protection processes of the device based on the repair instructions and the detection data.

[0082] The device knowledge base module is used to store parameter data corresponding to different battery models. The parameter data includes at least: rated capacity, capacity judgment threshold (including first threshold capacity and second threshold capacity), charging termination threshold, discharging termination threshold, deep discharge threshold, pulse parameters of standard repair strategy and deep repair strategy, and repair effect evaluation criteria.

[0083] The repair control module is used to retrieve the corresponding pulse repair parameters from the device knowledge base module and issue control commands when the state machine engine determines that the battery is in a standard repair state or a deep repair state, so as to complete the multi-stage composite pulse repair.

[0084] The data log and effect verification module is used to record data throughout the entire repair process. This data includes at least voltage, current, internal resistance, temperature, pre-repair capacity C1, and post-repair capacity C2. Based on the repair effect evaluation criteria, the module judges and grades the repair effect and generates a repair result report. The state machine engine is configured to: perform an initial safety diagnosis of the battery at the start of the repair process; and determine whether the battery has a short circuit, overheating, or failure risk based on the detected voltage, internal resistance, and temperature; if the diagnosis fails, output fault information.

[0085] When the diagnosis is successful, perform a preset charging and discharging test to obtain the discharge duration and discharge current and calculate the capacity C1 before repair.

[0086] The pre-repair capacity C1 is compared with the capacity judgment threshold of the corresponding battery model in the device knowledge base module, and the battery is determined to be in one of the following states: healthy state, standard repair state, or deep repair state. Specifically: when C1 ≥ first threshold capacity, the battery is judged to be healthy, the process ends and a health report is output; when second threshold capacity ≤ C1 < first threshold capacity, the battery enters the standard repair state; when C1 < second threshold capacity, the battery enters the deep repair state.

[0087] In standard repair or deep repair mode, the repair control module is invoked to implement the corresponding pulse repair strategy, and supplementary charging is performed after repair.

[0088] After recharging, a capacity retest is performed to obtain the repaired capacity C2. C2 is then compared with the repair effect evaluation criteria to automatically determine whether the repair is successful or the repair effect is poor, and a repair result report is output.

[0089] This is to achieve automated repair control of lead-acid batteries and closed-loop verification of repair effects.

[0090] Specifically, the execution logic of "one-click repair" is solidified as follows, and each process stage is defined as an independent state (initial diagnosis state, charging state, capacity assessment state, standard repair state, deep repair state, supplementary charging state, and effect verification state).

[0091] graph TD

[0092] A [User presses the "One-Click Repair" button] --> B {Initial Security Diagnosis};

[0093] B -- Voltage / Internal Resistance Abnormality / Short Circuit / Over Temperature --> C [Terminate Process, Alarm + Display Fault Cause];

[0094] B -- Diagnosis passed --> D [Execute standard charging procedure];

[0095] D --> E [Perform capacity test discharge];

[0096] E --> F{Capacity Assessment (C1)};

[0097] F -- C1 ≥ 80% of rated capacity --> G [Determine battery health, output report, process ends];

[0098] F -- 40% ≤ C1 < 80% rated capacity --> H [Trigger standard repair procedure];

[0099] F -- C1 < 40% rated capacity --> I [Trigger deep repair process (deep discharge first)];

[0100] H & I --> J [Perform multi-stage composite pulse repair];

[0101] J --> K [Execute supplementary charging process];

[0102] K --> L [Perform final capacity test discharge (C2)];

[0103] L --> M{Effect Evaluation};

[0104] M -- C2 ≥ 70% of rated capacity --> N [Output "Repair successful" report];

[0105] M -- C2 < 70% rated capacity --> O [Output "Ineffective Repair" report + replacement recommendation];

[0106] Each state has clearly defined "entry conditions", "execution logic", and "exit conditions". For example, the entry condition for "charging state" is "initial diagnosis passed", the execution logic is "constant current → constant voltage charging", and the exit condition is "voltage reaches the full charge threshold and current ≤ the lamp current for 10 minutes".

[0107] The status transition is triggered by comparing real-time collected data with knowledge base thresholds, requiring no manual intervention and ensuring process automation.

[0108] The device knowledge base module stores unified judgment thresholds, repair parameters, and battery model data, and supports configurable and adaptive parameters.

[0109] The specific implementation steps include: a built-in standardized threshold library, including charging thresholds (2.40V±0.05V per cell, lamp switching current 0.5%~1.0C), discharge thresholds (normal termination 1.75V / cell, deep discharge 1.50V / cell), and repair effect evaluation criteria (capacity ≥70% of rated value, internal resistance ≤1.3 times factory value).

[0110] It has a built-in battery model database that stores the rated capacity and factory standard internal resistance of different battery models (12V / 6V, different Ah capacity), and supports manually adding new models.

[0111] It supports dynamic parameter updates. By exporting / importing configuration files, thresholds can be optimized and parameters can be fixed without modifying the core code.

[0112] The repair control module, based on state machine decisions, invokes the corresponding repair strategy and dynamically adjusts the repair parameters. Its specific implementation steps include: a standard repair strategy, which, for batteries with a capacity of 40%~80% of their rated capacity, executes a "multi-stage composite pulse repair" with parameters of (8~12kHz) frequency, (30%~50%) duty cycle, and a duration of 6 hours.

[0113] The deep repair strategy targets batteries with a capacity of less than 40% of their rated capacity. First, deep discharge (0.05C current to 9.0V / 12V battery) is performed, followed by extended pulse repair (lasting 12 hours, with strong resonance for the first 6 hours (8~10kHz) and fine repair at 10~12kHz for the last 6 hours).

[0114] Polarization elimination logic: Embed a cycle of "5 minutes of charging → 1 second of negative pulse → 10 seconds of rest" during the charging and discharging process to eliminate electrode polarization.

[0115] The data logging and effect verification module records data throughout the entire process, enabling closed-loop verification of repair effectiveness and report generation. Its specific implementation steps include: the logging module records key data (voltage, current, internal resistance, temperature, time) at each stage in real time, including the pre-repair capacity C1 and post-repair capacity C2; the effect verification module executes a cycle from supplementary charging to complete discharge, calculates the ratio of C2 to rated capacity and C1, compares it against the knowledge base evaluation criteria, and automatically determines whether the repair was successful or ineffective. A repair report is automatically generated, including a comparison of parameters before and after repair, the effect level, and recommendations (e.g., "Repair successful, capacity recovered from 35% to 78%)," supporting screen display, printing, or export.

[0116] Taking a 12V / 100Ah battery as an example. First, step S1: The user connects the battery and presses the "One-Click Repair" button. The device enters the initial diagnostic state: it collects the open circuit voltage (e.g., 11.2V) and internal resistance (e.g., 1.8 times the factory value) and determines "No short circuit / over-temperature, repairable".

[0117] Step S2: Enter the charging state and charge at a constant current of 10~20A (0.1~0.2C). After the voltage rises to (14.4~14.7V), it switches to constant voltage. The current drops to (0.5~1.0A) (0.5%~1.0C) and is maintained for 10 minutes. The charging is then considered complete.

[0118] Step S3: Enter the capacity assessment state, discharge to 10.5V at 5A (0.05C), record the discharge duration, calculate C1=35Ah (35% of the rated value), and trigger the deep repair strategy.

[0119] Step S4: Enter deep repair state, first discharge to 9.0V with 5A (deep discharge), then perform 12 hours of composite pulse repair (the first 6 hours are 8kHz strong resonance, and the last 6 hours are 12kHz fine repair).

[0120] Step S5: Enter the supplementary charging state, repeat step S2, and complete the full charge.

[0121] Step S6: Enter the effect verification state, discharge to 10.5V at 5A, calculate C2=80Ah (80% of the rated value), and determine "repair successful".

[0122] Step S7: Output the repair report, and the process ends.

[0123] According to embodiments of the present invention, other alternative solutions are also possible. For example, pulse parameters can be replaced, the pulse frequency can be extended to (6kHz~15kHz), and the duty cycle can be adjusted to (25%~55%). By configuring the knowledge base to adapt to the sulfation characteristics of different brands of batteries, the repair effect is not significantly different.

[0124] The charging current replacement allows the constant current charging current range to be extended to (0.08C~0.25C), making it suitable for low-temperature environments or severely aged batteries, thus extending battery life.

[0125] The deep discharge current can be used as an alternative (0.03C~0.07C) to reduce the discharge rate of batteries with extremely low capacity and prevent them from being depleted and damaged.

[0126] In addition to capacity and internal resistance, the evaluation criteria can be improved by adding "charging acceptance" as an auxiliary evaluation standard (constant current charging time improved by ≥30% after repair), further optimizing the accuracy of the evaluation.

[0127] The second aspect of this invention discloses a method for repairing lead-acid batteries, such as... Figure 2 As shown. It includes the following steps:

[0128] Step S1: The user connects the lead-acid battery to be repaired to the device and issues a repair command through the interaction unit.

[0129] Step S2: The state machine engine controls the data acquisition unit to perform initial safety diagnosis. If the safety diagnosis conditions are not met, the process is terminated and fault information is output.

[0130] Step S3: When the safety diagnosis is passed, control the power execution unit to perform charging and enter the capacity assessment state, and calculate the capacity C1 before repair through the discharge test.

[0131] Step S4: Compare C1 with the capacity judgment threshold for the corresponding battery model in the device knowledge base module, and automatically select one of the three paths: health judgment, standard repair, and deep repair.

[0132] Step S5: When standard repair or deep repair is selected, the repair control module calls the corresponding pulse repair strategy from the device knowledge base module, controls the power execution unit to perform multi-stage composite pulse repair, and performs supplementary charging after the repair is completed.

[0133] Step S6: After the supplementary charging is completed, perform the final capacity test discharge, calculate the repaired capacity C2, and compare C2 with the rated capacity, C1 and internal resistance change by the data log and effect verification module.

[0134] Step S7: Based on the repair effect evaluation criteria in the equipment knowledge base module, automatically determine whether the repair is successful or unsatisfactory, generate a repair report and output it.

[0135] According to one embodiment of the present invention, in step S4, the capacity determination threshold includes: when C1 is greater than or equal to a first threshold, the battery is determined to be a healthy battery, and a health report is directly output. When C1 is between the first threshold and a second threshold, a standard repair path is executed. When C1 is less than the second threshold, a deep repair path is executed.

[0136] According to an embodiment of the present invention, in step S6, the data log and effect verification module further calculates the charging acceptance capability improvement ratio based on the change in the duration of the constant current charging stage before and after the repair, and compares the ratio with the charging acceptance capability evaluation threshold in the knowledge base, using it as an auxiliary criterion for evaluating the capacity and internal resistance results.

[0137] The beneficial effects achieved by this invention are as follows: First, this invention solidifies the steps of charging, discharging, deep discharging, pulse repair, supplementary charging and effect verification into a repair process in the form of a state machine, and drives the automatic jump of each state by real-time data acquisition, without the need for manual intervention; it avoids the defects of traditional equipment that require manual switching of processes and manual judgment, realizes the automated control of the repair process, and improves the operating efficiency and process repeatability.

[0138] Secondly, by employing an architecture combining a state machine engine and a device knowledge base, the system can automatically select the health assessment, standard repair, or deep repair path based on real-time battery voltage, current, internal resistance, temperature, and capacity parameters, and dynamically invoke appropriate pulse repair strategies. This solution enables the device to perform adaptive repair for different types and degrees of battery aging, improving the targeting and accuracy of the repair process.

[0139] By storing standardized charging thresholds, discharge termination thresholds, deep discharge thresholds, and effectiveness evaluation criteria in a knowledge base, all devices execute the repair process according to the same parameter system. This design effectively avoids the inconsistencies in results caused by reliance on human experience in traditional repair operations, achieving standardization and consistency in repair parameters and repair quality.

[0140] This invention employs a voltage / current sampling circuit with an accuracy of ±0.5%, a 16-bit ADC, and an automatic internal resistance measurement module, which can provide high-precision detection data in real time. This high-precision acquisition effectively improves the reliability of capacity detection, internal resistance estimation, and effect judgment, providing an accurate basis for repair strategy selection and state transitions.

[0141] By incorporating temperature sensors, overcurrent / short circuit detection circuits, overtemperature protection switches, and electromagnetic shielding, along with software-level anomaly warning mechanisms, power output can be promptly interrupted in cases of overtemperature, short circuit, or abnormal voltage, effectively avoiding safety risks caused by battery malfunctions or high-frequency pulse operation. Combined with a cooling system consisting of a fan and heat sink, the device can maintain stable operation during prolonged high-frequency pulse repair processes.

[0142] This invention constructs a configurable battery model database, and realizes dynamic updates of repair parameters, thresholds and models through import / export methods, enabling the device to adapt to lead-acid batteries of different brands, capacities and usage conditions, and further expands the scope of application by adjusting pulse frequency, duty cycle, charging current and deep discharge current.

[0143] The data log module can record voltage, current, temperature, internal resistance, and capacity data throughout the entire process, forming a closed-loop repair mechanism in conjunction with the effect verification module. By comprehensively comparing the changes in capacity C1 before and after repair, the improvement in internal resistance, and the increase in charging acceptance, the repair effect can be automatically determined and a report generated, achieving quantification, traceability, and verifiability of the repair effect.

[0144] In summary, this invention achieves automation, intelligence, and standardization in the lead-acid battery repair process by constructing a one-click automated repair workflow composed of a state machine engine, a knowledge base, and multiple collaborative modules. Through high-precision data acquisition, configurable repair strategies, and a unified threshold system, consistency and repeatability are ensured in diagnosis, charging / discharging, pulse repair, and effect verification. Dual hardware and software safety protection and optimized heat dissipation structure improve the safety and stability of long-term, high-frequency operation. A model library and configurable parameter mechanism enhance the device's adaptability to different battery specifications. Full-process data recording and multi-dimensional verification of capacity, internal resistance, and other parameters enable quantitative evaluation of repair effects and traceability of results.

[0145] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A lead-acid battery repair device, characterized in that, The device is used to detect the voltage, current, internal resistance and temperature of the lead-acid battery to be repaired and output the detection data, perform charging, discharging and pulse repair operations on the lead-acid battery, receive operation instructions and display the device operation status and result information; It includes a state machine engine, a device knowledge base module, a repair control module, and a data log and effect verification module; The state machine engine is used to perform state-based control on the charging, discharging, pulse repair, and safety protection processes of the device according to the repair instructions and the detection data. The device knowledge base module is used to store parameter data corresponding to different battery models. The parameter data includes at least: rated capacity, capacity judgment threshold (including first threshold capacity and second threshold capacity), charging termination threshold, discharging termination threshold, deep discharge threshold, pulse parameters of standard repair strategy and deep repair strategy, and repair effect evaluation criteria. The repair control module is used to retrieve the corresponding pulse repair parameters from the device knowledge base module and issue control commands when the state machine engine determines that the battery is in a standard repair state or a deep repair state, so as to complete the multi-stage composite pulse repair. The data log and effect verification module is used to record the data of the entire repair process. The data of the entire process includes at least voltage, current, internal resistance, temperature, capacity C1 before repair and capacity C2 after repair. Based on the repair effect evaluation criteria, the repair effect is judged and graded, and a repair result report is generated. The state machine engine is configured to at least: At the start of the repair process, an initial safety diagnosis is performed on the battery, and the battery is assessed for short circuit, overheating, or failure risk based on the detected voltage, internal resistance, and temperature; if the diagnosis fails, fault information is output. When the diagnosis is successful, perform the preset charging and discharging test, obtain the discharge duration and discharge current, and calculate the capacity C1 before repair. The pre-repair capacity C1 is compared with the capacity judgment threshold of the corresponding battery model in the device knowledge base module, and the battery is determined to be in one of the following states: healthy state, standard repair state, or deep repair state. Specifically: when C1 ≥ first threshold capacity, the battery is judged to be healthy, the process ends and a health report is output; when the second threshold capacity ≤ C1 < first threshold capacity, the battery enters the standard repair state; when C1 < second threshold capacity, the battery enters the deep repair state. In standard repair or deep repair mode, the repair control module is invoked to implement the corresponding pulse repair strategy, and supplementary charging is performed after repair. After recharging, a capacity retest is performed to obtain the repaired capacity C2. C2 is then compared with the repair effect evaluation criteria to automatically determine whether the repair is successful or the repair effect is poor, and a repair result report is output. This is to achieve automated repair control of lead-acid batteries and closed-loop verification of repair effects.

2. The lead-acid battery repair equipment according to claim 1, characterized in that, The device knowledge base module stores the rated capacity, factory standard internal resistance, charging termination threshold, discharging termination threshold, and deep discharge threshold for each battery model. The state machine engine performs normalized calculations of C1 and C2 based on the rated capacity of the model in both the capacity evaluation state and the effect verification state, thereby unifying the repair process and effect judgment standards for different battery models.

3. The lead-acid battery repair equipment according to claim 1 or 2, characterized in that, The device knowledge base module further supports updating the threshold, pulse parameters, and battery model data by importing or exporting configuration files without modifying the program code of the state machine engine and repair control module, thereby enabling configurable and adaptive optimization of the repair strategy.

4. The lead-acid battery repair equipment according to claim 1, characterized in that, The repair strategy corresponding to the standard repair state is as follows: without performing deep discharge, output multi-stage composite pulses with a first preset frequency range and a first preset duty cycle range to perform repair on the battery for a continuous first preset duration. The repair strategy corresponding to the deep repair state is as follows: first, perform deep discharge with a small current to the deep discharge threshold stored in the device knowledge base module, and then output multi-stage composite pulses with a second preset frequency range and a second preset duty cycle range to perform repair on the battery for a continuous second preset duration, wherein the second preset duration is greater than the first preset duration.

5. The lead-acid battery repair equipment according to claim 4, characterized in that, In the deep repair state, the repair control module divides the composite pulse repair process into two stages: the first stage uses pulse parameters in a first frequency range, and the second stage uses pulse parameters in a second frequency range, where the second frequency range is higher than the first frequency range. The combination of pulse parameters from the two stages is then written into the device knowledge base module as the deep repair strategy for this battery model.

6. The lead-acid battery repair equipment according to claim 1, characterized in that, The state machine engine pre-sets entry conditions, execution logic, and exit conditions for each state. The conditions for exiting the charging state include at least: the voltage reaches the full charge threshold of the corresponding battery model, and the current is less than the corresponding indicator light current threshold and continues for a third preset time. The exit conditions for the effect verification state include at least the following: the discharge capacity C2 has been calculated; or an over-temperature / over-current abnormality is detected midway; when an over-temperature / over-current abnormality occurs, the state is terminated and a fault report is output.

7. The lead-acid battery repair equipment according to claim 1, characterized in that, When determining the repair effect, the data log and effect verification module, in addition to comparing C2 with the rated capacity and C1, also calculates the improvement ratio of charging acceptance capability based on the change in the duration of the constant current charging stage before and after the repair. When the improvement ratio exceeds the preset charging acceptance capability threshold in the device knowledge base module, the improvement ratio is used as auxiliary evidence to improve the confidence of the repair success determination.

8. A method for repairing lead-acid batteries using the lead-acid battery repair equipment as described in any one of claims 1 to 7, characterized in that, Includes the following steps: Step S1: Connect the lead-acid battery to be repaired to the device and issue a repair command; Step S2: The state machine engine performs an initial safety diagnosis. If the safety diagnosis conditions are not met, the process is terminated and fault information is output. Step S3: When the safety diagnosis is passed, perform charging and enter the capacity assessment state, and calculate the capacity C1 before repair through the discharge test; Step S4: Compare C1 with the capacity judgment threshold for the corresponding battery model in the device knowledge base module, and automatically select one of the three paths: health judgment, standard repair, and deep repair. Step S5: When standard repair or deep repair is selected, the repair control module calls the corresponding pulse repair strategy from the device knowledge base module, performs multi-stage composite pulse repair, and performs supplementary charging after the repair is completed; Step S6: After the supplementary charging is completed, perform the final capacity test discharge, calculate the repaired capacity C2, and compare C2 with the rated capacity, C1 and internal resistance change by the data log and effect verification module. Step S7: Based on the repair effect evaluation criteria in the equipment knowledge base module, automatically determine whether the repair is successful or unsatisfactory, generate a repair report and output it.

9. The method according to claim 8, characterized in that, In step S4, the capacity determination threshold includes: When C1 is greater than or equal to the first threshold, the battery is determined to be a healthy battery and a health report is directly output. When C1 is between the first and second thresholds, the standard repair path is executed; When C1 is less than the second threshold, a deep repair path is executed.

10. The method according to claim 8 or 9, characterized in that, In step S6, the data log and effect verification module also calculates the charging acceptance capability improvement ratio based on the change in the duration of the constant current charging stage before and after the repair, and compares the ratio with the charging acceptance capability evaluation threshold in the knowledge base, using it as an auxiliary criterion for evaluating the capacity and internal resistance results.

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