A control method for an intelligent maintenance device of a lead-acid storage battery

Abstract

The application relates to the technical field of charge control, in particular to a control method of a lead-acid storage battery intelligent maintenance device, which can finely divide a voltage curve and calculate the mean value of each interval through real-time collection and analysis of terminal voltage and charging current, provides higher accuracy and real-time performance for state determination of the battery, improves dynamic tracking capability of the battery charging process, greatly improves dynamic adaptability of the battery charging process through adoption of fuzzy logic matching, avoids inefficiency and inadaptation under a fixed voltage and current mode in a traditional method, ensures that each link in the charging process can be optimized and monitored, thereby prolonging the service life of the battery and reducing the fault risk, generates a correction voltage through multiplication of a constant voltage target and a factor, can real-time adjust the voltage in the charging process of the battery, ensures that the battery is always in a suitable working state, and reduces the risk of overcharging and overdischarging.

Description

Technical Field

[0001] This invention relates to the field of charging control technology, and in particular to a control method for an intelligent maintenance device for lead-acid batteries. Background Technology

[0002] The field of charging control technology mainly studies the adjustment methods of current, voltage, and temperature parameters of batteries during charging and discharging. Through circuit control, charging efficiency is improved, battery life is extended, and safety is ensured. In lead-acid battery application scenarios, charging control includes constant current charging, constant voltage charging, float charging, and equalization charging modes. Combined with temperature compensation, capacity estimation, and polarization effect suppression measures, the battery maintains stable performance and energy output capability during long-term operation.

[0003] A control method for an intelligent maintenance device for lead-acid batteries refers to a method that implements orderly management of the charging, discharging, and maintenance operations of lead-acid batteries during operation through set circuit logic and control algorithms. The purpose is to extend the cycle life of the battery, improve the capacity retention rate, and reduce the risk of failure. By adjusting the charging parameters and operating environment in real time, overcharging and over-discharging are avoided, electrode sulfation is reduced, and the battery maintains a high energy conversion efficiency, thereby achieving the effects of extending service life and improving stability.

[0004] While existing technologies for lead-acid battery charging management include basic charging modes such as constant current charging, constant voltage charging, and float charging, and employ temperature compensation and capacity estimation techniques to improve charging efficiency to some extent, they still have many shortcomings. Traditional methods often rely on fixed charging current and voltage values, causing the battery to be unable to adapt to changes in the external environment in real time during long-term use. In cases of significant temperature changes or battery aging, the charging parameters cannot be adjusted in time, easily leading to overcharging or undercharging, which in turn affects the battery's lifespan and stability. Traditional technologies have not effectively solved the problems of battery polarization and sulfation, causing the battery capacity to gradually decrease after prolonged use, resulting in the battery's inability to function properly. Existing technologies have significant limitations in extending battery life and ensuring charging safety, and cannot fully utilize the battery's performance and stability in practical applications. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a control method for an intelligent maintenance device for lead-acid batteries.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a control method for an intelligent maintenance device for lead-acid batteries, comprising the following steps:

[0007] Step 1: Based on the acquisition of terminal voltage and charging current, the terminal voltage curve is segmented by time interval and the mean is calculated. The current differential rate is extracted and the slope of the electrolyte temperature trend is obtained. The state of charge interval is determined and a basic operating feature set is generated.

[0008] Step 2: Based on the basic operating feature set, divide the plate conductivity level and obtain the current rate differential level. After setting the terminal voltage fluctuation amplitude range, divide the temperature gradient range of the temperature control unit. Use fuzzy logic to combine and match conditions to establish a dynamic voltage factor sequence.

[0009] Step 3: Based on the dynamic voltage factor sequence, calculate the constant voltage target and factor product and generate the correction voltage, obtain the difference between the correction voltage and the terminal voltage, compare the difference with the threshold and confirm the result, and generate the stage voltage regulation result;

[0010] Step 4: Based on the stage voltage regulation results, compare the constant current node current with the constant voltage range voltage after Kalman filtering correction, match the float charging preparation point and determine the node transfer frequency, identify abnormal nodes and insert delayed observation nodes to obtain the charging path node set.

[0011] Step 5: Based on the charging path node set, traverse and compare nodes in order, identify and delete nodes with low probability, arrange the delay observation node and the limiting node in order and connect them to the path link, reorganize the link and generate a continuous control chain to obtain the maintenance control execution flow.

[0012] As a further embodiment of the present invention, the basic operating feature set includes the average terminal voltage, the differential charging current rate, the electrolyte temperature trend slope, and the state of charge range; the dynamic voltage factor sequence includes the plate conductivity level, the differential current rate level, the voltage fluctuation amplitude range, and the temperature gradient range of the temperature control unit; the stage voltage regulation result includes the corrected voltage, the set of differences between the corrected voltage and the terminal voltage, and the threshold comparison result; the charging path node set includes the constant current node current difference sequence, the float charging preparation point, the voltage lookup table, and the transfer frequency calculation result; and the maintenance control execution flow includes a set of effective nodes arranged in sequence, the node set including the delay observation node, the limiting node, and the connection relationship.

[0013] As a further aspect of the present invention, the specific steps for generating the basic operational feature set are as follows:

[0014] Based on the acquisition of terminal voltage and charging current, the terminal voltage curve is divided into fixed time intervals and the mean of each interval is calculated. The difference between adjacent sampling points of charging current is calculated and the rate is obtained. The rate sequence is continuously arranged and a numerical set is established to generate voltage and current segmentation results.

[0015] Based on the voltage and current segmentation results, the difference in electrolyte temperature between continuous sampling points is calculated to obtain the trend slope. The trend slope is compared with the state of charge value range one by one and the corresponding range is confirmed. The comparison results are merged with the existing segmentation results to generate a basic operating feature set.

[0016] As a further aspect of the present invention, the specific steps for establishing the dynamic voltage factor sequence are as follows:

[0017] Based on the aforementioned basic operating feature set, the electrode conductivity values ​​are sampled and divided into set intervals. The levels of values ​​in each interval are labeled and an index table is established. The index table and the original conductivity data are associated and stored to generate a set of electrode conductivity levels.

[0018] Based on the electrode conductivity level set, the difference between adjacent time points of charging current is calculated and the difference is grouped according to its magnitude. The upper and lower limits of the terminal voltage fluctuation amplitude are set and a boundary interval is formed. When combining the boundary interval with the current grouping result, fuzzy logic condition matching is introduced to generate the corresponding output and generate a current and voltage interval set.

[0019] Based on the current and voltage interval set, the difference between adjacent temperature sampling values ​​of the temperature control unit is calculated and gradient intervals are divided. The gradient intervals and current and voltage intervals are merged and matched one by one. The matching results are arranged according to conditions to form a sequence, and a dynamic voltage factor sequence is established.

[0020] As a further aspect of the present invention, the specific execution process of the fuzzy logic is as follows: based on the electrode conductivity level set, the charging current grouping result, and the terminal voltage fluctuation boundary interval, fuzzy language values ​​of input parameters are set and an input variable level mapping table is established. A rule correspondence table is constructed according to the combination relationship of parameters. In rule matching, multiple inputs are jointly identified according to fuzzy conditions. Conditional reasoning is performed through fuzzy membership degrees. The voltage regulation level value corresponding to the rule is output and merged to generate a fuzzy output set, thereby obtaining the fuzzy control output result corresponding to the combination of input parameters.

[0021] As a further aspect of the present invention, the step of merging and matching the gradient intervals and current-voltage intervals one by one specifically involves combining the gradient intervals obtained by dividing the temperature difference of the temperature control unit with each interval in the current-voltage interval set one by one, forming a pair of combination items with each temperature gradient interval and each current-voltage interval, and comparing them according to a preset matching rule after forming the combination items. The matching rule is set to confirm the combination item is valid and output the matching result when the numerical range of the temperature gradient interval and the numerical range of the current-voltage interval simultaneously meet the preset judgment condition. The valid combination items are arranged in the order of generation and form a matching sequence.

[0022] As a further aspect of the present invention, the specific steps for generating the stage voltage regulation result are as follows:

[0023] Based on the dynamic voltage factor sequence, the constant voltage target value is multiplied by the factor item by item to generate a corrected voltage point series. The point series values ​​are arranged in chronological order to form a sequence. The sequence is archived and stored to generate the corrected voltage result.

[0024] Based on the corrected voltage result, the corrected voltage is subtracted from the terminal voltage point by point to obtain a set of differences. The set of differences is compared with a set threshold and the judgment result is output. The judgment results are summarized to construct a set and generate the stage voltage regulation result.

[0025] As a further aspect of the present invention, the specific steps for obtaining the charging path node set are as follows:

[0026] Based on the voltage regulation results of the stage, the constant current node current is compared with the results point by point to form a difference sequence. Kalman filtering is applied to smooth the difference sequence and correct noise. A comparison table is generated by comparing the difference sequence with the constant voltage interval voltage. The comparison table is numbered and organized to generate a current-voltage comparison set.

[0027] Based on the current-voltage reference set, locate the float charging preparation point in the reference table and extract the starting voltage value. Match the starting voltage value with the node set item by item and generate matching points. Merge the matching points with the transfer frequency calculation sequence and sort them to generate a node matching frequency set.

[0028] Based on the node matching frequency set, nodes with large transfer frequency deviations are identified and marked with an index. The marked nodes and delay observation nodes are inserted into the original path sequence in sequence. The adjusted path sequence is organized and output uniformly to obtain the charging path node set.

[0029] As a further embodiment of the present invention, the execution process of the Kalman filter is as follows: based on the difference sequence generated by the stage voltage regulation result, the sequence is input into the process point by point in time order, the initial state quantity is set and the state prediction value is updated point by point, the correction amount is calculated using the difference between the prediction value and the actual measurement, the correction amount is applied to the state prediction value to form a new state estimate value, the prediction and correction are performed cyclically, the difference sequence is continuously smoothed and random noise is corrected in the update, and the smoothed difference sequence after filtering and correction is output.

[0030] As a further aspect of the present invention, the specific steps for obtaining the maintenance control execution flow are as follows:

[0031] Based on the charging path node set, the node order is traversed point by point and the index of each node is recorded. The traversal results are compared with the probability threshold item by item and nodes below the threshold are extracted. Low probability nodes in the sequence are deleted and the remaining nodes are retained. The order of the retained nodes is rearranged to generate node filtering results.

[0032] Based on the node selection results, the delay observation node and the amplitude limiting node are inserted into the selected links in a set order. The inserted links are connected point by point to generate a continuous path. The integrity of the continuous path is checked and the sequence is recorded to obtain the maintenance control execution flow.

[0033] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0034] 1. In this invention, by real-time acquisition and analysis of terminal voltage and charging current, the voltage curve can be finely segmented and the average value of each interval can be calculated, which provides higher accuracy and real-time performance for battery status determination and improves the dynamic tracking capability of battery charging process.

[0035] 2. In this invention, by employing fuzzy logic matching, the dynamic adaptability of the battery charging process is greatly improved, avoiding the inefficiency and unsuitability of the fixed voltage and current mode in traditional methods, ensuring that every link in the charging process can be optimized and monitored, thereby extending battery life and reducing the risk of failure.

[0036] 3. In this invention, a corrected voltage is generated by calculating the product of the constant voltage target and the factor, and the difference between the terminal voltage and the corrected voltage is compared in real time. This allows for real-time voltage adjustment during battery charging, ensuring that the battery is always in a suitable working state and reducing the risk of overcharging and over-discharging. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the main steps of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0039] Please see Figure 1 This invention provides a technical solution: a control method for an intelligent maintenance device for lead-acid batteries, comprising the following steps:

[0040] Step 1: Based on the acquisition of terminal voltage and charging current, the terminal voltage curve is segmented by time interval and the mean is calculated. The current differential rate is extracted and the slope of the electrolyte temperature trend is obtained. The state of charge interval is determined and a basic operating feature set is generated.

[0041] Step 2: Based on the basic operating feature set, classify the plate conductivity level and obtain the current rate differential classification. After setting the terminal voltage fluctuation amplitude range, classify the temperature gradient range of the temperature control unit. Use fuzzy logic to combine and match conditions to establish a dynamic voltage factor sequence.

[0042] Step 3: Based on the dynamic voltage factor sequence, calculate the constant voltage target and factor product and generate the correction voltage, obtain the difference between the correction voltage and the terminal voltage, compare the difference with the threshold and confirm the result, and generate the stage voltage regulation result;

[0043] Step 4: Based on the stage voltage regulation results, compare the constant current node current with the constant voltage range voltage after Kalman filtering correction, match the float charging preparation point and determine the node transfer frequency, identify abnormal nodes and insert delayed observation nodes to obtain the charging path node set.

[0044] Step 5: Based on the charging path node set, traverse and compare nodes in order, identify and delete nodes with low probability, arrange the delay observation node and the limiting node in order and connect them to the path link, reorganize the link and generate a continuous control chain to obtain the maintenance control execution flow.

[0045] The basic operating feature set includes the average terminal voltage, the differential charging current rate, the electrolyte temperature trend slope, and the state of charge range. The dynamic voltage factor sequence includes the plate conductivity level, the differential current rate level, the voltage fluctuation amplitude range, and the temperature gradient range of the temperature control unit. The stage voltage regulation results include the corrected voltage, the set of differences between the corrected voltage and the terminal voltage, and the threshold comparison results. The charging path node set includes the constant current node current difference sequence, the float charging preparation point, the voltage comparison table, and the transfer frequency calculation results. The maintenance control execution flow includes the effective node set arranged in sequence. The node set includes the delay observation node, the limiting node, and the connection relationship.

[0046] The specific steps for generating the basic runtime feature set are as follows:

[0047] Based on the acquisition of terminal voltage and charging current, the terminal voltage curve is divided into fixed time intervals and the mean of each interval is calculated. The difference between adjacent sampling points of charging current is calculated and the rate is obtained. The rate sequence is continuously arranged and a numerical set is established to generate voltage and current segmentation results.

[0048] Based on the voltage and current segmentation results, the difference in electrolyte temperature between continuous sampling points is calculated to obtain the trend slope. The trend slope is compared with the state of charge numerical range one by one and the corresponding range is confirmed. The comparison results are merged with the existing segmentation results to generate a basic operating feature set.

[0049] Based on the acquisition of terminal voltage and charging current, a fixed time interval of 10 seconds is set to divide the terminal voltage curve, calculate the average voltage value in each time interval, obtain the average terminal voltage of the time interval, obtain the current difference rate between adjacent sampling points of charging current, and generate the rate sequence by continuously arranging the current difference rates in each time interval. The average terminal voltage and the current difference rate are combined in time order to establish a numerical set and generate voltage and current segmentation results.

[0050] Based on the voltage and current segmentation results, the slope of the electrolyte temperature change trend is obtained by calculating the difference between continuous sampling points. The temperature difference between two continuous sampling points is calculated to generate a temperature difference sequence. The slope of the temperature change trend is used as the trend of electrolyte temperature change. The trend slope is compared with the state of charge (SOC) value range one by one. The SOC value range is set to 0.2V to 1.5V. The SOC value range is matched according to the magnitude of the trend slope to confirm the SOC range corresponding to each temperature trend slope. The comparison results are merged with the existing voltage and current segmentation results to generate a basic operating feature set.

[0051] The specific steps for establishing a dynamic voltage factor sequence are as follows:

[0052] Based on the basic operating feature set, the electrode conductivity values ​​are sampled and divided into set intervals. The level of each interval value is labeled and an index table is established. The index table and the original conductivity data are linked and stored to generate a set of electrode conductivity levels.

[0053] Based on the plate conductivity level set, the difference between adjacent time points of charging current is calculated and the difference is grouped according to the size. The upper and lower limits of the terminal voltage fluctuation amplitude are set and the boundary interval is formed. When combining the boundary interval with the current grouping results, fuzzy logic condition matching is introduced to generate the corresponding output and generate the current voltage interval set.

[0054] Based on the current and voltage interval set, the difference between adjacent temperature sampling values ​​of the temperature control unit is calculated and gradient intervals are divided. The gradient intervals and current and voltage intervals are merged and matched one by one. The matching results are arranged according to conditions to form a sequence and a dynamic voltage factor sequence is established.

[0055] Based on the basic operating feature set, the conductivity values ​​of the electrode plates are sampled, with a sampling time interval of once every 10 seconds. The conductivity data is then divided according to the set time interval, with each interval corresponding to a specific value range. The conductivity values ​​of each interval are classified into levels according to preset standards: conductivity less than 0.5 S / m is low conductivity, between 0.5 S / m and 1.0 S / m is medium conductivity, and greater than 1.0 S / m is high conductivity. Each interval is assigned a level identifier. The conductivity level information is associated with the corresponding original conductivity data to generate an index table. The index table is then merged with the original conductivity data through data association to generate a set of electrode plate conductivity levels.

[0056] Based on the electrode conductivity level set, the difference between adjacent time points of charging current and the current difference between adjacent current sampling points are calculated to obtain the current difference value of each adjacent sampling point. According to the preset standard, the current difference value is divided into multiple groups. The current difference threshold is set to 0.1A. The current difference less than 0.1A is regarded as one group, and the current difference greater than 0.1A is regarded as another group, generating multiple current groups. The upper and lower limits of the terminal voltage fluctuation amplitude are set to ±0.2V. The voltage fluctuation amplitude within the range is regarded as an interval, and the voltage data outside the range is distinguished to generate voltage fluctuation intervals. The current grouping results are paired with the voltage fluctuation amplitude intervals one by one. After combining each current group and voltage fluctuation amplitude interval, a fuzzy logic algorithm is used for matching. According to the set conditions, the current difference rate and the voltage fluctuation are matched as high load, generating a combination item that meets the conditions, and obtaining the current and voltage interval set.

[0057] Based on the current and voltage range set, the temperature difference between adjacent sampling points of the temperature control unit is calculated. The temperature sampling interval is set to acquire data once every 5 seconds. The temperature difference between two adjacent sampling points is calculated to obtain a temperature difference sequence. According to the magnitude of the temperature difference, temperature gradient ranges are divided: a temperature difference less than 0.2℃ is a low temperature gradient, a temperature difference between 0.2℃ and 0.5℃ is a medium temperature gradient, and a temperature difference greater than 0.5℃ is a high temperature gradient. Different temperature gradient ranges are generated. The generated temperature gradient ranges are combined one by one with the current and voltage ranges in the current and voltage range set to generate temperature gradient combination items corresponding to each current and voltage range. According to the set matching rules, combinations with smaller temperature changes are matched when the current fluctuation is large, and combinations with larger temperature changes are matched when the voltage fluctuation is small. The combinations are filtered and sorted to generate a dynamic voltage factor sequence that meets the conditions and is arranged in chronological order.

[0058] The specific execution process of fuzzy logic is as follows: based on the plate conductivity level set, charging current grouping results and terminal voltage fluctuation boundary interval, fuzzy language values ​​of input parameters are set and an input variable level mapping table is established. According to the combination relationship of parameters, a rule correspondence table is constructed. In rule matching, multiple inputs are jointly identified according to fuzzy conditions. Conditional reasoning is performed through fuzzy membership degree. The voltage regulation level value corresponding to the rule is output and merged to generate a fuzzy output set to obtain the fuzzy control output result corresponding to the combination of input parameters.

[0059] Fuzzy logic, according to the formula: in: This indicates the final output voltage regulation level. Indicates input parameters membership function, Indicates the first The weight coefficients of each input. Indicates the first The correction coefficient for each input. Indicates the first The environmental adaptability coefficient of each input. Indicates the total number of input parameters;

[0060] Execution process: First, the relevant input parameters are fuzzy processed using the membership function. The input fuzzy values ​​are converted into fuzzy linguistic values, reflecting their influence on the overall voltage regulation. Based on the influence of each input parameter, and combined with weighting coefficients... Weighting is performed, with weighting coefficients. The system will set parameters based on the relative importance of each input during the charging process, and will adjust these parameters using a correction factor. and environmental adaptability coefficient To dynamically adjust the influence of each input parameter, the impact of temperature gradients needs to be enhanced or weakened in high and low temperature environments, and this is achieved by dynamically adjusting the correction coefficient. and environmental adaptability coefficient To control the battery charging voltage and prevent voltage fluctuations caused by environmental changes, the adjusted input parameters are weighted and summed using a formula to calculate the voltage regulation level. .

[0061] The specific method of matching gradient intervals and current-voltage intervals by merging them one by one is as follows: the gradient intervals obtained by dividing the temperature difference of the temperature control unit are matched one by one with each interval in the current-voltage interval set. Each temperature gradient interval and each current-voltage interval form a pair of combination items. After forming the combination items, they are compared according to the preset matching rules. The matching rule is set as follows: when the numerical range of the temperature gradient interval and the numerical range of the current-voltage interval simultaneously meet the preset judgment conditions, the combination item is confirmed to be valid and the matching result is output. The valid combination items are arranged in the order of generation and form a matching sequence.

[0062] The specific steps for generating the voltage regulation results are as follows:

[0063] Based on the dynamic voltage factor sequence, the constant voltage target value is multiplied by the factor item by item to generate a corrected voltage point series. The point series values ​​are arranged in chronological order to form a sequence. The sequence is archived and stored to generate the corrected voltage result.

[0064] Based on the corrected voltage result, the corrected voltage is subtracted from the terminal voltage point by point to obtain a set of differences. The set of differences is compared with a set threshold and the judgment result is output. The judgment results are summarized to construct a set and generate the stage voltage regulation result.

[0065] Based on the dynamic voltage factor sequence, the constant voltage target value is multiplied by the factor item by item. The constant voltage target value is set to 4.2V. The constant voltage target value is multiplied by each factor to generate a corrected voltage point series. The corrected voltage point series is the product of the constant voltage target and each voltage factor. The values ​​of the corrected voltage point series are arranged in chronological order, and the values ​​are arranged in chronological order to form a corrected voltage point series sequence. After the corrected voltage point series sequence is formed, the values ​​in the sequence are archived and stored to generate the corrected voltage result.

[0066] Based on the corrected voltage result, the corrected voltage is subtracted from the terminal voltage point by point to obtain a set of differences. The terminal voltage data is set to be recorded once every 5 seconds. Each corrected voltage point is compared with the corresponding terminal voltage point and the difference is calculated. Each difference is compared with the set threshold ±0.05V. The difference is marked as abnormal if it is greater than the threshold and as normal if it is less than the threshold. The judgment results are summarized and a judgment result set is constructed to generate the stage voltage regulation result.

[0067] The specific steps to obtain the charging path node set are as follows:

[0068] Based on the stage voltage regulation results, the constant current node current is compared with the results point by point to form a difference sequence. Kalman filtering is applied to smooth the difference sequence and correct noise. A comparison table is generated by comparing the difference sequence with the constant voltage interval voltage. The comparison table is numbered and organized to generate a current-voltage comparison set.

[0069] Based on the current and voltage reference set, the float charging preparation point in the reference table is located and the starting voltage value is extracted. The starting voltage value is matched with the node set item by item to generate matching points. The matching points are merged with the transfer frequency calculation sequence and sorted to generate a node matching frequency set.

[0070] Based on the node matching frequency set, nodes with large transfer frequency deviations are identified and marked with an index. The marked nodes and delay observation nodes are inserted into the original path sequence in order. The adjusted path sequence is then sorted and output uniformly to obtain the charging path node set.

[0071] Based on the stage voltage regulation results, the constant current node current and the stage voltage regulation results are compared point by point. The difference between the constant current node current and the stage voltage regulation results is calculated to form a difference sequence. The Kalman filter algorithm is applied to smooth the difference sequence and correct the noise. The initial state, state transition matrix, measurement matrix and noise covariance matrix of the Kalman filter are set. The difference sequence is smoothed through a recursive process to obtain the corrected difference sequence. The corrected difference sequence is compared with the set constant voltage range voltage to generate a comparison table, including the voltage range information corresponding to the difference. The comparison table is numbered and organized to generate a current-voltage comparison set.

[0072] Based on the current-voltage reference set, the float charge preparation point in the reference table is located, and the starting voltage value of the float charge preparation point is extracted as the reference voltage for subsequent matching. The starting voltage value is matched with each node in the node set one by one to generate matching points between nodes and float charge preparation points. The matching points are merged and combined with the transfer frequency calculation sequence. The transfer frequency calculation sequence is calculated and sorted according to the frequency of voltage change to generate a node matching frequency set.

[0073] Based on the node matching frequency set, firstly, nodes with large transfer frequency deviations are identified, the transfer frequency deviation of each node is calculated, and nodes with deviations exceeding a set threshold are marked as abnormal nodes. The marked abnormal nodes and delay observation nodes are inserted into the original path sequence in sequence, the adjusted path sequence is sorted, the nodes in the path are arranged in the correct order, and the path sequence is output uniformly to obtain the charging path node set.

[0074] The Kalman filter is executed as follows: based on the difference sequence generated by the stage voltage regulation result, the sequence is input into the process point by point in time order, the initial state quantity is set and the state prediction value is updated point by point, the correction amount is calculated using the difference between the prediction value and the actual measurement, the correction amount is applied to the state prediction value to form a new state estimate, the prediction and correction are performed in a loop, the difference sequence is continuously smoothed and random noise is corrected in the update, and the smoothed difference sequence after filtering and correction is output.

[0075] Kalman filtering, according to the formula: in: This represents the estimated state value at the current moment. This represents the state estimate at the previous moment. This represents the Kalman gain, the ratio of prediction error to measurement error. This represents the measurement value at the current moment. Represents the measurement matrix. This represents the error correction factor. This represents the square of the measurement error;

[0076] Execution process: First, set the initial state estimate. Using the initial prediction error covariance matrix and the state estimate from the previous time step... Predict the state at the current moment And based on actual measured values Calculate the Kalman gain based on the difference between the predicted and actual values. According to the standard Kalman filtering process, the measurement error and prediction error are combined to adjust the weights and obtain the corrected state estimate.

[0077] The specific steps to obtain the maintenance control execution flow are as follows:

[0078] Based on the charging path node set, the node order is traversed point by point and the index of each node is recorded. The traversal results are compared with the probability threshold item by item and the nodes below the threshold are extracted. The low probability nodes in the sequence are deleted and the remaining nodes are retained. The order of the retained nodes is rearranged to generate the node filtering results.

[0079] Based on the node selection results, the delay observation node and the amplitude limiting node are inserted into the selected links in a set order. The inserted links are connected point by point to generate a continuous path. The integrity of the continuous path is checked and the sequence is recorded to obtain the maintenance control execution flow.

[0080] Based on the charging path node set, the path nodes are traversed point by point, and the index of each node is recorded. The path nodes are arranged in order of time and current. The traversal results are compared item by item. The probability threshold is set to 0.1. The matching probability of each node is judged. Nodes with a matching probability lower than the threshold are judged as having unstable charging paths. Nodes with a matching probability lower than the threshold are extracted and deleted. The remaining nodes are kept and rearranged in order so that the nodes are arranged in the order of the actual charging process, generating node filtering results.

[0081] Based on the node screening results, the delay observation node and the limiting node are inserted into the screened node link in a set order. The delay observation node is a node that controls the voltage and current fluctuations within a safe range in response to abnormal situations that occur during charging. The delay observation node and the limiting node are connected to the screened node link point by point, so that the nodes are connected in the order required by the actual charging control. The integrity of the inserted link is checked to ensure that there are no omissions between each node in the path and that the connection is smooth and there are no data anomalies. The sorted continuous path link is recorded as a sequence to make the time sequence of the path, the node order and the voltage and current control logic consistent, so as to obtain the maintenance control execution flow.

[0082] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A control method for an intelligent maintenance device for a lead-acid battery, characterized in that, Includes the following steps: Step 1: Based on the acquisition of terminal voltage and charging current, the terminal voltage curve is segmented by time interval and the mean is calculated. The current differential rate is extracted and the slope of the electrolyte temperature trend is obtained. The state of charge interval is determined and a basic operating feature set is generated. Step 2: Based on the basic operating feature set, divide the plate conductivity level and obtain the current rate differential level. After setting the terminal voltage fluctuation amplitude range, divide the temperature gradient range of the temperature control unit. Use fuzzy logic to combine and match conditions to establish a dynamic voltage factor sequence. The specific steps for establishing the dynamic voltage factor sequence are as follows: Based on the aforementioned basic operating feature set, the electrode conductivity values ​​are sampled and divided into set intervals. The levels of values ​​in each interval are labeled and an index table is established. The index table and the original conductivity data are associated and stored to generate a set of electrode conductivity levels. Based on the electrode conductivity level set, the difference between adjacent time points of charging current is calculated and the difference is grouped according to its magnitude. The upper and lower limits of the terminal voltage fluctuation amplitude are set and a boundary interval is formed. When combining the boundary interval with the current grouping result, fuzzy logic condition matching is introduced to generate the corresponding output and generate a current and voltage interval set. Based on the current and voltage interval set, the difference between adjacent temperature sampling values ​​of the temperature control unit is calculated and gradient intervals are divided. The gradient intervals and current and voltage intervals are merged and matched one by one. The matching operation results are arranged according to conditions to form a sequence, and a dynamic voltage factor sequence is established. Step 3: Based on the dynamic voltage factor sequence, calculate the constant voltage target and factor product and generate the correction voltage, obtain the difference between the correction voltage and the terminal voltage, compare the difference with the threshold and confirm the result, and generate the stage voltage regulation result; Step 4: Based on the stage voltage regulation results, compare the constant current node current with the constant voltage range voltage after Kalman filtering correction, match the float charging preparation point and determine the node transfer frequency, identify abnormal nodes and insert delayed observation nodes to obtain the charging path node set. Step 5: Based on the charging path node set, traverse and compare nodes in order, identify and delete nodes with low probability, arrange the delay observation node and the limiting node in order and connect them to the path link, reorganize the link and generate a continuous control chain to obtain the maintenance control execution flow. The specific steps to obtain the maintenance control execution flow are as follows: Based on the charging path node set, the node order is traversed point by point and the index of each node is recorded. The traversal results are compared with the probability threshold item by item and nodes below the threshold are extracted. Low probability nodes in the sequence are deleted and the remaining nodes are retained. The order of the retained nodes is rearranged to generate node filtering results. Based on the node selection results, the delay observation node and the amplitude limiting node are inserted into the selected links in a set order. The inserted links are connected point by point to generate a continuous path. The integrity of the continuous path is checked and the sequence is recorded to obtain the maintenance control execution flow.

2. The control method of the intelligent maintenance device for lead-acid batteries according to claim 1, characterized in that, The basic operating feature set includes the average terminal voltage, the differential charging current rate, the electrolyte temperature trend slope, and the state of charge range. The dynamic voltage factor sequence includes the plate conductivity level, the differential current rate level, the voltage fluctuation amplitude range, and the temperature gradient range of the temperature control unit. The stage voltage regulation results include the corrected voltage, the set of differences between the corrected voltage and the terminal voltage, and the threshold comparison results. The charging path node set includes the constant current node current difference sequence, the float charging preparation point, the voltage lookup table, and the transfer frequency calculation results. The maintenance control execution flow includes a set of effective nodes arranged in sequence. The node set includes delay observation nodes, limit nodes, and connection relationships.

3. The control method of the intelligent maintenance device for lead-acid batteries according to claim 1, characterized in that, The specific steps for generating the basic operational feature set are as follows: Based on the acquisition of terminal voltage and charging current, the terminal voltage curve is divided into fixed time intervals and the mean of each interval is calculated. The difference between adjacent sampling points of charging current is calculated and the rate is obtained. The rate sequence is continuously arranged and a numerical set is established to generate voltage and current segmentation results. Based on the voltage and current segmentation results, the difference in electrolyte temperature between continuous sampling points is calculated to obtain the trend slope. The trend slope is compared with the state of charge value range one by one and the corresponding range is confirmed. The comparison results are merged with the existing segmentation results to generate a basic operating feature set.

4. The control method of claim 1, wherein, The specific execution process of the fuzzy logic is as follows: based on the electrode conductivity level set, charging current grouping results, and terminal voltage fluctuation boundary interval, fuzzy language values ​​of input parameters are set and an input variable level mapping table is established. A rule correspondence table is constructed according to the combination relationship of parameters. In rule matching, multiple inputs are jointly identified according to fuzzy conditions. Conditional reasoning is performed through fuzzy membership degrees. The voltage regulation level value corresponding to the rule is output and merged to generate a fuzzy output set, thereby obtaining the fuzzy control output result corresponding to the combination of input parameters.

5. The control method for the intelligent maintenance device of lead-acid batteries according to claim 1, characterized in that, The specific steps of matching the gradient intervals and current-voltage intervals one by one are as follows: the gradient intervals obtained by dividing the temperature difference of the temperature control unit are matched one by one with each interval in the current-voltage interval set. Each temperature gradient interval and each current-voltage interval form a pair of combination items. After forming the combination items, they are compared according to the preset matching rules. The matching rules are set as follows: when the numerical range of the temperature gradient interval and the numerical range of the current-voltage interval simultaneously meet the preset judgment conditions, the combination item is confirmed to be valid and the matching result is output. The valid combination items are arranged in the order of generation and form a matching sequence.

6. The control method of claim 1, wherein, The specific steps for generating the stage voltage regulation result are as follows: Based on the dynamic voltage factor sequence, the constant voltage target value is multiplied by the factor item by item to generate a corrected voltage point series. The point series values ​​are arranged in chronological order to form a sequence. The sequence is archived and stored to generate the corrected voltage result. Based on the corrected voltage result, the corrected voltage is subtracted from the terminal voltage point by point to obtain a set of differences. The set of differences is compared with a set threshold and the judgment result is output. The judgment results are summarized to construct a set and generate the stage voltage regulation result.

7. The control method of claim 1, wherein, The specific steps for obtaining the charging path node set are as follows: Based on the voltage regulation results of the stage, the constant current node current is compared with the results point by point to form a difference sequence. Kalman filtering is applied to smooth the difference sequence and correct noise. A comparison table is generated by comparing the difference sequence with the constant voltage interval voltage. The comparison table is numbered and organized to generate a current-voltage comparison set. Based on the current-voltage reference set, locate the float charging preparation point in the reference table and extract the starting voltage value. Match the starting voltage value with the node set item by item and generate matching points. Merge the matching points with the transfer frequency calculation sequence and sort them to generate a node matching frequency set. Based on the node matching frequency set, nodes with large transfer frequency deviations are identified and marked with an index. The marked nodes and delay observation nodes are inserted into the original path sequence in sequence. The adjusted path sequence is organized and output uniformly to obtain the charging path node set.

8. The control method of claim 1, wherein, The Kalman filter execution process is as follows: based on the difference sequence generated by the stage voltage regulation result, the sequence is input into the process point by point in time order, the initial state quantity is set and the state prediction value is updated point by point, the correction amount is calculated using the difference between the prediction value and the actual measurement, the correction amount is applied to the state prediction value to form a new state estimate value, the prediction and correction are performed cyclically, the difference sequence is continuously smoothed and random noise is corrected in the update, and the smoothed difference sequence after filtering and correction is output.

Images