Storage battery on-line monitoring system

Abstract

The invention relates to the technical field of storage battery monitoring, and discloses a storage battery online monitoring system, which comprises the following components: a multi-parameter sensor integration module, a comprehensive signal processing and conversion module, an intelligent data processing and analysis unit and an efficient communication and display module, according to the invention, through integration of the multi-parameter sensor, key state parameters of the storage battery can be comprehensively and accurately obtained, meanwhile, the comprehensive signal processing and conversion module efficiently processes and converts output signals of the sensor, and the intelligent data processing and analysis unit adopts an advanced fuzzy neural network algorithm to establish a storage battery health model. According to the method, the processed signals are comprehensively analyzed, so that the health state of the storage battery can be accurately judged in real time, and compared with a traditional monitoring means, the monitoring accuracy and real-time performance are greatly improved, potential problems of the storage battery can be timely found and processed, and accidents are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring, and specifically to an online battery monitoring system. Background Art

[0002] With the development of modern industry and information technology, batteries, as energy storage devices, have been widely used in various fields. The performance of batteries directly affects the stability and reliability of these systems. However, due to the complexity of internal chemical reactions and the variability of working environments of batteries, the state monitoring thereof has become a technical problem. Accurately and real-time monitoring of battery state parameters, such as power, voltage, internal resistance, and temperature, is of great significance for preventing battery failures, extending battery life, and ensuring the safe and stable operation of the system.

[0003] Traditional battery monitoring technologies have many deficiencies. On the one hand, traditional sensors often can only monitor a single parameter, such as only monitoring voltage or internal resistance, lacking the ability of multi-parameter comprehensive monitoring, which leads to an incomplete and inaccurate judgment of the battery state. On the other hand, traditional signal processing technologies are relatively simple and difficult to effectively remove noise interference in the sensor output signal, affecting the accuracy of monitoring results. In addition, traditional data processing and analysis methods mostly use simple threshold judgments, lacking in-depth analysis and prediction capabilities for the health state of batteries, and it is difficult to timely discover potential problems of batteries. In terms of fault diagnosis, traditional technologies often rely on empirical judgments and lack scientific algorithm support, resulting in low accuracy of fault diagnosis and low maintenance efficiency.

[0004] In summary, due to the deficiencies of traditional battery monitoring technologies in terms of comprehensiveness, accuracy, real-time performance, and fault diagnosis capabilities, it is particularly important to develop an online battery monitoring system. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide an online battery monitoring system, which can achieve comprehensive monitoring of the battery state and accurate fault diagnosis by integrating multi-parameter sensors, advanced signal processing and conversion technologies, intelligent data processing and analysis algorithms, and efficient communication and display modules, providing strong guarantee for the safe and stable operation of the battery.

[0006] To solve the above technical problems, the present invention provides the following technical solution: An online battery monitoring system, which includes the following components: a multi-parameter sensor integration module, a comprehensive signal processing and conversion module, an intelligent data processing and analysis unit, and an efficient communication and display module;

[0007] The multi-parameter sensor integration module: includes a power sensor, a voltage sensor, an internal resistance measurement sensor, and a temperature sensor based on the principle of quantum tunneling;

[0008] The integrated signal processing and conversion module: is used to process and convert the output signals of various sensors;

[0009] The intelligent data processing and analysis unit: comprehensively analyzes the processed signals and judges the state of the battery;

[0010] The high-efficiency communication and display module: is responsible for transmitting and displaying the monitoring results.

[0011] Furthermore, the voltage sensor adopts a high-precision Hall voltage sensor, which integrates a magnetosensitive element inside. Through the magnetoelectric conversion mechanism, the magnetosensitive element converts the voltage across the battery into a proportional change in magnetic field strength. In practical applications, for the voltage ranges of different types of batteries, by adjusting the material properties and structural parameters of the magnetosensitive element, such as changing the concentration and distribution of doped atoms in semiconductor materials, the sensitivity of the sensor is accurately calibrated. For lead-acid batteries (with a single-cell voltage range of 2V - 12V), by optimizing the magnetosensitive element, the measurement accuracy of the sensor can reach ±0.01V within this voltage range. During the measurement process, the analog voltage signal output by the sensor first passes through a low-pass filter circuit to remove high-frequency noise interference, and then is amplified by an operational amplifier with an amplification factor of 100 to ensure that the signal strength meets the requirements of subsequent analog-to-digital conversion, providing reliable data support for accurately monitoring the battery voltage.

[0012] Even further, the internal resistance measurement sensor is based on the principle of AC injection method. This sensor injects an AC sine signal with a frequency of into the battery through a signal generator, and its amplitude is . After injecting the signal, a differential amplifier is used to measure the response voltage generated across the battery. Let the response voltage be . The internal resistance is calculated through an algorithm, and the calculation formula is , where is the injected AC current, which can be obtained by measuring the voltage across the sampling resistor in the injection circuit and combining Ohm's law, and is the correction factor. The determination of the correction factor is based on a large amount of experimental data. By conducting internal resistance measurement experiments on batteries of different types and different service life, analyzing the deviation between the measurement results and the actual internal resistance, the functional relationship between and the battery type and service life is obtained by least squares fitting. In this way, the internal resistance of the battery can be accurately calculated, providing key parameters for judging the quality of the battery.

[0013] Even further, the temperature sensor adopts a thermistor, and its resistance value is related to the temperature Satisfy the following non-linear relationship: , where is the resistance value at temperature , is the material constant of the thermistor. To improve the accuracy of temperature measurement, in the hardware design, the thermistor is closely attached to the battery shell, and a four-wire connection method is adopted to eliminate the influence of wire resistance on the measurement result. In terms of software algorithm, by collecting the resistance values of the thermistor at different temperatures, a resistance value-temperature lookup table is established. When measuring the battery temperature, first measure the resistance value of the thermistor, and then use the linear interpolation algorithm to find the corresponding temperature value in the lookup table. If the measured resistance value is between and in the lookup table, and the corresponding temperatures are and respectively, then the actual temperature . Through this combination of hardware and software, the rapid and accurate perception of the battery temperature is realized.

[0014] Furthermore, in the comprehensive signal processing and conversion module, for the analog signals output by the voltage and temperature sensors, a second-order Butterworth low-pass filter is used for filtering. For the signal output by the voltage sensor, the transfer function of the second-order Butterworth low-pass filter is , where is a complex variable. Through this filter, the noise signals with frequencies higher than the cut-off frequency can be effectively removed. The cut-off frequency is determined according to the highest frequency component and noise characteristics of the battery voltage signal, and is generally set to . The filtered signal is then amplified by an operational amplifier with an adjustable amplification factor. The amplification factor is dynamically adjusted according to the amplitude range of the sensor output signal and the input range of the analog-to-digital converter. When the amplitude range of the signal output by the voltage sensor is 0 - 50 mV and the input range of the analog-to-digital converter is 0 - 3 V, the amplification factor of the operational amplifier is adjusted to 60 to amplify the signal amplitude to an appropriate range, and then it is converted into a digital signal by a 16-bit successive approximation analog-to-digital converter to ensure the accuracy and stability of signal processing.

[0015] Furthermore, the battery health model established by the intelligent data processing and analysis unit adopts the fuzzy neural network algorithm. In this algorithm, the input layer nodes respectively correspond to the voltage , internal resistance , temperature of the battery, and the battery power obtained based on the quantum tunneling effect. In the hidden layer, the output of the neuron is calculated by the following formula: , where is the input value of the -th node in the input layer, is the weight between the -th node in the input layer and the -th node in the hidden layer, is the bias of the -th node in the hidden layer, is the fuzzy membership function, and the Gaussian membership function and are the center and width of the Gaussian function respectively, which are determined by clustering analysis of a large amount of historical battery data. The output layer obtains the health state evaluation value of the battery through weighted summation , , where is the weight between the -th node in the hidden layer and the output layer, and is continuously adjusted according to the training data through the backpropagation algorithm to improve the accuracy of model prediction. Through this algorithm, the health state of the battery can be accurately evaluated by integrating multiple parameters.

[0016] Furthermore, when the intelligent data processing and analysis unit performs fault diagnosis, it adopts a fault diagnosis algorithm based on a Bayesian network. Let the set of possible fault types of the battery be , and the set of measurement parameters be . First, according to a large amount of historical fault data, a conditional probability table between fault types and measurement parameters is established . When the parameter value of the battery is monitored as , through Bayes' formula:

[0017]

[0018] calculate the probability of occurrence of each fault type , where is the prior probability of fault type , which is obtained by counting the frequencies of various faults in historical fault data. If the calculated is the largest, it is determined that the battery has a fault type , such as the fault of plate vulcanization. Through this algorithm, the fault type of the battery can be accurately judged by combining multiple parameters, providing precise guidance for maintenance.

[0019] Furthermore, the local display screen in the communication and display module uses a TFT-LCD liquid crystal display screen with a resolution of 480×272. In the display interface design, a hierarchical display strategy is adopted. The main interface displays the power, voltage, current, internal resistance, and temperature parameters of the battery in real time, presented in a combination of numbers and bar charts, intuitively showing the real-time values and change trends of the parameters. When the battery state is abnormal, it automatically switches to the warning interface, highlighting the abnormal parameters with a red background and displaying the fault type and handling suggestions on the interface. At the same time, a touch control function is integrated in the display module, and users can switch the display interface and query historical data through touch operations, improving the convenience of user operation and the interaction experience.

[0020] Compared with the prior art, this on-line battery monitoring system has the following beneficial effects:

[0021] First, by integrating multi-parameter sensors, this system can comprehensively and accurately obtain the key state parameters of the battery. At the same time, the integrated signal processing and conversion module efficiently processes and converts the output signals of the sensors. The intelligent data processing and analysis unit uses an advanced fuzzy neural network algorithm to establish a battery health model and comprehensively analyzes the processed signals, enabling real-time and accurate judgment of the battery health state. Compared with traditional monitoring methods, this greatly improves the accuracy and real-time performance of monitoring, helps to promptly discover and handle potential problems of the battery, and avoids accidents.

[0022] Second, when the intelligent data processing and analysis unit of this system performs fault diagnosis, it uses a fault diagnosis algorithm based on the Bayesian network. This algorithm establishes a conditional probability table between the fault types and measurement parameters according to a large amount of historical fault data. When abnormal parameters of the battery are detected, it can quickly and accurately calculate the probabilities of various fault types occurring, thus accurately judging the fault type of the battery. This not only improves the accuracy of fault diagnosis but also provides accurate fault handling guidance for maintenance personnel, greatly improving the maintenance efficiency of the battery. At the same time, the local display screen in the communication and display module uses a TFT-LCD liquid crystal display screen and adopts a hierarchical display strategy, which can intuitively display the real-time values and change trends of the parameters. When the battery state is abnormal, it automatically switches to the warning interface and highlights the abnormal parameters, further improving the usability of the system and the user experience.

[0023] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 It is a flowchart for realizing the functions of a battery online monitoring system.

[0026] Figure 2 It is a flowchart of the overall architecture of a battery online monitoring system. Specific embodiments

[0027] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the drawings and preferred embodiments, detail the specific embodiments, structures, features and their effects of the present invention as follows.

[0028] Embodiment 1: This embodiment describes a communication base station in a remote mountainous area with a complex geographical environment and unstable mains power supply. The communication equipment completely relies on lead-acid battery packs as backup power. If a battery fails, it will cause communication interruption within a range of dozens of kilometers around, affecting important matters such as residents' daily communication and emergency rescue. Therefore, accurate and real-time online monitoring of the battery to promptly discover and solve potential problems has become the key to ensuring the stable operation of the communication base station.

[0029] The battery charge sensor based on the principle of quantum tunneling continuously monitors the remaining charge of the battery. Its unique working principle can accurately sense the charge change inside the battery and provide reliable data for subsequent charge management.

[0030] The voltage sensor selects a high-precision Hall voltage sensor. The internal magnetosensitive element converts the voltage across the battery into a proportional change in magnetic field strength through the magnetoelectric conversion mechanism. Since there are various electronic devices in the communication base station, which are prone to generate high-frequency electromagnetic interference, the analog voltage signal output by the voltage sensor first passes through a low-pass filter circuit to remove these high-frequency noise interferences and ensure the purity of the signal. Then, the signal is amplified by an operational amplifier with an amplification factor of 100 for subsequent processing.

[0031] The internal resistance measurement sensor works based on the principle of AC injection method. In actual operation, the signal generator injects an AC sine signal with a frequency and amplitude into the battery. After injecting the signal, a differential amplifier is used to measure the response voltage generated across the battery. Suppose after measurement, the response voltage is obtained. Given the injected AC current known injected AC current , correction factor , according to the formula , the internal resistance of the storage battery can be calculated .

[0032] The temperature sensor uses a thermistor, which is closely attached to the outer shell of the storage battery to obtain the measured value closest to the actual temperature of the battery. The thermistor uses a four-wire connection method to effectively reduce the influence of line resistance on the measurement result. The resistance value of the thermistor and the temperature satisfy a non-linear relationship: , after the hardware installation is completed, technicians collect the resistance values of the thermistor at different temperatures in a laboratory environment, establish a resistance value-temperature look-up table. When actually measuring the temperature of the storage battery, first measure the resistance value of the thermistor. If the measured resistance value is between and , and the corresponding temperatures are and , then the actual temperature is calculated through a linear interpolation algorithm .

[0033] There is a certain amount of noise and interference in the analog signals output by the voltage and temperature sensors. To ensure the signal quality, a second-order Butterworth low-pass filter is used for filtering. For the signal output by the voltage sensor, the transfer function of the second-order Butterworth low-pass filter is , this filter can effectively remove the noise signals with frequencies higher than the cut-off frequency . The filtered signal is then amplified by an operational amplifier with an adjustable amplification factor. The amplification factor will be dynamically adjusted according to the amplitude range of the sensor output signal and the input range of the analog-to-digital converter to ensure that the amplitude of the signal input to the subsequent intelligent data processing and analysis unit is within an appropriate range, improving the accuracy of data processing.

[0034] The intelligent data processing and analysis unit receives the processed voltage, internal resistance, temperature, and power data, and establishes a storage battery health model through a fuzzy neural network algorithm. The input layer nodes respectively correspond to the voltage , internal resistance , temperature of the storage battery, and the power obtained based on the quantum tunneling effect. In the hidden layer, the output of the neuron , where is the input value of the th node of the input layer, is the weight between the th node of the input layer and the th node of the hidden layer, is the bias of the -th node in the hidden layer, is the fuzzy membership function, and the Gaussian membership function is adopted , and are the center and width of the Gaussian function respectively. By continuously adjusting the weights and biases, the model can accurately reflect the health state of the battery.

[0035] When abnormal parameters of the battery are detected, the intelligent data processing and analysis unit uses a fault diagnosis algorithm based on Bayesian network for fault diagnosis. Let the set of possible fault types of the battery be , and the set of measured parameters be . According to a large amount of historical fault data, technicians establish a conditional probability table between the fault types and the measured parameters. When the measured parameter value of the battery is , the probability of each fault type occurring is calculated through Bayes' formula . Among them, is the prior probability of the fault type . For example, after calculation, it is found that the probability of a certain fault type occurring exceeds the set threshold, and the system immediately issues a warning to prompt technicians to check and maintain the battery.

[0036] The communication base station is locally configured with a TFT-LCD liquid crystal display screen, adopting a hierarchical display strategy. The main interface displays the power, voltage, current, internal resistance, and temperature parameters of the battery in real time, presented in a combination of numbers and bar charts. The digital display can accurately show the specific values of the parameters, and the bar chart intuitively shows the change trend of the parameters, facilitating the staff to quickly understand the real-time state of the battery.

[0037] When the battery state is abnormal, the system automatically switches to the warning interface. On the warning interface, the abnormal parameters are highlighted with a red background, enabling the staff to notice the problem at the first time. At the same time, the fault type and treatment suggestions will be displayed on the interface, such as "The internal resistance of the battery is too high. It is recommended to check the connection line and consider replacing the battery". In addition, the display module integrates a touch control function. The staff can switch the display interface and query historical data through touch operations. By querying historical data, the staff can analyze the performance change trend of the battery and formulate a maintenance plan in advance to prevent potential faults from occurring.

[0038] Embodiment 2: This embodiment describes a large data center, such as a data center responsible for the core business of an e-commerce platform, where a large number of servers are running and carrying out key tasks such as user data storage and online transaction processing. This data center operates year-round. Once a power outage occurs, not only will transactions be interrupted and data lost, but it will also seriously affect the user experience and cause huge economic losses. Therefore, the stable operation of the backup power supply is extremely important, and the battery is the core part of the backup power supply. Real-time and accurate monitoring of the battery is of great significance.

[0039] The multi-parameter sensor integration module plays a key role. The power sensor based on the principle of quantum tunneling can keenly sense the changes in the battery's power and provide power information in a timely manner. The high-precision Hall voltage sensor measures the voltage by converting the voltage across the battery into changes in magnetic field strength through internal magnetic sensitive elements. For lead-acid batteries, the analog voltage signal output by the sensor first passes through a low-pass filter circuit to remove high-frequency noise, and then is amplified to make the voltage data more accurate. The internal resistance measurement sensor uses the AC injection method to send a specific AC signal to the battery, and then measures the response voltage generated at both ends of the battery to calculate the internal resistance and judge the battery performance. The temperature sensor uses a thermistor, which is tightly attached to the battery casing and can quickly sense temperature changes. It is connected via a four-wire system and uses a resistance-temperature lookup table established by the software algorithm to accurately obtain the battery temperature.

[0040] The integrated signal processing and conversion module is like a "master of signal optimization". For the analog signals from the voltage and temperature sensors, it uses a second-order Butterworth low-pass filter to filter out the noise, and then flexibly adjusts the amplification factor of the operational amplifier according to the signal size and the requirements of the analog-to-digital converter, so that the signal can be stably transmitted to the next unit.

[0041] The intelligent data processing and analysis unit is the "smart center" of the entire monitoring system. It uses a fuzzy neural network algorithm to build a battery health model, integrating data such as voltage, internal resistance, temperature and power to evaluate the health of the battery. When the battery is abnormal, it will use a fault diagnosis algorithm based on a Bayesian network, refer to a large amount of historical fault data, and quickly determine the type of fault, such as battery aging, leakage, or other problems.

[0042] The local display screen of the efficient communication and display module adopts a TFT-LCD liquid crystal display screen. The main interface uses numbers and bar graphs to simultaneously display the battery's power, voltage, current, internal resistance and temperature parameters, allowing staff to clearly see real-time data and changing trends. Once the battery status is abnormal, the screen will automatically switch to the early warning interface, highlighting the abnormal parameters in eye-catching red, and will also give the fault type and handling suggestions. Moreover, the display screen integrates a touch control function, and staff can switch interfaces and view historical data through touch operations to keep abreast of the battery's operating conditions.

[0043] As described above, it is only the preferred embodiment of the present invention and does not impose any formal restrictions on the present invention. Although the present invention has been disclosed above in the preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the equivalent embodiments of equivalent changes within the scope of the technical solution of the present invention by using the above-disclosed technical content. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A battery online monitoring system, characterized in that: The system includes the following components: multi-parameter sensor integration module, comprehensive signal processing and conversion module, intelligent data processing and analysis unit, and efficient communication and display module; The multi-parameter sensor integrated module includes a power sensor, a voltage sensor, an internal resistance measurement sensor and a temperature sensor based on the quantum tunneling principle; The comprehensive signal processing and conversion module is used to process and convert the output signals of various sensors; The intelligent data processing and analysis unit performs a comprehensive analysis on the processed signal and determines the battery status; The efficient communication and display module is responsible for transmitting and displaying monitoring results.

2. A battery online monitoring system according to claim 1, characterized in that: The voltage sensor adopts a high-precision Hall voltage sensor, which integrates a magnetic sensitive element. The magnetic sensitive element converts the voltage across the battery into a proportional change in magnetic field strength through a magnetoelectric conversion mechanism. For lead-acid batteries, by optimizing the magnetic sensitive element, during the measurement process, the analog voltage signal output by the sensor first passes through a low-pass filter circuit to remove high-frequency noise interference, and then is amplified by an operational amplifier with an amplification factor of 100.

3. A battery online monitoring system according to claim 1, characterized in that: The internal resistance measurement sensor is based on the principle of AC injection method. The sensor injects a frequency of The AC sinusoidal signal has an amplitude of After the signal is injected, the response voltage generated at both ends of the battery is measured using a differential amplifier. The response voltage is , calculate the internal resistance through the algorithm , the calculation formula is ,in is the injected AC current, is the correction factor.

4. A battery online monitoring system according to claim 1, characterized in that: The temperature sensor adopts a thermistor, whose resistance value is With temperature The following nonlinear relationship is satisfied: ,in For temperature The resistance value when is the material constant of the thermistor. In hardware design, the thermistor is tightly fitted to the battery casing and connected in a four-wire system. In software algorithm, the resistance value of the thermistor at different temperatures is collected to establish a resistance value-temperature lookup table. When measuring the battery temperature, the resistance value of the thermistor is first measured, and then the corresponding temperature value is found in the lookup table through the linear interpolation algorithm. If the measured resistance value In the lookup table and The corresponding temperatures are and , then the actual temperature .

5. A battery online monitoring system according to claim 1, characterized in that: In the integrated signal processing and conversion module, the analog signals output by the voltage and temperature sensors are filtered by a second-order Butterworth low-pass filter. The transfer function of the voltage sensor output signal and the second-order Butterworth low-pass filter is: ,in is a complex variable. This filter can effectively remove frequencies higher than the cutoff frequency. The noise signal is filtered and then amplified by an operational amplifier with adjustable amplification factor. The amplification factor is dynamically adjusted according to the amplitude range of the sensor output signal and the input range of the analog-to-digital converter.

6. A battery online monitoring system according to claim 1, characterized in that: The battery health model established by the intelligent data processing and analysis unit adopts a fuzzy neural network algorithm, in which the input layer nodes correspond to the battery voltage 、Internal resistance ,temperature And the amount of electricity obtained based on the quantum tunneling effect , in the hidden layer, the output of the neuron Calculated by the following formula: ,in The input layer The input value of the node, The input layer nodes and the hidden layer The weights between nodes, The hidden layer The bias of the node, is the fuzzy membership function, and the Gaussian membership function is used and are the center and width of the Gaussian function respectively. The output layer obtains the health status assessment value of the battery through weighted summation. , ,in The hidden layer The weights between each node and the output layer.

7. A battery online monitoring system according to claim 1, characterized in that: When the intelligent data processing and analysis unit performs fault diagnosis, it adopts a fault diagnosis algorithm based on the Emerson network, assuming that the set of possible fault types of the battery is , the measurement parameter set is First, based on a large amount of historical fault data, a conditional probability table between fault types and measurement parameters is established. When the battery parameter value is When , through the Bayesian formula: , calculate each fault type The probability of occurrence, Fault Type The prior probability of .

8. A battery online monitoring system according to claim 1, characterized in that: The local display screen in the communication and display module adopts a TFT-LCD liquid crystal display screen. In the display interface design, a layered display strategy is adopted. The main interface displays the battery power, voltage, current, internal resistance and temperature parameters in real time, and presents them in a combination of numbers and bar graphs to intuitively display the real-time values ​​and change trends of the parameters. When the battery status is abnormal, it automatically switches to the early warning interface, highlights the abnormal parameters with a red background, and displays the fault type and processing suggestions on the interface. At the same time, a touch control function is integrated in the display module, and the user can switch the display interface and query historical data through touch operation.

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