CN117996243A - Lithium battery energy storage battery pack control method and system - Google Patents
Lithium battery energy storage battery pack control method and system Download PDFInfo
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- 238000004146 energy storage Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims abstract description 27
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 24
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 24
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 4
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Abstract
The invention discloses a control method and a system for a lithium battery energy storage battery pack, which belong to the technical field of batteries and energy storage, and the method comprises the following steps: data acquisition and real-time monitoring; data processing and feature extraction; establishing and training a prediction model, establishing the prediction model of the battery performance by utilizing historical data and a machine learning algorithm, and training and optimizing the model; predicting the performance index of the battery pack in a future period based on a prediction model, and performing optimization control according to a prediction result; abnormality detection and protection functions; remote control and management, and remote control, data storage and remote diagnosis functions are realized through the cloud platform. The invention can improve the prediction accuracy of the battery pack, prolong the service life, improve the safety and stability, optimize the use efficiency and provide a more reliable and efficient battery pack application and management scheme for users.
Description
Technical Field
The invention relates to the technical field of batteries and energy storage, in particular to a control method and a control system for a lithium battery energy storage battery pack.
Background
Lithium ion batteries are widely used as core energy supply components in the fields of new energy automobiles, electrochemical energy storage and the like, but the problems of performance degradation and thermal runaway caused by aging of the lithium ion batteries always endanger long-term application safety. The state of health is an important quantization index that can effectively evaluate the performance degradation degree of the battery, but in the prior art, the index is often difficult to directly measure through a sensor and depends on online estimation, which leads to inaccurate index acquisition, is unfavorable for the control and management of a battery pack, and affects the service life of the battery.
Disclosure of Invention
The technical task of the invention is to provide the control method and the control system for the lithium battery energy storage battery pack, aiming at the defects, which can improve the prediction accuracy of the battery pack, prolong the service life, improve the safety and stability, optimize the use efficiency and provide a more reliable and efficient battery pack application and management scheme for users.
The technical scheme adopted for solving the technical problems is as follows:
a lithium battery energy storage battery pack control method is realized, which comprises the following steps:
(1) Data acquisition and real-time monitoring, wherein real-time data of the battery pack are acquired through a sensor;
(2) Data processing and feature extraction, wherein preprocessing and feature extraction are carried out on the acquired real-time data, and the preprocessing and feature extraction comprise filtering, noise reduction and feature extraction algorithms;
(3) Establishing and training a prediction model, establishing the prediction model of the battery performance by utilizing historical data and a machine learning algorithm, and training and optimizing the model;
(4) Predicting the performance index of the battery pack in a future period based on a prediction model, and performing optimization control according to a prediction result;
(5) The system comprises an abnormality detection and protection function, a prediction model and a battery pack monitoring module, wherein the abnormality detection and protection function is used for monitoring the state of the battery pack in real time, comparing the state with the prediction model, detecting whether an abnormality occurs or not, and taking corresponding protection measures according to the abnormality;
(6) Remote control and management, and remote control, data storage and remote diagnosis functions are realized through a cloud platform; and the monitoring and management functions comprise the steps of remotely setting a charging and discharging strategy, receiving real-time data and alarm notification and diagnosing faults.
The method combines with artificial intelligence, improves prediction accuracy, prolongs battery life, improves safety and stability, improves energy utilization efficiency, realizes remote control and management, and provides a more reliable, efficient and intelligent battery pack application and management solution for users.
Preferably, the data acquisition and real-time monitoring includes:
(1.1) acquiring real-time data of the battery pack by using a sensor, wherein the real-time data comprises current, voltage and temperature data;
(1.2) transmitting the data acquired by the sensor to a control system through a data acquisition module;
and (1.3) adjusting the data acquisition frequency according to the real-time state of the battery pack so as to acquire the latest state of the battery pack.
Preferably, the data processing and feature extraction includes:
(2.1) preprocessing the data acquired by the sensor, including data cleaning, filtering and normalization;
And (2.2) extracting characteristic information, including the slope of a charge-discharge curve and the internal resistance of a battery, from the processed data by using a characteristic extraction algorithm, including a time domain characteristic and a frequency domain characteristic algorithm, for subsequent prediction and optimization.
Preferably, the prediction model is established and trained as follows:
(3.1) establishing a prediction model based on an artificial intelligence technology, wherein the prediction model is a regression model based on machine learning or a neural network model based on deep learning;
(3.2) training a predictive model using existing historical data to learn the performance laws and life distribution of the battery pack.
Preferably, the battery pack performance prediction and optimization control includes:
(4.1) monitoring state data of the battery pack in real time, including voltage, current and temperature;
(4.2) predicting performance indexes of the battery pack in a future period of time, including residual available energy and health state, by inputting real-time state data based on a prediction model;
and (4.3) performing optimal control according to the prediction result, wherein the optimal control comprises dynamic adjustment of a charging and discharging strategy and temperature management so as to prolong the service life and improve the safety performance.
Preferably, the anomaly monitoring and protection mechanism comprises:
(5.1) the control system detects whether abnormal conditions, including overvoltage, overcharge and charge imbalance, occur by monitoring the state of the battery pack in real time and comparing the state with a prediction model;
And (5.2) according to the threshold value and the rule defined in the prediction model, the control system takes corresponding protection measures according to abnormal conditions, including stopping charge and discharge, alarming, notifying and the like, so as to protect the safety and stability of the battery pack.
The invention also claims a lithium battery energy storage battery pack control system, which comprises a sensor module, a data acquisition module, a data processing and feature extraction module, an artificial intelligent model module, a control module and Yun Mokuai:
The data acquisition module is used for acquiring real-time data of the battery pack and realizing data acquisition and real-time monitoring;
The data processing and feature extraction module is used for preprocessing and extracting features of the acquired real-time data, and comprises filtering, noise reduction and feature extraction algorithms;
the artificial intelligent model module is used for building and training a prediction model: establishing a prediction model of battery performance by utilizing historical data and a machine learning algorithm, and training and optimizing the model;
The control module comprises an abnormality detection and protection function, and realizes the optimization and adjustment of the charge-discharge strategy and the temperature control parameters by inputting real-time data and a prediction model; monitoring state data of the battery pack in real time, and performing abnormality detection by using an artificial intelligent algorithm, wherein the abnormality detection comprises overvoltage, overcharge, overdischarge, temperature abnormality and the like, and timely taking protection measures, including stopping charge and discharge, alarming and the like;
The cloud module comprises remote control and management functions, and realizes the remote control, monitoring and management functions through the cloud platform, and comprises the steps of remotely setting a charging and discharging strategy, receiving real-time data and diagnosing faults;
The system realizes the control method of the lithium battery energy storage battery pack.
Preferably, the artificial intelligence model module performs data analysis and prediction by using an artificial intelligence algorithm according to the real-time state data of the battery pack; by analyzing the historical data and the real-time data, key indexes of the battery pack, including residual available energy, health state and service life, are accurately predicted.
Preferably, the control module further optimizes a charge-discharge strategy to maximally extend the life of the battery and improve the energy utilization efficiency by using the result of the prediction model: according to the predicted battery performance and load demand, the system dynamically adjusts the charge and discharge power and rate to avoid the bad operation of overcharging, overdischarging and over-temperature, thereby ensuring the safety and stability of the battery system.
The abnormal monitoring and protecting sub-module in the control module monitors the state data of the battery pack in real time, and performs abnormal detection by using an algorithm in the artificial intelligent model module, once any abnormal situation is detected, the control system immediately takes corresponding protection measures, including stopping charge and discharge, alarming, notifying and the like, so as to prevent the damage of the battery system or the occurrence of safety problems.
Preferably, the remote control and management module in the cloud module enables a user to remotely control and manage the battery system through the cloud platform, and the user remotely sets and adjusts the charge and discharge strategy, monitors the battery state data in real time and performs fault diagnosis operation, so that the convenience and manageability of the system are improved.
Compared with the prior art, the control method and the control system for the lithium battery energy storage battery pack have the following beneficial effects:
the prediction accuracy is improved: through artificial intelligence technology, the control system can establish a prediction model based on historical data and real-time data, and predict by utilizing machine learning and deep learning algorithms; the method can improve the accurate prediction of key indexes such as future performance, residual available energy, health state and the like of the battery pack, help users reasonably plan the use and maintenance of the battery, and timely prevent possible faults and performance degradation.
Prolonging the service life of the battery: by monitoring the state and performance of the battery pack in real time, the control system can take optimized charge-discharge strategies and temperature control measures to avoid harmful conditions such as overcharge, overdischarge, overhigh temperature and the like, and adjust the charge and discharge processes according to the prediction result, so that the abrasion and ageing of the battery pack are reduced to the greatest extent, and the service life of the battery is prolonged.
Safety and stability are improved: the control system can timely identify possible risks and abnormal conditions of the battery pack, such as overvoltage, overcharge, unbalanced charge and the like, by monitoring the state data of the battery pack in real time and utilizing an artificial intelligent algorithm to perform abnormal detection; the system can take corresponding protection measures, such as stopping charge and discharge, alarming and notifying, etc., so as to protect the safety and stability of the battery pack.
The energy utilization efficiency is improved: the control system can dynamically adjust the charge and discharge strategy according to the state of the battery pack, the load demand and other factors by optimizing control through an artificial intelligence technology, so that the capacity and the energy of the battery pack are utilized to the maximum extent, and the energy loss is reduced. This can improve the energy utilization efficiency of battery package, reduces the energy waste.
Realize remote control and management: the connection with the cloud platform enables the control system to realize remote control and management functions, including remote operation, real-time monitoring, fault diagnosis and the like. This has improved the management convenience to the battery package greatly, has reduced maintenance cost and staff's working strength.
Drawings
Fig. 1 is a structural diagram of a lithium-ion battery pack control method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an implementation of a method for controlling a lithium battery energy storage battery pack according to an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples.
The embodiment of the invention provides a control method of a lithium battery energy storage battery pack, which comprises the following steps:
1. data acquisition and real-time monitoring, wherein real-time data of the battery pack are acquired through a sensor;
2. Data processing and feature extraction, wherein preprocessing and feature extraction are carried out on the acquired real-time data, and the preprocessing and feature extraction comprise filtering, noise reduction and feature extraction algorithms;
3. establishing and training a prediction model, establishing the prediction model of the battery performance by utilizing historical data and a machine learning algorithm, and training and optimizing the model;
4. predicting the performance index of the battery pack in a future period based on a prediction model, and performing optimization control according to a prediction result;
5. The system comprises an abnormality detection and protection function, a prediction model and a battery pack monitoring module, wherein the abnormality detection and protection function is used for monitoring the state of the battery pack in real time, comparing the state with the prediction model, detecting whether an abnormality occurs or not, and taking corresponding protection measures according to the abnormality;
6. remote control and management, and remote control, data storage and remote diagnosis functions are realized through a cloud platform; and the monitoring and management functions comprise the steps of remotely setting a charging and discharging strategy, receiving real-time data and alarm notification and diagnosing faults.
The data acquisition and real-time monitoring comprises the following steps:
1.1, acquiring real-time data of a battery pack by using a sensor, wherein the real-time data comprise current, voltage, temperature data and the like;
1.2, transmitting the data acquired by the sensor to a control system through a data acquisition module;
And 1.3, adjusting the data acquisition frequency according to the real-time state of the battery pack so as to acquire the latest state of the battery pack.
The data processing and feature extraction comprises:
2.1, preprocessing the data acquired by the sensor, including data cleaning, filtering and normalization processing;
And 2.2, extracting characteristic information, including the slope of a charge-discharge curve and the internal resistance of a battery, from the processed data by using a characteristic extraction algorithm, including a time domain characteristic and a frequency domain characteristic algorithm, for subsequent prediction and optimization.
The prediction model is established and trained as follows:
3.1, establishing a prediction model based on an artificial intelligence technology, wherein the prediction model is a regression model based on machine learning or a neural network model based on deep learning;
and 3.2, training a prediction model by using the existing historical data so as to learn the performance rule and the service life distribution of the battery pack.
The battery pack performance prediction and optimization control comprises the following steps:
4.1, monitoring state data of the battery pack in real time, including voltage, current and temperature;
4.2, based on a prediction model, predicting performance indexes of the battery pack in a future period of time, including residual available energy, health state and the like by inputting real-time state data;
and 4.3, performing optimal control according to the prediction result, wherein the optimal control comprises dynamic adjustment of charge-discharge strategies, temperature management and the like so as to prolong the service life and improve the safety performance.
The anomaly monitoring and protection mechanism comprises:
5.1, the control system detects whether abnormal conditions, including overvoltage, overcharge and charge imbalance, occur or not by monitoring the state of the battery pack in real time and comparing the state with a prediction model;
And 5.2, according to the threshold value and the rule defined in the prediction model, the control system takes corresponding protection measures according to abnormal conditions, including stopping charge and discharge, alarming, notifying and the like, so as to protect the safety and stability of the battery pack.
The remote control and management system can be communicated with the battery pack through a remote communication technology, so that remote control and management are realized:
And 6.1, accessing the cloud platform to perform remote control and management, and realizing functions of remote monitoring, data storage, remote diagnosis and the like.
And 6.2, communicating with a control system through a cloud platform, realizing remote setting and adjustment of a charging and discharging strategy, receiving real-time data, alarming notification and the like.
The lithium battery energy storage battery pack is a high-energy-density, light, reliable and environment-friendly energy storage solution. The performance and life of a lithium battery pack are key factors affecting the stable operation and service life of the overall energy storage system.
In order to monitor the state and performance of the battery pack in real time, parameters such as current, voltage, temperature and the like of the battery pack need to be collected. The data acquisition technology can acquire various data in the running process of the battery pack and transmit the data to the subsequent data processing and analysis links. By processing and analyzing the collected battery pack data, useful characteristic information can be extracted for predicting the performance and life of the battery pack. The data processing technology comprises the methods of data cleaning, feature extraction, data normalization and the like.
Artificial intelligence techniques include machine learning, deep learning, neural networks, etc., which can build predictive models by learning and training historical data and utilize real-time data for prediction. The artificial intelligence technology can be applied to aspects of feature selection, model training, optimization of prediction results and the like in battery pack prediction.
Through combining artificial intelligence with lithium electricity energy storage battery package technique, can realize the accurate prediction to battery package performance and life-span to provide the optimization suggestion for the user, improve battery package's availability factor and security.
The embodiment of the invention also provides a lithium battery energy storage battery pack control system which is used for predicting the performance and the service life of the lithium battery energy storage battery pack, predicting the possible thermal runaway of the battery in the battery pack according to historical data and regulating and controlling a control strategy according to a prediction result; the system realizes the control method of the lithium battery energy storage battery pack.
The system comprises a sensor module, a data acquisition module, a data processing and feature extraction module, an artificial intelligent model module, a control module and a cloud module.
The data acquisition module is used for acquiring real-time data of the battery pack and realizing data acquisition and real-time monitoring;
The data processing and feature extraction module is used for preprocessing and extracting features of the acquired real-time data, and comprises filtering, noise reduction and feature extraction algorithms;
the artificial intelligent model module is used for building and training a prediction model: establishing a prediction model of battery performance by utilizing historical data and a machine learning algorithm, and training and optimizing the model;
The control module comprises an abnormality detection and protection function, and realizes the optimization and adjustment of the charge-discharge strategy and the temperature control parameters by inputting real-time data and a prediction model; monitoring state data of the battery pack in real time, and performing abnormality detection by using an artificial intelligent algorithm, wherein the abnormality detection comprises overvoltage, overcharge, overdischarge, temperature abnormality and the like, and timely taking protection measures, including stopping charge and discharge, alarming and the like;
The cloud module comprises remote control and management functions, and realizes the remote control, monitoring and management functions through the cloud platform, and comprises the steps of remotely setting a charging and discharging strategy, receiving real-time data and diagnosing faults;
The artificial intelligent model module is used for carrying out data analysis and prediction by utilizing an artificial intelligent algorithm according to the real-time state data of the battery pack; by analyzing the historical data and the real-time data, key indexes of the battery pack, including residual available energy, health state and service life, are accurately predicted.
The control module also optimizes a charge-discharge strategy by utilizing the result of the prediction model so as to prolong the service life of the battery to the maximum extent and improve the energy utilization efficiency: according to the predicted battery performance and load demand, the system dynamically adjusts the charge and discharge power and rate to avoid the bad operation of overcharging, overdischarging and over-temperature, thereby ensuring the safety and stability of the battery system.
The abnormal monitoring and protecting sub-module in the control module monitors the state data of the battery pack in real time, and performs abnormal detection by using an algorithm in the artificial intelligent model module, once any abnormal situation is detected, the control system immediately takes corresponding protection measures, including stopping charge and discharge, alarming, notifying and the like, so as to prevent the damage of the battery system or the occurrence of safety problems.
The remote control and management module in the cloud module enables a user to remotely control and manage the battery system through the cloud platform, and the user remotely sets and adjusts the charging and discharging strategy, monitors the battery state data in real time and performs fault diagnosis operation, so that the convenience and manageability of the system are improved.
The present invention can be easily implemented by those skilled in the art through the above specific embodiments. It should be understood that the invention is not limited to the particular embodiments described above. Based on the disclosed embodiments, a person skilled in the art may combine different technical features at will, so as to implement different technical solutions.
Other than the technical features described in the specification, all are known to those skilled in the art.
Claims (10)
1. The lithium battery energy storage battery pack control method is characterized by comprising the following steps of:
(1) Data acquisition and real-time monitoring, wherein real-time data of the battery pack are acquired through a sensor;
(2) Data processing and feature extraction, wherein preprocessing and feature extraction are carried out on the acquired real-time data, and the preprocessing and feature extraction comprise filtering, noise reduction and feature extraction algorithms;
(3) Establishing and training a prediction model, establishing the prediction model of the battery performance by utilizing historical data and a machine learning algorithm, and training and optimizing the model;
(4) Predicting the performance index of the battery pack in a future period based on a prediction model, and performing optimization control according to a prediction result;
(5) The system comprises an abnormality detection and protection function, a prediction model and a battery pack monitoring module, wherein the abnormality detection and protection function is used for monitoring the state of the battery pack in real time, comparing the state with the prediction model, detecting whether an abnormality occurs or not, and taking corresponding protection measures according to the abnormality;
(6) Remote control and management, and remote control, data storage and remote diagnosis functions are realized through a cloud platform; and the monitoring and management functions comprise the steps of remotely setting a charging and discharging strategy, receiving real-time data and alarm notification and diagnosing faults.
2. The method of claim 1, wherein the data acquisition and real-time monitoring comprises:
(1.1) acquiring real-time data of the battery pack by using a sensor, wherein the real-time data comprises current, voltage and temperature data;
(1.2) transmitting the data acquired by the sensor to a control system through a data acquisition module;
and (1.3) adjusting the data acquisition frequency according to the real-time state of the battery pack so as to acquire the latest state of the battery pack.
3. The method for controlling a lithium-ion battery pack according to claim 2, wherein the data processing and feature extraction comprises:
(2.1) preprocessing the data acquired by the sensor, including data cleaning, filtering and normalization;
And (2.2) extracting characteristic information, including the slope of a charge-discharge curve and the internal resistance of a battery, from the processed data by using a characteristic extraction algorithm, including a time domain characteristic and a frequency domain characteristic algorithm, for subsequent prediction and optimization.
4. The method for controlling a lithium battery energy storage battery pack according to claim 3, wherein the prediction model is established and trained as follows:
(3.1) establishing a prediction model based on an artificial intelligence technology, wherein the prediction model is a regression model based on machine learning or a neural network model based on deep learning;
(3.2) training a predictive model using existing historical data to learn the performance laws and life distribution of the battery pack.
5. The method of claim 4, wherein the predicting and optimizing control of the performance of the battery pack comprises:
(4.1) monitoring state data of the battery pack in real time, including voltage, current and temperature;
(4.2) predicting performance indexes of the battery pack in a future period of time, including residual available energy and health state, by inputting real-time state data based on a prediction model;
And (4.3) performing optimal control according to the prediction result, wherein the optimal control comprises dynamic adjustment of a charge-discharge strategy and temperature management.
6. The method of claim 5, wherein the abnormality detection and protection function comprises:
(5.1) the control system detects whether abnormal conditions, including overvoltage, overcharge and charge imbalance, occur by monitoring the state of the battery pack in real time and comparing the state with a prediction model;
And (5.2) according to the threshold value and the rule defined in the prediction model, the control system takes corresponding protection measures according to the abnormal condition, including stopping charge and discharge and notifying an alarm.
7. The lithium battery energy storage battery pack control system is characterized by comprising a sensor module, a data acquisition module, a data processing and feature extraction module, an artificial intelligent model module, a control module and Yun Mokuai:
The data acquisition module is used for acquiring real-time data of the battery pack and realizing data acquisition and real-time monitoring;
The data processing and feature extraction module is used for preprocessing and extracting features of the acquired real-time data, and comprises filtering, noise reduction and feature extraction algorithms;
the artificial intelligent model module is used for building and training a prediction model: establishing a prediction model of battery performance by utilizing historical data and a machine learning algorithm, and training and optimizing the model;
The control module comprises an abnormality detection and protection function, and realizes the optimization and adjustment of the charge-discharge strategy and the temperature control parameters by inputting real-time data and a prediction model; monitoring state data of the battery pack in real time, and performing abnormality detection by using an artificial intelligent algorithm, wherein the abnormality detection comprises overvoltage, overcharge, overdischarge, temperature abnormality and the like, and timely taking protective measures;
The cloud module comprises remote control and management functions, and realizes the remote control, monitoring and management functions through the cloud platform, and comprises the steps of remotely setting a charging and discharging strategy, receiving real-time data and diagnosing faults;
the system realizes the control method of the lithium battery energy storage battery pack according to any one of claims 1 to 6.
8. The lithium battery energy storage battery pack control system according to claim 7, wherein the artificial intelligence model module performs data analysis and prediction by using an artificial intelligence algorithm according to real-time state data of the battery pack; by analyzing the historical data and the real-time data, key indexes of the battery pack, including residual available energy, health state and service life, are accurately predicted.
9. The lithium battery energy storage battery pack control system according to claim 8, wherein the control module further optimizes the charge-discharge strategy using the results of the predictive model: according to the predicted battery performance and load requirements, the system dynamically adjusts the charge and discharge power and rate to avoid poor operation of overcharging, overdischarging and overtemperature;
The abnormal monitoring and protecting sub-module in the control module monitors the state data of the battery pack in real time, and performs abnormal detection by using an algorithm in the artificial intelligent model module, and once any abnormal situation is detected, the control system immediately takes corresponding protecting measures, including stopping charge and discharge and alarming notification.
10. The lithium battery energy storage battery pack control system according to claim 9, wherein the remote control and management module in the cloud module enables a user to remotely control and manage the battery system through the cloud platform, and the user remotely sets and adjusts the charge and discharge strategy, monitors battery state data in real time, and performs fault diagnosis operation.
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| CN117445755A (en) * | 2023-11-01 | 2024-01-26 | 山东大学 | Cloud computing-based remote monitoring system for batteries of electric vehicle |
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