2.1

Technological progress

In recent years, significant technological progress has been made in the field of grid batteries. Various types of battery technologies, such as lithium-ion batteries, sodium-sulfur batteries, and lead-acid batteries, are continuously improving and innovating^2-4. For example, lithium-ion batteries have improved energy density and charge-discharge efficiency, while sodium-sulfur batteries and lead-acid batteries are also enhancing their cycle life and safety performance. These technological advances help to improve the reliability and performance of batteries.

2.2

The cost breakdown

With the continuous maturity of grid battery technology and the expansion of market scale, the cost of grid batteries is gradually decreasing. Technological innovation and production efficiency have effectively controlled the manufacturing cost of batteries, and the scale effect has further reduced the cost. These cost reduction trends make the application of grid batteries in power systems more economically feasible and provide better support for the large-scale application of clean energy.

2.3

The capacity augmentation

With the continuous improvement of grid battery technology, the capacity of the battery is also increasing⁵. At present, some power grid battery projects have been able to achieve a hundred megawatts of operation, thus providing a large-scale energy regulation capability for the power grid. Due to the expansion of capacity, the power grid can better cope with energy fluctuations and peak demand, enhance the flexibility and reliability of the power system, and provide important support for the smooth operation of the power system.

2.4

Energy density increase

With the continuous development of science and technology and the continuous improvement of material technology, the energy density of the grid battery has also been continuously improved.

The high energy density battery can store more energy in a smaller volume so that the battery system has a larger energy storage capacity within the specified space range⁶. This increase in energy density not only improves the efficiency of the grid battery system but also helps to reduce the volume and weight of the system, further enhancing the reliability and applicability of the system.

2.5

Intellectualized management

With the progress of information technology, the management and control of the power grid battery system are gradually becoming intelligent. By combining artificial intelligence and big data analysis technology, advanced monitoring and scheduling systems can monitor the state and performance of the battery in real-time, thereby optimizing the energy distribution and supply and demand balance of the power grid. Intelligent management improves the response speed and accuracy of the battery system, better supports the dispatching operation of the power grid, and improves the energy utilization efficiency and system stability.

3.1

Cycle life test

A cycle life test is one of the commonly used methods to evaluate the battery life of a power grid. The capacity decay of the battery under different cycle times was recorded by multiple cycle charge-discharge tests^7-9. This intuitive evaluation method can help us to understand the performance changes of the battery during long-term use, thus providing an important reference for future operation and maintenance. However, it takes a lot of time and resources to carry out the cycle life test, so the test plan and data recording should be carefully considered in the evaluation.

3.2

Forecasting model

Using mathematical models, such as chemical models and statistical models, to predict the battery life is an effective evaluation method^10-12. The equivalent circuit is based on the chemical model, as shown in Figure 1. By establishing a model to simulate the performance of the battery under different working conditions, the life of the battery can be predicted and the maintenance and management work in practical applications can be guided.

Figure 1.

The equivalent circuit based on the chemical model.

3.3

Real-time monitoring

Real-time monitoring is a real-time data acquisition and analysis method based on battery temperature, voltage, current, and other parameters, which is used to evaluate battery life. By timely monitoring the battery status and analyzing the data, the abnormal situation of the battery can be detected early, the service life of the battery can be effectively extended, and the safe and stable operation of the battery system can be ensured. This method helps to improve the reliability and performance of the battery system and ensure the smooth and long-term operation of the power grid.

3.4

Evaluation criteria of circular life

The development of cycle life assessment standards is an important way to evaluate battery life^13-16. The battery is evaluated and compared by setting standards and according to the standards. This standard helps us to better understand the performance of the battery^17,18. It can also improve the comparability and credibility of the evaluation results.

4.1

Data collection

When establishing a data-driven battery life assessment model for power grids, the primary task is to collect a large amount of data related to battery life. By recording the use of the battery, the number of charge and discharge cycles, ambient temperature, charging rate, and other related information, a complete data set can be established to lay the foundation for subsequent model construction. These data will provide key information to help us understand the performance of the battery in different environments and conditions of use, and further predict its life.

In the process of data collection, it is necessary to ensure the accuracy and integrity of the data. In addition to battery information, factors such as the type of charging device, charging strategy, and grid load should also be considered. Through long-term comprehensive monitoring and recording of these data, a large-scale data set covering battery usage in different scenarios can be constructed to provide sufficient support for model training and optimization.

As the amount of data increases, we can use data analysis and machine learning techniques to explore the rules and associations in the data. A model is established to analyze the data, predict the battery life, and guide the maintenance and replacement of the battery in the operation and management of the power grid, so as to improve the utilization efficiency of the battery and prolong its life while reducing the operation cost of the power grid.

Therefore, data collection plays a vital role in building a data-driven grid battery life assessment model. Only through sufficient and detailed data collection and analysis can we lay a solid foundation for the establishment of a battery life assessment model and bring greater benefits and sustainable development prospects for grid energy management.

4.2

Data pre-processing

In the data preprocessing stage, it is necessary to clean, process, and convert the collected data to ensure data quality and consistency. First of all, data cleaning is a necessary step, which can purify data and improve the accuracy of modeling by identifying and processing duplicate data, abnormal data, etc. Next, in the face of missing values, we can choose appropriate filling methods, such as using techniques such as mean, median, or interpolation, to ensure data integrity and availability. At the same time, dealing with outliers is also essential. We can use methods such as elimination, smoothing, or conversion to avoid the interference of outliers on model construction.

Through these data processing methods, we can prepare for the next feature engineering and lay the foundation for building an accurate and reliable grid battery life assessment model. In the process of data preprocessing, after careful data processing, it can fully support the training and evaluation of the model, ensure that the model has high prediction accuracy and stability, and provide better decision support for power grid operation and management.

4.3

Feature engineering

Selecting and extracting relevant feature parameters can find key information from a large amount of data. The initial feature set can be constructed based on domain knowledge related to battery life, such as the number of charge and discharge cycles, ambient temperature, charging rate, and current size. For example, for the characteristics of nonlinear relationships, polynomial extensions or kernel techniques can be used to extract richer information.

Therefore, the importance of feature engineering cannot be ignored when constructing the life assessment model of power grid batteries, which is the key link to constructing an efficient and accurate model.

4.4

Model selection

In the model selection stage, it is necessary to comprehensively consider the actual needs and data characteristics to ensure that the model can accurately predict the service life of the battery. For the problem of evaluating the service life of power grid batteries, various models such as regression models, decision trees, random forests, and neural networks are generally considered. The regression model is suitable for the prediction of continuous values and can be modeled by linear regression or polynomial regression. Decision tree is suitable for scenes with many features and complex relationships. It can not only provide interpretable results but also deal with nonlinear relationships. Random forest is good at dealing with the relationship between large amounts of data and complex features and has excellent generalization ability. Neural networks are suitable for processing large-scale data and complex nonlinear relationships, which can discover hidden patterns and features.

In the process of model selection, it is necessary to use cross-validation and other methods to evaluate and compare different models, so as to select the most suitable model and optimize the parameters, so as to achieve good prediction results.

4.5

Model training

By using historical data for model training, the change rule of battery life can be simulated more accurately. This process needs to collect a large number of battery operation data, including the number of charge and discharge cycles, temperature, current, and voltage parameters. Appropriate data processing methods, such as data cleaning, feature extraction, etc., can be used to more effectively use these data for modeling. By repeatedly adjusting the model parameters, such as learning rate and regularization parameters, the performance and generalization ability of the model are improved.

4.6

Model evaluation

When establishing such a model, a large amount of grid battery operation data is generally collected as a training data set, and a prediction model is constructed through machine learning or deep learning. By comparing with the actual test data, we can evaluate the performance of the model in practical applications and provide a reference for optimizing the model. Optimizing the model and adjusting it according to the feedback of the evaluation index can improve the prediction ability and accuracy of the model, further guide the management and maintenance of the power grid battery, prolong the service life of the battery and improve the reliability and economic benefits of the power grid system.

4.7

Model optimization

In order to improve the prediction accuracy and stability of the model, we can continuously adjust the operation of feature selection and parameter adjustment by improving the model according to the evaluation results. In order to optimize the model, it is necessary to repeatedly verify and evaluate its performance. Cross-validation, grid search, and other technical methods can be used to evaluate the performance of the model on different data sets.

By continuously optimizing the model, the performance of evaluation indicators can be improved, such as accuracy, precision, and recall, which can provide reliable support for grid battery management and maintenance.

4.8

Model deployment

Model deployment refers to its practical application and is used to monitor the life of the battery in real time. By monitoring the real-time operation data of the battery, the model can predict the life of the battery in real time, and take corresponding maintenance and management measures according to the predicted results. For example, when the model predicts that the battery is about to run out of life, it can be replaced or repaired in time, so as to effectively extend the service life of the battery and improve the sustainable utilization and efficiency of the grid battery. In order to expand the application scope of the model, it can be integrated into the power grid management system to carry out real-time data exchange and collaborative control with other equipment and systems.

By combining battery life prediction with power grid operation optimization, the reliability and efficiency of power grid can be further improved, and more intelligent power grid management can be realized. It is also possible to apply the model to other types of energy storage equipment, such as wind and solar energy storage systems, so as to achieve comprehensive intelligent energy management and optimization.