The Remaining Useful Life Forecasting Method of Energy Storage Batteries Using Empirical Mode Decomposition to Correct the Forecasting Error of the Long Short-Term Memory Model

By inergency On Feb 26, 2024

1. Introduction

Traditional power generation consumes fossil fuels such as oil and coal; this has a negative impact on the environment and is not conducive to sustainable development. Therefore, new energy power generation has developed rapidly in recent years, with clean and pollution-free characteristics. However, due to the influence of weather conditions, new energy power generation has the characteristic of uncertainty [1,2,3]. Therefore, the large-scale integration of new energy generation into the grid will bring new challenges to the power quality and safe and stable operation of the power grid [4,5,6]. In addition, due to the rapid development of the economy and society, the electricity consumption in many countries has also shown a rapid growth trend, and the large load and peak valley difference will also bring new problems.

Electrochemical energy storage plays an important role in alleviating the above problems. It has been widely applied on the new energy side, power grid side, and user side in recent years, which is conducive to new energy consumption and sustainable development. The current high price of energy storage systems has become one of the main reasons limiting their development and application [7,8]. By studying the remaining useful life (RUL) of batteries, energy management methods for energy storage systems can be formulated, thereby extending the useful life of energy storage batteries and improving the economic benefits of energy storage power station owners. In addition, the safety of energy storage plants is very important, and there have been some accidents, such as fires and explosions, in energy storage stations before [9,10]. The forecasting of RUL can avoid this issue in advance.

Therefore, it is very necessary to forecast the RUL of energy storage batteries, and many scholars have also conducted related research. The main methods are divided into model-based methods [11,12] and data-driven methods [13]. The data-driven model is currently the most popular method, because it has the advantage of being able to analyze the data to obtain the relationships between various parameters and forecast the RUL of energy storage batteries. These methods mainly include neural networks (NN), support vector machines (SVM), long short-term memory (LSTM) neural networks, and so on.

Sun et al. [14] propose a simultaneous estimation scheme using SVM for state of charge (SOC) and state of health (SOH) based on shock response characteristics. Simulation results show that the proposed method can accurately estimate the SOH and SOC of a battery, with strong robustness and generalization ability. Li et al. [15] propose a lithium-ion health online estimation method based on particle swarm optimization (PSO) and SVM. The PSO was used to optimize the kernel function of the SVM. From experiments (such as dynamic stress tests), it can be seen that it has good adaptability and feasibility. Lin et al. [16] propose an RUL forecasting model for lithium-ion batteries that combines variational mode decomposition (VMD) and SVM. Compared with a single SVM regression model and a Gaussian process regression model, the VMD–SVM method achieved more accurate forecasting results. However, SVM methods are prone to fall into a local optimal state, making it difficult to obtain the most accurate forecasting results or ensure the stability of the forecasting results.

A recurrent neural network (RNN) considers the influence of the information of diffident time and is often used for time series prediction or language processing. Song et al. [17] propose a battery RUL forecasting method based on a new RNN, which overcomes the disadvantage of dealing with the long-term relationships of RNN. The average error of different battery cells is less than 3%, which means that the proposed method is accurate and robust for battery RUL forecasting. The RNN is prone to have problems of gradient vanishing or gradient explosion, and many scholars have improved it. LSTM is a highly successful improved model. Zhao et al. [18] propose a joint forecasting method for health status and RUL based on LSTM neural networks and Gaussian process regression. They established a Gaussian process regression forecasting model for remaining useful life. On this basis, an LSTM neural network is used to forecast the trend of health factors over time. Li et al. [19] propose an online forecasting method for electric vehicle battery fault based on LSTM. The model based on LSTM can effectively forecast battery fault with an accuracy rate of over 85%. It can complete online preprocessing of vehicle operation data and fault forecasting of power batteries, improve vehicle monitoring capabilities, and ensure the safety of electric vehicle use. In addition to the battery RUL, LSTM is also used for other battery-related research. Wang et al. [20] propose a fault diagnosis method based on a hybrid model, which estimates the internal temperature and SOC of the battery through its physical model. It combines the surface temperature, voltage, and current of the battery as inputs to the LSTM to accurately forecast the surface temperature and internal temperature. In the above literature, the RUL of energy storage batteries is mostly forecasted by using a single method. We can find that LSTM has good forecasting performance, but there is still room for improvement.

A soft-shared multitask deep learning method for multi-node load prediction in power systems was proposed to achieve the simultaneous prediction of multi-node loads [21]. The simulation results showed that this method can effectively explore the spatiotemporal coupling characteristics of multi-node load data and improve prediction performance. Wang et al. [22] combined the hybrid convolutional neural network (CNN)–Bidirectional Gated Recursive Unit (BiGRU) model with the Bootstrap method, endowing deep learning (DL)-based prediction methods with the ability to quantify prediction intervals. Experimental verification was conducted on a bearing dataset, and this method outperformed the other four methods in most cases. Guo et al. [23] propose a deep feature learning method that combines a convolutional neural network (CNN)–convolutional block attention module (CBAM) and a transformer network into a parallel channel method to predict the RUL of drilling pumps, confirming that the proposed method has higher accuracy. Zhang et al. [24] propose a combination of offline global models developed by different machine learning methods and online adaptive cellular personalized models. Training and testing were conducted on three large datasets, demonstrating the predictive performance. A new dual Gaussian process regression (GPR) framework was proposed in reference [25], and quantitative experimental results show that the state of charge estimation is significantly improved compared to traditional methods. Liu et al. [26] propose an effective prediction of battery calendar aging trajectory by deriving a knowledge-driven data-driven model with transfer concepts. The results show that the model has good prediction accuracy under known conditions. A reliable cycle aging-prediction method based on data-driven models was proposed in reference [27]. A multi-core RVM model containing two different feature kernel functions was constructed. Quantitative experimental results showed that the proposed model can accurately predict the failure cycle and capacity decay trajectory of different types of batteries.

Although the above literature combines multiple models, there is no relevant research focusing on the forecasting error of the RUL of energy storage batteries. This article explores how to improve the forecasting accuracy by dealing with the forecasting error. We propose the method that uses empirical mode decomposition (EMD) to correct the forecasting error of LSTM, thereby improving the forecasting accuracy of RUL. Firstly, we use the LSTM model to forecast the RUL of energy storage batteries and obtain the RUL forecasting error. Then, EMD is used to decompose the forecasting error to obtain components with different characteristics. Finally, we use the LSTM model to forecast the time series of each component, perform inverse transformation on the forecasting results to obtain the corrected forecasting error, and add it to the original RUL forecasting result to obtain the forecasting result of LSTM–EMD. The research in this article will accurately forecast the RUL of energy storage batteries, which is beneficial for improving the safety of new energy power plants containing energy storage batteries.

The main contributions of this paper are as follows:

(1): This paper proposes a novel framework for forecasting the remaining useful life of the energy storage battery, which includes the LSTM model for forecasting the RUL and the EMD model for correcting the forecasting error.
(2): The EMD method is proposed to decompose the LSTM forecasting error into multiple components. Each error component is trained separately to correct the forecasting error, which improves the forecasting accuracy.
(3): The established forecasting model uses simple input data to solve the forecasting accuracy problem in the case of insufficient data types.

The rest of this paper are as follows: Section 2 introduces the framework of the RUL forecasting of the energy storage battery. Section 3 is the RUL forecasting method of the energy storage battery, which is the main part of this paper. Section 4 is the simulation analysis and verification. Section 5 is the conclusion.

2. Framework for Predicting the Remaining Useful Life of Energy Storage Batteries

The framework of the RUL forecasting of energy storage batteries is shown in Figure 1, which is mainly divided into three parts.

Part A: We have collected the data of different batteries. Before using the LSTM model for forecasting, we divide the data into training set and test set and carry out normalization processing. The normalization formula is shown in the equation below. The LSTM model is trained by using the data in the training set. The specific model is explained in Section 3.1. When the expected results are achieved by using training set, the test set is used to forecast the RUL and verify the proposed method. The forecasting value of the remaining capacity of the battery is obtained. In this paper, we assume that the remaining capacity represents the RUL. However, the forecasting results have a forecasting error, which is used as the input in Part B.

Part B: In this part, the forecasting error is obtained by using the forecasting value minus the real value. We use the method of empirical mode decomposition to decompose the forecasting error of the RUL, where several components can be obtained. For each component, we divide the training set and the test set and construct the corresponding LSTM model. The LSTM models are used for time series forecasting of several components. The method of empirical mode decomposition is described in Section 3.2.

Part C: Multiple trained LSTM models are used for time series forecasting of several components. Then, we invert the forecasting component to determine the correction value of the LSTM forecasting error in Part A. We add the correction value to the forecasting value by the LSTM in Part A. We determine the forecasting result of the correction, forecasting the remaining capacity of the battery, and the forecasting effect has been significantly improved.

5. Conclusions

For the current problems in the process of determining the RUL forecasting error, this paper proposes a method for forecasting the RUL of energy storage batteries using EMD to correct the LSTM forecasting error. An RUL forecasting model for energy storage batteries based on the LSTM neural network was constructed, and the EMD method was used to process the forecasting error of LSTM, thereby improving RUL forecasting accuracy. The following conclusions have been drawn through this study: (1) By using different evaluation indexes, similar numbers of hidden layers can be obtained; (2) Taking RMSE as the index, the forecasting effect of LSTM is improved by 16% compared with the NN method and 32.26% compared with the SVM method; (3) The forecasting effects of each component obtained through EMD are good, providing a good foundation for error correction; (4) The forecasting effect of LSTM–EMD is improved by 28.57% compared with that of LSTM, which verifies the effectiveness of this method of correcting forecasting errors.

In future research, we will further analyze the statistical patterns of forecasting errors and develop a more accurate forecasting model based on deep learning. Furthermore, the factors that impact the RUL of batteries will also be considered and analyzed. The energy management strategies for energy storage plants based on the forecasting results will be studied. Combining RUL forecasting with energy management will delay the lifespan decay of energy storage battery.