State of Health Estimation and Remaining Useful Life Prediction Using Hybrid Kmeans CNN-Lstm Network

Lithium-ion batteries have become among the most used storage systems in different fields such as electric vehicles, lighting, robotics, etc. These storage systems are known for their fast charging and high energy density (they can store 3 or 4 times more energy per unit mass than other battery technologies). Lithium-ion batteries are being used in more and more areas; however, the major drawback of this system is its high price, and the degradation of its performance makes good maintenance necessary to optimize the battery operation. This good maintenance consists on the real-time follow-up of its state of health (SOH) and the prediction of its remaining useful life (RUL). For this purpose, there are two main categories of methods: model-based methods (such as the sliding mode, and the Kalman filter, etc.) and data-based methods (such as fuzzy logic, genetic programming and artificial intelligence algorithms, etc.). Many authors have based their studies on artificial intelligence models and more specifically artificial neural networks which are known for their high accuracy and their ability to solve complex problems that model based methods find difficulties to deal with. Among the most used neural networks, we find recurrent neural networks (RNN) and more specifically Long Short Term Memory (LSTM). LSTM networks have an internal memory allowing them to process time series flexibly and with high accuracy. These networks have shown good performance in SOH estimation and RUL prediction in several studies. Convolutional neural networks are networks originally dedicated to image processing, but recently several studies have proposed to use these networks for processing time series and have demonstrated good performance in this field. Some studies propose to combine different models for the estimation of the SOH and the prediction of the RUL, among these models, we find the CNN LSTM combination which improves the accuracy of the model and decreases its calculation time. This is the case for our study, where the CNN network was used for data filtering and the LSTM network for processing the filtered data. In addition to that, we added a K-means clustering network which is used to classify the data and makes their processing by the CNN LSTM hybrid model, easier and faster. The major drawback of neural network models is that they need a lot of data for their training. In our study, we used the NASA open source dataset which was extracted from an experiment consisting on charging and discharging LCO 18650 lithium ion batteries with randomly chosen currents between -4.5A and 4.5A to simulate the operation of a lithium battery in electric vehicle driving conditions. The dataset contains the data of 4 batteries, we used the data of 3 of them for training the model and we validated our model on the data of the 4th one. The following figure shows the estimation results of the state of health of the 4th battery obtained by our model. To evaluate the performance of our model, we used three metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Error (MAE). The performance evaluation demonstrated the improvement in the accuracy of the model and the computation time compared to each of the networks used separately. The following table summarizes the obtained results: Metric/ Model MSE RMSE MAE Hybrid model 0.0002 0.01 0.008 For the prediction of the RUL, we developed a model that predicts the evolution of the capacitance value. For its training, we used the same dataset. The model trains on a percentage of 90% of the data from each battery and its role is to predict the remaining capacitance values until the end of life of the battery is reached. The following figure shows the model results for the prediction of the 4th battery data Figure 1