Description:Field of the Invention
A hybrid deep learning and attention based CNN model for predicting the battery remaining useful life (RUL). It uses modern technologies like deep neural network, sequential data, machine learning algorithms to analyze data of various parameters and estimates the accurate RUL of battery.
Background of the Invention
Electric vehicles have seen a great rise in the recent days, and sustainability of the battery health and the longevity of the battery is important. For the prediction of Battery Remaining Useful Life (RUL) of an Electric Vehicle using the deep learning technique and the machine learning algorithms, failure and life of the EV's batteries are reduced. Traditional methods that are required to predict battery health and prediction RUL with various factors based on life cycle battery degradation methods need to be advanced to calculate the right results as per expert knowledge and domain knowledge. It allows scheduling advance replacement for batteries or any changes according to the remaining useful of batteries and is essential for electric vehicles. The innovation disclosed in US20230140727A1 it is an electronic device that predicts the remaining useful life (RUL) of a battery based on various parameters such as temperature, voltage, charge and discharge cycles it uses resistance attached to the electronic device and it uses artificial intelligence (AI) model to train the data on the various parameters and evaluates the RUL of a battery. CN110824364B this invention is proposed to estimate the remaining useful life (RUL) by leveraging an AST-LSTM neural network and it uses various parameters to predict the RUL and SOH estimation by collecting voltage, current, temperature and corresponding capacity values and discharge cycles of a battery and use neural network model for Lithium-ion batteries. KR102362532B1 the present invention is related to a method of predicting the remaining battery life based on a neural network and this invention uses a dilated convolutional neural network (CNN) in the first and middle part and in the second part is uses 1D-CNN, it stores the data of each and every battery in the memory and based on the stored memory data the model evaluates the battery RUL. US20200081070A1 this invention utilizes the neural network for estimating the SOH (state of health) and SOC (state of charge) for battery by receiving one or more battery attributes for lithium-ion battery and it uses the artificial neural network (ANN) that includes at least one of a recurrent neural network (RNN) and a convolutional neural network (CNN) and battery attributes that includes voltage, temperature, charge and discharge values of the batteries.

Summary of the Invention
The model proposes a CNN with attention to predict the RUL of the battery. This model uses a hybrid deep learning approach, which focuses attention and makes use of two powerful methods: deep neural networks combined with very recent machine learning algorithms to estimate an accurate estimation of the RUL of the lithium-ion battery.The model is designed to manage the health of the batteries, and the data source is varying with datatype parameters, which includes voltage, charge, discharge time cycle index, and current charge time, based on which these predictions in the model predict the remaining useful life of the battery and are very essential to electric vehicles and also reduce the failures of the battery and improve usage along with health management of batteries. Lithium-ion batteries will be the most used since they have a big energy storage capacity and long lifespan.The project main idea is the estimation of the RUL of the battery used mainly in electric vehicles. This model is used to schedule the period of replacement and life expectancy for a battery, which reduces failures in the process. It is a key parameter for the improvement of electric vehicles, and also is economical that utilizes only the first hundred life cycles of the battery and predicts the remaining life of the batteries in cycles.

Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure-1: Flow chart representing the work flow of the system

Figure-2: Detailed architecture of a hybrid deep learning model structure.

Detailed Description of the Invention
This proposed project leverages a hybrid ensemble deep learning model for the early prediction of the Remaining Useful Life (RUL) of lithium-ion batteries (LIBs). Many electronic devices and energy storage systems rely mainly on lithium-ion batteries (LIBs) such as electric vehicles (EV), electric wheel chairs, cameras, smart phones, laptop and desktops, etc., and other electronic devices, hence it is essential to accurately predict the Remaining Useful Life (RUL) of lithium-ion batteries for high performance and safety. But, accurately predicting the remaining battery life is quite a difficult task because of the complexity in the battery degradation process which is non-linear and this makes it challenging in the process of studying the behavior of the batteries and because of this, the traditional data-driven models struggle to identify these battery degradation signals and the predictions are made with low accuracy. So, by focusing only on the early cycles (charge and discharge) of the battery, before the significant degradation begins, this proposal aims to achieve the reliable RUL estimation, thus helping the industries or manufacturers of clear understanding in battery degradation process which in turn helps them for a better optimizing battery management and minimizing the test costs.
This hybrid deep learning model’s architecture combines the both data-driven features or the latent-features through deep neural networks and handcrafted features from domain knowledge to increase the prediction accuracy. The data-driven statistical features are obtained at each cycle (charge and discharge of battery) in order to capture the patterns in the battery degradation process. These data-driven statistical features consist of variables such as mean, variance and skewness that tends to the battery degradation process through cycle -to-cycle. And additionally the handcrafted features from the domain knowledge obtained from the principles related to the battery operation which consist of battery discharge capacity and temperature variance. And these domain specific features are important for capturing the nonlinear behavior of battery degradation in LIBs.
This model also proposes a non-linear correlation-based feature selection method “Spearman’s correlation and recursive feature elimination (RFE)” which is used to retain the most relevant domain based features that contributes to prediction accuracy, by allowing the model to ignore the non-informative features which also helps in avoiding the overfitting of the model. This way the model can be mainly focused on the useful features and increasing the chances of accurate prediction in the aspects of battery behavior by reducing the noise in data. Now once the useful features are being selected then these features are combined with the local statistical features and fed into a hybrid deep learning model through a convolutional neural network (CNN) structure. And these both features are being processed concurrently where the convolutional neural network (CNN) structure captures the temporal dependencies and makes sure about the battery degradation patterns throughout the cycles for predicting the RUL effectively.The key aspect of this project is the use of a novel snapshot ensemble learning technique that overall increases the model’s generalization capabilities among various battery datasets. Since we are training the model with a large dataset there is a chance of the model overfitting because of the excess amount of data for training. Which can be solved by model ensemble where we train multiple models on the same data set by dividing the dataset into equal numbers of data samples according to the number of models. And finally we combine all the model output’s together to obtain a highly predictable decision. But here, the problem is this process is not budget friendly or it requires more computational power. So, in order to overcome this issue we use the snapshot ensemble learning technique where the entire dataset is being trained by a single model and throughout the process we save a particular number of Epochs or snapshots at a certain number of iterations. By this process we can avoid the issue of overfitting and we can achieve the best prediction with low computational power. Through this technique we can make sure that the model is well generalized across various battery batches and with different operational distributions. This results in the hybrid model which will be capable of variations in LIB performance and well suited for real world applications.
Finally this approach will be a significant advancement in the early prediction of a battery's remaining useful life by the integration of battery domain knowledge with deep learning. This has real world applications in battery quality control, lifecycle management and safety measures in high stake applications like electric vehicles and energy storage systems. Which will be also beneficial for manufacturers by reducing the development time, optimizing the battery management and data driven decision making.
3 Claims and 2 Figures
Equivalents
The present invention, Enhanced Battery RUL Prediction System using Hybrid Deep Learning and Attention-Based CNN Models, is adaptable beyond lithium-ion batteries. Its broad applicability extends to a wide range of battery technologies used in consumer electronics, renewable energy systems, and industrial machinery. The system’s hybrid architecture, including 1D-CNN, attention mechanisms, feature selection, and snapshot ensemble techniques, is applicable to other time-series prediction tasks, such as equipment failure forecasting and energy usage trends. The invention’s scalability and efficiency make it a versatile solution for predictive modeling and real-time applications across various industries. , Claims:The scope of the invention is defined by the following claims:

Claim:
1. The Enhanced Battery RUL Prediction Using Hybrid Deep Learning and Attention-Based CNN Models comprising,
a) The model integrates with a hybrid deep learning model which consists of 1D-CNN and attention mechanisms to emphasize critical features and improve prediction accuracy for battery remaining useful life (RUL).
b) The system uses SelectKBest technique, for feature selection to identify the most relevant features that enhances model efficiency by reducing computational complexity while maintaining prediction accuracy.
c) The incorporation of a Snapshot Ensemble Technique to facilitate robust model training and mitigating overfitting without additional computational cost.
2. According to claim 1, the integration of attention mechanisms within the 1D-CNN layers dynamically prioritizes significant features in the dataset, improving computational efficiency while maintaining prediction accuracy for battery RUL. This hybrid model is adaptable to various datasets and battery chemistries, ensuring scalability for real-time deployment such as in electronic vehicles (EVs).
3. As per claim 1, the system addresses the growing need for accurate RUL predictions of batteries by providing a scalable and adaptable solution. It enables users to schedule battery replacements proactively, minimizing sudden failures and ensuring the system's robustness for real-world deployment through the integration of feature selection, attention mechanisms, and ensemble techniques.