End-to-End Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries

• URL: http://arxiv.org/abs/2505.16664v2
• Date: Sat, 24 May 2025 12:35:39 GMT
• Title: End-to-End Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries
• Abstract summary: This paper proposes a RUL prediction approach that leverages data from recent charge-discharge cycles to estimate the number of remaining usable cycles.<n>The approach introduces both a novel signal processing pipeline and a deep learning prediction model.<n> Experimental results on two publicly available large-scale datasets demonstrate that the proposed method outperforms a baseline deep learning approach and machine learning techniques.
• Score: 0.04660328753262073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of the Remaining Useful Life (RUL) is essential for enabling timely maintenance of lithium-ion batteries, impacting the operational efficiency of electric applications that rely on them. This paper proposes a RUL prediction approach that leverages data from recent charge-discharge cycles to estimate the number of remaining usable cycles. The approach introduces both a novel signal processing pipeline and a deep learning prediction model. In the signal preprocessing pipeline, a derived capacity feature $\dot{Q}(I, Q)$ is computed based on current and capacity signals. Alongside original capacity, voltage and current, these features are denoised and enhanced using statistical metrics and a delta-based method to capture differences between the current and previous cycles. In the prediction model, the processed features are then fed into a hybrid deep learning architecture composed of 1D Convolutional Neural Networks (CNN), Attentional Long Short-Term Memory (A-LSTM), and Ordinary Differential Equation-based LSTM (ODE-LSTM) blocks. This architecture is designed to capture both local signal characteristics and long-range temporal dependencies while modeling the continuous-time dynamics of battery degradation. The model is further evaluated using transfer learning across different learning strategies and target data partitioning scenarios. Results indicate that the model maintains robust performance, even when fine-tuned on limited target data. Experimental results on two publicly available large-scale datasets demonstrate that the proposed method outperforms a baseline deep learning approach and machine learning techniques, achieving an RMSE of 101.59, highlighting its strong potential for real-world RUL prediction applications.

Related papers

• C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge [0.0]
  We propose C2L-Net, a novel context-to-latest data-driven framework for realistic online SOC estimation.<n>Unlike existing short-receptive-field or long-history models, the proposed framework explicitly separates contextual encoding from latest-measurement updating.<n>C2L-Net achieves up to 60 times faster inference and requires fewer parameters than recent data-driven baselines.
  arXiv  Detail & Related papers  (2026-05-09T03:47:08Z)
• Hybrid Deep Learning with Temporal Data Augmentation for Accurate Remaining Useful Life Prediction of Lithium-Ion Batteries [10.601010008014587]
  CDFormer is a hybrid deep learning model that integrates convolutional neural networks, deep residual shrinkage networks, and Transformer encoders.<n>It provides accurate and reliable forecasts, supporting effective battery health monitoring and data-driven maintenance strategies.
  arXiv  Detail & Related papers  (2026-03-28T08:19:28Z)
• On Neural Scaling Laws for Weather Emulation through Continual Training [76.70310203875866]
  We study neural scaling in Scientific Machine Learning, focusing on models for weather forecasting.<n>To analyze scaling behavior in as simple a setting as possible, we adopt a minimal, scalable, general-purpose Swin Transformer architecture.<n>We show that models trained in this minimalist way follow predictable scaling trends and even outperform standard cosine learning rate schedules.
  arXiv  Detail & Related papers  (2026-03-26T17:37:25Z)
• A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life [4.687035026089796]
  This paper proposes a multi-task targeted learning framework for predicting state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries.<n>It integrates multiple neural networks, including a multi-scale feature extraction module, an improved extended LSTM, and a dual-stream attention module.<n>It reduces the average RMSE for SOH and RUL predictions by 111.3% and 33.0%, respectively, compared to traditional and state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-03-20T12:22:10Z)
• SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling [0.0]
  We propose SeqBattNet, a discrete-state PINN with built-in aging adaptation for battery modeling.<n>The model requires only three basic battery parameters and, when trained on data from a single cell, still achieves robust performance.
  arXiv  Detail & Related papers  (2025-09-22T11:33:17Z)
• A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction [5.657115189763182]
  This paper introduces a hybrid deep learning framework, termed PCA-PR-Seq2Seq-Adam-LSTM.<n>It integrates Principal Component Analysis (PCA), Poisson Regression (PR), a Sequence-to-Sequence (Seq2Seq) architecture, and an Adam-optimized LSTM.<n>Results indicate that the proposed approach significantly improves forecasting accuracy and robustness compared to existing methods.
  arXiv  Detail & Related papers  (2025-09-20T17:13:25Z)
• HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction [0.6306103927990603]
  We propose a novel RUL prediction framework that incorporates a domain adaptation (DA) technique.<n>Our framework integrates a signal preprocessing pipeline including noise reduction, feature extraction, and normalization with a robust deep learning model.<n> Experimental results show that HybridoNet Adapt significantly outperforms traditional models.
  arXiv  Detail & Related papers  (2025-03-27T11:35:25Z)
• DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence [21.867940190460704]
  The Remaining Useful Life (RUL) of lithium-ion batteries is essential for ensuring safety, reducing maintenance costs, and optimizing usage.<n>This study introduces an accurate RUL prediction approach based on feature engineering and DLinear, applied to the dataset from NASA's Prognostics Center of Excellence.
  arXiv  Detail & Related papers  (2025-01-20T15:28:20Z)
• CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation [0.0]
  We propose a hybrid approach combining Convolutional Neural Networks with Long Short-Term Memory (LSTM) networks for RUL estimation.<n>Our results demonstrate that the hybrid CNN-LSTM model achieves the highest accuracy, offering a superior score compared to the other methods.
  arXiv  Detail & Related papers  (2024-12-20T15:48:57Z)
• Remaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making [0.0]
  We develop machine learning-based models to predict and classify battery RUL.<n>The proposed TLE model consistently outperforms baseline models in RMSE, MAE, and R squared error.<n>XGBoost achieves an impressive 99% classification accuracy, validated through cross-validation techniques.
  arXiv  Detail & Related papers  (2024-09-26T15:08:38Z)
• Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting [0.9208007322096533]
  Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem. A number of algorithms have incorporated features that are available from data collected by battery management systems. We develop a highly-accurate method that can overcome this limitation.
  arXiv  Detail & Related papers  (2022-12-03T12:59:51Z)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design [68.1682448368636]
  We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
  arXiv  Detail & Related papers  (2022-03-29T21:18:47Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning representations with end-to-end models for improved remaining useful life prognostics [64.80885001058572]
  The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. We propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. We will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T16:45:18Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.