ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning

• URL: http://arxiv.org/abs/2501.10492v1
• Date: Fri, 17 Jan 2025 12:13:04 GMT
• Title: ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning
• Authors: James Sadler, Rizwaan Mohammed, Michael Castle, Kotub Uddin,
• Abstract summary: This work establishes a foundational battery degradation model, providing reliable forecasts across a range of battery types and operating conditions. Our novel framework uses contrastive learning to map the relationship between the underlying physical degradation parameters and observable operational quantities.
• Score: 0.0
• License:
• Abstract: Modeling lithium-ion battery (LIB) degradation offers significant cost savings and enhances the safety and reliability of electric vehicles (EVs) and battery energy storage systems (BESS). Whilst data-driven methods have received great attention for forecasting degradation, they often demonstrate limited generalization ability and tend to underperform particularly in critical scenarios involving accelerated degradation, which are crucial to predict accurately. These methods also fail to elucidate the underlying causes of degradation. Alternatively, physical models provide a deeper understanding, but their complex parameters and inherent uncertainties limit their applicability in real-world settings. To this end, we propose a new model - ACCEPT. Our novel framework uses contrastive learning to map the relationship between the underlying physical degradation parameters and observable operational quantities, combining the benefits of both approaches. Furthermore, due to the similarity of degradation paths between LIBs with the same chemistry, this model transfers non-trivially to most downstream tasks, allowing for zero-shot inference. Additionally, since categorical features can be included in the model, it can generalize to other LIB chemistries. This work establishes a foundational battery degradation model, providing reliable forecasts across a range of battery types and operating conditions.

Related papers

• Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics [1.5728609542259502]
  This paper presents a data-inspired approach for improving the fidelity of reduced-order li-ion battery models. The proposed method combines a Genetic Algorithm with Sequentially Thresholded Ridge Regression (GA-STRidge) to identify and compensate for discrepancies between a low-fidelity model (LFM) and data generated either from testing or a high-fidelity model (HFM) The hybrid model, combining physics-based and data-driven methods, is tested across different driving cycles to demonstrate the ability to significantly reduce the voltage prediction error compared to the baseline LFM.
  arXiv  Detail & Related papers  (2024-11-20T00:00:11Z)
• DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis [0.7342676110939172]
  We introduce a novel general-purpose model for battery degradation prediction and synthesis, DiffBatt. It operates as a probabilistic model to capture uncertainty in aging behaviors and a generative model to simulate battery degradation. It provides accurate results with a mean RMSE of 196 cycles across all datasets, outperforming all other models and demonstrating superior generalizability.
  arXiv  Detail & Related papers  (2024-10-31T12:53:53Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Revisiting Essential and Nonessential Settings of Evidential Deep Learning [70.82728812001807]
  Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation. We propose Re-EDL, a simplified yet more effective variant of EDL.
  arXiv  Detail & Related papers  (2024-10-01T04:27:07Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction Based on Relaxation Voltage Curves [7.07637687957493]
  This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning. Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.
  arXiv  Detail & Related papers  (2023-08-15T15:07:32Z)
• Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction [2.670887944566458]
  We introduce a novel Transformer-based deep learning architecture which is able to simultaneously infer the ageing state from a limited number of voltage/current samples. Our experiments show that the trained model is effective for input current profiles of different complexities and is robust to a wide range of degradation levels.
  arXiv  Detail & Related papers  (2022-06-01T15:31:06Z)
• Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis [6.7143928677892335]
  Lithium-ion batteries (LIBs) are key to promoting electrification in the coming decades. Inadequate understanding of LIB degradation is an important bottleneck that limits battery durability and safety. Here, we propose hybrid physics-based and data-driven modeling for online diagnosis and prognosis of battery degradation.
  arXiv  Detail & Related papers  (2021-10-25T11:14:12Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
  Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently. There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
  arXiv  Detail & Related papers  (2021-06-17T00:49:55Z)
• Universal Battery Performance and Degradation Model for Electric Aircraft [52.77024349608834]
  Design, analysis, and operation of electric vertical takeoff and landing aircraft (eVTOLs) requires fast and accurate prediction of Li-ion battery performance. We generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning.
  arXiv  Detail & Related papers  (2020-07-06T16:10:54Z)

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.