BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling

• URL: http://arxiv.org/abs/2404.05802v1
• Date: Mon, 8 Apr 2024 18:05:24 GMT
• Title: BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling
• Authors: Yunyi Zhao, Wei Zhang, Erhai Hu, Qingyu Yan, Cheng Xiang, King Jet Tseng, Dusit Niyato,
• Abstract summary: We introduce a machine learning-based approach for battery-type classification and address the problem of data scarcity for the application. We propose BatSort which applies transfer learning to utilize the existing knowledge optimized with large-scale datasets. We conducted an experimental study and the results show that BatSort can achieve outstanding accuracy of 92.1% on average and up to 96.2%.
• Score: 42.453194049264646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries. However, it is challenging, largely because sorting batteries is costly and hardly automated to group batteries based on battery types. In this paper, we introduce a machine learning-based approach for battery-type classification and address the daunting problem of data scarcity for the application. We propose BatSort which applies transfer learning to utilize the existing knowledge optimized with large-scale datasets and customizes ResNet to be specialized for classifying battery types. We collected our in-house battery-type dataset of small-scale to guide the knowledge transfer as a case study and evaluate the system performance. We conducted an experimental study and the results show that BatSort can achieve outstanding accuracy of 92.1% on average and up to 96.2% and the performance is stable for battery-type classification. Our solution helps realize fast and automated battery sorting with minimized cost and can be transferred to related industry applications with insufficient data.

Related papers

• BatteryML:An Open-source platform for Machine Learning on Battery Degradation [15.469939183346467]
  We present BatteryML - a one-step, all-encompass, and open-source platform designed to unify data preprocessing, feature extraction, and the implementation of both traditional and state-of-the-art models. This streamlined approach promises to enhance the practicality and efficiency of research applications.
  arXiv  Detail & Related papers  (2023-10-23T08:51:05Z)
• A Mapping Study of Machine Learning Methods for Remaining Useful Life Estimation of Lead-Acid Batteries [0.0]
  State of Health (SoH) and Remaining Useful Life (RUL) contribute to enhancing predictive maintenance, reliability, and longevity of battery systems. This paper presents a mapping study of the state-of-the-art in machine learning methods for estimating the SoH and RUL of lead-acid batteries.
  arXiv  Detail & Related papers  (2023-07-11T10:41:41Z)
• A Deep Learning Approach Towards Generating High-fidelity Diverse Synthetic Battery Datasets [0.0]
  We introduce few Deep Learning-based methods to synthesize high-fidelity battery datasets. These augmented synthetic datasets will help battery researchers build better estimation models.
  arXiv  Detail & Related papers  (2023-04-09T05:41:21Z)
• Continual Learning For On-Device Environmental Sound Classification [63.81276321857279]
  We propose a simple and efficient continual learning method for on-device environmental sound classification. Our method selects the historical data for the training by measuring the per-sample classification uncertainty.
  arXiv  Detail & Related papers  (2022-07-15T12:13:04Z)
• Evaluating feasibility of batteries for second-life applications using machine learning [0.0]
  This paper presents a combination of machine learning techniques to enable prompt evaluation of retired electric vehicle batteries. The proposed algorithm generates features from available battery current and voltage measurements with simple statistics. It selects and ranks the features using correlation analysis, and employs Gaussian Process Regression enhanced with bagging.
  arXiv  Detail & Related papers  (2022-03-08T18:07:33Z)
• Data Driven Prediction of Battery Cycle Life Before Capacity Degradation [0.0]
  This paper utilizes the data and methods implemented by Kristen A. Severson, et al, to explore the methodologies that the research team used. The fundamental effort is to find out if machine learning techniques may be trained to use early life cycle data in order to accurately predict battery capacity.
  arXiv  Detail & Related papers  (2021-10-19T01:35:12Z)
• Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning [69.68068088508505]
  A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems. In this work, we use the deep deterministic policy algorithm to optimise the charging and discharging behaviour of a battery within such a system.
  arXiv  Detail & Related papers  (2021-09-10T10:59:14Z)
• ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
  We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. This dataset contains over1800fully segmented video frames collected from a real waste sorting plant. We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
  arXiv  Detail & Related papers  (2021-06-04T22:17:09Z)
• State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks [68.8204255655161]
  We build a Deep Forward Network for a lithium-ion battery and its performance assessment. The contribution of this work is to present a methodology of building a Deep Forward Network for a lithium-ion battery and its performance assessment.
  arXiv  Detail & Related papers  (2020-09-20T23:47:11Z)
• 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.