Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning

• URL: http://arxiv.org/abs/2011.00841v2
• Date: Sat, 23 Jan 2021 10:59:27 GMT
• Title: Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning
• Authors: Arnab Bhattacharjee, Ashu Verma, Sukumar Mishra, Tapan K Saha
• Abstract summary: We propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability.
• Score: 0.4129225533930966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability.

Related papers

• Forecasting Lithium-Ion Battery Longevity with Limited Data Availability: Benchmarking Different Machine Learning Algorithms [3.4628430044380973]
  This work aims to compare the relative performance of different machine learning algorithms, both traditional machine learning and deep learning. We investigated 14 different machine learning models that were fed handcrafted features based on statistical data. Deep learning models were observed to perform particularly poorly on raw, limited data.
  arXiv  Detail & Related papers  (2023-12-10T00:51:50Z)
• Mobile Traffic Prediction at the Edge through Distributed and Transfer Learning [2.687861184973893]
  The research in this topic concentrated on making predictions in a centralized fashion, by collecting data from the different network elements. We propose a novel prediction framework based on edge computing which uses datasets obtained on the edge through a large measurement campaign.
  arXiv  Detail & Related papers  (2023-10-22T23:48:13Z)
• Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing [59.40731173370976]
  We investigate the application of energy-efficient brain-inspired machine learning models for on-board radio resource management. For relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$times$ as compared to the CNN-based reference platform.
  arXiv  Detail & Related papers  (2023-08-22T03:13:57Z)
• Transfer learning for atomistic simulations using GNNs and kernel mean embeddings [24.560340485988128]
  We propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) to represent chemical environments together with kernel mean embeddings. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance.
  arXiv  Detail & Related papers  (2023-06-02T14:58:16Z)
• Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet and IGTD Algorithm [1.700438015014627]
  Convolutional Neural Networks (CNN) have been a good solution for understanding a vast image dataset. We implemented deep learning approaches to analyze the datasets to understand their state of charge and which charge levels they would choose. We integrated other CNN architecture such as EfficientNet to prove that CNN is a great learner for reading information from images.
  arXiv  Detail & Related papers  (2022-06-07T22:56:40Z)
• Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems [79.58173794910631]
  A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data.
  arXiv  Detail & Related papers  (2022-05-31T17:09:46Z)
• 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)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation [51.66271681532262]
  Online Self-Acquired Knowledge Distillation (OSAKD) is proposed, aiming to improve the performance of any deep neural model in an online manner. We utilize k-nn non-parametric density estimation technique for estimating the unknown probability distributions of the data samples in the output feature space.
  arXiv  Detail & Related papers  (2021-08-26T14:01:04Z)
• 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)
• Multistep Electric Vehicle Charging Station Occupancy Prediction using Mixed LSTM Neural Networks [1.3706331473063877]
  We propose a new mixed long short-term memory neural network incorporating both historical charging state sequences and time-related features for discrete charging occupancy state prediction. The model is compared to a number of state-of-the-art machine learning and deep learning approaches based on the EV charging data obtained from the open data portal of Dundee, UK. The results show that the proposed method produces very accurate predictions (99.99% and 81.87% for 1 step (10 minutes) and 6 step (1 hour) ahead, respectively, and outperforms the benchmark approaches significantly.
  arXiv  Detail & Related papers  (2021-06-09T11:10:14Z)

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.