scholarly journals An End-to-End Learning Framework for Battery Capacity-Fade Trajectory Prediction Using Early Life Data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jinqiang Liu ◽  
Adam Thelen ◽  
Chao Hu ◽  
Xiao-Guang Yang
Keyword(s):  
Machine Learning ◽  
Early Life ◽  
Life Prediction ◽  
Capacity Fade ◽  
Learning Problem ◽  
Learning Framework ◽  
Life Data ◽  
Machine Learning Model ◽  
Li Ion ◽  
End To End

Predicting the capacity-fade trajectory of a lithium-ion (Li-ion) battery cell is a critical task given its broad utility throughout the battery product life cycle. Even more useful is estimating a battery cell’s capacity-fade trajectory when this cell has not exhibited any noticeable capacity degradation. Accurately predicting the entire capacity-fade trajectory using early life data enables more efficient cell design, operation, maintenance, and evaluation for second-life use. To accomplish this challenging task, we propose an end-to-end learning framework combining empirical capacity fade models and data-driven machine learning models, in which the two types of models are closely coupled. First, we evaluate the accuracy of a library of relevant empirical models which have been shown to model the observed capacity fade of Li-ion cells with reasonable accuracy. After selecting a model, we formulate an end-to-end learning problem that simultaneously fits the chosen empirical model to estimate the capacity fade curve and trains a machine learning model to estimate the best-fit parameters of the empirical model. By solving this end-to-end learning problem, rather than sequentially executing the separate tasks of fitting the capacity fade model and training the machine learning model, we achieve a more optimal solution which is shown to better balance these two objectives. Our proposed end-to-end learning framework is evaluated using a publicly available battery dataset consisting of 124 lithium-iron-phosphate/graphite cells charged with various fast-charging protocols. This dataset was split into training, primary test, and secondary test datasets. Our method performs on par with existing early prediction methods in terms of cycle life prediction, attaining root-mean-square errors of 84 cycles and 169 cycles for primary and secondary test datasets, respectively. In addition to the cycle life prediction, our method possesses a unique ability to predict the entire capacity-fade trajectory.

scholarly journals Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries

RSC Advances ◽  
2018 ◽  
Vol 8 (69) ◽  
pp. 39414-39420 ◽  
Author(s):  
Omar Allam ◽  
Byung Woo Cho ◽  
Ki Chul Kim ◽  
Seung Soon Jang
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
High Throughput Screening ◽  
Density Functional ◽  
Electrode Materials ◽  
Screening Method ◽  
Li Ion Batteries ◽  
Functional Theory ◽  
Learning Framework ◽  
Li Ion

In this study, we utilize a density functional theory-machine learning framework to develop a high-throughput screening method for designing new molecular electrode materials.

Predicting Thermal Runaway in Li-Ion Battery Employing Machine Learning Framework

2020 ◽  
Vol MA2020-01 (2) ◽  
pp. 429-429 ◽  
Author(s):  
Marco Ragone ◽  
Vitaliy Yurkiv ◽  
Ajaykrishna Ramasubramanian ◽  
Reza Shahbazian-Yassar ◽  
Farzad Mashayek
Keyword(s):  
Machine Learning ◽  
Thermal Runaway ◽  
Li Ion Battery ◽  
Learning Framework ◽  
Li Ion

A Recurrent Neural Network Model for Battery Capacity Fade Curve Prediction Using Early Life Data

2021 ◽  
Vol MA2021-01 (1) ◽  
pp. 65-65
Author(s):  
Saurabh Saxena ◽  
Logan Ward ◽  
Joseph Kubal ◽  
Hong-Keun Kim ◽  
Wenquan Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Early Life ◽  
Capacity Fade ◽  
Life Data ◽  
Battery Capacity

scholarly journals CarEnvision: A Data-Driven Machine Learning Framework for Automated Car Value Prediction

2021 ◽  
Author(s):  
TianGe (Terence) Chen ◽  
Angel Chang ◽  
Evan Gunnell ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Retail Price ◽  
Data Driven ◽  
Value Prediction ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Negative Sentiment

When people want to buy or sell a personal car, they struggle to know when the timing is best in order to buy their favorite vehicle for the best price or sell for the most profit. We have come up with a program that can predict each car’s future values based on experts’ opinions and reviews. Our program extracts reviews which undergo sentiment analysis to become our data in the form of positive and negative sentiment. The data is then collected and used to train the Machine Learning model, which will in turn predict the car’s retail price.

scholarly journals IFed: A novel federated learning framework for local differential privacy in Power Internet of Things

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091969
Author(s):  
Hui Cao ◽  
Shubo Liu ◽  
Renfang Zhao ◽  
Xingxing Xiong
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Differential Privacy ◽  
Learning Model ◽  
Wireless Sensor ◽  
Trade Off ◽  
Privacy Requirements ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Internet Of Thing

Nowadays, wireless sensor network technology is being increasingly popular which is applied to a wide range of Internet of Things. Especially, Power Internet of Things is an important and rapidly growing section in Internet of Thing systems, which benefited from the application of wireless sensor networks to achieve fine-grained information collection. Meanwhile, the privacy risk is gradually exposed, which is the widespread concern for electricity power consumers. Non-intrusive load monitoring, in particular, is a technique to recover state of appliances from only the energy consumption data, which enables adversary inferring the behavior privacy of residents. There can be no doubt that applying local differential privacy to achieve privacy preserving in the local setting is more trustworthy than centralized approach for electricity customers. Although it is hard to control the risk and achieve the trade-off between privacy and utility by traditional local differential privacy obfuscation mechanisms, some existing obfuscation mechanisms based on artificial intelligence, called advanced obfuscation mechanisms, can achieve it. However, the large computing resource consumption to train the machine learning model is not affordable for most Power Internet of Thing terminal. In this article, to solve this problem, IFed was proposed—a novel federated learning framework that let electric provider who normally is adequate in computing resources to help Power Internet of Thing users. First, the optimized framework was proposed in which the trade-off between local differential privacy, data utility, and resource consumption was incorporated. Concurrently, the following problem of privacy preserving on the machine learning model transport between electricity provider and customers was noted and resolved. Last, users were categorized based on different levels of privacy requirements, and stronger privacy guarantee was provided for sensitive users. The formal local differential privacy analysis and the experiments demonstrated that IFed can fulfill the privacy requirements for Power Internet of Thing users.

scholarly journals Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing

2017 ◽  
Vol 5 ◽  
pp. 263-278 ◽  
Author(s):  
Tim Vieira ◽  
Jason Eisner
Keyword(s):  
Machine Learning ◽  
Dynamic Programming ◽  
Pareto Frontier ◽  
Change Propagation ◽  
Prior Work ◽  
Learning Problem ◽  
Speed Up ◽  
End To End

Pruning hypotheses during dynamic programming is commonly used to speed up inference in settings such as parsing. Unlike prior work, we train a pruning policy under an objective that measures end-to-end performance: we search for a fast and accurate policy. This poses a difficult machine learning problem, which we tackle with the lols algorithm. lols training must continually compute the effects of changing pruning decisions: we show how to make this efficient in the constituency parsing setting, via dynamic programming and change propagation algorithms. We find that optimizing end-to-end performance in this way leads to a better Pareto frontier—i.e., parsers which are more accurate for a given runtime.

scholarly journals A Novel Machine Learning Model for the Detection of Epilepsy and Epileptic Seizures Using Electroencephalographic Signals Based on Chaos and Fractal Theories

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zayneb Brari ◽  
Safya Belghith
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Epileptic Seizures ◽  
Chaotic Signal ◽  
Research Area ◽  
New Method ◽  
Classification Problems ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Electroencephalographic Signals

Machine learning is an expanding research area. Its main application is in the medical field and particularly the detection of epilepsy and epileptic seizures through electroencephalographic signals (EEG). It aims to design an intelligent framework that enables an immediate diagnosis of this disease without neurological consultation and thus saves the lives of the epileptic patients by detecting seizures and warning them before it happens. However, as a real-time application, this kind of framework faces several challenges such as accuracy, fast responses, and optimal memory usage. Within this context, our work was carried out. We propose a new machine learning framework based on chaos and fractal theories. Two main novelties are presented in this paper. Firstly, we propose a new method for signal preprocessing, and we reconstruct new versions of studied EEG signals using derivative determination and chaotic injection. Secondly, we suggest a new method for fractal analysis using Higuchi fractal dimension (HFD). In fact, HFDs extracted from EEG derivatives lead to detect epilepsy, whereas HFDs extracted from EEG with a chaotic signal injection lead to seizure detection. In addition, feature fusion helped to linearize all classification problems. An experimental study using the Bonn EEG database proves the efficiency of our contributions in comparison to published research. An accuracy of 100% was achieved in different classification cases using few features and a simple linear classifier.

Sign in / Sign up

Export Citation Format

Share Document