2021 IEEE International Symposium on Circuits and Systems (ISCAS) 2021
DOI: 10.1109/iscas51556.2021.9401541
|View full text |Cite
|
Sign up to set email alerts
|

A Battery Management System with Charge Balancing and Aging Detection Based on ANN

Abstract: A battery management system with aging detection based on artificial neural network (ANN) for the state of charge (SOC) balancing is proposed in this paper. The charger adopts a single-inductor multiple-output architecture to achieve charge balancing among different battery cells. In constant current mode, the pulse charging is utilized to improve the charging speed and slow down the aging rate. Moreover, an ANN is proposed to detect the state of health (SOH) of the battery cells and improve the accuracy of th… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
3
3

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(3 citation statements)
references
References 13 publications
0
3
0
Order By: Relevance
“…Large set of datasets is required to train the models in deep learning. In this project the battery aging model is trained using LSTM algorithm where the actual and predicted data's can be compared to obtain the accurate prediction results [5].…”
Section: Deep Learningmentioning
confidence: 99%
“…Large set of datasets is required to train the models in deep learning. In this project the battery aging model is trained using LSTM algorithm where the actual and predicted data's can be compared to obtain the accurate prediction results [5].…”
Section: Deep Learningmentioning
confidence: 99%
“…Based on the average DC resistance, the SoH of the battery is estimated. In [18], both the internal capacitance and resistance of the batteries are used to characterize the SoH based on a nonlinear autoregressive exogenous architecture. An alternative solution to obtain the general model of batterie is based on a new regression generative adversarial network (RGAN) [19].…”
Section: Introductionmentioning
confidence: 99%
“…Data-driven models include classical machine learning models, deep learning models, and hybrid models. In early battery states estimation research, classical machine learning models are mainly used, and common models include artificial neural networks (ANN) [18,19], support vector machine (SVM) [20,21], and Gaussian process regression (GPR) [22,23], hidden Markov model (HMM) [24,25], random forest (RF) [26,27], fuzzy control [28,29], autoregressive(AR) [30,31], relevance vector machine (RVM) [32,33], etc. Although classic machine learning models can estimate battery states based on a small number of data samples, the estimation quality relies on expert experience to manually extract features, and the estimation accuracy is greatly affected by the selected features.…”
Section: Introductionmentioning
confidence: 99%