State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

The state of charge (SOC) is a crucial parameter of a battery management system for Li-ion batteries. The SOC indicates the amount of charge left in the battery of electric vehicles-akin to the fuel gauge in combustion vehicles. An accurate SOC knowledge contributes largely to the longevity, perform...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on industry applications Vol. 56; no. 5; pp. 5565 - 5574
Main Authors:	How, Dickshon N. T., Hannan, Mahammad A., Lipu, Molla S. Hossain, Sahari, Khairul S. M., Ker, Pin Jern, Muttaqi, Kashem M.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01-09-2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial neural networks Deep learning deep neural network (DNN) electric vehicle (EV) Electric vehicles Error analysis Estimation Fuel gages Li-ion battery Lithium Lithium-ion batteries Mathematical model Neural networks Neurons Performance evaluation Rechargeable batteries Schedules State of charge state-of-charge (SOC) estimation Test procedures
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The state of charge (SOC) is a crucial parameter of a battery management system for Li-ion batteries. The SOC indicates the amount of charge left in the battery of electric vehicles-akin to the fuel gauge in combustion vehicles. An accurate SOC knowledge contributes largely to the longevity, performance, and reliability of the battery. However, the SOC of Li-ion batteries cannot be easily measured by any apparatus. Furthermore, the SOC can also be influenced by numerous incalculable factors such as battery chemistry, ambient environment, aging factor, etc. In this article, we propose an SOC estimation model for a Li-ion battery using an improved deep neural network (DNN) approach for electric vehicle applications. We found that a DNN with a sufficient number of hidden layers is capable of predicting the SOC of the unseen drive cycles during training. We developed a series of DNN models with a varying number of hidden layers, and its training algorithm was to investigate their respective performance when evaluated on different drive cycles. We observe that the increasing number of hidden layers in the DNN (up to four hidden layers) decreases the error rate and improves SOC estimation. An additional increase in the number of hidden layers beyond that increases the error rate. In this study, we show that a four-hidden-layer DNN trained on Dynamic Stress Test drive cycle is capable of predicting SOC values unexpectedly well of other unseen drive cycles such as Federal Urban Driving Schedule, Beijing Dynamic Stress Test, and Supplemental Federal Test Procedure, respectively.
ISSN:	0093-9994 1939-9367
DOI:	10.1109/TIA.2020.3004294