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

Dickson N.T. How, M. A. Hannan, M. S.Hossain Lipu, K. S.M. Sahari, P. J. Ker, K. M. Muttaqi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The state-of-charge (SOC) estimation is a crucial parameter of a lithium-ion battery it depends on numerous incalculable factors such as battery chemistry, ambient environment, aging factor, etc. This paper develops a state of charge (SOC) estimation model for a lithium-ion battery using an improved deep neural network (DNN) approach in electric vehicle applications. The DNN is suitable for SOC estimation due its sufficient hidden layer which is capable of predicting the SOC of unseen drive cycle during training. A series of DNN models with varying number of hidden layers and its training algorithm is developed to investigate the training performance of different drive cycles. It is observed that adding hidden layers in DNN decreases the error rate and improves the SOC estimation. This study also shows that the 7-layer of DNN training on dynamic stress test (DST) drive cycle is capable of predicting SOC values unexpectedly well of other unseen drive cycles such as federal urban driving schedule (FUDS), Beijing dynamic stress test (BJDST), and supplemental federal test procedure (US06), respectively.

Original languageEnglish
Title of host publication2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538645390
DOIs
Publication statusPublished - Sep 2019
Event2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 - Baltimore, United States
Duration: 29 Sep 20193 Oct 2019

Publication series

Name2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Conference

Conference2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
CountryUnited States
CityBaltimore
Period29/9/193/10/19

Fingerprint

electric vehicle
Electric vehicles
neural network
chemistry
Aging of materials
Deep neural networks
Lithium-ion batteries
performance
Values

Keywords

  • deep learning
  • deep neural network
  • DNN
  • lithium ion battery
  • state of charge estimation

ASJC Scopus subject areas

  • Filtration and Separation
  • Fluid Flow and Transfer Processes
  • Process Chemistry and Technology
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Transportation

Cite this

How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J., & Muttaqi, K. M. (2019). State-of-Charge Estimation of Li-ion Battery in Electric Vehicles: A Deep Neural Network Approach. In 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 [8912003] (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAS.2019.8912003

State-of-Charge Estimation of Li-ion Battery in Electric Vehicles : A Deep Neural Network Approach. / How, Dickson N.T.; Hannan, M. A.; Lipu, M. S.Hossain; Sahari, K. S.M.; Ker, P. J.; Muttaqi, K. M.

2019 IEEE Industry Applications Society Annual Meeting, IAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8912003 (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

How, DNT, Hannan, MA, Lipu, MSH, Sahari, KSM, Ker, PJ & Muttaqi, KM 2019, State-of-Charge Estimation of Li-ion Battery in Electric Vehicles: A Deep Neural Network Approach. in 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019., 8912003, 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019, Baltimore, United States, 29/9/19. https://doi.org/10.1109/IAS.2019.8912003
How DNT, Hannan MA, Lipu MSH, Sahari KSM, Ker PJ, Muttaqi KM. State-of-Charge Estimation of Li-ion Battery in Electric Vehicles: A Deep Neural Network Approach. In 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8912003. (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019). https://doi.org/10.1109/IAS.2019.8912003
How, Dickson N.T. ; Hannan, M. A. ; Lipu, M. S.Hossain ; Sahari, K. S.M. ; Ker, P. J. ; Muttaqi, K. M. / State-of-Charge Estimation of Li-ion Battery in Electric Vehicles : A Deep Neural Network Approach. 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019).
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