State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state o...

Full description

Saved in:
Bibliographic Details
Published inElectronics (Basel) Vol. 9; no. 9; p. 1546
Main Authors Hossain Lipu, M. S., Hannan, M. A., Hussain, Aini, Ayob, Afida, Saad, Mohamad H. M., Muttaqi, Kashem M.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2020
Subjects
Accuracy
Aging
Algorithms
Aluminum oxide
Back propagation
Bias
Charge efficiency
Climate change
Cobalt oxides
Electric vehicles
Fault diagnosis
Heuristic methods
Lithium
Lithium-ion batteries
Manganese
Network management systems
Neural networks
Nickel
Noise
Optimization
Optimization techniques
Rechargeable batteries
Root-mean-square errors
State of charge
Time lag
Online AccessGet full text

Cover

More Information
Summary:The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics9091546