Abstract：
:State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Hannan MA,Lipu MSH,Hussain A,Ker PJ,Mahlia TMI,Mansor M,Ayob A,Saad MH,Dong ZYdoi
10.1038/s41598-020-61464-7subject
Has Abstractpub_date
2020-03-13 00:00:00pages
4687issue
1issn
2045-2322pii
10.1038/s41598-020-61464-7journal_volume
10pub_type
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