On-line remaining energy prediction: A case study in embedded battery management system

Yujie Wang, Zonghai Chen and Chenbin Zhang

Applied Energy, 2017, vol. 194, issue C, 688-695

Abstract: Modern electric vehicles (EVs) and hybrid electric vehicles (HEVs) require a reliable battery management system (BMS). The remaining energy and the state-of-energy (SoE) are very important indexes for the embedded BMS used in both EV and HEV applications. As a case study in the embedded BMS, this paper presents the implementation of remaining energy prediction based on the μC/OS-II real time operating system (RTOS). In considering that there are accumulated errors caused by inevitable drift noise of the current or voltage sensors, a model based SoE estimator is developed based on a first-order RC equivalent circuit model. Moreover, the Bayesian learning technique is used for SoE estimation to get accurate and robustness estimation results. Lastly, two different kinds of batteries are carried out under laboratory experiments and real road test to verify the robustness of the proposed SoE estimation approach. The results indicate that the maximum absolute estimation error (MAEE) and the root-mean square error (RMSE) are within 2% and 1% for both LiFePO4 and Li(Ni1/3Co1/3Mn1/3)O2 batteries.

Keywords: Battery management system; Embedded system; μC/OS-II RTOS; Remaining energy prediction (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626191630678X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:194:y:2017:i:c:p:688-695

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2016.05.081

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().