Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7730587 | Journal of Power Sources | 2015 | 10 Pages |
Abstract
This paper deals with the contradiction between simplicity and accuracy of the LiFePO4 battery states estimation in the electric vehicles (EVs) battery management system (BMS). State of charge (SOC) and state of health (SOH) are normally obtained from estimating the open circuit voltage (OCV) and the internal resistance of the equivalent electrical circuit model of the battery, respectively. The difficulties of the parameters estimation arise from their complicated variations and different dynamics which require sophisticated algorithms to simultaneously estimate multiple parameters. This, however, demands heavy computation resources. In this paper, we propose a novel technique which employs a simplified model and multiple adaptive forgetting factors recursive least-squares (MAFF-RLS) estimation to provide capability to accurately capture the real-time variations and the different dynamics of the parameters whilst the simplicity in computation is still retained. The validity of the proposed method is verified through two standard driving cycles, namely Urban Dynamometer Driving Schedule and the New European Driving Cycle. The proposed method yields experimental results that not only estimated the SOC with an absolute error of less than 2.8% but also characterized the battery model parameters accurately.
Related Topics
Physical Sciences and Engineering
Chemistry
Electrochemistry
Authors
Van-Huan Duong, Hany Ayad Bastawrous, KaiChin Lim, Khay Wai See, Peng Zhang, Shi Xue Dou,