Real-time state-of-charge tracking system using mixed estimation algorithm for electric vehicle battery system
RIS ID
132785
Abstract
The state-of-charge (SoC) in electric vehicles (EVs) is a key piece of information, which plays a crucial role in reducing drivers' range anxiety (fear of being stranded due to insufficient EV battery power) and also in amping up EVs uptake into the global transport market. This paper proposes a new real-time mixed SoC estimation algorithm for lithium-ion batteries used in EVs. The mixed algorithm is a combination of an improved Coulomb Counting (CC), a Model-Based (MB), and a Bottom-up (BU) methods in order to improve the accuracy of the SoC estimation. To extract the battery parameters, experimental tests have been conducted on a 2012 Nissan Leaf battery cell (i.e. 31.1 Ah Manganese-oxide Li-ion cell). The effectiveness of the proposed algorithm is then validated using the measured data from an actual driving cycle test on the battery cell. The results demonstrate a great accuracy with a maximum error of 0.15% for the SoC estimation in comparison with conventional models.
Publication Details
K. Sarrafan, K. M. Muttaqi & D. Sutanto, "Real-time state-of-charge tracking system using mixed estimation algorithm for electric vehicle battery system," in 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018, 2018, pp. 1-8.