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Energies 2014, 7(12), 8446-8464; doi:10.3390/en7128446

Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles

1
Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, the University Town, Shenzhen 518055, China
2
Sunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, China
*
Author to whom correspondence should be addressed.
Received: 22 August 2014 / Revised: 17 November 2014 / Accepted: 10 December 2014 / Published: 17 December 2014
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Abstract

State of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as ambient temperature, current rate and battery aging. In this paper, two model-based adaptive algorithms, including the adaptive unscented Kalman filter (AUKF) and adaptive slide mode observer (ASMO) are applied and compared in terms of convergence behavior, tracking accuracy, computational cost and estimation robustness against parameter uncertainties of the battery model in SOC estimation. Two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the two algorithms. Comparison results show that the AUKF has merits in convergence ability and tracking accuracy with an accurate battery model, while the ASMO has lower computational cost and better estimation robustness against parameter uncertainties of the battery model. View Full-Text
Keywords: lithium-ion battery; state of charge; adaptive unscented Kalman filter; adaptive slide mode observer lithium-ion battery; state of charge; adaptive unscented Kalman filter; adaptive slide mode observer
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tian, Y.; Xia, B.; Wang, M.; Sun, W.; Xu, Z. Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles. Energies 2014, 7, 8446-8464.

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