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

Wei Sun,Yong Tian,Bizhong Xia,Mingwang Wang,Zhihui Xu

doi:10.3390/en7128446

Wei Sun, Yong Tian + Show 3 more

Open Access

https://doi.org/10.3390/en7128446

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Journal: Energies	Publication Date: Dec 17, 2014
Citations: 86	License type: CC BY 4.0

Affiliation: University Town of Shenzhen, Tsinghua University

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.

Highlights

Electric vehicles (EVs) have rapidly developed in the past few years due to the increasing cost of energy and global warming constraints
It can be seen that two algorithms both can quickly track the reference state of charge (SOC) values with different initial SOCs, while the adaptive unscented Kalman filter (AUKF) performs better with a faster convergence ability and a higher accuracy
The computational cost of AUKF algorithm obtained by the Maltab commands, including tic and toc is about 0.145 ms/point, while the value of adaptive slide mode observer (ASMO) algorithm is about

Summary

Introduction

Electric vehicles (EVs) have rapidly developed in the past few years due to the increasing cost of energy and global warming constraints. It is not suitable for online estimation due to the long rest time to reach the steady-state of the battery Computational intelligence algorithms, such as artificial neural networks (ANNs) [6,7,8], fuzzy-logic [9,10,11], and support vector machines (SVMs) [12,13] have been developed to estimate the SOC. These methods do not require detailed knowledge of the battery systems, they can be applied to all battery types and have excellent estimation performance if the training data is sufficient to cover all loading conditions.

Results

Discussion

Conclusion