Hardware Implementation Optimization of Extended Kalman Filter for the Estimation of State of Charge of Li-Ion Battery

Hong Ce Zhang; Pu Shen Wang; Jiang Jiang; Hong Fei Cao

doi:10.4028/www.scientific.net/AMM.713-715.1094

Paper Titles

Research and Analysis of Business Systems for County Grid
p.1077

Suppression of High-Frequency Interference in Signal Loop
p.1081

Research of Electric Energy-Saving Evaluation Analysis in Colleges and Universities
p.1086

Distributed Cooperative Spectrum Sensing Algorithm Based on Average Consensus
p.1090

Hardware Implementation Optimization of Extended Kalman Filter for the Estimation of State of Charge of Li-Ion Battery
p.1094

Hoeffding Evolutionary Algorithm and its Application in Building Equivalent Circuit Models for Lithium-Ion Battery
p.1099

Research on Overlapped Time Division Multiplexing in the Amplify-and-Forward Cooperative Mode
p.1103

A Wind Power Prediction Method Based on RBF Neural Network
p.1107

Impact of Large-Scale Wind Power Penetration on the Static Voltage Stability Based on the L Index
p.1111

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 713-715Hardware Implementation Optimization of Extended...

Hardware Implementation Optimization of Extended Kalman Filter for the Estimation of State of Charge of Li-Ion Battery

Abstract:

Extended Kalman Filter (EKF) is widely studied in the field of State of Charge (SOC) estimation of Li-ion batteries, however, in applications like Electric Vehicles (EV), there are usually a large number of individual battery cells. In order to meet the demand of real-time computation, MCU of high performance is essential. In this paper, we proposed a hardware structure to implement EKF which is economical in area and power consumption and could be easily integrated in a larger design and at the same time could satisfy the real-time restriction.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 713-715)

Pages:

1094-1098

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.713-715.1094

Citation:

Cite this paper

Online since:

January 2015

Authors:

Hong Ce Zhang*, Pu Shen Wang, Jiang Jiang, Hong Fei Cao

Keywords:

Extended Kalman Filter (EKF), Hardware Optimization, State of Charge (SOC)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Andrew F. Burke: Batteries and Ultracapacitors for Electric, Hybrid, and Fuel Cell Vehicles, Proceedings of the IEEE, Vol. 95, No. 4 (2007), pp.806-820.

DOI: 10.1109/jproc.2007.892490

Google Scholar

[2] H. Rahimi-Eichi, Mo-Yuen Chow: Adaptive parameter identification and State-of-Charge estimation of lithium-ion batteries, " IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, Oct. 2012, pp.4012-4017.

DOI: 10.1109/iecon.2012.6389248

Google Scholar

[3] Gregory L. Plett: Kalman-Filter SOC Estimation for LiPB HEV Cells, the 19th International Battery, Hybrid and Fuel Electric Vehicle Symposium and Exhibition; Oct. 2002, pp.1-12.

Google Scholar

[4] P. Spagnol, S. Rossi, S.M. Savaresi: Kalman Filter SoC estimation for Li-Ion batteries, 2011 IEEE International Conference on Control Applications (CCA), pp.587-592.

DOI: 10.1109/cca.2011.6044480

Google Scholar

[5] Hen-Geul Yeh: Kalman filtering and systolic processors, IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 11 (1986), pp.2139-2142.

DOI: 10.1109/icassp.1986.1168641

Google Scholar