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Energies 2018, 11(2), 283; https://doi.org/10.3390/en11020283

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

1
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2
School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
Received: 10 January 2018 / Revised: 18 January 2018 / Accepted: 19 January 2018 / Published: 24 January 2018
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Abstract

The paper aims to realize a rapid online estimation of the state-of-power (SOP) with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC) model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF) method, the battery parameters and states, including open-circuit voltage (OCV), are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery’s peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS) with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method. View Full-Text
Keywords: state of power; peak power; dual extended Kalman filters; state of charge; parameter identification state of power; peak power; dual extended Kalman filters; state of charge; parameter identification
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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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Xiang, S.; Hu, G.; Huang, R.; Guo, F.; Zhou, P. Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints. Energies 2018, 11, 283.

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