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Energies 2017, 10(3), 390; doi:10.3390/en10030390

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

1
National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
2
School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China
*
Author to whom correspondence should be addressed.
Academic Editor: Rui Xiong
Received: 22 January 2017 / Revised: 27 February 2017 / Accepted: 7 March 2017 / Published: 19 March 2017
(This article belongs to the Special Issue Advanced Energy Storage Technologies and Their Applications (AESA))
View Full-Text   |   Download PDF [8277 KB, uploaded 19 March 2017]   |  

Abstract

The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference. View Full-Text
Keywords: power estimation; parameter identification; ratio vector; cell difference; recursive least squares power estimation; parameter identification; ratio vector; cell difference; recursive least squares
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Jiang, B.; Dai, H.; Wei, X.; Zhu, L.; Sun, Z. Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs. Energies 2017, 10, 390.

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