Next Article in Journal
Design and Experimental Analysis of an Exhaust Air Energy Recovery Wind Turbine Generator
Previous Article in Journal
Nonlinear Passive Control of a Wave Energy Converter Subject to Constraints in Irregular Waves
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(7), 6543-6565;

A New Data-Stream-Mining-Based Battery Equalization Method

National Engineering Laboratory for Electric Vehicles, Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China
Author to whom correspondence should be addressed.
Academic Editor: Peter J. S. Foot
Received: 4 March 2015 / Revised: 11 June 2015 / Accepted: 16 June 2015 / Published: 30 June 2015
Full-Text   |   PDF [1471 KB, uploaded 30 June 2015]   |  


Balancing battery cells is a key task for battery management systems (BMS). Imbalances of cells decrease the capacity and lifetime of the battery pack. Many balancing topologies and strategies have been proposed to balance the electric charges among cells and most of the intelligent control strategies select cells (to shuttle charges) by comparing their terminal voltages. However, the nature of battery equalization is to balance the energy stored in individual cells. The measured terminal voltage is just an external characteristic and cannot accurately reflect the state of charge (SOC) of the cell, especially in a noisy environment. Additionally, when the consistencies of cells are very poor, balancing the cells with terminal voltages will lead to serious errors. In this paper, we introduced a novel battery balancing method, in which the charge-balancing criterion was not the cell voltage, but the shuttling capacities among cells. Data stream mining (DSM) technique was used to calculate the shuttling capacities. A single switched capacitor (SSC) based cell balancing topology was used to test the performance of the proposed method. With the obtained summary information, the cells, the sequence, and the quantity of the equalized charge can be decided automatically by the proposed algorithm. The simulation and experiment results have shown that the proposed method was effective and convenient. View Full-Text
Keywords: battery management systems (BMS); state of charge (SOC); single switched capacitor (SSC); data stream mining (DSM) battery management systems (BMS); state of charge (SOC); single switched capacitor (SSC); data stream mining (DSM)

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Lin, C.; Mu, H.; Zhao, L.; Cao, W. A New Data-Stream-Mining-Based Battery Equalization Method. Energies 2015, 8, 6543-6565.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top