Design and implementation of an open circuit voltage prediction mechanism for lithium-ion battery systems

Abstract

This paper describes an open circuit voltage (OCV) prediction technique for lithium cells. The work contains an investigation to examine the charge and mixed state relaxation voltage curves, to analyse the potential for the OCV prediction technique in a practical system. The underlying principal of the technique described in this paper employs a simple equation paired with a polynomial to predict the equilibrated cell voltage after a small rest period. The polynomial coefficients are devised by the use of curve fitting and system identification techniques. The practical work detailed in this paper was conducted at the Centre for Automotive and Power System Engineering (CAPSE) battery laboratories at the University of South Wales.The results indicate that the proposed OCV prediction technique is highly effective and may be implemented with a simple battery management system.

Enhanced OCV prediction mechanism for a stand-alone PV-lithium ion renewable energy system

“…The work in this paper shows the advantages of the OCV prediction technique in both a theoretical and practical application. The results achieved from the testing work in Section 3 concur greatly with the tests conducted previously in Stockley et al (2013Stockley et al ( , 2014aStockley et al ( , 2014b. This is a positive feat as not only were the tests conducted on a seven-cell-connected module, but also on a different cell type to previous cell samples.…”

“…From the results in Table 3, it can be seen that the optimized results are very close to the initial results produced as in Stockley et al (2014aStockley et al ( , 2014b and previous in this paper. The use of the optimized values from the SSM algorithm increased the error by 1 mV when compared to the derived K V parameter values.…”

“…Stockley et al (2013) and Williams (2014a, 2014b) proved that the summing method could be used on lithium cells using a 30-minute measurement, and then reduced this time to 8 minutes to make the prediction mechanism more available to a wider range of applications. The maximum error obtained from the tests conducted in Stockley et al (2013Stockley et al ( , 2014aStockley et al ( , 2014b was just 6 mV for the 30-minute rate and 8.3 mV for the 8-minute rate, less than a 1%SoC error.…”

“…The voltage at the measurement interval (V tr ) was initially set as the voltage after 30 minutes of an opencircuit condition (Stockley et al, 2013(Stockley et al, , 2014a. However, advances in the research allowed the simple equation to be successfully tested at an improved measurement time of 8 minutes with a maximum SoC error of less than 1% (Stockley et al, 2014b). The work in this paper investigates the further improvement in the rate of prediction with an aim to reduce the time to within 5 minutes.…”

“…The module under test was made for use in a PV microsystem, and was constructed of seven 40 Ah LiNiMnCoO 2 cells as shown in Figure 2. Single cell tests were conducted on a 20-Ah LiNiMnCoO 2 cell and although the 40 Ah cells are the focus of this work, the previous work in Stockley et al (2013Stockley et al ( , 2014aStockley et al ( , 2014b was conducted on the 20 Ah cells and they are referenced throughout this paper.…”