Measurement results obtained with new SoC algorithms using fresh batteries
A complete description of EMF and overpotential behaviour of US18500G3 Li-ion batteries has been given in chapters 4 and 5 of this book. In this chapter, a new SoC algorithm inferred from the SoC system described in  will be developed. The algorithm combines adaptive and predictive systems with Electro- Motive Force (EMF) measurement during the equilibrium state and Coulomb counting (cc) – during the charge and discharge states. Besides cc, the effect of the battery overpotential (?) during discharge will be also considered. The goal of the SoC system is to predict the remaining run-time (tr) of an Li-ion battery with an uncertainty of 1 minute or less under all realistic user conditions, including a wide variety of load currents and a wide temperature range.
Basic issues concerning SoC and tr are presented in section 7.1. A new SoC algorithm and the method of implementing the algorithm states in a real-time SoC evaluation system are presented in section 7.2. The focus in section 7.3 is on the experimental results obtained with the real-time SoC evaluation system. The identification of sources of error in each state of the real-time SoC evaluation system is discussed in section 7.4. The conclusions of the error sources analysis are used to develop an improved SoC algorithm in section 7.5. They are also compared with the SoC algorithm introduced in section 7.2. Section 7.6 presents a comparison with a competitive SoC indication system, i.e. the bq26500 developed by Texas Instruments. These results prove that it is indeed possible to test SoCindication algorithms with the newly developed SoC evaluation system and show that the new approach is effective in improving remaining run-time indication accuracy. Finally, section 7.7 presents concluding remarks.
KeywordsTransitional State Texas Instrument Battery Voltage Uncertainty Calculation Battery Temperature
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