Lithium-ion batteries have developed as the key energy storage solutions for various applications from power tools and consumers’ applications to electric vehicles and a renewable energy storage applications. Besides offering better performance (e.g., high gravimetric and volumetric energy density, high power capability during both charging and discharging, long lifetime) in comparison to other energy storage devices, Lithium-ion batteries are non-linear systems with their performance and degradation behavior strongly dependent on the operating conditions [1]. Thus, knowledge about the battery state-of-health (SOH) has become mandatory for real-life applications. Consequently, the topic of Lithium-ion battery was brought at the forefront of the research.

Many battery SOH estimation methods were developed and are available in the literature [2]. Most of these methods, which show very good accuracy, are applicable and validated only for perfect laboratory testing conditions. However, in real-life applications, the batteries are subjected to various constraints imposed by the battery management system (BMS); the constraints refer among others to impossibility of applying high current peaks to measure the internal resistance, discharge the battery to measure the capacity by other means then driving the car and/or have access to the whole battery voltage window to measure the battery capacity.

Thus, in this paper we proposed a method for estimating the SOH of the battery, which focuses on the measurement of the battery capacity for a certain voltage interval (e.g., 10%-90% SOC), which is not the entire voltage window allowed by the manufacturer as presented in Figure 1 and Figure 2. The proposed SOH estimation method is applied and its accuracy is assessed to different Li-ion battery chemistries.

[1] M. Kassem, J. Bernard, R. Revel, S. Pelissier, F. Duclaud, C. Delacourt, J. of Power Sources, 208, 296-305, (2012)

[2] M. Berecibar et al., Renewable and Sustainable Energy Reviews, 56, 572-587 (2016).