Marco Mussi, Luigi Pellegrino, Marcello Restelli, Francesco Trovò.
In recent years, the use of Lithium-ion batteries in smart power systems and hybrid/electric vehicles has become increasingly popular since they provide a flexible and cost-effective way to store and deliver power. Their full integration into more complex systems requires an accurate estimate of the energy a battery is currently storing, a.k.a. State of Charge (SoC). However, the standard techniques present in the literature provide an accurate estimation of the SoC only having a priori knowledge about the battery. Moreover, their accuracy degrades if the battery working conditions (e.g., external temperature) are variable over time, or battery measurements necessary for the SoC estimation are affected by offset or gain biases. To overcome these limitations, this paper proposes a novel data-driven optimization based methodology for battery SoC estimation, namely VDB-SE. The proposed methodology provides accurate SoC estimations without knowing battery model parameters, such as capacity and internal resistance, whose characterization would require complex and long laboratory tests. Experimental verification and comparisons demonstrate that VDB-SE performance are comparable to the state-of-the-art algorithms over a wide range of working conditions. Indeed, the difference in terms of performance is smaller than 0.2%. Moreover, experimental results showed that on a real energy storage system the proposed method provides a SoC estimation with an error of less than 2.1%.