A binary PSO approach for real time optimal balancing of electrochemical cells

An effective management of Electrochemical Energy Storage Systems (ESSs) is nowadays of utmost importance for the technological evolution in both automotive and sustainable power networks applications. In particular, Battery Managements Systems (BMSs) are the electronic devices devolved to this management. One of the most important task of any BMS is cells balancing, aiming at leveling the operating points of the cells composing the ESS. Therefore, a novel online balancing algorithm is proposed in this work. Differently to the most commonly used methods, the proposed approach works by leveling the State of Charge (SoC) of the cells instead of their voltages. The balancing procedure has been formulated as a zero-one integer programming to be solved online by means of a Hybrid Genetic Binary Particle Swarm optimization (BPSO). Furthermore, a sparsity regularization has been considered for improving the energetic efficiency of the algorithm. Both the baseline and the regularized balancing systems have been tested and compared with a standard voltage based approach. The results show that the proposed method achieves a better and more robust balancing of the ESS, keeping a comparable energetic efficiency with respect to the voltage based technique.