Heuristic estimation of temperature-dependant model parameters of Li-Po batteries for UAV applications

This work deals with the system identification of Thevenin models of Li-Po batteries for UAV applications. Starting from the results of an experimental hybrid pulse-power characterization of a battery pack carried out at different temperatures (0°C, 15°C, 49°C) and within the operative range of state-of-charge (>10%), the model parameters are identified via three heuristic optimization algorithms, based on particle-swarm, teaching-learning and differential evolution techniques. Differently from conventional approaches typically applied by commercial CAE tools (e.g. Matlab), the proposed techniques are directly applied to the whole time history of the measurements. The results highlight that the particle-swarm method exhibits the fastest convergence, but it requires to initially define the algorithm weighing coefficients. This is not needed for teaching-learning based optimization, but computational effort strongly increases to achieve satisfactory accuracy. The differential evolution technique provides intermediate performances, especially if the total computation time is also considered. The case study is referred to the 1850 mAh/6 cells/22.2 V Li-Po battery pack employed in the lightweight fixed-wing UAV Rapier X-25, developed by Sky Eye Systems (Italy).