Online Parameters Estimation in Battery Systems for EV and PHEV Applications

The main target of this thesis is to assess whether two among of the most advanced algorithms are able to perform an online parameters estimation. Starting from a current profile generated by a real driving cycle and applied to an Electric Circuit Model (ECM) with known parameters, a voltage profile is generated. Then, Extended Kalman Filter (EKF) and Varied-Parameters Approach (VPA) will be employed both to the known system and to a real battery cell profile with unknown parameters. The research has led to the result that even if the two algorithms present opposite characteristics in terms of accuracy and computational effort, the are some common results. Convergence and accuracy are strictly dependent on the prior knowledge of the ECM parameter curves and on the hypothesis done to simplify the model, such as variables dependences, circuital complexity etc. Therefore, when applying the algorithms to a known system, perfect correspondence between estimated and real parameters is found, whereas when they are applied to an unknow system the converge in not reached. Therefore, for future researches might be recommend introducing Temperature, Current and Aging dependence in the system model, as well as generating voltage profiles from more complex ECMs and performing simulations with the same ECM used in this thesis.