Multi-phase method of estimation and adaptation of parameters of electrical battery models

Mathematical modeling of the battery lifetime is an important tool for the design of more efficient batteries, as well as for the optimization of their use. The electrical models class is among the classes of mathematical models used for this purpose, and a fundamental step to their application is the correct estimation of their parameters. This paper performs the mathematical modeling of Lithium-Ion Polymer batteries lifetime through the electrical model of Tremblay, in which a multi-phase method of estimation and adaptation of parameters is proposed, divided into three phases: discovery, learning, and inference. The multi-phase method is based on two Artificial Intelligence techniques: genetic algorithms and artificial neural networks. The proposed method is validated by the simulation and experimental studies. From the results, it is concluded that the application of the multi-phase method improves the effective accuracy of the Tremblay model, when it comes to adapt its parameters to the battery during runtime. For constant discharge currents, the average error reduction was 79%, when compared to the best set of parameters obtained by GA without the adaptation process. For variable current discharge curves, the method was able to reduce the error more than 35%. This method can be applied to other battery lifetime prediction models.

Automated summary

This study proposes a multi-phase method for predicting the lifetime of Lithium-Ion Polymer batteries by estimating and adapting parameters of Tremblay's electrical model using genetic algorithms and artificial neural networks. Experimental results show that this method significantly reduces parameter estimation error, demonstrating good adaptability for both constant discharge currents and variable discharge curves.