The physics-based lithium-ion battery model used in this work to demonstrate the OED methodology is based on the work of Doyle, Fuller and Newman . However, the proposed optimal parametrization strategy is not limited to this specific model but instead widely applicable for electrochemical battery models and beyond.
The increasing adoption of batteries in a variety of applications has highlighted the necessity of accurate parameter identification and effective modeling, especially for lithium-ion batteries, which are preferred due to their high power and energy densities.
Forgez et al., in developed a simple thermal mo del for a cylindrical lithium ion battery. In the internal temperature. Then, with another thermocouple used to measure the temperature on the 1.5 °C. In , the model proposed by Forgez et al ., was used and integrated with an electric model. Figure 8.
In the literature, an electrochemical approach is the pseudo-two-dimensional model developed by Doyle [ 1 ], which proved to be able to predict quite well the dynamics of Li-ion batteries. The main disadvantage of such a model is the high computational required time.
These criteria are essential for a number of reasons: Selection and Sizing: Engineers can select the best battery for a certain application by knowing the parameters and calculating the size and number of batteries required to match the specifications.
We developed and implemented a new robust framework for model validation and parameter identification for lithium-ion batteries, leveraging a hybrid optimization approach that combines the Gauss–Newton algorithm and gradient descent technique, the so-called Levenberg–Marquardt algorithm.