Data-driven modeling of Li-ion battery based on the manufacturer specifications and laboratory measurements

Abstract

Accurate modeling of Lithium-ion battery is essential in the development and testing of state estimation and lifetime prediction algorithms. The desired features of the model include flexibility, fast development, accuracy and reliability. There are many different ways to model a battery, depending on the level of abstraction desired, the data available and the target application environment. This paper shows how to extract equivalent circuit model parameters from manufacturer datasheets and laboratory measurement to build robust battery simulation models. A step-by-step methodology for data preparation is presented for both datasheet and measurement-based methods. The benefits and the disadvantages of both approaches are also discussed. A simple equivalent circuit model is firstly derived from manufacturer specification and its robustness is enhanced by collecting more extensive experimental data in the laboratory. Furthermore, an advanced model to better capture the battery dynamics is developed. The aging effects are added to this battery model, to reflect the internal parameters variation according to the health condition of the battery. To measure the accuracy of the developed models, the relative error is computed. An initial relative error of 2.8% of the model build with manufacturer specifications is reduced to 1.0% using laboratory measurements and finally to less than 0.4% by incorporating aging effects.