Nonlinear Modeling of Lithium-Ion Battery Cells for Electric Vehicles using a Hammerstein–Wiener Model

Lithium-ion batteries are a popular electrical storage choice for electric vehicles. This is motivated by their many advantages, such as their high energy density and cycling performance. This article aims to present a nonlinear model for the dynamic behavior of lithium-ion battery cells. For this purpose, we use measurements of electric vehicles at different driving cycles. This allows us to model lithium-ion battery cells using their dynamic behavior in real-world use cases. The proposed model is developed using a nonlinear Hammerstein–Wiener model to accurately predict the behavior of these cells. The proposed model prediction performance is evaluated using mean squared error (MSE), final prediction error (FPE), and goodness of fit between the Hammerstein–Wiener (H–W) model and the measured battery cell output. The results show that the model is able to predict battery cell behavior with great accuracy: The goodness of fit value shows that the presented Hammerstein–Wiener model matches the battery cell data by 93.77% in the identification phase, and by 93.74% in the validation phase for LA-92 drive cycle. In order to show the efficiency of the selected model (H–W), a comparative study is also conducted with other model types including a neural network model, a linear model and an equivalent electrical circuit model using the same measurements. The (H–W) model presented in this paper achieved better results in term of MSE compared to the other models.