A Comparative Study between Physics, Electrical and Data Driven Lithium-Ion Battery Voltage Modeling Approaches 2022-01-0700

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models. The models are tested and compared using four standard drive cycles at six ambient temperatures ranging from -20⁰C to 40 ⁰C. The proposed models are benchmarked using various qualitative and quantitative means including, accuracy, engineering effort to parametrize and create the model, and the ability of each model to represent the nonlinear behavior of the battery. The comparison between the three models shows that the ECM and the LSTM models have better accuracy than the ESPM. However, the ESPM requires a reduced set of calibration data, is highly capable of incorporating the complex nonlinear behavior of the battery, and the parameters have physical meaning.