Lithium-ion Battery Parameter Identification for Hybrid and Electric Vehicles using Drive Cycle Data

This paper proposes an approach for the accurate and efficient parameter identification of lithium-ion battery packs using only drive cycle data obtained from hybrid or electric vehicles. The approach was experimentally validated using data collected from a BMW i8 hybrid vehicle. The dual polarization model was used, and a new open circuit voltage equation was proposed based on a simplification of the combined model, with the aim of reducing the number of parameters to be identified. The parameter identification was performed using NEDC data collected on a rolling road dynamometer; the results showed that the proposed model improved the accuracy of terminal voltage estimation, reducing the peak voltage error from 2.16% using the Nernst model to 1.28%. Furthermore, the robustness of these models in maintaining accuracy when new drive cycles were used was evaluated by comparing WLTC simulations with experimental measurements. The proposed model showed improved robustness, with a reduction in RMS error of more than 50% compared to the Nernst model. These findings are significant because they will improve the accuracy of modelbased battery management systems used in electric vehicles, allowing for improved performance prediction without the requirement of recharacterization for different drive cycles or individual cell characterization.