Multi-layer optimisation of hybrid energy storage systems for electric vehicles

TL;DR

This paper introduces a multi-layer optimization framework for hybrid energy storage systems in electric vehicles, enhancing performance by optimizing capacity and power split between different battery chemistries based on drive cycle data.

Contribution

It develops a data-driven battery model and jointly optimizes capacity distribution and power split for hybrid systems, improving energy efficiency in electric vehicles.

Findings

NCA-NMC hybrid yields lowest energy consumption

Hybrid systems offer optimal efficiency-weight trade-offs

Model captures voltage, temperature, and degradation from datasheets

Abstract

This research presents a multi-layer optimization framework for hybrid energy storage systems (HESS) for passenger electric vehicles to increase the battery system's performance by combining multiple cell chemistries. Specifically, we devise a battery model capturing voltage dynamics, temperature and lifetime degradation solely using data from manufacturer datasheets, and jointly optimize the capacity distribution between the two batteries and the power split, for a given drive cycle and HESS topology. The results show that the lowest energy consumption is obtained with a hybrid solution consisting of a NCA-NMC combination, since this provides the best trade-off between efficiency and added weight.