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

TL;DR

This work develops a multi-layer optimization framework for hybrid energy storage systems in electric vehicles, jointly optimizing the relative sizing of two cell chemistries and the energy flow between them under a drive cycle. A datasheet-based dynamic battery model incorporating voltage, thermal, and aging effects drives the optimization, with a PMP-based inner loop for the power-split and an outer loop for sizing and chemistry selection. Across NCA-NMC, NCA-LFP, and NCA-LTO configurations, the study finds that a NCA-NMC hybrid minimizes energy consumption, reflecting a favorable balance between efficiency and added weight. The approach provides a rigorous method to design HESS configurations with practical implications for BEV efficiency and lifecycle considerations.

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.

Figures (6)

• Figure 1: Schematic overview of the vehicle model and HESS topology. The DC-DC converter is connected to the energy battery, and acts as the main connecting element between the two batteries and controls the power-split. The power battery is connected in parallel to the DC-DC, thereby determining the bus voltage.
• Figure 2: Overview of the battery model structure. Given the battery scaling parameters and chemistry dependent cell characteristics, the model outputs energy consumption, cell degradation and cost. We include several dynamics, such as the voltage response, temperature and degradation.
• Figure 3: Battery cell dynamics model without temperature dependency. Both the internal resistance and the open-circuit voltage are dependent on the state of charge.
• Figure 4: Optimization layer scheme, where the inner layer solves the power-split between the two batteries and the outer layers determine the relative battery sizing and cell chemistries.
• Figure 5: Energy breakdown and $\Delta SoC$ for all feasible combinations for different capacity distributions between the high-power battery and the high-energy battery. The bar in Figures \ref{['fig:resultnmcE']}, \ref{['fig:resultsLFPE']} and \ref{['fig:resultsLTOE']} represents the energy breakdown for a single cell battery constructed of only the power battery cell. The constraint represents the required maximum battery power of the total HESS.