EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure

TL;DR

EV-EcoSim introduces a Python-based grid-aware co-simulation platform that integrates electric vehicle charging, battery degradation, solar PV, and power distribution dynamics. It employs a high-fidelity battery ECM with system identification, coupled with transformer thermal dynamics and a GridLAB-D power-flow interface, and supports MILP-based control in RHC or one-shot modes for planning. The paper demonstrates the platform through a case study sizing a collocated battery for an EV charging site, revealing how controller and battery-model fidelity can dramatically alter economic outcomes such as LCOE and grid impact. This work provides a configurable, open-source tool to evaluate DER sizing and operation under uncertainty, enabling more reliable and equitable deployment of EV charging infrastructure.

Abstract

To enable the electrification of transportation systems, it is important to understand how technologies such as grid storage, solar photovoltaic systems, and control strategies can aid the deployment of electric vehicle charging at scale. In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid. This python-based platform can run a receding horizon control scheme for real-time operation and a one-shot control scheme for planning problems, with multi-timescale dynamics for different systems to simulate realistic scenarios. We demonstrate the utility of EV-EcoSim through a case study focused on economic evaluation of battery size to reduce electricity costs while considering impacts of fast charging on the power distribution grid. We present qualitative and quantitative evaluations on the battery size in tabulated results. The tabulated results delineate the trade-offs between candidate battery sizing solutions, providing comprehensive insights for decision-making under uncertainty. Additionally, we demonstrate the implications of the battery controller model fidelity on the system costs and show that the fidelity of the battery controller can completely change decisions made when planning an electric vehicle charging site.

Figures (13)

• Figure 1: System diagram for simulation framework showing the various objects and flow of inputs/outputs.
• Figure 2: Second order ECM (Thevenin) circuit diagram for the cell
• Figure 3: Plot showing fitted $R_o$ vs SoC for a single cell. It is observed that the cell's resistance reduces with increasing SoC up to a certain point and marginally increases at high SoC.
• Figure 4: Plot showing the effect of OCV correction on model accuracy.
• Figure 5: The identification algorithm is run on each cell for 10 trials. In all cases the errors are below 0.5%. The black dashed lines are the means; the yellow lines are medians. The sample names are retained from the data source.