simses¶

A Python simulator for battery energy storage systems (BESS).

simses models battery systems using equivalent-circuit cells in series-parallel configurations, AC/DC converter loss models, calendar and cyclic degradation, and optional thermal environments. Every subsystem is an independent, composable piece — so the same API runs a cell-only study, a full converter-backed pack, or a containerised BESS with HVAC and solar loading. Primary focus is stationary storage, though the building blocks extend naturally to mobile battery applications.

Install¶

pip install simses

Python 3.12 or newer is required. See Getting Started for uv, dev, and docs setups.

Quick-start¶

from simses.battery import Battery
from simses.model.cell.sony_lfp import SonyLFP

battery = Battery(
    cell=SonyLFP(),
    circuit=(13, 1),                                    # 13 serial, 1 parallel
    initial_states={"start_soc": 0.5, "start_T": 25.0},
)

for _ in range(30):
    battery.step(-50.0, dt=60)   # discharge at 50 W, one minute per step

print(f"SOC: {battery.state.soc:.3f}, V: {battery.state.v:.2f}")
# SOC: 0.303, V: 42.17

Where to go¶

New to simses. → Getting Started — a five-minute walkthrough.
Running a study. → Concepts for understanding the subsystems, User Guides for applied recipes.
Extending simses. → one extension guide per subsystem: cells, converters, degradation, non-battery storage.
Full walkthrough. → the interactive demo tutorial notebook.

Contributing¶

Contributions are welcome. See the Contributing guide for development setup, test commands, and PR conventions.

Citation¶

If you use simses in academic work, please cite the original SimSES paper:

Möller, M., Kucevic, D., Collath, N., Parlikar, A., Dotzauer, P., Tepe, B., Englberger, S., Jossen, A., & Hesse, H. (2022). SimSES: A holistic simulation framework for modeling and analyzing stationary energy storage systems. Journal of Energy Storage, 49, 103743. doi:10.1016/j.est.2021.103743

Acknowledgements¶

simses is a ground-up rewrite of the original SimSES, developed at the TUM Chair of Electrical Energy Storage Technology (EES). This version builds directly on the models, data, and years of research from that project.