Tractable lithium-ion storage models for optimizing energy systems

Fiodar Kazhamiaka,Srinivasan Keshav,Catherine Rosenberg

doi:10.1186/s42162-019-0070-6

Fiodar Kazhamiaka, Srinivasan Keshav + Show 1 more

Open Access

https://doi.org/10.1186/s42162-019-0070-6

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Journal: Energy Informatics	Publication Date: May 7, 2019
Citations: 8	License type: open-access

Affiliation: University of Waterloo

Abstract

Model-based optimization of energy systems with batteries requires a battery model that is accurate, tractable, and easy to calibrate. Developing such a model is challenging because electrochemical batteries exhibit complex behaviours. In this paper, we propose and evaluate a family of battery models that have different trade-offs between accuracy and complexity. We derive our models from a recently developed battery model, which is accurate and easy to calibrate but is not tractable. We evaluate our models against the commonly-used benchmark tractable model using a set of experiments that characterize the cycling behaviour of two Lithium-ion battery chemistries, as well as dynamic charge/discharge experiments. We further compare the models for two typical energy system applications, solar farm firming and grid regulation, to show the impact of the choice of battery model on the results. Finally, we evaluate the increase in accuracy when battery models are calibrated with the proper operating range.

Highlights

The rapid decline in the cost of energy storage, Lithium-ion batteries, has motivated a wide range of storage applications (Scrosati et al 2011)
Evaluation In our evaluation, for each of the analytic models discussed in the “Models” section, we quantify the loss of accuracy in the energy content estimate with respect to the Power-based integrated battery model (PI) model
To evaluate our models, we use LTO (Leclanché 2014) and LithiumFerrous-Phosphate (LiFePO4) (A123 Systems 2009) battery measurements collected while running charge/discharge cycling experiments on these batteries at different C-rates with 2-3 cycles per C-rate, and use the PI model to calculate the energy content of these batteries over the course of the experiment

Summary

Introduction

The rapid decline in the cost of energy storage, Lithium-ion batteries, has motivated a wide range of storage applications (Scrosati et al 2011). RC circuit models (Chen and Rincon-Mora 2006; Hu et al 2012) require pulse charge/discharge measurements to set the resistance and capacitance parameters of the (2019) 2:4 circuit, and while there are attempts to reduce the calibration effort of these models (Einhorn et al 2013; Hentunen et al 2014; Jackey et al 2013), calibrating them is still not an easy task. These models are described using complex mathematical functions such as differential equations, and this makes them unsuitable for optimization studies using mathematical programs

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Discussion

Conclusion