.png){kind=icon}
.png){kind=icon}
Return
Model-based state estimation for lithium-ion batteries
.png){kind=icon}
PRE
AI
DOI：10.1515/auto-2013-1064.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Batteries, in particular lithium-ion batteries, are becoming one of the dominant energy storage devices, especially in the field of electric mobility. This is due to both their high energy density and capacity retention during cycling. A long-term, safe and robust operation of batteries requires accurate estimates of battery parameters and states, most importantly the state of charge (SOC). The estimation of these quantities proves to be rather difficult due to measurement uncertainties, model mismatch, parameter variations, the rather low number of measured signals, as well as inherent nonlinearities and large operating regime. The first part of this work describes the difficulties in state estimation for lithium-ion batteries. We present theoretical and practical challenges of the estimation problem and give a brief overview of previously applied estimation techniques, and a short insight into the modelling of lithium-ion batteries. The second part describes two methods for estimating (among other states) the state of charge of lithium-ion batteries. The first approach employs set-based methods to tackle the estimation problem. The set-based estimator provides a set of consistent states, explicitly taking into account uncertainties in measurements and model parameters. The second approach presented here is a distributed observer, which is based on a reduced distributed model of the battery. The observer not only estimates the SOC of the battery, it also reconstructs other immeasurable internal states, thus providing valuable information about the physical processes occurring inside the battery. Both approaches are evaluated considering a detailed distributed model for a realistic drive cycle. Furthermore, the benefits and difficulties of the presented approaches, in comparison to existing methods, are discussed.
Keywords:
Lithium-ion batteries
State estimation
Set-based
Distributed system
Electrochemistry
Equivalent circuit model
Electric mobility
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
A
IF:
0.9
Papers:
22
Citations:
613