New technique more accurately determines charge remaining in battery; better charge management and longer functional life
Researchers from North Carolina State University have å a new technique that allows users to better determine the amount of charge remaining in a battery in real time. While the technique was developed specifically for batteries in plug-in electric vehicles, the approach is also applicable to battery use in any other application.
A paper on their work, “Adaptive Parameter Identification and State-of-Charge Estimation of Lithium-Ion Batteries,” will be presented at the 38th Annual Conference of the IEEE Industrial Electronics Society in Montreal, 25–28 October.
This improved accuracy will also give us additional insight into the dynamics of the battery, which we can use to develop techniques that will lead to more efficient battery management. This will not only extend the life of the charge in the battery, but extend the functional life of the battery itself.—Dr. Mo-Yuen Chow, a professor of electrical and computer engineering at NC State and co-author
At present, it is difficult to determine how much charge a battery has left. Existing computer models for estimating the remaining charge are not very accurate. The inaccuracy stems, in part, from the number of variables that must be plugged in to the models. For example, the capacity of a battery to hold a charge declines with use, so a battery’s history is a factor. Other factors include temperature and the rate at which a battery is charged, among many others.
Existing models only allow data on these variables to be plugged in to the model once. Because these variables— such as temperature— are constantly changing, the models can become increasingly inaccurate.
The researchers developed software that identifies and processes data that can be used to update the computer model in real time, allowing the model to estimate the remaining charge in a battery much more accurately.
Using the new technique, models are able to estimate remaining charge within ±5%.
Lead author of the paper is Habiballah Rahimi-Eichi, a Ph.D. student at NC State. The research was supported by the National Science Foundation, in collaboration with the foundation’s Engineering Research Center for Future Renewable Electric Energy Delivery and Management, which is based at NC State.
Habiballah Rahimi-Eichi and Mo-Yuen Chow (2012) Adaptive Parameter Identification and State-of-Charge Estimation of Lithium-Ion Batteries