Battery applications (computer, cell phones or even in cars) have been extensively used in our daily life. The reasons for their success and extensive usage in the real world applications are their light weight, smaller sizes and greater energy densities. These unique characteristics render this class of battery an ideal candidate for powering electrical vehicles. However, due to lack of battery information, often time we will observe machine down time, operation malfunctioning, and even some catastrophic failure due to fast battery degradation and depletion. Thus, much of the attention has been focused on prognostics and health management of battery technologies for the stated purpose. In this paper, we will present two main algorithms that cannot only estimate a one-step-ahead prediction of the battery state but also can estimate the battery remaining useful life. The first method is the linear prediction error method. The second approach is the neural network algorithms. Both methods can predict the battery information accurately. However, particular algorithm specializes in different area of interest.
- Manufacturing Engineering Division
Battery Prognostics: SoC and SoH Prediction
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Lee, S, Cui, H, Rezvanizaniani, M, & Ni, J. "Battery Prognostics: SoC and SoH Prediction." Proceedings of the ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. ASME 2012 International Manufacturing Science and Engineering Conference. Notre Dame, Indiana, USA. June 4–8, 2012. pp. 689-695. ASME. https://doi.org/10.1115/MSEC2012-7345
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