System Identification Using Regular and Quantized Observations by Qi He Le Yi Wang & G. George Yin

Author:Qi He, Le Yi Wang & G. George Yin
Language: eng
Format: epub
Publisher: Springer New York, New York, NY

This chapter uses a battery diagnosis problem to illustrate the use of the LDP in industrial applications. Management of battery systems plays a pivotal role in electric and hybrid vehicles, and in support of distributed renewable energy generation and smart grids. The state of charge (SOC), the state of health (SOH), internal impedance, open circuit voltage, and other parameters indicate jointly the state of the battery system. Battery systems’ behavior varies significantly among individual cells and demonstrates time-varying and nonlinear characteristics under varying operating conditions such as temperature, charging/discharging rates, and different SOCs. Such complications in capturing battery features point to a common conclusion that battery monitoring, diagnosis, and management should be individualized and adaptive. Although battery health diagnosis has drawn substantial research effort recently [26, 56] using various estimation methods or signal-processing algorithms [6, 43], the LDP has never been used before in this application. In the following, we describe a joint estimation algorithm for real-time battery parameter and SOC estimation that supports a diagnosis method using LDPs. For further reading on power control and battery systems, see [4, 5, 9, 13, 20, 21, 24, 25, 34, 44, 52] and references therein.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.