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Adaptive Battery Monitoring using Parameter Estimation

Parthasarathy, Nandakumar
http://rave.ohiolink.edu/etdc/view?acc_num=osu1236714224

Abstract Details

2009, Master of Science, Ohio State University, Electrical and Computer Engineering.
The popularity of Hybrid-electric vehicles (HEV) in today's automotive industry is because of its higher fuel-efficiency and reduced emissions of polluting gases . In a HEV, during the acceleration process, the power to run the vehicle is provided by the DC motor (powered by the battery) and during coasting (constant speed) period, the power is provided by the engine (powered by the fuel). This ensures optimum use of fuel and also the energy of the fuel is utilized to its best. However, the battery is constantly subjected to charging (during deceleration) and discharging (acceleration) process which causes depreciation in its performance with time. Since the battery forms an integral part in the operation of the vehicle it becomes essential to know the available charge left in the battery, how long will it be able to provide required energy and also about the life of the battery. This calls for an efficient model of the battery which can accurately monitor the performance of the battery and other critical battery parameters like State of Charge, State of Health, Time to Run, etc. In this thesis, the battery is modeled as an R-C circuit comprising of elements each of which represents certain battery characteristics. Based on the input (current demand) – output (voltage profile) data that can be measured from the experimental results, the apt battery model is selected using model identification. The parameters of the model are computed on-line using parameter estimation techniques with the help of input-output data while the vehicle is in operation. This monitoring algorithm provides the customer with the flexibility to use a wide range of batteries in their automobile so that the monitoring algorithm adapts itself to the battery based on the input-output data.
Dr. Vadim Utkin (Advisor)
Dr. Ali Keyhani (Advisor)
Donald G. Kasten (Committee Member)
125 p.
Electrical Engineering; Energy; Engineering
Battery monitoring; adaptive; parameter estimation;

Recommended Citations

Citations

  • Parthasarathy, N. (2009). Adaptive Battery Monitoring using Parameter Estimation [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1236714224

    APA Style (7th edition)

  • Parthasarathy, Nandakumar. Adaptive Battery Monitoring using Parameter Estimation. 2009. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1236714224.

    MLA Style (8th edition)

  • Parthasarathy, Nandakumar. "Adaptive Battery Monitoring using Parameter Estimation." Master's thesis, Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1236714224

    Chicago Manual of Style (17th edition)

osu1236714224
6,272
© 2009, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.