Publication Date


Document Type


Committee Members

Hong Huang (Committee Member), Kuldip Rattan (Committee Member), Xiaodong Zhang (Advisor)

Degree Name

Master of Science in Engineering (MSEgr)


Effective vehicular power management requires accurate knowledge of battery state, including state-of-charge (SOC) and state-of-health (SOH). An essential functionality of automotive batteries is delivering high power in short periods to crank the engine. A well-known approach to battery SOH monitoring is to infer battery state-of-health from battery impedance or resistance, which is not robust to variation of battery types. The research and development of more reliable battery state-of-health monitoring methods to ensure vehicle start-up ability are presented in this thesis. The methods include a battery cranking voltage based method, a parity-relation based method using battery voltage and cranking current signals, and a support vector machine based pattern recognition method utilizing battery voltage and engine cranking speed. The performances of these methods have been evaluated and compared through analysis of extensive real vehicle cranking data from 2 vehicles and 20 batteries. Cost benefit analysis is also conducted with different sensor options.

Page Count


Department or Program

Department of Electrical Engineering

Year Degree Awarded