| Summary: | 碩士 === 國立臺灣大學 === 機械工程學研究所 === 102 === With the growing consciousness of green power in recent years, the emissions of
carbon dioxide and oil crisis have been two critical issues. The electric vehicle is one of
many products developed with this trend. Batteries are indispensable to electric vehicles.
In particular, lithium batteries have gradually become the main power sources of electric
vehicles for the merits such as the stability and high efficiency of discharge, long cycle
life, small size, etc. When it comes to the way to evaluate the efficiency of electric
vehicles, State of Health (SOH) and State of Charge (SOC) are often applied. The latter
is mainly based on Coulomb integral method, and its property of convenience and
precision is widely applied to the SOC estimation of electric vehicles. However, no
available techniques can thoroughly be applied to estimate SOC. Therefore, this
research aims to direct the random errors of the estimation of the Coulomb integral
method and establish a Markov model for assessing the exhaustion of SOC along with
any time of discharge. This research is based on the voltage and capacity data from the
reference to establish the Markov model and take random differences and aging of
batteries into account. By doing so, we can come to realize the possibility of any battery
exhaustion clearly and then apply the system reliability theory to estimate the SOC of
batteries pack. With this research, we can efficiently estimate the SOC of dynamic
discharge of battery systems, and through the Markov model obtain the probability
distribution of battery exhaustion at any time. The results can be used for manufacturing
companies to carry out risk assessment for standards such as ISO26262.
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