Jingsi Lilly

Date of Award


Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering


Graduate Studies

Committee Chair

Dr. Richard "Pat" Anderson

First Committee Member

Dr. Mark Balas

Second Committee Member

Dr. D. Steven Daniel

Third Committee Member

Dr. Richard J. Prazenica


Aviation propulsive battery pack research is in high demand with the development of electric and hybrid aircraft. Accurate inflight state-of-charge and state-of-health estimations of aviation battery packs still remain challenging. This thesis puts efforts on estimating the state-of-charge, state-of-health, and remaining energy of a lithium- ion propulsive battery pack with a recursive least squares based adaptive estimator. By reading the system measurements (discharging currents and terminal voltages) with persistent excitation, the proposed estimator can determine the present internal parameters of the battery cells and further interpolate them into state-of-charge, state-of-health, and the remaining energy information. The validation results indicate that the recursive least squares based estimator achieves convergence within a very short time period (_ 1 second) with desirable estimation accuracy (normally under 1%).

To validate the recursive least squares based estimator, a lithium-ion single cell simulation model is developed to simulate a NCR18650GA single cell's performance during discharge at 25oC. Validations of the single cell simulation model with both constant discharging current and HK-36 flight mission profile show simulation errors less than 1.3%.

This thesis also empirically analyzes the propulsive battery system weight and weight fractions based on the HK-36 electric airplane propulsive battery system designing experiences. As a result, the entire HK-36 propulsive battery system takes approximately 27% of the aircraft gross weight. 58% of the battery system weight is the cells' weight, and 42% is the auxiliary components weight. Taking the weight fraction into consideration, NCR18650GA cells' effective specific energy reduces from 0.16 HP-hr/lb (259 W-hr/kg) to 0.09 HP-hr/lb (150 W-hr/kg).