This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is required, particularly for the UAM that uses a battery as its primary power source. To ensure the suitability of the framework for the UAM, a two-pronged approach is taken. First, realistic test profiles, reflecting actual operational scenarios for the UAM, are used to model the battery and validate its state estimator. These profiles incorporate variations in battery power flow, namely, charge-depleting and charge-sustaining modes, during the different phases of the UAM’s flight, including take-off, cruise, and landing. Moreover, the current sensor bias is estimated and corrected concurrently with the SOC. An extended Kalman filter-based bias estimator is developed and experimentally validated using actual current measurements from a Hall sensor, which is prone to noise. With this correction, a SOC estimation error is consistently maintained at 2% or lower, even during transitions between operational modes.

The plate-fin heat exchanger was designed for the liquid cooling thermal management system of the hybrid electric propulsion system for an electric vertical take-off and landing (eVTOL) vehicle. The offset-strip fin design was applied, and the performance of the heat exchanger was evaluated, particularly with respect to the inclination of the airflow entering the heat exchanger. The estimated performance during the design phase matched well with the experimental results. The inclination of the heat exchanger had a minimal effect on thermal performance, with a slight increase in performance as the inclination increased. However, the pressure difference along the airflow was affected, likely increasing as the inclination increased. The sensitivity of various parameters on coolant temperature was also investigated. The air inlet temperature had a significant effect on coolant temperature, followed by the coolant flow rate. Therefore, when designing the thermal management system, careful consideration should be given to the ambient air temperature and coolant flow rate.

Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery’s lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery’s lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources.

This study presents a hybrid CFD–ε–NTU–neural network framework for evaluating the thermal performance of compact heat exchangers installed in confined nacelle environments under severe space and weight constraints. Airflow was simulated using steady Reynolds-Averaged Navier–Stokes (RANS) equations with the shear stress transport (SST) k–ω turbulence model, while heat-exchanger performance was evaluated using the effectiveness–number of transfer units (ε–NTU) method. To provide a continuous functional interface between the analytical model and the CFD solver, a neural network was employed as a representation of ε–NTU-based thermal performance. The proposed framework was validated against wind tunnel experiments, showing good agreement with measured data, with a maximum deviation of approximately 2.2 °C in coolant outlet temperature. Using the validated framework, a multi-objective redesign of a compact heat exchanger was performed under practical installation constraints. The selected design achieved a 28.4% reduction in heat exchanger mass while maintaining cooling performance comparable to the baseline design. Installation performance was evaluated numerically in a modified nacelle configuration, where the redesigned heat exchanger increased the air mass flow rate by approximately 13% under identical fan operating conditions because of its reduced air-side flow resistance. The proposed framework provides a practical and efficient approach for system-level design and installation analysis of compact heat exchangers for aircraft thermal management systems.

The hot-section components of the gas turbine engine operate under high-temperature and high-pressure conditions beyond the melting point of nickel-base alloys, and high reliability and structural integrity are required. However, as with other systems, this system is exposed to manufacturing tolerances and perturbations of the operating environment; in harsh environments, these perturbations are major factors that weaken the structural integrity of hot-section components. There are various methods to cope with these perturbations, such as worst-condition design that are based on the safety factor, and reliability-based design optimization and robust design based on statistical data. The worst-condition design is simple to apply and is efficient; thus, it is applied to practical product design. This study statistically models the perturbation of the turbine inlet temperature and generates candidates for the worst-case scenario, and compares them with the worst condition presented in the existing literature. For this purpose, low cycle fatigue life is evaluated through conjugate heat transfer analysis and structural analysis.