Hybrid-Electric Aircraft Operations and Design Analysis

Wed, 15 Oct 2025 00:00:00 +0000

Hybrid-electric aircraft (HEA) design is highly dependent on the operational context in which the aircraft will be used. To be viable, new HEA concepts must not only reduce fuel burn and operating costs, but also remain compatible with the realities of today’s air traffic system. Our research considered new HEA designs specifically in the context of regional jet routes and their operational constraints.

Our core methodology focused on coupling early-stage aircraft design with detailed operational analysis. This integrated approach ensures that design decisions remain feasible when applied to real-world airline operations. To support this work, we compiled a large dataset of regional airline routes, capturing key mission values such as time at gate, cruise altitude, and mission range.

A central factor in HEA performance is the power management strategy—the split between the gas turbine and the electric during a mission. Hybrid-electric aircraft benefits are highly dependent on this strategy; without it, the additional weight of onboard batteries could increase fuel burn compared to conventional aircraft. Importantly, the optimal strategy is not defined solely by mission variables like range or altitude, but also by operational factors such as available charging time at the gate. We optimize the HEA power management strategy based on an aircraft’s full day of flights to consider mission and operations.

In further work with LATTICE, HEAs were incorporated into a full fleet assignment model representing a legacy U.S. carrier. Results showed that HEAs using optimized power management strategies were assigned to a significantly larger share of routes than those without, underscoring the importance of operation-based design and analysis.

Our findings demonstrate that operation-driven design is integral to making hybrid-electric aircraft practical and impactful. By aligning aircraft design with real operational environments, we can maximize the value of hybrid-electric systems and accelerate their adoption in commercial aviation.

This research on Hybrid-Electric Aircraft Operations was sponsored by RTX.

Li-ion Battery Pack-level Design, Life-Cycle Prediction, and Cost Modeling

Fri, 10 Oct 2025 00:00:00 +0000

We developed a framework for lithium-ion battery pack-level design and optimization for aircraft propulsion energy sources. The framework includes the three main parts: (i) mission-profile-based design, where the battery power/energy required is derived from the physics performance analysis and aircraft architecture; (ii) model-based (an equivalent circuit model) dynamics simulation of the Li-ion battery cell, other components including Constant Current-Constant Voltage (CC–CV) charging protocol, sizing optimization based on battery C-rate and SOC constraints, and an empirical cycling aging model of NMC and LFP battery parameterized by stress factors such as Depth of Discharge (DoD), C-rate, temperature, Full Equivalent Cycles (FECs), and so on; and (iii) lifecycle analysis, simulating aircraft off-design for monitoring battery State-of-Health (SoH) trends and predicting End-of-Life (EoL) while cycling.

In addition to the battery’s physical/empirical models, we have a battery economic model, coupled with regressed NMC and LFP battery unit prices and Battery Management System (BMS) cost allocation trends. This function enables forward-looking predictions of battery replacement costs. The general concept would be presented as a trade study, for example, between the NMC and LFP chemistry battery performance degradation influence on aircraft performance, operation, and economics to achieve a techno-econ lifecycle study. Providing insights and thoughts on the progress of next-gen batteries in electric aviation.

Example of charging method for battery model.

Battery replacement cost validation.

Power Management Optimization | IDEAS Lab at University of Michigan

Hybrid-Electric Aircraft Operations and Design Analysis

Li-ion Battery Pack-level Design, Life-Cycle Prediction, and Cost Modeling