# Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control

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## Abstract

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## 1. Introduction

_{2}emissions due to in-flight combustion. Furthermore, the global aviation demand is anticipated to grow at around 4.8% annually [1]. By the year 2050, a greater than 60% increase in global commercial air travel seat miles and a 38% increase in energy use are projected by the U.S. Energy Information Administration, with corresponding CO

_{2}emissions projections of 209 million metric tons CO

_{2}e [2]. Thus, it is critical to reduce the environmental footprint of the aviation sector, and the civil aviation sector plays an increasingly significant role in transportation sustainability in the environmental, economic, and social dimensions. The major organizations and research councils have all published pathways for sustainable aviation decarbonization to reduce aviation-related pollutant emissions. For example, the NASA ‘N+3′ strategic implementation plan aims for −75% NOx emissions, −70% fuel burn, and −55 dB noise at the airport boundary in the year 2035 [3], and Flightpath 2050 targets a 75% reduction in CO

_{2}emissions per passenger kilometer and a 90% reduction in NOx emissions relative to the technology level of the year 2000 [4]. However, the potential efficiency improvements achieved by conventional technology progress in airframes, structure, propulsion, and air-traffic management are already reaching a plateau. Driven by the concerns of environmental sustainability in the aviation sector, electrified aircraft propulsion technologies have emerged and have been identified as the most promising approach to realize sustainable and decarbonized aviation.

## 2. Electrified Aircraft Propulsion System Architectures

#### 2.1. All-Electric Architecture

_{2}emissions can be reduced by 15%. To reach cost effectiveness with conventional aircraft, carbon taxes and batteries should be below USD 100 per kWh [17]. To enable a flight mission of 900 nmi with an all-electric propulsion system, the battery specific energy is required to reach 1200 Wh/kg [18].

#### 2.2. Series Hybrid Electric Architecture

#### 2.3. Parallel Hybrid Electric Architecture

#### 2.4. Turboelectric Architecture

## 3. Electrified Aircraft Propulsion System Dynamic Modeling and Control System Design

#### 3.1. Classical Control

#### 3.2. Hierarchical Control

#### 3.3. Robust Control

#### 3.4. Optimal Control

#### 3.5. Model Predictive Control

## 4. Electrified Aircraft Propulsion System Supervisory Energy Management Strategy

#### 4.1. Rule-Based EMS

#### 4.2. Convex Programming

#### 4.3. Dynamic Programming

#### 4.4. Pontryagin’s Minimum Principle

- The state and co-state should satisfy the following conditions, as represented by Equations (6)–(9):$${\dot{x}}^{*}\left(t\right)={\left(\frac{\partial H}{\partial \lambda}\right|}_{{u}^{*}\left(t\right)}=f\left({x}^{*}\left(t\right),{u}^{*}\left(t\right),t\right)$$$${\dot{\lambda}}^{*}\left(t\right)=-{\left(\frac{\partial H}{\partial x}\right|}_{{u}^{*}\left(t\right)}=h\left({x}^{*}\left(t\right),{u}^{*}\left(t\right),{\lambda}^{*}\left(t\right),t\right)$$$$=-\frac{\partial L}{\partial x}\left({x}^{*}\left(t\right),{u}^{*}\left(t\right),t\right)-{\lambda}^{*}\left(t\right)\cdot {\left[\frac{\partial f}{\partial x}\left({x}^{*}\left(t\right),{u}^{*}\left(t\right),t\right)\right]}^{T}$$$${x}^{*}\left({t}_{0}\right)={x}_{0}$$$${x}^{*}\left({t}_{f}\right)={x}_{\mathrm{target}}$$
- For all $t\in \left[{t}_{0},{t}_{f}\right]$, ${u}^{*}\left(t\right)$ globally minimizes the Hamiltonian function:$$H\left(u\left(t\right),{x}^{*}\left(t\right),{\lambda}^{*}\left(t\right),t\right)\ge H\left({u}^{*}\left(t\right),{x}^{*}\left(t\right),{\lambda}^{*}\left(t\right),t\right),\forall u\left(t\right)\in U\left(t\right),\forall t\in \left[{t}_{0},{t}_{f}\right]$$$$i.e.,{u}^{*}\left(t\right)=\underset{u\left(t\right)\in U\left(t\right)}{\mathrm{arg}min}H\left(u\left(t\right),x\left(t\right),\lambda \left(t\right),t\right)$$

#### 4.5. Metaheuristic Algorithms

#### 4.6. Equivalent Consumption Minimization Strategy

#### 4.7. Model Predictive Control

## 5. Summary

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

HEA | Hybrid Electric Aircraft |

EM | Electric Machine |

GE | Generator |

MTOW | Maximum Take-Off Weight |

SE | Specific Energy |

SP | Specific Power |

PAX | Passenger |

YEIS | Year of Entry Into Service |

MIPH | Mechanically Integrated Parallel Hybrid |

CIPH | Cycle-Integrated Parallel hybrid |

DC | Direct Current |

EAP | Electrified Aircraft Propulsion |

EEC | Electronic Engine Control |

VAFN | Variable-Area Fan Nozzle |

VBV | Variable Bleed Valve |

PID | Proportional Integral Derivative |

SISO | Single Input Single Output |

RBF | Radial Basis Function |

PWM | Pulse Width Modulation |

MPC | Model Predictive Control |

LPV | Linear Parameter Varying |

SMC | Sliding Mode Control |

LQR | Linear Quadratic Regulator |

LQG | Linear Quadratic Gaussian |

LTR | Loop Transfer Recovery |

EMSs | Energy Management Strategies |

CP | Convex Programming |

DP | Dynamic programming |

PMP | Pontryagin’s Minimum Principle |

GA | Genetic Algorithm |

PSO | Particle Swarm Optimization |

DEA | Differential Evolution Algorithm |

ECMS | Equivalent Consumption Minimization Strategy |

SHAPSO | Systematic Hybrid Aircraft Power Schedule Optimizer |

FSM | Fuzzy State Machine |

FLPU | Fuzzy Logic Parameter Updating |

ADMM | Alternating Direction Method of Multipliers |

SA | Simulated Annealing |

ACO | Ant Colony Optimization |

CS | Cuckoo Search |

NSGA II | Nondominated Sorting Genetic Algorithm II |

ABC | Artificial Bee Colony |

GWO | Grey Wolf Optimization |

MSA | Moth Swarm Algorithm |

SMS-EMOA | S-Metric Selection Evolutionary Multi-Objective Algorithm |

ALO | Ant Lion Optimizer |

AFO | Moth Flame Optimization |

DA | Dragonfly Algorithm |

SCA | Sine Cosine Algorithm |

MVO | Multi-Verse Optimizer |

WOA | Whale Optimization Algorithm |

UAVs | Unmanned Aerial Vehicles |

SOH | State of Health |

MILP | Mixed-Integer Linear Programming Algorithm |

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**Figure 1.**All-electric architecture [9].

**Figure 2.**Series hybrid electric architecture [9].

**Figure 3.**Parallel hybrid electric architecture [9].

**Figure 4.**Parallel hybrid electric operation in take-off conditions [36].

**Figure 5.**Turboelectric architecture [9].

**Figure 6.**Control architecture of conventional gas-turbine propulsion system [48].

**Figure 7.**Control architecture of electrified propulsion system [48].

**Figure 9.**Hierarchical control scheme classification [73].

Study | Category | Technical Specification | Key Findings |
---|---|---|---|

Airbus E-Fan [19] | Two-seat electric aircraft for pilot training. Endurance: 60 min; first flight year: 2014 | Battery: Li-ion, 207 Wh/kg EM power: 30 kW | Two electric motors via eight-blade ducted fans, each producing a thrust of 0.75 kN. |

Magnus eFusion [20] | Two-seat training aircraft with light aerobatic capabilities, serving as flying testbed for the sub-100 kW electric propulsion system. First flight year: 2016. | Propulsion system: RRP70D EM: continuous power 70 kW | |

Siemens Extra 330 LE [20] | Two-seat aerobatic aircraft as a flying testbed for motors in the class of 0.25 to 0.5 MW. First flight year: 2016 | Propulsion system: RRP 260D EM power: 260 kW EM power density: 5.2 kW/kg | |

Airbus Vahana [21] | Single-seat eVTOL. Range: 50 km; first flight year: 2019 | EM power: 8 × 45 kW Batteries: 38 kWh | All-electric propulsion system for a tilt-wing aircraft configuration; flight testing culmination time: 12 hrs, totaling 500 nmi. |

Boeing Aurora [22] | 2 PAX eVTOL. Target YEIS: 2020 | 21 ft long with a 36 ft wingspan, 12 independent lift fans. | |

Rolls-Royce/YASA ACCEL Project [23] | Single-passenger light sport and training aircraft. Range: 200 miles; first flight year: 2020 | Developing the world’s fastest electric aircraft, expected to reach 480 km/h. | |

NASA X-57 Maxwell [24] | Two-seat Tecnam P2006T general aviation aircraft. Maximum operational altitude: 14,000 ft; cruise speed: 172 mph; first flight year: 2020 | Aircraft weight: 3000 lb Batteries: Li-ion, 69.1 kWh Power distribution: 460 V | 14 motors and propellers (two large cruise motors and propellers and 12 small high-lift motors and propellers located across the wing to increase airflow). |

EVIATION ALICE [25] | 9 PAX commuter aircraft. Range: 440 nmi; maximum cruise speed: 250 kts; maximum payload: 2500 lbs; target YEIS: 2021 | MTOW: 16,500 lbs Propulsion: magni650 Max Power: 2 × 640 kW | |

Airbus CityAirbus [26] | Four-seat eVTOL. Range: 80 km; cruise speed: 120 km/h; target YEIS: 2023 | MTOW: 2200 kg Propulsion system: RRP200D EM power: 8 × 200 kW EM torque density: 30 Nm/kg | A fixed wing with V-shaped tail; eight electric-powered propellers with distributed propulsion system. |

Wright Electric/Easy Jet [27] | 100 PAX large commercial aircraft on the platform of BAe 146. Endurance: 1 h; target YEIS: 2026 | EM power: 2 MW EM SP: 10 kW/kg Inverter power: 2 MW Inverter frequency: 300 kHz Inverter volume density: 20 kW/L | 10 × 2 MW motors totaling 20 MW, as powerful as an A320 Airbus aircraft. |

Rolls-Royce/Siemens CleanSky 2 ELICA [28,29] | 19 PAX commuter aircraft. Target YEIS: 2060 | Battery SE: 500 Wh/kg Battery SP: 1 kW/kg EM SP: 7.7 kW/kg | All-electric concept with 16 distributed propellers for design range of 200 nmi and 400 nmi. |

Aviation Sector | EM Power Capacity | EM Specific Power | Battery Specific Energy |
---|---|---|---|

General aviation | Motor: <1 MW | Motor: >6.5 kW/kg | >400 Wh/kg |

Regional/single-aisle | Motor: 1–11 MW | Motor: >6.5 kW/kg | >1800 Wh/kg |

Twin-aisle | Not feasible | Not feasible | Not feasible |

Study | Category | Technical Specification | Key Findings |
---|---|---|---|

Zunum Aero [31] | Seat capacity: 12 economy, 9 premium, 6 executives; max payload: 2500 lbs; range: 700 nmi; target YEIS: 2020 | Propulsion system: series hybrid with range extender Max power: 1 MW Turbogenerator: 500 kW | Emissions: 0.0 to 0.3 lbs CO_{2}/ASMOperating cost: 8 cents/seat mile, USD 250 per hour. |

XTI TriFan 600 [32] | 6-seat fixed-wing aircraft with VTOL. Range: 600 nmi in VTOL, 900 nmi for conventional take-off and landing; YEIS: 2024 | Propulsion system: a turboshaft engine driving 3 generators for electrical energy generation, powering motors which are mechanically connected to propellers | Three ducted fans, hybrid energy system (hydrogen fuel cell, sustainable aviation fuel compatible). |

Airbus E-Fan X [33] | 100-seat regional jet. Payload: 6650 kg; YEIS: 2030 | Motor power: 2 MW Generator power: 2.5 MW EM power density: 10 kW/kg Power distribution: 3 kV DC | One of the four jet engines (AE2100) was replaced by a 2 MW electric motor. |

Study | Category | Technical Specification | Key Findings |
---|---|---|---|

NASA Boeing SUGAR Volt [37,38] | 150 PAX single-aisle aircraft. Range: 900 nmi | Battery SE: 750 Wh/kg EM SP: 3–5 kW/kg EM efficiency: 93% EM power capacity: 1.3 MW (balanced), 5.3 MW (core shutdown) | Transonic truss-braced wing, ‘balanced’ version: reduces fuel burn by 60% and energy use by 54%. ‘Core shutdown’ version: EM for cruise on 100% electric power, reduces fuel burn by 64% and energy use by 46%. |

NASA UTRC hGTF [38,39] | 150 PAX single-aisle aircraft. Range: 900 nmi | EM power capacity: 2.1 MW Battery SE: 1000 Wh/kg | Optimized geared turbofan engine for cruise, electric power boosting for take-off and climb; 7–9% block fuel burn reduction and 3–5% energy saving. |

NASA R-R LibertyWorks EVE [39,40] | 150 PAX single-aisle aircraft, exploring mission optimization using battery power for taxiing, idle decent, and take-off power augmentation | EM power capacity: 1 MW-2.6 MW | 28% fuel burn reduction for 900 nmi mission, 10% energy saving for 500 nmi mission, 18% reduction in total fleet fuel usage. |

Horizon 2020 H3PS (High-Power High-Scalability Aircraft Hybrid Powertrain) [40] | 4-seat general aviation aircraft developed on the platform of Tecnam P2010 | Engine: Rotax 915 (141 hp) EM power capacity: 30 kW (thrust booster motor during take-off and climb, operating as a generator to recharge batteries during cruise) | TECNAM: airframe and system integration, BRP-ROTAX: design and integration of combustion engine and e-motor, ROLLS-ROYCE: e-motor and power storage. |

Clean Sky 2 NOVAIR project [41,42] | 150 PAX single-aisle large passenger aircraft (retrofitted on A320 NEO). Range: 800 nmi | Technology level: 2040+ Battery efficiency: 95.0% Battery SE: 1 kWh/kg EM efficiency: 98.0% EM SP: 15.0 kW/kg | With downscaled, more efficient turbofan engine core, potential trip fuel reduction is about 14%. |

**Table 5.**Electrical system component performance requirements for parallel hybrid electric propulsion systems.

Aviation Sector | EM Power Capacity | EM Specific Power | Battery Specific Energy |
---|---|---|---|

General aviation | Motor: <1 MW | Motor: >3 kW/kg | >250 Wh/kg |

Regional/single-aisle | Motor: 1–6 MW | Motor: >3 kW/kg | >800 Wh/kg |

Twin-aisle | Not studied | Not studied | Not studied |

Study | Category | Technical Specification | Key Findings |
---|---|---|---|

NASA N3-X [38,43] | 300 PAX hybrid wing body with fully distributed propulsion, 16-aft motor-driven fans. Range: 7500 nmi | Superconducting electric machines and power distribution. Power distribution: 7500 V Fully distributed power: 50 MW | 70% fuel burn reduction compared to Boeing 777–200 LR. |

Empirical Systems Aerospace ECO-150 R [38,44,45] | 150 PAX regional jet, fully distributed propulsion system with a split-wing concept. Maximum payload range: 1500 nmi | Superconducting electrical machines cooled with liquid hydrogen | 16-wing motor-driven fans. |

NASA STARC-ABL [38,46] | 154 PAX single-aisle aircraft with tube and wing airframe. Range: 900 nmi; target YEIS: 2035 | EM SP: 8 hp/lb EM efficiency: 96% Inverter SP: 10 hp/lb Inverter efficiency: 98% Power distribution: 1000 V Motor power capacity: 2.6 MW | 12% reduction in start of cruise TSFC, 9% reduction in economic mission block fuel, 15% reduction in design mission block fuel. |

Boeing SUGAR Freeze [38,47] | 154 PAX single-aisle aircraft using a truss-braced wing combined with boundary-layer ingestion. Range: 900 nmi | Solid oxide fuel cell, superconducting motor, cryogenic power management system | 56% reduction in fuel burn. |

Aviation Sector | EM Power Capacity | EM Specific Power | Battery Specific Energy |
---|---|---|---|

General aviation | Motor and generator: <1 MW | Motor and generator: >6.5 kW/kg | NA |

Regional/single-aisle | Motor: 1.5–3 MW Generator: 1–11 MW | Motor and generator: >6.5 kW/kg | NA |

Twin-aisle | Motor: 4 MW Generator: 30 MW | Motor and generator: >10 kW/kg | NA |

**Table 8.**Aircraft propulsion control systems and performance requirements [54].

Control System | Performance Requirements |
---|---|

Setpoint control | Regulate the gas turbine performance near a desired operating condition, e.g., idle, take-off, cruise |

Transient control | Transient operation (performance variables change with time) |

Limit protection | Physical limits: shaft speed, turbine blade maximum temperature, maximum combustion pressure, surge/stall of compressor |

**Table 9.**Summary of aircraft propulsion system control methodologies [61].

Methodology | Benefits and Limitations | Applications |
---|---|---|

Classical Control PID controller | Simple implementation in practice with relative efficiency. Only applied in single-input–single-output (SISO) systems, requires the system model to be linear-time-invariant (LTI), cannot incorporate constraints, and lacks overall performance optimization. | Conventional propulsion system: [49,52,55,64,65,66,67,68] Electrified propulsion system: [69,70] |

Hierarchical Control Centralized control, decentralized control, distributed control | A form of networked control system; a computationally efficient approach for multidomain, multi-timescale system dynamics; decomposes a complicated control problem into different time-based modules and, in turn, organizes them into layers. | Conventional propulsion system: [74,75,76] Electrified propulsion system: [77,78,79] |

Robust Control H-infinity, sliding mode control | Explicitly deals with bounded system uncertainty and disturbances. Decouples the overall system motion into independent partial subsystems of lower dimension and consequently reduces the complexity of feedback design. | Conventional propulsion system: [81,82,83,84,87,88,89] Electrified propulsion system: [85,86,90,91] |

Optimal Control LQR, LQG, H _{2} | Determines the control signals of a dynamic system to satisfy the physical constraints whilst minimizing the cost function. | Conventional propulsion system: [93,94,95,97,98] Electrified propulsion system: [96] |

Model Predictive Control | Flexible nonlinearity and constraint handling; closed-loop stability cannot be guaranteed; poor robustness. | Conventional propulsion system: [101,102,103,104] Electrified propulsion system: [59,105,106,107] |

**Table 10.**Overall characteristics of classical control theory and modern control theory [61].

Classical Control Theory | Advanced Control Theory | |
---|---|---|

Domain | Frequency, S-domain | Time domain |

Model representation | Transfer function | State-space |

Continuity | Continuous | Continuous, discrete, hybrid |

Linearity | Linear | Linear, nonlinear |

Time variance | Time-invariant | Time-variant |

Dimensions | Single input, single output | Multiple input, multiple output |

Determinism | Deterministic | Deterministic, stochastic |

Implementation | Cheap, easy | Expensive, complex |

Methodologies | Advantages | Disadvantages |
---|---|---|

Rule-Based EMS Deterministic rule-based EMS, fuzzy rule-based EMS. | Easy to implement, low computational load. | Aircraft performance is determined by predefined rules; highly dependent on designers’ expertise; highly sensitive to flight mission profile. |

Global Optimization EMS Convex programming (CP), dynamic programming (DP), Pontryagin’s minimum principle (PMP); metaheuristic algorithms: genetic algorithm (GA), particle swarm optimization (PSO), differential evolution algorithm (DEA). | Provide a globally optimal solution (all); stochastic solution generation to avoid local optima (metaheuristic algorithms). | Analytical methods are frequently not applicable for complicated problems with complex constraints (PMP); strong model simplification (CP, DP); require a planned flight mission profile as a priori knowledge (DP, metaheuristic algorithms); high computation effort (metaheuristic algorithms). |

Instantaneous Optimization EMS Equivalent consumption minimization strategy (ECMS), model predictive control (MPC). | Easy to implement (ECMS); reduced computation load; allow the current timeslot to be optimized while keeping account of future timeslots; handle many system constraints simultaneously; applicable to a multivariable problem (energy balance of multiple sources of energy); applicable to a multiobjective optimization problem (MPC). | Achieves optimal instantaneous equivalent fuel consumption but cannot guarantee the optimal aircraft performance at mission level (ECMS); highly sensitive to flight mission profile (ECMS); single-objective optimization, cannot be expanded to operating costs, emissions, etc. (ECMS). |

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**MDPI and ACS Style**

Zhang, J.; Roumeliotis, I.; Zolotas, A.
Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control. *Sustainability* **2022**, *14*, 5880.
https://doi.org/10.3390/su14105880

**AMA Style**

Zhang J, Roumeliotis I, Zolotas A.
Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control. *Sustainability*. 2022; 14(10):5880.
https://doi.org/10.3390/su14105880

**Chicago/Turabian Style**

Zhang, Jinning, Ioannis Roumeliotis, and Argyrios Zolotas.
2022. "Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control" *Sustainability* 14, no. 10: 5880.
https://doi.org/10.3390/su14105880