Review of electrically powered propulsion for aircraft

This paper presents a review of the state-of-the-art in aircraft electrical propulsion technology. A comparison is provided of differing propulsion mechanisms such as propellers, open fans, ducted fans, multi-stage rim driven fans and distributed thrust designs and their suitability to particular flight profiles and mission applications. Electrical motor architectures are also reviewed with particular attention being given to synchronous machines, such as Brushless Direct Current (BLDC) and Switched Reluctance Motor (SRM) technologies, and the recent advances that have been made in solid-state switching and High Temperature Superconducting (HTS) material applications. Present day electrical power generation, storage and control technologies are also reviewed including hybrid and fuel cell technologies and regeneration techniques. Electrical storage capabilities with regard to specific power and energy characteristics are discussed and the extent to which existing system technology can be integrated onto a Hybrid-electric and an All Electric Aircraft (AEA) is also investigated. Finally, a conclusion is provided highlighting the current technological challenges facing the development of commercial aircraft in terms of performance, airframe configuration and legislative and operational infrastructural requirements

Progress and summary of reinforcement learning on energy management of MPS-EV

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS

Efficient Flatness Based Energy Management Strategy for Hybrid Supercapacitor/Lithium-ion Battery Power System

This article offers a flatness theory-based energy management strategy (FEMS) for a hybrid power system consisting of a supercapacitor (SC) and lithium-ion battery. The proposed FEMS intends to allocate the power reference for the DC/DC converters of both the battery and SC while attaining higher effciency and stable DC bus voltage. First, the entire system model is analyzed theoretically under the differential flatness approach to reduce the model order as a at system. Second, the proposed FEMS is validated under different load conditions using MATLAB/Simulink. Thus, this FEMS provides high-quality energy to the load and reduces the fluctuations in the bus voltage. Moreover, the performance of the FEMS is compared with the load following (LF) strategy. The obtained results show that the proposed FEMS meet the real load power under fast variations with good power quality compared to the classical LF strategy, where the maximum overshoot of the bus voltage is 5%

Optimal energy management for a grid-tied solar PV-battery microgrid: A reinforcement learning approach

There has been a shift towards energy sustainability in recent years, and this shift should continue. The steady growth of energy demand because of population growth, as well as heightened worries about the number of anthropogenic gases released into the atmosphere and deployment of advanced grid technologies, has spurred the penetration of renewable energy resources (RERs) at different locations and scales in the power grid. As a result, the energy system is moving away from the centralized paradigm of large, controllable power plants and toward a decentralized network based on renewables. Microgrids, either grid-connected or islanded, provide a key solution for integrating RERs, load demand flexibility, and energy storage systems within this framework. Nonetheless, renewable energy resources, such as solar and wind energy, can be extremely stochastic as they are weather dependent. These resources coupled with load demand uncertainties lead to random variations on both the generation and load sides, thus challenging optimal energy management. This thesis develops an optimal energy management system (EMS) for a grid-tied solar PV-battery microgrid. The goal of the EMS is to obtain the minimum operational costs (cost of power exchange with the utility and battery wear cost) while still considering network constraints, which ensure grid violations are avoided. A reinforcement learning (RL) approach is proposed to minimize the operational cost of the microgrid under this stochastic setting. RL is a reward-motivated optimization technique derived from how animals learn to optimize their behaviour in new environments. Unlike other conventional model-based optimization approaches, RL doesn't need an explicit model of the optimization system to get optimal solutions. The EMS is modelled as a Markov Decision Process (MDP) to achieve optimality considering the state, action, and reward function. The feasibility of two RL algorithms, namely, conventional Q-learning algorithm and deep Q network algorithm, are developed, and their efficacy in performing optimal energy management for the designed system is evaluated in this thesis. First, the energy management problem is expressed as a sequential decision-making process, after which two algorithms, trading, and non-trading algorithm, are developed. In the trading algorithm case, excess microgrid's energy can be sold back to the utility to increase revenue, while in the latter case constraining rules are embedded in the designed EMS to ensure that no excess energy is sold back to the utility. Then a Q-learning algorithm is developed to minimize the operational cost of the microgrid under unknown future information. Finally, to evaluate the performance of the proposed EMS, a comparison study between a trading case EMS model and a non-trading case is performed using a typical commercial load curve and PV generation profile over a 24- hour horizon. Numerical simulation results indicated that the algorithm learned to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility based on the time-varying tariff and battery wear cost) in both summer and winter case studies. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one decreased cost by 4.033% in the summer season and 2.199% in the winter season. Secondly, a deep Q network (DQN) method that uses recent learning algorithm enhancements, including experience replay and target network, is developed to learn the system uncertainties, including load demand, grid prices and volatile power supply from the renewables solve the optimal energy management problem. Unlike the Q-learning method, which updates the Q-function using a lookup table (which limits its scalability and overall performance in stochastic optimization), the DQN method uses a deep neural network that approximates the Q- function via statistical regression. The performance of the proposed method is evaluated with differently fluctuating load profiles, i.e., slow, medium, and fast. Simulation results substantiated the efficacy of the proposed method as the algorithm was established to learn from experience to raise the battery state of charge and optimally shift loads from a one-time instance, thus supporting the utility grid in reducing aggregate peak load. Furthermore, the performance of the proposed DQN approach was compared to the conventional Q-learning algorithm in terms of achieving a minimum global cost. Simulation results showed that the DQN algorithm outperformed the conventional Q-learning approach, reducing system operational costs by 15%, 24%, and 26% for the slow, medium, and fast fluctuating load profiles in the studied cases

Advances in Supercapacitor Technology and Applications Ⅱ

Energy storage is a key topic for research, industry, and business, which is gaining increasing interest. Any available energy-storage technology (batteries, fuel cells, flywheels, and so on) can cover a limited part of the power-energy plane and is characterized by some inherent drawback. Supercapacitors (also known as ultracapacitors, electrochemical capacitors, pseudocapacitors, or double-layer capacitors) feature exceptional capacitance values, creating new scenarios and opportunities in both research and industrial applications, partly because the related market is relatively recent. In practice, supercapacitors can offer a trade-off between the high specific energy of batteries and the high specific power of traditional capacitors. Developments in supercapacitor technology and supporting electronics, combined with reductions in costs, may revolutionize everything from large power systems to consumer electronics. The potential benefits of supercapacitors move from the progresses in the technological processes but can be effective by the availability of the proper tools for testing, modeling, diagnosis, sizing, management and technical-economic analyses. This book collects some of the latest developments in the field of supercapacitors, ranging from new materials to practical applications, such as energy storage, uninterruptible power supplies, smart grids, electrical vehicles, advanced transportation and renewable sources

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio

Optimal operation of hybrid AC/DC microgrids under uncertainty of renewable energy resources : A comprehensive review

The hybrid AC/DC microgrids have become considerably popular as they are reliable, accessible and robust. They are utilized for solving environmental, economic, operational and power-related political issues. Having this increased necessity taken into consideration, this paper performs a comprehensive review of the fundamentals of hybrid AC/DC microgrids and describes their components. Mathematical models and valid comparisons among different renewable energy sources’ generations are discussed. Subsequently, various operational zones, control and optimization methods, power flow calculations in the presence of uncertainties related to renewable energy resources are reviewed.fi=vertaisarvioitu|en=peerReviewed

A hybrid energy storage solution based on supercapacitors and batteries for the grid integration of utility scale photovoltaic plants

This paper presents a 2-level controller managing a hybrid energy storage solution (HESS) for the grid integration of photovoltaic (PV) plants in distribution grids. The HESS is based on the interconnection of a lead-acid battery pack and a supercapacitor pack through a modular power electronics cabinet. The inclusion of the HESS into the PV plant –and not an state-of-the-art energy storage system based on a single technology–, is motivated by the diversity of technical requirements for the provision of the services of grid peak power shaving and PV output power ramp limitation. The 2-level controller ensures a synergistic exploitation of the two storage technologies aiming for an optimal service level of the HESS and minimum battery degradation. The higher level of the controller is based on a mathematical optimization problem that solves with the optimal schedule of the storage technologies for peak power shaving purposes. The power setpoints of this optimization are then complemented by a real time controller managing PV plant output ramp limitation. The HESS performance and associated controller has been proved effective through two case studies. The first one adopts a 6.6 MW PV plant including a HESS solution combining a 5.5 MWh and 2.64 MW lead-acid battery pack with a 0.25 MWh and 1.32 MW supercapacitor pack. The second one reports experimental data from an analogous scenario scaled down to kW level and using a laboratory scale prototype for the HESS. All in all, the hardware and software solutions proposed in this paper contribute to a feasible exploitation of multi-purpose energy storages targeting the needs of renewables' and distribution system operators.Peer ReviewedPostprint (published version