Recently, the thermal management of power electronic converters has gained significant attention due to the continuous trend of developing very compact power electronic converters with high power density. With the evolution of power semiconductor devices, high operating temperatures and large thermal cycles have become possible, necessitating a significant improvement in thermal system designs. Researchers have made significant efforts to develop effective thermal management systems to improve the reliability and lifetime of power electronic converters. This article intends to present a thorough review of thermal management systems employed in power electronics cooling. The applied thermal management techniques have been reviewed from the perspective of electrical parameter regulation and heat dissipation control. Regulation of electrical parameters involves active thermal control, which is a method for controlling junction temperature and thermal cycling of power semiconductor devices. The active thermal control implementation processes reviewed in this article consist of increasing overload capacity, manipulating switching and conduction losses, employing modified modulation processes, balancing thermal stress at the converter level, and controlling thermal stress at the system level. Control of heat dissipation can be achieved through direct and indirect cooling of power electronic converters with air or liquid as the coolant. The effectiveness and implementation methods of these cooling techniques, such as channel cooling, phase change material-based cooling, immersion cooling, jet impingement and spray cooling, are reviewed in this paper. Moreover, performance-enhancing ideas and challenges for these techniques are discussed. The primary objective of this review paper is to bridge the existing gap in the literature by offering a comprehensive comparison of commonly employed thermal management techniques.

Modern and autonomous hybrid electric vehicles (HEVs), as complex cyber-physical systems, represent a key innovation in the future of transportation. However, the increasing interconnectivity and reliance on digital components expose these vehicles to significant cybersecurity risks. To address these challenges, Zero Trust Architecture (ZTA) has emerged as a promising security framework. Operating on the principle of ‘never trust, always verify,’ ZTA offers a comprehensive approach to ensuring continuous trust verification in HEV systems. Despite its potential, the application of ZTA within cyber-physical vehicular systems remains underexplored, and its practical benefits and limitations are not yet fully understood by the engineering community. To bridge this gap, this article presents a detailed survey of ZTA tailored specifically to the needs of vehicular CPSs, highlighting existing technologies, security challenges, and the application of zero-trust principles in HEVs. Additionally, this work proposes a deep learning-based replay attack detection scheme for the battery management system (BMS) of HEVs. The approach leverages a deep learning model to estimate the battery's State of Charge (SoC), analyzing the Error of Estimation using the Inter-Quartile Range (IQR) technique. The detection system analyzes the Error of Estimation using the IQR technique, demonstrating a 74.25% containment ratio and detecting deviations up to 2.39 units during attack scenarios. The system maintains a balanced detection sensitivity with 25.75% detection density. While the proposed method demonstrates high effectiveness in detecting stealth replay attacks through simulation results, it faces certain limitations including computational overhead for real-time processing, dependence on high-quality training data, and potential vulnerability to adversarial attacks on the underlying deep learning model. These challenges highlight the need for careful consideration in practical implementations while opening avenues for future research.

Autonomous electric vehicles provide benefits to both drivers and the environment compared to conventional vehicles; however, they are burdened with an increase in potential pathways for cyber-attacks. Therefore, reliable cyber-security strategies for these vehicles must be pursued. This paper addresses this concern by implementing a threat detection strategy that utilizes an observer and a neural network. These tools monitor discrepancies between the vehicle’s lateral metrics, which are produced via sensor data, neural network output, and an observer. Previous literature focuses on physics-based analytics to create the threat decision, but here, a data based approach is utilized. The vehicle used in this study is a four-motor-drive autonomous electric vehicle that is propelled with brushless DC motors. The motors are controlled by direct torque control. In this study, three forms of cyber-attacks are implemented. These include data integrity attacks, replay attacks, and denial-of-service attacks. A performance metric is also created, which indicates the data-driven approach outperforms the physics-based approaches. All modeling and simulation were conducted in the MATLAB/Simulink environment.

In modern maritime operations, electric ship power systems (SPSs) play a crucial role as they integrate distributed energy resources to meet stringent environmental targets while ensuring reliability in dynamic and isolated marine environments. Energy management systems (EMS) are critical to optimizing fuel efficiency, reducing emissions, and maintaining power quality under variable load demands and the harsh conditions in which ships operate. This paper presents a comprehensive review of EMS methodologies for SPS, covering traditional methods such as evolutionary algorithms, model predictive control (MPC), fuzzy logic control, and more modern approaches such as machine learning (ML) and deep learning (DL). ML and DL for EMS offer predictive and adaptive capabilities for real-time optimization, but face limitations in data security and centralized computational demands. Federated learning (FL), which is a decentralized, privacy-preserving paradigm, is a viable solution to the disadvantages of the conventional centralized form of training in ML. FL enables collaborative model training across distributed systems without raw data sharing, addressing critical concerns in cybersecurity and communication overhead. However, FL does require individual devices or clients to have enough computational power to carry out distributed optimization locally. A detailed overview of FL, including the FL training process, categories, architectures and various applications of FL in ship and energy systems, is presented in this paper. To further establish the usefulness of conducting energy management (EM) in ship systems, the paper presents a case study on a notional four-zone DC SPS in which FL has been utilized to collaboratively train DL models across multiple generators without sharing sensitive local data. The results demonstrate better generator output power prediction and effective load management in comparison to the conventional centralized learning setup, indicating the potential of FL to enhance energy efficiency and reliability in ship systems. Finally, some challenges and possible research gaps in applying FL to ship systems are discussed.

This article presents an innovative approach to improve hybrid powertrains’ cyber-physical security for high-speed, tracked, off-road vehicles against subtle and sophisticated cyberattacks. While cyber threats against hybrid tracked vehicles (HTVs) have been acknowledged, exploring advanced cyberattacks targeting battery lifetime and energy efficiency within the powertrain remains underexplored. This article proposes a dual-pronged intelligent methodology to construct novel FDIAs targeting vehicle control decisions from the energy management system (EMS). The optimal cyberattack strategy by a minimally informed adversary is formulated as a partial observable Markov decision process (POMDP), employing deep reinforcement learning (DRL) for online learning and attacking. Simultaneously, a comprehensive reward system is devised that incorporates sniffing features to elevate the stealthiness and efficacy of the cyberattacks. This augmentation ensures that the orchestrated attack vectors remain inconspicuous to human drivers. Evaluation metrics are formulated to assess the impact and stealth characteristics of the cyberattack. Furthermore, a sliding window-based controller data monitoring scheme combining iForest and dynamic time warping (DTW) algorithms is proposed to automatically detect the stealthy FDIAs in real-time, ensuring secure control of energy-efficient powertrain systems. Through the proposed metrics and detection module, the article thoroughly examines the impact of cyberattacks on the energy consumption of the HTV. It also provides vital insights for defending vehicles that operate in austere environments against sophisticated controller attacks.