Elutunji Buraimoh

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.

Virtual integration of geographically dispersed laboratories through real-time co-simulation presents powerful capabilities for co-simulating massive complex systems but it is hindered by communication delays that compromise accuracy and stability. This challenge is particularly concerning for real-time power system co-simulation, where delays can induce synchronization loss and limit dynamic and transient studies. This study proposes an adaptive, multi-parameter model-free framework for predicting and compensating delays in co-simulated systems, addressing this critical issue. This framework leverages the improved damping impedance method interface algorithm and an adaptive, parameter-tuning predictor system that predicts and compensates for delays without requiring complex interface signal transformation, processing, decomposition, reconstruction, phase estimation, system models, and no human interference. The proposed approach is validated using a joint experiment between two laboratories at Clemson, SC, USA and Greenville, SC, USA, with the Damping Impedance Method as an Interface Algorithm between the two partitioned subsystems. The coupling errors, state tracking errors, and residual complex power are used as evaluation metrics for the proposed delay compensation. This approach enhances co-simulation accuracy and stability, facilitating reliable dynamic and transient analyses.

Power electronics building block (PEBB) concept involves integrating fundamental components into functional blocks that can be stacked, extending converter power ratings for all-electric ships (AESs). This modular approach reduces costs, size, weight, design complexity, and maintenance. PEBBs can be realized as modular multi-level converters (MMCs), which offer advantages like modularity, low switching losses, minimal voltage/current quantization, high reliability, and efficiency. However, effective switching control methods are crucial to balance capacitor voltages and suppress circulating currents. This paper proposes an improved nearest level control (NLC) method that employs smoothed trapezoidal reference signals instead of sinusoidal references, aiming to enhance capacitor voltage balancing, suppress circulating currents, and improve the output power quality of PEBBs in AESs. The proposed NLC method is analyzed in real-time for an N-level PEBB connected to an induction machine (IM) with variable speed and torque load. The real-time verification is conducted in the Typhoon HIL606 digital real-time simulator (DRTS). The results validate the feasibility and effectiveness of the proposed NLC method for a three-phase N-level PEBB concept for AESs.

In recent years, data-driven methods have shown promise in diagnosing various open circuit fault (OCF) modes in inverter drive applications. However, existing studies primarily evaluate the reliability of these methods based solely on classification accuracy, neglecting critical real-time factors, such as computational delays and data transfer latency associated with the data-driven approach and communication protocols, respectively, which can affect real-time operational reliability. This article addresses these gaps by proposing a universally applicable reliability score criterion that integrates classification accuracy with system timing profiles. In addition, a model predictive control strategy employing active thermal management (ATM) is applied to the drive system, enabling a detailed analysis of the impact of OCF modes on the junction temperature of mosfets. Moreover, a novel feature extraction dataset is introduced, leveraging resistive/conduction loss data from the ATM scheme without requiring signal preprocessing. The proposed reliability score quantification and the dataset's diagnostic potential are validated using various data-driven classifiers. The most reliable classifier, achieving a 99.95% diagnosis accuracy, is further tested under diverse operating conditions using a control hardware-in-the-loop setup on an OPAL-RT testbed and Raspberry Pi.

Real-time cosimulation of geographically dispersed laboratories enables extensive system simulations but faces significant challenges from communication delays, impacting accuracy and stability. This issue is crucial in real-time power system cosimulation, where delays can disrupt synchronism and hinder dynamic analyses. This article proposes model-free predictive delay compensation methods as viable alternative signal transformation-based methods. This study explores the frequency domain stability of a model-free framework for delay prediction and compensation at power system interfaces using the ideal transformer method as interface algorithm. Similarly, time-domain implementation reveals signal amplitude magnification, addressed by introducing a delay- and frequency-dependent normalizing factor. This framework adapts interface coupling signals, enabling real-time parameter tuning without complex processing or system models, enhancing co-simulation accuracy and stability for distributed power system analyses.

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.

Inverter-based electric drive systems (EDS) in electric vehicles (EVs) are susceptible to various common cyber and physical anomalies (CPAs), such as cyber-attacks (CAs) and open-circuit faults (OCFs) in power switches. Most existing data-driven diagnostic schemes for motor drives, solely rely on residual and 3-phase output current signals and are prone to misclassification due to overlapping data features associated with these anomalies. Moreover, these methods concentrate on detecting either CAs or OCFs independently, lacking a unified approach that can be universally applied to both types of anomalies. Therefore, in this work, a physics-informed machine learning (PIML) approach is proposed which can effectively distinguish between CAs and OCFs in EDS. In this regard, the unique features associated with stator current, resulting from various CPAs, are used to estimate the stator voltage characteristics through a synchronous motor mathematical model. The proposed voltage-based features characterizing better correlation via distinctive EDS transients in response to these anomalies are further used as the system’s prior information by a machine learning (ML) classifier. Moreover, active-thermal management (ATM)-based model predictive control (MPC) is utilized in the control layer due to its benefits in device-level thermal cycling and power quality. In addition, various variations in electrical, mechanical, and thermal characteristics due to CPAs in EDS have also been analyzed. The presented approach is evaluated for the US06 standard drive cycle in real-time via controller-hardware-in-the-loop (CHIL) experiment for various case study scenarios, resulting in a classification accuracy of 98.92%.

The increasing popularity of electric vehicles is driven by their compatibility with sustainable energy goals. However, the decline in the performance of energy storage systems, such as batteries, due to their degradation puts electric vehicles and hybrid electric vehicles at a disadvantage compared to traditional internal combustion engine vehicles. This paper presents a prognostic-based control framework for hybrid electric vehicles to reduce the cost of operating hybrid electric vehicles by considering the degradation of energy storage systems. The strategy utilizes a degradation forecasting model of electrical components to predict their degradation pattern and uses the prediction to control hybrid electric vehicles via their energy management systems to reduce the degradation of components. A real-time controller hardware-in-the-loop is set up to run the proposed strategy. An hybrid electric vehicle model is developed on Typhoon (i.e., a real-time simulator), which is connected to two layers, energy management and degradation forecasting layer, deployed in Raspberry Pis, respectively. All these components are communicated through CAN communication, where the actual operating condition of the vehicle is sent from Typhoon to each Raspberry Pis to implement the proposed control strategy. With this approach, the cost of operating hybrid electric vehicles can be reduced, making them more competitive than their combustion engine counterparts shown in both numerical simulations and the CHIL experiment.

In recent years, there has been growing research interest in the virtual integration of hardware and software assets across different geographical locations. However, achieving joint real-time simulation in virtually connected laboratories presents several challenges that must be addressed. One critical aspect involves selecting suitable interface algorithms to conserve energy, ensure signal decomposition, and reconstruct data accurately, thereby preventing distortions. Additionally, communication latencies must be taken into account to maintain experiment fidelity. The establishment of fast and reliable communication between real-time simulators in different laboratories through coupling interfaces, is of utmost importance for successful real-time co-simulation. Nevertheless, assuming the constant availability of reliable and delay-free communication is unrealistic, which can lead to performance degradation and system instability. These requirements pose significant obstacles to implementing virtual integration involving various real-time simulators and hardware-in-the-loop setups across diverse laboratories. The real-time co-simulation of power systems, in particular, is highly susceptible to ineffective interface algorithms, signal decomposition, and reconstruction, as well as communication delays, potentially causing loss of synchronism and negatively impacting simulation fidelity. Consequently, these limitations render such setups unsuitable for dynamic and transient studies. In light of these challenges, this paper aims to provide an overview of interface algorithms, signal decomposition and reconstruction techniques, communication protocols, and delay compensation approaches. The goal is to ensure system fidelity, enhance coupling point modeling, and improve the accuracy of real-time co-simulation in virtual environments across long distances while preserving ongoing research confidentiality, safeguarding intellectual property, and facilitating collaborative research.