In this article, the challenges and a future vision of the cyber–physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyberattacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision-making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyberattacks in software and cybernetworks is described. Simulation and experimental results that show the impact of cyberattacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cybersecure power electronics systems. These opportunities include multiscale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

The development of high-definition (HD) maps has enabled predictive cruise control (PCC) systems to access additional road and traffic information. This study provides a novel control scheme of PCC, which utilizes HD map information. To minimize fuel consumption, the problem of the PCC is formulated as a nonlinear model predictive control (MPC), and the derivation and implementation of the fast solver are discussed. Then, a novel shift-map is proposed to define the different working regions to allow the application of the proposed PCC system. The use of the real-time HD map is discussed, and the proposed control scheme is evaluated through simulation and experimental tests. The total fuel-savings rates obtained with the PCC system and factoryinstalled ACC system over a 370 km route were compared. An average fuel-savings rate of as high as 8.73% can be obtained by the proposed PCC system.

The roundabout is a typical changeable, interactive scenario in which automated vehicles should make adaptive and safe decisions. In this article, an optimization embedded reinforcement learning (OERL) is proposed to achieve adaptive decision-making under the roundabout. The promotion is the modified actor of the Actor-Critic framework, which embeds the model-based optimization method in reinforcement learning to explore continuous behaviors in action space directly. Therefore, the proposed method can determine the macroscale behavior (change lane or not) and medium-scale behaviors of desired acceleration and action time simultaneously with high sample efficiency. When scenarios change, medium-scale behaviors can be adjusted timely by the embedded direct search method, promoting the adaptability of decision-making. More notably, the modified actor matches human drivers' behaviors, macroscale behavior captures the human mind's jump, and medium-scale behaviors are preferentially adjusted through driving skills. To enable the agent adapts to different types of the roundabout, task representation is designed to restructure the policy network. In experiments, the algorithm efficiency and the learned driving strategy are compared with decision-making containing macroscale behavior and constant medium-scale behaviors of the desired acceleration and action time. To investigate the adaptability, the performance under an untrained type of roundabout and two more dangerous situations are simulated to verify that the proposed method changes the decisions with changeable scenarios accordingly. The results show that the proposed method has high algorithm efficiency and better system performance.

Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this article, we propose a novel high-dimensional data-driven cyber physical attack detection and identification (HCADI) approach. First, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in the distribution power grids. Then, we construct a high-dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score-based attack detection and binary matrix factorization-based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.

In this article, a systematic assessment of cyber-physical security on the energy management system for connected and automated electric vehicles is proposed, which, to our knowledge, has not been attempted before. The generalized methodology of impact analysis of cyber attacks is developed, including novel evaluation metrics from the perspectives of steady state and transient performance of the energy management system and innovative index-based resilience and security criteria. Specifically, we propose a security criterion in terms of dynamic performance, comfortability, and energy, which are the most critical metrics to evaluate the performance of an electronic control unit (ECU). If an attack does not impact these metrics, it perhaps can be negligible. Based on the statistical results and the proposed evaluation metrics, the impact of cyber attacks on ECU is analyzed comprehensively. The conclusions can serve as guidelines for attack detection, diagnosis, and countermeasures.