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Register a new userA Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks
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Cooperative Adaptive Cruise Control (CACC) is a vehicular technology that allows groups of vehicles on the highway to form in closely-coupled automated platoons to increase highway capacity and safety, and decrease fuel consumption and CO2 emissions. The underlying mechanism behind CACC is the use of Vehicle-to-Vehicle (V2V) wireless communication networks to transmit acceleration commands to adjacent vehicles in the platoon. However, the use of V2V networks leads to increased vulnerabilities against faults and cyberattacks at the communication channels. Communication networks serve as new access points for malicious agents trying to deteriorate the platooning performance or even cause crashes. Here, we address the problem of increasing robustness of CACC schemes against cyberattacks by the use of multiple V2V networks and a data fusion algorithm. The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side. We exploit this redundancy to obtain attack-free estimates of acceleration commands. To accomplish this, we propose a data-fusion algorithm that takes data from all channels, returns an estimate of the true acceleration command, and isolates compromised channels. Note, however, that using estimated data for control introduces uncertainty into the loop and thus decreases performance. To minimize performance degradation, we propose a robust $H_{infty}$ controller that reduces the joint effect of estimation errors and sensor/channel noise in the platooning performance (tracking performance and string stability). We present simulation results to illustrate the performance of our approach.
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Even with state-of-the-art defense mechanisms, cyberattacks in the electric power distribution sector are commonplace. Particularly alarming are load-altering (demand-side) cyberattacks launched through high-wattage assets, which are not continuously monitored by electric power utilities. Electric Vehicle Charging Stations (EVCSs) are among such high-wattage assets and, therefore, cyber insurance can be an effective mechanism to protect EVCSs from economic losses caused by cyberattacks. This paper presents a data-driven cyber insurance design model for public EVCSs. Under some mildly restrictive assumptions, we derive an optimal cyber insurance premium. Then, we robustify this optimal premium against uncertainty in data and investigate the risk of insuring the EVCSs using Conditional Value-at-Risk. A case study with data from EVCSs in Manhattan, New York illustrates our results.
Cooperative Adaptive Cruise Control (CACC) is an autonomous vehicle-following technology that allows groups of vehicles on the highway to form in tightly-coupled platoons. This is accomplished by exchanging inter-vehicle data through Vehicle-to-Vehicle (V2V) wireless communication networks. CACC increases traffic throughput and safety, and decreases fuel consumption. However, the surge of vehicle connectivity has brought new security challenges as vehicular networks increasingly serve as new access points for adversaries trying to deteriorate the platooning performance or even cause collisions. In this manuscript, we propose a novel attack detection scheme that leverage real-time sensor/network data and physics-based mathematical models of vehicles in the platoon. Nevertheless, even the best detection scheme could lead to conservative detection results because of unavoidable modelling uncertainties, network effects (delays, quantization, communication dropouts), and noise. It is hard (often impossible) for any detector to distinguish between these different perturbation sources and actual attack signals. This enables adversaries to launch a range of attack strategies that can surpass the detection scheme by hiding within the system uncertainty. Here, we provide risk assessment tools (in terms of semidefinite programs) for Connected and Automated Vehicles (CAVs) to quantify the potential effect of attacks that remain hidden from the detector (referred here as emph{stealthy attacks}). A numerical case-study is presented to illustrate the effectiveness of our methods.
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.
This paper proposes a distributed framework for vehicle grid integration (VGI) taking into account the communication and physical networks. To this end, we model the electric vehicle (EV) behaviour that includes time of departure, time of arrival, state of charge, required energy, and its objectives, e.g., avoid battery degradation. Next, we formulate the centralised day ahead distribution market (DADM) which explicitly represents the physical system, supports unbalanced three phase networks with delta and wye connections, and incorporates the charging needs of EVs. The solution of the centralised market requires knowledge of EV information in terms of desired energy, departure and arrival times that EV owners are reluctant in providing. Moreover, the computational effort required to solve the DADM in cases of numerous EVs is very intensive. As such, we propose a distributed solution of the DADM clearing mechanism over a time-varying communication network. We illustrate the proposed VGI framework through the 13-bus, 33- bus, and 141-bus distribution feeders.
This paper proposes a novel Gap Reduced Minimum Error Robust Simultaneous (GRMERS) estimator for resource-constrained Nano Aerial Vehicle (NAV) that enables a single estimator to provide simultaneous and robust estimation for a given N unstable and uncertain NAV plant models. The estimated full state feedback enables a stable flight for NAV. The GRMERS estimator is implemented utilizing a Minimum Error Robust Simultaneous (MERS) estimator and Gap Reducing (GR) compensators. The MERS estimator provides robust simultaneous estimation with minimal largest worst-case estimation error even in the presence of a bounded energy exogenous disturbance signal. The GR compensators reduce the gap between the graphs of N linear plant models to decrease the estimation error generated by the MERS estimator. A sufficient condition for the existence of a simultaneous estimator is established using LMIs and robust estimation theory. Further, MERS estimator and GR compensator design are formulated as non-convex tractable optimization problems and are solved using the population-based genetic algorithms. The performance of the GRMERS estimator consisting of MERS estimator and GR compensators from the population-based genetic algorithms is validated through simulation studies. The study results indicate that a single GRMERS estimator can produce state estimates with reduced errors for all flight conditions. The results indicate that the single GRMERS estimator is robust than the individually designed H inifinity filters.
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