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The proliferation of electric vehicles in recent years has significantly expanded the charging infrastructure while introducing new security risks to both vehicles and chargers. In this paper, we investigate the security of major charging protocols such as SAE J1772, CCS, IEC 61851, GB/T 20234, and NACS, uncovering new physical signal spoofing attacks in their authentication mechanisms. By inserting a compact malicious device into the charger connector, attackers can inject fraudulent signals to sabotage the charging process, leading to denial of service, vehicle-induced charger lockout, and damage to the chargers or the vehicle's charge management system. To demonstrate the feasibility of our attacks, we propose PORTulator, a proof-of-concept (PoC) attack hardware, including a charger gun plugin device for injecting physical signals and a wireless controller for remote manipulation. By evaluating PORTulator on multiple real-world chargers, we identify 7 charging standards used by 20 charger piles that are vulnerable to our attacks. The root cause is that chargers use simple physical signals for authentication and control, making them easily spoofed by attackers. To address this is

Wireless power transfer (WPT) is increasingly used to sustain Internet-of-Things (IoT) systems by wirelessly charging embedded devices. Mobile chargers further enhance scalability in wireless-powered IoT (WP-IoT) networks, but pose new challenges due to dynamic channel conditions and limited energy budgets. Most existing works overlook such dynamics or ignore real-time constraints on charging schedules. This paper presents a bandit-based charging framework for WP-IoT systems using mobile chargers with practical beamforming capabilities and real-time charging constraints. We explicitly consider time-varying channel state information (CSI) and impose a strict charging deadline in each round, which reflects the hard real-time constraint from the charger's limited battery capacity. We formulate a temporal-spatial charging policy that jointly determines the charging locations, durations, and beamforming configurations. Area discretization enables polynomial-time enumeration with constant approximation bounds. We then propose two online bandit algorithms for both stationary and non-stationary unknown channel state scenarios with bounded regrets. Our extensive experimental results validat

Advancements in information technology have increased demand for natural human-computer interaction in areas such as gaming, smart homes, and vehicles. However, conventional approaches like physical buttons or cameras are often limited by contact requirements, privacy concerns, and high costs.Motivated by the observation that these EM signals are not only strong and measurable but also rich in gesture-related information, we propose EMGesture, a novel contactless interaction technique that leverages the electromagnetic (EM) signals from Qi wireless chargers for gesture recognition. EMGesture analyzes the distinctive EM features and employs a robust classification model. The end-to-end framework enables it capable of accurately interpreting user intent. Experiments involving 30 participants, 10 mobile devices, and 5 chargers showed that EMGesture achieves over 97% recognition accuracy. Corresponding user studies also confirmed higher usability and convenience, which demonstrating that EMGesture is a practical, privacy-conscious, and cost-effective solution for pervasive interaction.

In this paper we discuss a protocol for charging a two-level quantum battery using a bipartite charger composed of two quantum harmonic oscillators. As one of its features, it allows us to fully charge the battery and is universally optimal in the regime of a single excitation added as energy input. We also make use of a selective interaction to extend the protocol for a different class of quantum states and show that, in this case, the presence of quantum coherence can be harnessed as energetic resource to charge multiple similar batteries. Among these, we also explore symmetries of the derived effective dynamics to quickly discuss how the same protocol can be adapted to the task of \textit{active state resetting}, a task which is particularly useful in the quantum computation area.

We investigate the performance of the charger-mediated quantum battery modeled by a two-qubit system. One of the qubits acts as the battery and the other acts as the charger which is subjected to a reservoir. We derived the time-local master equation in Lindblad form with a time-dependent dephasing rate. The dephasing rate may be negative in the early-stage of the charging process and thus indicate the presence of the memory effect. We find that such early-stage memory effect could increase the maximal ergotropy of the battery compared with the one under Markovian approximation with the corresponding asymptotic dephase rate. The enhancement of the performance is explained by means of the non-Markovian quantum jumps. Moreover, a discrete time scheme of the measurement-enhanced quantum battery is proposed in a quantum circuit with global and random local operations.

Since its introduction in 2012, the Combined Charging System (CCS) has emerged as the leading technology for EV fast charging in Europe, North America and parts of Asia. The charging communication of CCS is defined by the ISO 15118 standards, which have been improved over the years. Most notably, in 2014, important security features such as Transport Layer Security (TLS) and usability enhancements such as Plug and Charge were introduced. In this paper, we conduct the first measurement study of publicly deployed CCS DC charging stations to capture the state of deployment for different protocol versions and to better understand the attack surface of the EV charging infrastructure. In our evaluation, we examine 325 chargers manufactured between April 2013 and June 2023, and installed as late as May 2024 by 26 manufacturers across 4 European countries. We find that only 12% of the charging stations we analyzed implement TLS at all, leaving all others vulnerable to attacks that have already been demonstrated many years ago. We observe an increasing trend in support for ISO 15118-2 over the years, reaching 70% of chargers manufactured in 2023. We further notice that most chargers use a d

Quantum batteries modeled as two-qubit systems coupled to Markovian thermal reservoirs have been shown to benefit from measurement-assisted charging, where projective measurements enhance the charging rate at an infinite thermodynamic resource cost. In this work, we consider weak, continuous measurements implemented via quantum point contact detectors (QPC), which enhance the charging rate at a definite and quantifiable resource cost. We analyze three measurement configurations namely, a single QPC, two independent QPCs, and a series-coupled (coherent) two-QPC scheme, and study their effect on the steady-state charging rate, defined as the rate of energy flow from the charger qubit to the battery qubit, relative to the unmeasured baseline. We find that the charging rate enhancement is non-monotonic as a function of the temperature gradient and potential gradient required to drive the QPCs, exhibiting a plateau of near-optimal enhancement. Comparing the three configurations, the plateau of optimal enhancement contracts toward lower temperature and chemical potential for both the cases with two QPCs compared to the single QPC case. The coherent measurement further shows a lowering of

Growing EV adoption can worsen traffic conditions if chargers are sited without regard to their impact on congestion. We study how to strategically place EV chargers to reduce congestion using two equilibrium models: one based on congestion games and one based on an atomic queueing simulation. We apply both models within a scalable greedy station-placement algorithm. Experiments show that this greedy scheme yields optimal or near-optimal congestion outcomes in realistic networks, even though global optimality is not guaranteed as we show with a counterexample. We also show that the queueing-based approach yields more realistic results than the congestion-game model, and we present a unified methodology that calibrates congestion delays from queue simulation and solves equilibrium in link-space.

Electric Vehicle (EV) has become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. The security of EV charging, however, has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs, and exposes the lack of protection on the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states or charging rates of EV chargers with the goal of disrupting the charging schedules, causing a denial of service (DoS), or degrading the battery performance. ChargeX inserts a hardware attack circuit to strategically modify the charging control signals. We design and implement multiple attack systems, and evaluate the attacks on a public charging station and two home chargers using a simulated vehicle load in the lab environment. Extensive experiments on different types of chargers demonstrate the effectiveness and generalization of ChargeX. Sp

For electrifying the transportation sector, deploying a strategically planned and efficient charging infrastructure is essential. This paper presents a two-phase approach for electric vehicle (EV) charger deployment that integrates spatial point-of-interest analysis and maximum coverage optimization over an integrated spatial power grid. Spatial-focused studies in the literature often overlook electrical grid constraints, while grid-focused work frequently considers statistically modeled EV charging demand. To address these gaps, a new framework is proposed that combines spatial network planning with electrical grid considerations. This study approaches EV charger planning from a perspective of the distribution grid, starting with an estimation of EV charging demand and the identification of optimal candidate locations. It ensures that the capacity limits of newly established chargers are maintained within the limits of the power grid. This framework is applied in a test case for the Dallas area, integrating the existing EV charger network with an 8500-bus distribution system for comprehensive planning.