Author Correction: Dephasing enabled fast charging of quantum batteries
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Optimization of electric charging infrastructure: integrated model for routing and charging coordination with power-aware operations
With the increasing adoption of electric vehicles (EVs), optimizing charging operations has become imperative to ensure efficient and sustainable mobility. This study proposes an optimization model for the charging and routing of electric vehicles between Origin-Destination (OD) demands. The objective is to develop an efficient and reliable charging plan that ensures the successful completion of trips while considering the limited range and charging requirements of electric vehicles. This paper presents an integrated model for optimizing electric vehicle (EV) charging operations, considering additional factors of setup time, charging time, bidding price estimation, and power availability from three sources: the electricity grid, solar energy, and wind energy. One crucial aspect addressed by the model is the estimation of bidding prices for both day-ahead and intra-day electricity markets. The model also considers the total power availability from the electricity grid, solar energy, and wind energy. The alignment of charging operations with the capacity of the grid and prevailing bidding prices is essential.This ensures that the charging process is optimized and can effectively adapt to the available grid capacity and market conditions. The utilization of renewable energies led to a 42% decrease in the electricity storage capacity available in batteries at charging stations. Furthermore, this integration leads to a substantial cost reduction of approximately 69% compared to scenarios where renewable energy is not utilized. Hence, the proposed model can design renewable energy systems based on the required electricity capacity at charging stations. These findings highlight the compelling financial advantages associated with the adoption of sustainable power sources.
Dephasing enabled fast charging of quantum batteries
We propose and analyze a universal method to obtain fast charging of a quantum battery by a driven charger system using controlled, pure dephasing of the charger. While the battery displays coherent underdamped oscillations of energy for weak charger dephasing, the quantum Zeno freezing of the charger energy at high dephasing suppresses the rate of transfer of energy to the battery. Choosing an optimum dephasing rate between the regimes leads to a fast charging of the battery. We illustrate our results with the charger and battery modeled by either two-level systems or harmonic oscillators. Apart from the fast charging, the dephasing also renders the charging performance more robust to detuning between the charger, drive, and battery frequencies for the two-level systems case.
Large-scale empirical study of electric vehicle usage patterns and charging infrastructure needs
As global electric vehicle (EV) adoption accelerates, granular analysis of empirical usage and charging patterns remains scarce. This study presents a unique large-scale empirical examination of 1.6 million EVs, including a broad array of vehicle types—private, taxi, rental, official, bus, and special purpose vehicle—across seven major Chinese cities with over 854 million observations of driving and charging events. Our findings illuminate significant heterogeneity in EV usage, battery energy, and charging behavior across vehicle types with notable city differences. Day-time high-power charging presents high loads on the electricity grid across all vehicle types, particularly from service-oriented vehicles, including taxis, rental cars, and buses. The maximum loads also are the highest in the center of the cities. Our study of large-scale EV usage offers critical insights for developing charging infrastructure, managing energy grids, and providing flexibility services, which are pivotal to the evolution of future transport ecosystems.
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
Optimizing bus charging infrastructure by incorporating private car charging demands and uncertain solar photovoltaic generation
Integrating solar photovoltaic (PV) and battery energy storage (BES) into bus charging infrastructure offers a feasible solution to the challenge of carbon emissions and grid burdens. The deployment costs and uncertain power outputs of solar PV and BES need to be considered by public transportation agencies. This study presents a data-driven approach to optimize bus charging infrastructure and incorporates sharing charging and uncertain solar PV generation using the Latin Hypercube Sampling method. A case study in Yinchuan, China, reveals that integrating solar PV and BES at a single bus depot reduces total costs by 37.35%, carbon emissions by 41.46%, and grid loads by 49.35% over the 10-year lifetime of BES. Sharing this infrastructure with private cars offers further economic benefits. Global analysis shows this approach’s varying economic and environmental advantages. The proposed model offers practical implications for developing cost-effective and environmentally friendly electric bus charging infrastructure to advance sustainable transport.
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