To systematically improve the public charging experience, EV charging industry stakeholders need to define and measure it precisely. Many stakeholders currently measure aspects of the charging experience, but they typically employ metrics that are either operational in nature, such as charger uptime and mean time between failures, or composite customer satisfaction indices. To improve the customer experience most effectively, the industry needs metrics that define the charging experience from the perspective of the customer, not business operations. Furthermore, industry practitioners need granular metrics to know what specific aspects of the charging experience need improvement. This report defines such customer-focused metrics, called key performance indicators (KPIs).

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies.

Nuclear energy systems present unique challenges in terms of ensuring safety, reliability, and efficiency during their design and operation. Early fault detection is critical for mitigating risks and fostering system resilience. However, current methods often fall short at identifying faults during early stages, potentially leading to costly delays and safety risks. The present work proposes a comprehensive digital engineering approach that leverages digital twins, digital threads, model-based systems engineering (MBSE), artificial intelligence (AI), and immersive extended reality (XR) to support early fault detection in nuclear systems. Through a series of case studies, we highlight specific gaps in the fault detection mechanisms of traditional nuclear design and operation processes, then demonstrate a suite of solutions we are working to implement to address these shortcomings in similar projects. Our findings suggest that a digital engineering approach to design and operation can significantly improve fault detection, ultimately leading to reductions in risk.

With the growing adoption of Electric Vehicles (EVs), there is an increasing need for a reliable EV charging infrastructure. To help meet this need, the report “Recommendations for Minimum Required Error Codes for Electric Vehicle Charging Infrastructure,” recommends a set of minimum required error codes (MRECs) and their functional and responsibility classification. Charger manufacturers, charging station operators, EV manufacturers, and other stakeholders in the North American market are encouraged to uniformly adopt the MRECs to enhance EV charging error reporting, interpretation, and diagnostics. This document serves as a guide to enable uniform implementation of the MRECs using the Open Charge Point Protocol (OCPP).

Background Nuclear power plants face increasing cost pressures, workforce constraints (aging workforce and skilled labor shortages), and safety requirements that are accelerating interest in robotic systems for inspection and maintenance. Methods We conducted semi-structured interviews with personnel from seven U.S. nuclear utilities and compared deployment models, operational use cases, and integration practices with those reported by participants in the oil, gas, and petrochemical sector. Results In nuclear plants, robotic use remains concentrated in inspection—particularly indoor unmanned aerial vehicles and submersible remotely operated vehicles—with limited application to physical maintenance tasks. Reported near-term value includes reduced radiological and industrial risk, reduced outage labor, and improved data for planning and condition assessment. Key barriers include integration and data-interoperability constraints, operator qualification requirements, cybersecurity review burden, and difficulty demonstrating reliability in plant-representative environments. Conclusions Cross-sector benchmarking highlights organizational and deployment practices that may help nuclear plants scale from pilots to routine use. We propose a deployment-oriented roadmap emphasizing modular payload strategies, representative qualification pathways and testing environments, and improved data governance to support safe and economically justified expansion of robotics in operating nuclear power plants.

This report describes how individual and unique messages that are sent over in OCPP sessions and/or transactions are used to calculate the interim set of key performance indicators (KPI) established by ChargeX Consortium’s Working Group 1: Defining the Charging Experience in the report entitles “Customer-Focused Key Performance Indicators for Electric Vehicle Charging.