Second-Life EV Batteries for PV–SLB Hybrid Petrol Stations: A Roadmap for Malaysia’s Urban Energy Transition

The rapid growth of electric vehicle (EV) adoption in Malaysia is projected to generate substantial volumes of end-of-life lithium-ion batteries, creating both environmental challenges and opportunities for repurposing into second-life batteries (SLBs). This study investigates the technical, economic, and regulatory feasibility of deploying SLBs for photovoltaic (PV) energy storage in petrol stations, an application aligned with the nation’s energy transition goals. Laboratory testing of Nissan Leaf ZE0 battery modules over a 120-day operation period demonstrated stable cycling performance with approximately 7% capacity fade, maintaining state-of-health (SOH) above 47%. A case study of a 12 kWp PV–SLB hybrid system for a typical Malaysian petrol station shows 45 kWh of usable storage, capable of offsetting a daily electricity demand of 45 kWh, reducing capital cost by 30–50% compared to new lithium-ion systems, and achieving 70–80% lifecycle CO2 emission reductions. The proposed architecture leverages SLBs’ suitability for slower, steady discharge to provide reliable nighttime operation and grid load relief, particularly in semi-urban and rural stations. Beyond technical validation, the paper evaluates economic benefits, environmental impacts, and Malaysia’s regulatory readiness, identifying gaps in certification standards, reverse logistics, and workforce skills. Strategic recommendations are proposed to enable large-scale SLB deployment and integration into hybrid PV–petrol station systems. Findings indicate that SLBs can serve as a cost-effective, sustainable energy storage solution, supporting Malaysia’s National Energy Transition Roadmap (NETR), advancing circular economy practices, and positioning the country as a potential ASEAN leader in battery repurposing

Performance Optimization of Grounding System for Multi-Voltage Electrical Installation

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations

AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks

AI-Driven Framework for Secure and Efficient Load Management in Multi-Station EV Charging Networks

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent

Performance Evaluation of Second-Life EV Batteries for Off-Grid Solar Energy Storage System

The increasing adoption of electric vehicles (EVs) has led to a growing volume of retired lithium-ion batteries that retain significant residual capacity, prompting interest in their repurposing for stationary energy storage systems (ESS). This study presents a comprehensive performance evaluation of second-life EV batteries sourced from three platforms: Nissan Leaf Gen 1 (LMO-LNO chemistry), Citroën C0 (Mitsubishi i-MiEV) (LMO), and China Aviation Lithium Battery Co., Ltd. (CALB) (LFP). Laboratory tests were conducted using Arbin battery testers to assess capacity retention, internal resistance, and state-of-health (SOH) across up to 5000 charge-discharge cycles. In parallel, real-world field tests were conducted using a solar-powered 120 W LED streetlight ESS to validate operational performance under ambient Malaysian climate conditions. Results from laboratory analysis showed that the CALB LFP battery exhibited the highest mid-life stability, retaining 70.9% of its capacity at 2500 cycles and 57.4% at 3000 cycles, with slower thermal-induced degradation. The Nissan Leaf battery maintained 72.6% capacity at 2500 cycles but experienced accelerated decay post-3000 cycles, dropping to 4.8% at 5000 cycles. The Citroën C0 battery showed comparable early-cycle behavior but degraded more sharply under thermal stress, retaining 63.6% at 3000 cycles and just 7.2% at 5000 cycles. Field test data revealed that actual SOH decline rates were 15–20% faster than in lab conditions, attributed to variable temperatures (30–38°C), inconsistent load patterns, and limited thermal management. The study confirms that second-life batteries are viable for low- to medium-demand ESS applications within a 2500–3000 cycle operating window, provided that chemistry-specific integration strategies and intelligent battery management systems are employed. The findings contribute to lifecycle extension modeling and support the deployment of second-life batteries as cost-effective, sustainable components in decentralized energy systems

Color Changes of Ceramic Materials Used for Inlay and Veneers Restorations after Staining with Different Time Intervals

Background: Stain color of CAD/CAM prosthetic materials is important for the long-term of these materials without affecting daily beverage consumption habits.&#x0D; Aims: To assess the influence of aging with Coca-Cola drinks on the color of the CAD/CAM materials used for inlay and veneer restoration over different time intervals.&#x0D; Methods: 48 specimens were fabricated from Vitablocs Mark II and zirconia CAD/CAM ceramic materials. Each group comprised 24 specimens with eight samples for every background (white [W], black [B], and gray). Baseline readings were taken by a spectrophotometer before staining (Baseline T0). After Coca-Cola staining and aging, the color measurement was repeated after 15 and 30 days (T1 and T2), while T3 was the difference between T2-T0. The data were analyzed with descriptive statistics, one-way ANOVA, and post hoc tests.&#x0D; Results: The ΔE00 color parameters of Mark II and zirconia materials were higher against the W background than other backgrounds. However, the zirconia materials recorded higher values than Mark II for the same parameters (L, a, and b) against the gray background during 15 and 30 days of immersion in Coca-Cola. Translucency parameters (TP) exhibited significant differences between ceramic types and immersion periods, with the TP values of zirconia being slightly higher than those of Mark II. The ΔE00 values for the three-time intervals were within clinically acceptable ranges. The ΔE00 values of both ceramic types at T3 were within 1.38–1.53 and 3.11–3.62 and lacked significant differences.&#x0D; Conclusions: Coca-Cola staining was obvious after 15 and 30 days of immersion and had a marked effect on the TP and ΔE00 of the tested CAD/CAM materials. Increasing the staining time resulted in a reduction in TP values at all time intervals. Zirconia samples had higher ΔE00 values than Mark II materials at different time periods. All of the colors of the tested materials had changed from B1 to different light colors in accordance with the Vita classical shade guide.</jats:p

Supervised Curricular Internship and the development of management skills: a perception of graduates, undergraduates, and professors

Abstract Objective: Analyze the perception of graduates, undergraduates and professors about the teaching-learning process of the Supervised Curricular Internship (SCI) of Nursing Undergraduate Courses and in light of the development of management competencies. Method: Qualitative study based on the Mayan content analysis from a questionnaire of objective and discursive questions that evaluated the perception of 87 undergraduates, 280 graduates, and 48 professors from two universities in the state of São Paulo. Results: Four thematic categories emerged from this process and the development of management skills, highlighting the fundamental role of the supervising nurse in SCI and the need for greater presence of professors. Regarding management skills, the subjects emphasized leadership, resource management and performance of bureaucratic functions as essential skills. Final considerations: SCI is an environment that favors learning the profession and developing managerial skills; however, better coordination is required between educational institutions and SCI practice sites