Journal of Automation and Automobile Engineering

Investigation of Optimized Design of a Rotating Solar Panel System for Sustainable EV Charging Stations

2026-03-31T08:45:05+00:00

The rapid growth of electric vehicles (EVs) has increased the demand for sustainable and efficient charging infrastructure. Conventional EV charging stations are often dependent on grid-based electricity, which limits their environmental benefits and contributes to energy stress. This study investigates the optimized design of a rotating solar panel system integrated with street light monitoring for sustainable EV charging applications. The proposed system utilises solar tracking mechanisms to maximise energy capture throughout the day, thereby improving the efficiency of solar-powered charging stations. By combining the functionality of street lights with EV charging, the design ensures dual-purpose utility while reducing infrastructure costs and land use. The street light monitoring system further enhances operational reliability by incorporating sensors to track energy usage, illumination requirements, and system health in real time. Experimental and simulation-based analyses are employed to assess solar energy generation, panel rotation efficiency, charging performance, and overall system sustainability. The results demonstrate that the rotating solar panel system provides significantly higher energy output compared to fixed-panel setups, enabling faster and more reliable EV charging while ensuring continuous street lighting. Additionally, the integrated monitoring system allows for predictive maintenance and adaptive energy distribution, further increasing efficiency and safety. This research contributes to the development of eco-friendly, self-sufficient charging solutions that can be deployed in urban and rural environments. The findings highlight the potential of hybrid infrastructure solutions in supporting sustainable mobility, optimizing energy utilisation, and advancing smart city initiatives.

Comprehensive Design, Assembly, Fault Diagnosis, and Preventive Maintenance Study of an Electric Two-Wheeler System

2026-01-03T05:39:32+00:00

The rapid expansion of electric two-wheelers (E2Ws) has intensified the need for reliable, safe, and efficient vehicle performance, highlighting the importance of systematic fault diagnosis and preventive maintenance strategies. This study presents a comprehensive investigation into the design, assembly-related faults, diagnostic methodologies, and maintenance practices applicable to modern electric two-wheeler systems. The research focuses on identifying critical failure points within the vehicle’s mechanical, electrical, and electronic subsystems, including the battery pack, motor, controller, wiring harness, braking components, chassis assembly, and drivetrain integrations that directly influence durability, safety, and performance. A hybrid methodology combining design analysis, Failure Mode and Effects Analysis (FMEA), condition monitoring, sensor-based diagnostics, and real-world case studies is adopted to map common assembly faults and their root causes. The study also leverages predictive maintenance techniques such as vibration analysis, thermal profiling, battery health assessment, and data-driven diagnostic models for early fault detection. Particular emphasis is given to the role of improper assembly practices, component misalignment, torque deviations, wiring inconsistencies, and inadequate quality checks, which often lead to premature failures and customer dissatisfaction. The preventive maintenance framework developed in this research integrates scheduled servicing, predictive diagnostics, and digital maintenance logs to establish a proactive approach for improving system reliability. Recommendations include enhanced design-for-maintenance principles, standardized assembly protocols, integration of on-board diagnostic sensors, and AI-enabled fault prediction tools. The findings of this study contribute to improving the lifecycle performance, safety, and operational efficiency of electric two-wheelers. The proposed fault diagnosis and preventive maintenance strategies offer practical insights for manufacturers, service centers, policymakers, and researchers, enabling more robust and sustainable adoption of electric mobility solutions.

Machine Learning-based Health Estimation and Optimal Allocation of Second-life Electric Vehicle Batteries for Grid Applications

2026-03-13T08:32:18+00:00

The rapid proliferation of electric vehicles (EVs) has generated a substantial and growing inventory of retired lithium-ion battery packs, which typically retain 70–80% of their original energy storage capacity upon automotive retirement. Repurposing these second-life batteries (SLBs) for stationary grid applications presents a compelling economic and environmental opportunity; however, their heterogeneous degradation histories introduce fundamental challenges for accurate health estimation and safe deployment. This study presents a comprehensive framework that integrates machine learning (ML) techniques with electrochemical characterization to precisely assess the state of health (SOH) of retired EV batteries and to formulate an optimal allocation strategy for diverse grid services, encompassing frequency regulation, peak shaving, and renewable energy integration. A hybrid model combining Gaussian process regression (GPR) and long short-term memory (LSTM) networks is proposed for SOH estimation, trained on a curated dataset of 1,240 retired battery modules spanning three distinct chemistries. The allocation optimization employs a multi-objective genetic algorithm that jointly minimizes degradation acceleration and system-level costs while maximizing grid service revenue. Simulation results demonstrate that the proposed framework achieves a mean absolute error (MAE) of 1.23% in SOH estimation and increases projected second-life revenue by 34.7% compared to heuristic allocation benchmarks. These findings underscore the practical viability of data-driven SLB management and provide actionable guidelines for grid operators and battery aggregators.