Solar-based home PV systems are the most amazing eco-friendly energy innovations in the world, which are not only climate-friendly but also cost-effective solutions. The tropical environment of Malaysia makes it difficult to adopt photovoltaic (PV) systems because of the protracted rainy monsoon season, which makes PV systems useless without backup batteries. Large quantities of lithium-ion battery (LIB) trash are being produced by the electric vehicle (EV) sector. A total of 75% of the highest capacity levels have been discarded. By 2035, it is predicted that the wasted LIBs held as a result of expensive recycling and difficult material separation would carry up to 1200 GWh. An economical and sustainable option is offered by our study, which prototypes a replicated LIB pack that is incorporated into a PV home system. This study investigates the transformational power of second-life electric vehicle batteries (SLEVBs) when incorporated into home photovoltaic (PV) systems. The concept entails reusing existing electric vehicle batteries for stationary applications, offering a unique approach to extending the life of these batteries while meeting the growing need for sustainable domestic energy storage. The study looks at the technological feasibility, economic viability, and environmental effect of introducing SLEVBs into household PV systems, giving vital insight into their role in revolutionizing energy storage techniques and promoting sustainability. In comparison to the Lead–Acid Battery (LAB) system, the SLEVB system has a cheaper total cost of ownership, with savings of 12.62% compared with new LABs. A CO₂ emission reduction of at least 20% is achieved by using the SLEVB system compared with LABs. Electricity can be provided in houses in rural areas where there is no electricity. As a result, the security and superiority of the life of rural residents will improve. It is anticipated that the suggested strategy will lower EV pricing, enabling EV adoption for M40 and B40 groups. Consequently, the Malaysian and worldwide EV business will remain viable.

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS).

As the global adoption of Electric Vehicles (EVs) surges, efficient management of retired EV batteries is becoming vital. Second-life Batteries (SLBs), repurposed from retired EV batteries, offer a sustainable energy solution. This paper provides a step-by-step technical assessment, covering battery removal from cars, assessment, and integration into second life applications, focusing on the Nissan Leaf Generation 1. The assessment includes comprehensive testing and presents the results of each test. It outlines the repurposing process, calculating capacity for second life application which was a solar-powered streetlight in this experiment. Economic aspects are explored, with a formula for retired battery purchasing price and the repurposing cost related. The paper also examines State of Health (SOH) degradation in the second life application, showing a decline from an initial 49.17% to 44.75% after 100 days and further to 29.25% after 350 days in the second life application. Factors affecting the degradation such as remaining capacity and internal resistance and their inter-relation were investigated. Challenges in SLB implementation are discussed, along with future recommendations. This study underscores the significance of SLBs in promoting sustainable energy systems amid growing EV usage.

This paper presents an advanced AI-based optimization framework for Electric Vehicle (EV) smart charging systems, focusing on efficient energy distribution to meet dynamic user demand. The study leverages machine learning models such as Random Forest, Support Vector Regression (SVR), Gradient Boosting Regressor, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) to forecast user demand and optimize energy allocation. Among the models, XGBoost demonstrated superior predictive performance, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), making it the most effective for real-time user demand prediction in smart charging scenarios. The framework introduces proportional and priority-based allocation strategies to distribute available energy effectively, with a focus on minimizing energy shortfalls and balancing supply with user demand. Results from the XGBoost model reduced prediction error by 15% compared to other models, significantly improving the station’s ability to meet user demand efficiently. The proposed AI framework enhances charging station operations, supports grid stability, and promotes sustainability in the context of increasing EV adoption.

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.

This paper presents a thorough review and analysis of solar photovoltaic (PV) home systems in Malaysia, offering a comprehensive exploration of their implementation, challenges, benefits, and future potential. As a nation striving to embrace sustainable and renewable energy solutions, Malaysia’s adoption of solar PV systems at the residential level is of paramount importance. The study delves into the current state of solar PV deployment, government initiatives, and policy frameworks that influence adoption trends. It examines the economic, environmental, and societal advantages of solar PV home systems, outlining their contribution to reducing electricity costs, carbon emissions, and energy dependence. Addressing barriers to wider acceptance, the paper scrutinizes challenges encompassing cost, awareness, intermittency, and regulatory constraints. Furthermore, the study evaluates the socio-economic implications of solar PV home systems, including potential job creation and energy independence. Technological advancements, such as enhanced efficiency and smart grid integration, are also explored for their role in surmounting obstacles. Drawing insights from practical case studies, the paper underscores successful installations and the lessons they offer. In examining policy and regulatory frameworks, it analyzes existing incentives while suggesting improvements to accelerate adoption. Finally, the paper offers a visionary perspective on the future trajectory of solar PV home systems in Malaysia, envisioning increased affordability, advanced energy storage solutions, and seamless integration with emerging technologies. This holistic analysis contributes valuable insights to propel Malaysia’s sustainable energy transition and inform strategic decision making for stakeholders across sectors.