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In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.

Energy generation from renewable sources is a global trend due to the carbon emissions generated by fossil fuels, which cause serious harm to the ecosystem. As per the long-term goals of the ASEAN countries, the Malaysian government established a target of 31% renewable energy generation by 2025 to facilitate ongoing carbon emission reductions. To reach the goal, a large-scale solar auction is one of the most impactful initiatives among the four potential strategies taken by the government. To assist the Malaysian government’s large-scale solar policy as detailed in the national renewable energy roadmap, this article investigated the techno-economic and feasibility aspects of a 10 MW floating solar PV system at UMP Lake. The PVsyst 7.3 software was used to develop and compute energy production and loss estimation. The plant is anticipated to produce 17,960 MWh of energy annually at a levelized cost of energy of USD 0.052/kWh. The facility requires USD 8.94 million in capital costs that would be recovered within a payback period of 9.5 years from the date of operation. The plant is expected to reduce carbon emissions by 11,135.2 tons annually. The proposed facility would ensure optimal usage of UMP Lake and contribute to the Malaysian government’s efforts toward sustainable growth.

Electric vehicles (EVs) have received widespread attention in the automotive industry as the most promising solution for lowering CO2 emissions and mitigating worldwide environmental concerns. However, the effectiveness of EVs can be affected due to battery health degradation and performance deterioration with lifespan. Therefore, an advanced and smart battery management technology is essential for accurate state estimation, charge balancing, thermal management, and fault diagnosis in enhancing safety and reliability as well as optimizing an EV’s performance effectively. This paper presents an analytical and technical evaluation of the smart battery management system (BMS) in EVs. The analytical study is based on 110 highly influential articles using the Scopus database from the year 2010 to 2020. The analytical analysis evaluates vital indicators, including current research trends, keyword assessment, publishers, research categorization, country analysis, authorship, and collaboration. The technical assessment examines the key components and functions of BMS technology as well as state-of-the-art methods, algorithms, optimization, and control surgeries used in EVs. Furthermore, various key issues and challenges along with several essential guidelines and suggestions are delivered for future improvement. The analytical analysis can guide future researchers in enhancing the technologies of battery energy storage and management for EV applications toward achieving sustainable development goals.