In recent years, lithium-ion batteries (LIBs) have gained attention and popularity due to their extended cycle life and high energy density. A hexagon-shaped 18,650 lithium-ion cylindrical cell battery pack was designed, incorporating paraffin wax (PA) as a phase change material (PCM) and nano-enhanced phase change material (Ne-PCM). However, the low thermal conductivity of the PCM causes a significant challenge to the development of electric vehicles (EVs). The highest temperature in the cylindrical cell battery pack is reached in the midregion, leading to an uneven temperature distribution across the cells. To overcome these challenges and achieve efficient battery module performance, phase change with nanomaterials such as graphene platelet nanopowder (GPN), multiwalled carbon nanotubes (MWCNTs), and graphite-synthetic powder (GSP) was placed in the center of four cells. Studies on the battery module were conducted without cooling, with PCM cooling, and with Ne-PCM cooling. The investigation revealed that the battery pack with Ne-PCM performed well, maintaining the temperature below 50 °C at different discharge rates of 1C, 2C, and 3C, and ensuring a uniform temperature variation within the cells. Ne-PCM decreased the temperature differential between the modules at 1C, 2C, and 3C discharge rates by 85.49%, 91.47%, and 84.21%, respectively, compared to PCM.

Electric vehicles (EVs) have grown in popularity in recent years due to their environmental friendliness and the potential scarcity of fossil fuels. Lithium-ion batteries (LIBs) are commonly utilized in EVs and hybrid electric vehicles (HEVs). They have a high specific charge, a high density of power, and a long life. A revolutionary design of a trapezoidal battery pack with a liquid cooling system based on composite phase change material (CPCM) is proposed in this research. The phase change material (PCM) is paraffin wax (PA), and the high thermal conductivity particles are graphite powder (GSP). CPCM is made in three different compositions and is filled in between cells with a 5 mm gap. Because PCM has a low thermal conductivity, it is filled with GSP, a high thermal conductive particle. The thermal conductivity is increased from 0.25 to 2.7 W/m K, which increases the heat transfer rate significantly. By adjusting different coolant flow velocities at varied discharge rates, the performance of the battery pack is examined. During the experiment, the discharge rates of 1 C, 2 C, and 3 C were used at a 28–30 °C ambient temperature. According to the findings, a trapezoidal battery pack based on CPCM exhibits a more efficient rate of heat transfer than a battery pack based on PCM. Moreover, BTMS with a liquid cooling system achieves consistent temperature distribution, with the maximum temperature remaining within the ideal range of below 45 °C under all test conditions.

Ocean Renewable Energy (ORE) systems—comprising wind, wave, tidal, and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels. However, their integration into the power grid is hindered by environmental sensitivity, dynamic ocean conditions, and high maintenance demands. Artificial Intelligence (AI) offers promising solutions to these challenges by enabling intelligent, adaptive, and resilient energy systems. This review explores AI applications in ORE, focusing on three critical domains: optimization, forecasting, and control. Optimization techniques, including Genetic Algorithms (GA) and Swarm Intelligence (SI), are employed to enhance device efficiency, improve energy capture, optimize farm layouts, reduce environmental impacts, and lower installation costs. Forecasting uses Machine Learning (ML) and Deep Learning (DL) models to predict wave height, tidal flow, and energy output, aiding in grid integration and energy scheduling. In control systems, AI approaches like Reinforcement Learning (RL) and Fuzzy Logic ensure real-time responsiveness and predictive maintenance, improving system reliability in dynamic marine environments. Emerging technologies such as Edge AI enable decentralized computation for real-time decision-making, while Digital Twin frameworks simulate and predict system performance before deployment. Explainable AI (XAI) is also discussed to ensure transparent and trustworthy decision-making. Ethical and regulatory concerns are acknowledged to ensure responsible AI integration in ocean settings. Overall this review offers a comprehensive synthesis of how AI enhances the performance, efficiency, and scalability of ORE systems. It serves as a valuable resource for researchers, policymakers, and industry professionals seeking to advance clean, smart, and sustainable ocean energy solutions.