The internal combustion engine-based transportation system is causing severe problems such as rising levels of pollution, rising petroleum prices, and the depletion of natural resources. To divide power between the engine and the battery in an effective manner, a sophisticated energy management system is required to be put into place. A power split strategy that is efficient may result in higher fuel economy and performance of Electric Vehicles (EVs). In this paper, we propose the reinforcement learning method using Deep Q learning (DQL), which is a novel Improved Swarm optimized Deep Reinforcement Learning Algorithm (IS-DRLA) designed for energy management control. To perform an update on the weights of the neural network, this method computes the use of a modified version of the swarm optimization technique. After that, the suggested IS-DRLA system goes through training and verification using high-precision realistic driving conditions, after which it is contrasted with the standard approach. The performance indices such as State of Charge (SOC) and fuel consumption and loss function are analyzed for the efficiency of the proposed method (IS-DRLA). According to the findings, the newly proposed IS-DRLA is capable of achieving a higher training pace with a lower overall fuel consumption than the conventional policy, and its fuel economy comes very close to matching that of the worldwide optimal. In addition to this, the adaptability of the suggested strategy is demonstrated by utilizing a different driving schedule.
The rapid development of capacitive materials can be attributed to the introduction of novel approaches to the design and production of energy storage materials. In this context, multiple grapheme-based spinal metal oxide nanoparticles display a significant capacitive potential. In addition, graphene nanocomposites that contain electron-donating inclusions boost the electronic importance of the chemicals that are supported. By utilizing the co-precipitation method, copper chromite nanoparticles implanted on graphene oxide (CuCr2O4/GO) were manufactured to produce a material that is capable of serving as an efficient energy storage medium. The production of CuCr2O4 was accomplished via the use of a basic sol-gel method, whereas the production of GO was accomplished through the use of a modified version of Hummer's strategy. For this purpose of determining the X-ray diffraction analysis was performed, and energy-dispersive spectroscopy and electrochemical analysis were utilized to determine chemical weight composition. The nano-composite, in its as-made state, is suitable for touchsensitive energy storage, as evidenced by the fact that the highest capacitance of 370.5 Fg1 that could be measured matched to an aqueous electrolyte of 0.1 M H2SO4; this finding supports the hypothesis that the nanocomposite was designed specifically for this purpose. As a result, the CuCr2O4/GO material, in the form in which it has been developed, has the potential to be an effective capacitive material for applications involving energy storage.
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