Batteries with high energy density are packed into compact groups to solve the range anxiety of new-energy vehicles, which brings greater workload and insecurity, risking thermal runaway in harsh conditions. To improve the battery thermal performance under high ambient temperature and discharge rate, a battery thermal management system (BTMS) based on honeycomb-structured liquid cooling and phase change materials (PCM) is innovatively proposed. In this paper, the thermal characteristics of INR18650/25P battery are studied theoretically and experimentally. Moreover, the influence of structure, material and operating parameters are studied based on verifying the simplified BTMS model. The results show that the counterflow, honeycomb structure of six cooling tubes and fins, 12% expanded graphite mass fraction and 25 mm battery spacing give a better battery thermal performance with high group efficiency. The maximum temperature and temperature difference in the battery in the optimal BTMS are 45.71 °C and 4.4 °C at the 40 °C environment/coolant, as against 30.4 °C and 4.97 °C at the 23.6 °C environment/coolant, respectively. Precooling the coolant can further reduce the maximum battery temperature in high temperature environments, and the precooling temperature difference within 5 °C could meet the uniformity requirements. Furthermore, this study can provide guidance for the design and optimization of BTMS under harsh conditions.
In recent years, with the progress of computer technology, artificial intelligence has been rapidly developed and begun to be applied in industry, economy and other aspects. Besides, with the pursuit of green hydrogen, hydrogen-based hybrid renewable energy systems have become the focus of the development of the hydrogen industry. This paper compares different artificial intelligence applications in hydrogen-based hybrid renewable energy systems and carries out a case study in a typical area. Firstly, this paper summarizes important works in literature, which use artificial intelligence methods to predict the supply chain of the renewable energy system, including the prediction of renewable energy system resources, output power, load demand and terminal electricity price. Secondly, main articles about artificial intelligence optimization algorithms used in renewable energy systems are also summarized, including swarm and non-swarm biological heuristics, physical or chemical heuristics and hybrid optimization algorithms. Finally, a case study is carried out in Tikanlik, Xinjiang, China. Tikanlik’s weather and load data train the artificial neural network to predict system output power. It shows that 99.32% of the relative error of the test set is less than 3%, which proves that this model can achieve good prediction results.
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