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The thermal management system of a Lithium-ion battery consisting of phasechange material (PCM) is considered in this study. The batteries are covered by a large volume of PCM. A 18650 battery is used in this numerical investigation as a case study and all achievements are implemented on it. The battery thermal management system (BTMS) parameters during discharge-restcharge-rest cycles are concerned to understand PCM behavior and battery resilience in the long run. The battery is placed adjacent to various PCMs and is charged and discharged at different rates. Results show that the paraffin wax has the worst performance, and hydrate salt and capric acid have the best performance. Furthermore, the use of polyethylene glycol as PCM improves the performance of the BTMS. Meanwhile, the composite of nano-graphite and PCM would increase a reasonable effect on the BTMS functionality.

Summary Latent heat thermal energy storage (LHTES) problems include a lot of boundary conditions that could not be solved by exact solution, so new approaches to solving such problems could revolutionize the advanced energy storage devices. This paper focuses on reformulating the generalized differential quadrature method (GDQM) for a one‐dimensional solidification/melting Stefan problem as a fundamental LHTES problem and solves some practical cases. Convergence and comparisons demonstrate that the proposed approach is sufficiently reliable. By checking the accuracy of the proposed approach for the LHTES problem (where Stefan number is below 0.2), it was demonstrated that for all Stefan numbers, the maximum error is less than 3.81% for temperatures. As the usual range of thermal energy storages, for Stefan numbers up to 0.2 the solution yields errors less than 0.2%. Then, the proposed approach is very ideal for such applications. In comparison, GDQM has a more accurate response than an integral solution for Stefan numbers less than 0.2. When this priority of GDQM comes with its low computational cost, it would undoubtedly be preferable.

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Problems with latent heat thermal storage (LHTS) often contain several boundary conditions that an exact solution cannot solve. Therefore, novel methods to tackle such issues could fundamentally change the design of innovative energy storage systems. This study concentrates on the reformulation of the generalized differential quadrature method (GDQM) for the two-region freezing/melting Stefan problem as an essential LHTS challenge. Comparison and convergence show that there is sufficient confidence in the proposed approach. By monitoring the precision of the suggested approach for the LHTS problem, it was indicated that this method's error depends on Stefan's number. The maximum error of all Stefan numbers up to 0.3 is less than 6%. For such applications in a standard array of LHTS (Stefan numbers between 0 and 0.2), the proposed method is appropriate as it predicts the answers with a maximum of 4.2% error. In comparison to the heat capacity method, GDQM delivers a more precise result at higher processing times. Additionally, this GDQM priority is accompanied by a low computational cost, which is unquestionably superior.