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Multi-objective optimization of PCM-fin structure for staggered Li-ion battery packs

Qiu Chenghui, Wu Chongtian, Yuan Xiaolu, Wu Linxu, Yang Jiaming, Shi Hong

Bulletin of the Polish Academy of Sciences. Technical Sciences

2023

Vol. 71, nr 4

art. no. e145677

Endurance capability is a key indicator to evaluate the performance of electric vehicles. Improving the energy density of battery packs in a limited space while ensuring the safety of the vehicle is one of the currently used technological solutions. Accordingly, a small space and high energy density battery arrangement scheme is proposed in this paper. The comprehensive performance of two battery packs based on the same volume and different space arrangements is compared. Further, based on the same thermal management system (PCM-fin system), the thermal performance of staggered battery packs with high energy density is numerically simulated with different fin structures, and the optimal fin structure parameters for staggered battery packs at a 3C discharge rate are determined using the entropy weight-TOPSIS method. The result reveals that increasing the contact thickness between the fin and the battery (X) can reduce the maximum temperature, but weaken temperature homogeneity. Moreover, the change of fin width (A) has no significant effect on the heat dissipation performance of the battery pack. Entropy weight-TOPSIS method objectively assigns weights to both maximum temperature (Tmax) and temperature difference (DT) and determines the optimal solution for the cooling system fin parameters. It is found that when X = 0:67 mm, A = 0:6 mm, the staggered battery pack holds the best comprehensive performance.

A novel method for islanding in active distribution network considering distributed generation

Wang Jun, Wang Xinran, Di Bart, Sun Chao, Zheng Wei

Journal of Power Technologies

2021

Vol. 101, nr 1

11--20

The output of distributed generation (DG) has strong randomness, and its randomness has a great inuence on the division of islands. To simulate the impact of DG output on island division when dividing islands, this study proposed an island division method that considers the randomness of DG output. The basic idea of this method is as follows. First, Monte Carlo sampling was used to obtain the output power of DG under different confidence levels to simulate the randomness of DG output. Furthermore, a multi-objective and multi-constraint considering the randomness of DG output were established. The niche genetic algorithm was used to solve the model, and the effectiveness of the proposed model and algorithm was verified through the analysis of examples. The results show that the risk reserve power introduced by simulating the randomness of DG output is inversely proportional to the confidence level. The minimum value of the system node voltage level after islanding is 0.9495 pu, which meets the requirements of the constraint. Under the same conditions, compared with the island division method of not considering the random DG, the method proposed in this study not only has a larger total load recovery and a higher priority load recovery rate but also has a higher DG utilization rate, which can meet the needs of practical applications. This study provides a certain reference for the establishment and solution method of the islanding model of the distribution network with DG.