Single and multi-objective optimization for the performance enhancement of lead-acid battery cell

Abstract

SUMMARYElectric energy storage systems are used considerably in industries and daily applications. The demand for batteries with high energy content has increased because of their use in hybrid vehicles. Lead-acid batteries have wide applications because of their advantages such as high safety factor and low cost of production. The major shortcoming of lead-acid batteries is low energy content and high dimension and weight. Nowadays, a common method to increase the energy content of lead-acid battery is the experimental method with trial and error, which is time consuming and expensive. In this paper, non-isothermal one-dimensional numerical simulation of lead-acid battery with finite volume method is performed. In addition, a cell with higher energy content and lower thickness is designed by using particle swarm optimization algorithm based on developed simulation code. The results of single objective optimization show that an optimal battery that has 27.6% higher energy can be made with the same cell dimension. The results also show that an optimum cell battery can be obtained with a decrease of 24% in thickness while keeping the energy the same. Moreover, a multi-objective optimization algorithm is utilized to find Pareto optimal solutions while considering the energy content and thickness objectives simultaneously.

Experimental investigation of the effect of C‐rate, electrode gaps, and electrode surface roughness on the performance characterization of lead‐acid batteries

“…As mentioned earlier, when the C-rate of FLAB increases, FLAB capacity decreases drastically the TR rate increases due to the thermo-electrochemical process so that the FLAB can suffer from thermal runaway (TRA) and it may damage the FLAB. [5][6][7] According to previous investigations, [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] it seems that by balancing the FLAB design parameters (such as C-rate, electrode gaps [electrolyte reservoir volume], and electrode surface roughness) can affect on the capacity and TR rate of FLAB and improve their performance characterization. Therefore, in this experimental study, parameters of average roughness wavelength of the electrode surfaces (λa) (roughness quality of electrode surfaces), the gap between the electrodes and C-rate according to Table 1 are considered as variables, and their effect on the capacity and TR rate of FLAB cells are considered as the response.…”

“…According to the authors' knowledge, there are different methods to DOE for both modeling and optimizing the complex processes in the technologies related to batteries. 11,29,30 Nowadays, among these methods, RSM as a combination of mathematical and statistical techniques is commonly used to reach the relationship between a set of controllable input experimental variables and some output variables (responses) of observed results. The main advantage of RSM compared to other methods is a minimum number of experiments to analyze and optimize the response of particular issues, which is influenced by several independent variables.…”

Reduced‐order modeling of lead‐acid battery using cluster analysis and orthogonal cluster analysis method

“…They showed that increasing the cell temperature improves the cell voltage wherein the effect on the concentration distribution is negligible. Pourmirzaagha et al employs electrochemical modeling to optimize the performance of Gu's cell such that the modified cell encompasses higher energy content and lower thickness. Nazghelichi et al studied the effect of electrodes' active area on Lewis number and thermal response of a LAB using non‐dimensional governing equations.…”

Multiobjective optimization for exergoeconomic analysis of an integrated cogeneration system

“…Bélanger and Gosselin obtained the effect of variables on the objective function and the potential relationship between different objectives through multiobjective optimization. Pourmirzaagha et al designed a new type of battery cell using the particle swarm optimization, and both single‐objective and multiobjective optimization methods were used to find the optimal battery thickness and energy. Mert et al used an improved genetic algorithm to analyze three important performance indicators of fuel cells: power, energy, and exergy.…”