Multi-Objective Optimal Design of Lithium-Ion Battery Cells

Lithium-Ion battery;Multi-objective optimization;Pareto front;Optimal design;Genetic algorithm;Industrial and Operations Engineering;Industrial and Systems Engineering, College of Engineering and Computer Science

Hong, YaoLiu, Yung-wen ;

University of Michigan

关键词: Lithium-Ion battery;    Multi-objective optimization;    Pareto front;    Optimal design;    Genetic algorithm;    Industrial and Operations Engineering;    Industrial and Systems Engineering, College of Engineering and Computer Science;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/136070/Multi-Objective%20Optimization%20Problems%20for%20Lithium-Ion%20Battery%20Designs.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Lithium ion battery has been considered as a promising candidate to improve the currentfossil fuels based energy economy. Massive efforts have been put in the optimal design oflithium ion batteries with the assistance of simulation models. But to our knowledge, theapplication of multi-objective optimization in this process has not been well discussed. Thepurpose of this thesis is to study the multi-objective optimization problems that could be applied on the optimal design of lithium ion batteries with the assistance of simulation models. A two-objective problem is firstly constructed with the performance measures of energy per unit separator area for the discharge rate of 0.5C and the mass per unit separator area. The reaction zone model and genetic algorithm are employed to solve this problem qualitatively. The resulted Pareto front comes out to be a concave curve in the 2D plane of the two performance measures. Three case studies are guided to illustrate the advantages and applications of employing the multi-objective optimization in the design process.A DAE based simulation model is then employed and tuned to have a satisfying fit to thecharge and discharge curves for the cycling rates up to 4C. With the assistance of this precise simulation model, the properties of the Pareto front of the two-objective optimization is then validated quantitatively.A three-objective optimization problem with the objectives of energy performance of0.25C and 4C discharge and mass performance is then constructed to extend the analysis ofapplications of multi-objective oriented studies in lithium-ion battery designs. The problem isquantitatively resolved with the assistance of the DAE based simulation model and genetic x algorithm. The Pareto front comes out to be a curved surface in the 3D space of the threeobjectives. The properties of the Pareto front are expected to offer perspectives and references to product designs in the industry.