4.6 Article

Multi-Objective Optimization of Kinetic Characteristics for the LBPRM-EHSPCS System

期刊

PROCESSES
卷 11, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/pr11092623

关键词

lithium-ion battery pole rolling mill (LBPRM); electro-hydraulic servo pump control system (EHSPCS); optimum design; NSGA-II; dynamic characteristics; efficiency characteristics; economic characteristics

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This paper proposes a multi-objective optimization model for the electro-hydraulic servo pump control system of the lithium-ion battery pole rolling mill. By comprehensively considering the dynamic, efficiency, and economic characteristics, the limitations in dynamic performance and large power loss of the EHSPCS are addressed. After optimization, the power loss is reduced by more than 7.2% and steady-state precision is greatly improved.
As the 'heart' of energy vehicles, the lithium-ion battery is in desperate need of precision improvement, green production, and cost reduction. To achieve this goal, the electro-hydraulic servo pump control system (EHSPCS) is applied to the lithium-ion battery pole rolling mill (LBPRM). However, this development can lead to limited dynamic performance and large power loss as a result of the EHSPCS unique volume direct-drive control mode. At present, how to solve this conflict has not been studied and how the EHSPCS component parameters influence the dynamic response, power loss, and economic performance is not clear. In this paper, a multi-objective optimization (MOO) model for the LBPRM-EHSPCS is proposed by comprehensively considering the dynamic, efficiency, and economic characteristics. Firstly, the evaluation model of the dynamic response, power loss, and cost is investigated. Then, the NSGA-II algorithm is introduced to address the Pareto front of the MOO model. Finally, the power loss and dynamic response of the LBPRM-EHSPCS before and after optimization are tested to validate the viability of the raised method. Results indicate that power loss is decreased by as much as 7.2% while steady-state precision is greatly improved after optimization. The proposed framework enhances the performance in lithium-ion battery manufacturing and can be applied to other kinds of hydraulic systems.

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