Energy-saving spring energy storage drive system optimized by a hybrid genetic algorithm

Abstract The growing demand for energy efficiency in industrial applications has led to increasing interest in mechanical energy storage systems that can recover and reuse energy. This study proposes a spring energy storage drive system optimized through a hybrid genetic algorithm and local nonlinear programming to improve energy conversion efficiency and recovery performance. Experimental results demonstrate that the optimized system reduces energy consumption by 13.6% compared to conventional motor-driven systems, with energy conversion efficiency improving from 75.5% to 88.1%. The proposed system offers significant potential for applications in industries requiring linear reciprocating motion, providing an effective solution for reducing energy costs and improving operational efficiency.