Global Optimization of he Hydraulic-Electromagnetic Energy-Harvesting Shock Absorber for Road Vehicles With Human-Knowledge-Integrated Particle Swarm Optimization Scheme

This article proposes a human-knowledge-integrated particle swarm optimization (Hi-PSO) scheme to globally optimize the design of the hydraulic-electromagnetic energy-harvesting shock absorber (HESA) for road vehicles. A newly developed k-fold swarm learning framework is the key to the Hi-PSO scheme, which runs k groups (folds) of individual local optimization (using a selected learning cycle), and validation (using the other k-1 testing cycles) with the concept of digital twin introduced into the design of the HESA. It aims to achieve the optimum energy recovery efficiency globally in both learning cycles and testing cycles. Within the learning framework, a nearest-neighborhood particle swarm learning algorithm is developed to incorporate human knowledge (e.g., ISO standards) for local optimization so that the computational load can be reduced through downsizing of the learning spaces. Experiments have been conducted to evaluate the energy recovery and damping performance under both local conditions (duty cycles used for learning) and global conditions (six duty cycles covering the main equivalent amplitudes and frequencies of the suspension's operation). Compared with the conventional PSO algorithm, Hi-PSO is shown to be more robust by achieving a 5.17% higher mean value in 10 trials while achieving the same maximum energy efficiency. The global optimum result is obtained under 20 mm/1.5 Hz condition and achieves an average energy efficiency of 59.07%.

Automated summary

This study proposes a human-knowledge-integrated particle swarm optimization scheme for optimizing the design of hydraulic-electromagnetic energy-harvesting shock absorbers for road vehicles. Experimental results show that the scheme achieved the optimal energy recovery efficiency under global conditions.