Modeling and simulation analysis of electric forklift energy prediction management. (September 2022)

• Record Type: Journal Article
• Authors: Cheng, Lili; Zhao, Dingxuan; Li, Tianyu; Wang, Yao
• Is Part Of: Energy reports. Volume 8(2022)Supplement 6
• Page Start: 353
• Page End: 365
• Publication Date: 2022-09
• Subjects: Electric forklift -- Hybrid energy storage -- SMPC -- Energy management; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.03.071
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: In recent years, the effective utilization of energy for electric forklift has been the focus of research. However, the load of forklift change markedly and frequently, leading to a great challenge in the performance of the composite energy system. To solve this problem, this paper presents the power system structure of electric forklift and the battery–supercapacitor hybrid energy management method of electric forklift truck. The working conditions and energy flow of electric forklift are analyzed and the power demand forecasting model of electric forklift power system is indispensable. Two predictive control based on Markov chain and neural network are developed, considering the performance of predictive control. The predictive model is applied to the stochastic model predictive (SMPC) energy management strategy. Simulations are carried out on the representative driving cycles of an electric forklift. As an auxiliary power supply on the energy management of composite power, the influence of the development depth of supercapacitor is discussed. Simulation results show that short-term Markov chain model is closer to the actual working condition. Fuzzy neural network model can give full play to the charging and discharging characteristics of supercapacitors. The state of charge of supercapacitor is stable between 0.5 and 1, which effectively stabilizes the rate of the charge and discharge of the battery.