access icon free Modelling of supercapacitors based on SVM and PSO algorithms

Supercapacitors present an attractive energy storage alternative for high-performance applications due to their compact size and high-power density. Therefore, the supercapacitors have broad prospects for the development in the field of electric vehicles and renewable energy. To describe the output characteristics of the supercapacitors with high accuracy for the simulation research and practical application, a dynamic modelling method is proposed for the supercapacitors based on support vector machine (SVM) and particle swarm optimisation (PSO) algorithm. In this study, the SVM is used to predict the output voltage of the supercapacitors with the key parameters (temperature, current and initial voltage). The PSO algorithm is adopted to optimise the parameters of the SVM to improve the performance of the dynamic modelling. An experimental platform is established, where an electric machine drive system powered by the supercapacitors is controlled to operate at frequent acceleration and deceleration modes, thus leading to the frequent charging and discharging of the supercapacitors. The experimental data is collected to validate the effectiveness of the proposed method. The results show that the proposed method can effectively predict the output voltage of the supercapacitors.

Inspec keywords: power engineering computing; machine control; supercapacitors; support vector machines; electric drives; particle swarm optimisation

Other keywords: parameter optimization; frequent mode; output voltage prediction; initial voltage; current; frequent supercapacitor charging; dynamic modelling method; energy storage; temperature; support vector machine algorithm; SVM algorithm; particle swarm optimisation algorithm; supercapacitors modelling; electric machine drive system; frequent acceleration mode; PSO algorithm; frequent supercapacitor discharging

Subjects: Control of electric power systems; Knowledge engineering techniques; a.c. machines; Other energy storage; Optimisation techniques; Control engineering computing; Optimisation techniques; d.c. machines; Power engineering computing; Drives

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