A deep learning approach for optimize dynamic and required power in electric assisted bicycle under a structure and operating parameters

Abstract

The purpose of this research is to study how the operating and structure parameter affect the dynamic, require power and electric consumption of electric assisted bicycle (EAB). The paper applied a combined an artificial neural network (ANN) with genetic algorithm (GA) method to predict the required power, electric consumption of EAB and find an effective performance area. The MATLAB-Simulink simulation model is established to create 1000 data point, that is applied in an ANN for training, testing and verifying. The ANN-GA method is applied to identify the optimized required power and electric consumption under four typical slope grades in the area of (0-0.65%), four typical wheel radius in the area of (0.3-0.0.39 m), four typical frontal areas in the range of (0.423-1.323 m2), four typical speed levels from speed level 1 to speed level 4. After the ANN is trained, it is applied into the genetic algorithm to identify the effective performance. The prediction results reflect the major physical mechanism that governs ES performance and are validated against the MATLAB-Simulink simulation model. Additionally, the electric assisted bicycle can reach an effective performance area at 27.16 km/h with the optimized required power of 545.5 W at slope grade of 0%, wheel radius of 0.39 m, speed level 4, frontal area of 0.423 m2. The results show that the ANN-GA method is suitable to identifying structure and operating parameters including various slope grades, frontal areas, wheel radius, speed level for optimizing effective performance of EAB, which contributes a helpful method for EAB design and control. Beside that, the experimental test was conducted on real road test at Taehwa river to verify the simulated results. The experiment results and simulation results have the same trend at the same conditions. (The short version of the paper was presented at ICAE2022, Bochum, Germany, Aug 8-11, 2022. This paper is a substantial extension of the short version of the conference paper.)