Cargo E-Bike Robust Speed Control Using an MPC Battery Thermal Lumped Model Approach

Abstract

Cargo e-bikes are expected to convey heavy loads in all aspects of daily life. Also, these vehicles are expected to maintain a consistent speed to meet mobility needs while optimizing the battery design. In this paper, a control model is developed to improve rotational speed motor control via the battery model predictive controller (MPC) thermal model designed based on experimental field test data. Experimental field tests are performed to provide the relation between battery surface and ambient temperatures in different road types and weight conditions. For this purpose, in different slope ranges, the pedal load/activity and voltage-current data are logged to use as experimental input in an MPC-integrated 1D model. To obtain the desired thermal conditions in the Li-Ion battery, the MPC battery thermal model is defined based on the thermal lumped model approach. In the next step, the generated MPC model is used as a function for longitudinal speed control in the MPC motor torque control model subjected to uncertain road disturbances. Then, the outputs of the control models are compared using the MPC parameters oc weight factors and prediction horizon. Thus, the speed control model for cargo e-bikes is presented with increased robustness using the MPC battery thermal lumped model approach considering energy and Li-Ion battery life-cycle efficiency methods regardless of driving performance needs.