Enhancing E-Bike Efficiency with Intelligent Battery Temperature Control

Abstract

This work presents an innovative approach to battery thermal management for e-bikes by addressing heat generation at its source rather than relying on conventional cooling techniques. Traditional systems rely on heat sinks, fans, phase change materials, or cooling fluids, which increase cost and complexity. In contrast, this study integrates embedded thermal management algorithms into the e-bike’s motor controller, enabling temperature regulation through performance limitation. Two models are investigated: a reactive algorithm that reduces speed as battery temperature nears a critical threshold, and a predictive algorithm that forecasts future temperature evolution and adjusts speed accordingly. Experimental results show that the reactive algorithm successfully limited battery temperature to 26.7% below the critical value but at the cost of speed reductions up to 40%. The predictive model, tested in two configurations, demonstrated improved performance, limiting speed by a maximum of 20% while maintaining stable temperature profiles. These findings confirm that embedded algorithms can effectively manage battery temperature, with the reactive model being suitable for low-complexity applications and the predictive model offering enhanced performance when more computational resources are available.