EV driver profiling and habits prediction : Deep Learning based method to forecast near future usage and optimal battery preconditioning for lifetime extension

Electric vehicle battery aging is a complex process fundamentally influenced by environmental and operational conditions. Temperature and usage patterns play critical roles in determining battery longevity and performance. Specifically, battery degradation is closely linked to two primary factors: environmental conditions and operational parameters.

From an environmental perspective, temperature variations significantly impact battery chemistry side reactions and battery performance. Extreme temperatures accelerate chemical degradation processes, making thermal management a crucial consideration in battery design and usage. Operationally, the battery’s state of charge window and charging/discharging profiles directly influence degradation processes and long-term health.

The key scientific insight emerges from understanding how daily vehicle usage patterns directly correlate with battery aging mechanisms. The idea behind the project is that precise management of operational conditions can substantially reduce battery degradation. This involves carefully controlling the state of charge window, implementing intelligent charging profiles, and maintaining optimal temperature conditions during charging and operation.

The core research challenge lies in developing advanced predictive models that can forecast vehicle usage patterns and anticipate energy requirements. By leveraging machine learning algorithms and comprehensive data collection, we aim to transform battery maintenance into a proactive, data-driven optimization process. The methodological framework involves collecting detailed usage data, developing sophisticated predictive algorithms, implementing real-time battery condition monitoring, and dynamically optimizing charging and operational parameters.

The potential impact is significant: enhanced battery durability, improved electric vehicle economic efficiency, reduced maintenance costs, and advancement of sustainable transportation technology. By understanding and predicting the intricate relationship between vehicle usage and battery degradation, we target more reliable, long-lasting electric vehicle technologies.

The goal is not merely to extend battery life but to create an intelligent battery management system that adapts to individual usage patterns, maximizing performance and minimizing dramatically battery degradations and ageing, and consequently environmental and economic costs.