Integrating ML and Material Characterization for Smart Electric Vehicle Battery Management in Urban Networks
Keywords:
Material Characterization, Machine Learning, Smart Battery Management System, Electric Vehicles, Microstructure–Property Relationships, Submicron Imaging, Nanoscale Diagnostics, Spectroscopy, Voltammetry, Predictive Feature Engineering, Remaining Life Prediction, Battery Health Monitoring, Kalman-Based Uncertainty Quantification, Vehicle-to-Grid Interaction, Supervisory Control, Demand Response, Peak Shaving, Microgrid Operations, Data Fusion, Urban EV Fleet ManagementAbstract
Advances in material characterization (MC) and machine learning (ML) are synthesized to generate a wide spectrum of data that supports a smart battery management system (BMS) for electric vehicles in an urban environment. Data-driven representations of how microstructure influences material properties—from battery health assessment to remaining life prediction and control—are established over a set of case studies. MC techniques that measure or image local structure on the submicron to nanoscale and electrochemical-metallic response test techniques such as spectroscopy or voltammetry are preferred; these image and measure damage associated with local particle/form interaction or route MC to succumb to first-order process closure. Data returned by MC or by standard diagnostics is processed to create additional predictive features and fed to various ML algorithms according to application. The methods are then integrated into the BMS for an electric vehicle network in an urban environment, joined with vehicle-to-grid interaction and network-level supervisory control for demand response and peak shaving in partnership with building-level loads, and subjected to a series of test scenarios.
The gathered results serve first to evaluate the BMS in an urban operating context, capturing different distribution patterns in a microgrid case and the interplay between deterioration, scheduling, and fleet-level operations. Included also is the implementation of a proof-of-concept data pipeline for data-driven remaining-life assessment with associated uncertainty quantification; a Kalman-centered structure is pursued for this purpose to facilitate uncertainty representation and to allow direct performance benchmarking against standard methods through response time for integration into short-tscale decision support. The findings are then summarized, remaining open questions identified, and future work endorsed that focuses on enhancing capability for large-scale application through data fusion or evident return for policy direction.
