Next-Generation Electric Vehicles: Structural Battery Composites, AI-Driven Charging, and Cybersecurity

Abstract

Electric vehicles (EVs) are rapidly proliferating, driving innovation in batteries, charging, and security. This paper explores three key pillars for next-generation EVs. First, structural battery composites integrate carbon-fiber electrodes and a solid electrolyte into load-bearing parts, cutting pack weight. Recent work by Chaudhary et al. (Chalmers U., 2024) demonstrated a carbon-fiber structural battery with ~30 Wh/kg energy density and >70 GPa stiffness, retaining ~100% Coulombic efficiency over 1000 cycles. Such “massless” energy storage greatly improves system density. Second, AI-driven smart charging aligns EV load with grid needs. Managed charging shifts energy to low-price hours and reduces grid upgrades. NREL found substantial system benefits even at ~15% EV participation, and five-state modeling (NREL, 2024) showed distribution upgrade costs falling from ~$2.3B to $1.6B with managed charging. Machine learning, including graph neural networks (EV-GNN) and reinforcement learning (PPO, TD3), can predict traffic and plan charging to minimize wait times and bills. Third, cybersecurity resilience is critical. The EV charging ecosystem cars, chargers, cloud, and grid faces attacks from hardware flaws, insecure protocols, and side channels. Side-channel research (“Leaky Batteries”) shows battery power traces can reveal driver identity, routes, and occupancy with ~95% accuracy. Charging protocols are evolving: ISO 15118 Plug&Charge uses X.509 PKI for EV identity, and OCPP 2.0.1 adds formal device models and security events. Yet incidents persist: researchers have shown OCPP 1.6 can be hijacked (session disruption, code injection) and plugged-in chargers can be commandeered to destabilize grids.