The adoption of electric vehicles (EVs) as part of Vehicle-to-Grid (V2G) networks introduces new challenges on how to make energy more sustainable and presents severe weaknesses to planned cyber-physical attacks and fraudulent transactions. To solve these problems, this paper offers the Ensemble Particle Swarm -Genetic Algorithm with Blockchain-Secured Energy Trading (EPGA-BET) framework. The core optimization engine combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) operators, augmented by a standard contraction–expansion Quantum-inspired PSO (QPSO) formulation to improve exploration capability without altering canonical update rules. Energy trading is supported through a permissioned blockchain architecture employing Practical Byzantine Fault Tolerance (PBFT) consensus and Merkle-tree verification to ensure transaction integrity and fault tolerance. An auxiliary reinforcement learning (RL) module performs anomaly-aware adaptation by adjusting optimization parameters in response to detected attack indicators. Importantly, optimization, trading security, and anomaly detection are architecturally decoupled yet interact through defined feedback channels, preventing subsystem over-dependence. Comparative experiments against established baseline strategies demonstrate statistically significant improvements in operational cost, resilience index, detection latency, and transaction success rate under attack scenarios. Ablation analysis further clarifies the contribution of each module, showing that the primary performance gains arise from the PSO–GA ensemble and adaptive RL mechanism, while blockchain enhances transactional trust without affecting optimization convergence. In general, this study has shown how a well-architectured combination of ensemble intelligence, evolution algorithms and blockchain technology can offer a strong, adaptive, and secure base of the next-generation V2G networks.
Lavanya et al. (Tue,) studied this question.