Thermodynamic optimization of Urban EV charging networks: A chaos-enhanced memetic approach via spatiotemporal demand potential fields

The rapid electrification of urban transportation constitutes a significant phase transition in modern energy systems, yet it faces a critical bottleneck: the optimal deployment of charging infrastructure. This facility location problem features high-dimensional, non-convex landscapes and stochastic demand, rendering deterministic approaches inadequate. Existing planning methods relying on precise time-series forecasting suffer from error propagation and premature convergence in complex urban topologies. This study proposes a novel Demand Modeling framework that estimates the relative “demand potential” of Traffic Analysis Zones (TAZs) by integrating historical trajectories with spatial semantic features to identify intrinsic demand attractor. To solve the resulting location-allocation problem, we introduce the Chaos-Enhanced Adaptive Memetic Algorithm (CE-AMA). This method synthesizes evolutionary global search, ergodic exploration via a hybrid Logistic-Tent chaotic map, and memetic hill-climbing local exploitation. Theoretical analysis maps the optimization objective to a system Hamiltonian, showing that chaotic perturbations couple with thermodynamic temperature to induce a “reheating” effect, allowing the system to escape metastable glassy states. Validated against the Shenzhen UrbanEV dataset under a strict 14,400-evaluation budget, CE-AMA achieves a superior scalarized optimum of 0.2226, outperforming Simulated Annealing (0.3541), Particle Swarm Optimization (0.5169) and Genetic Algorithms (0.2804). The optimized configuration delivers 62.1% coverage and an 86.9% service level with robust 94.4% cost efficiency. These results demonstrate that introducing mathematically grounded controlled disorder via chaos theory effectively navigates the rugged energy landscape of urban infrastructure planning, minimizing system configurational entropy.