Abstract This research paper develops a co-optimization framework using dynamic programming to optimize MESS by integrating sector coupling between the transportation and electricity networks. To capture market uncertainties, the framework incorporates traffic-aware, terrain-sensitive routing, empirical battery degradation models, and LMP. The framework enhances MESS performance across various scenarios by co-optimizing travel routes, traffic constraints, battery degradation, and charging costs. Comparative analyses establish cost trade-offs and scalability implications. Results demonstrate that cost-optimized routing reduces degradation costs while maintaining efficiency. The proposed approach improves MESS sustainability and economic viability in sector-coupled electricity markets.