Dynamic Pricing Model for Techno Economic Analysis of Battery Swapping Options as a Service

Battery as a Service (BaaS) offers a scalable alternative to plug-in charging, yet its adoption is hindered by economic uncertainties, unfair pricing structures, and limited integration of life cycle cost (LCC) principles. Existing models often ignore key aspects such as degradation rates, state of health (SOH), state of charge (SOC), residual resale value, and demand fluctuations, leading to inaccurate cost recovery and non-compliance with standards. Therefore, this work develops first dynamic phase-wise cost recovery model (DCRM) comprising pre-swap (procurement, logistics and testing), swap (age and usage-based User Behaviour Incentive Pricing (UBIP)), and post-swap (residual and second-life recovery) phases. Furthermore, Monte Carlo simulation (MCS) is applied to capture uncertainty in procurement cost, demand elasticity, degradation, and resale value. Case study results show that subsidies significantly enhance financial performance. At 60% subsidy, 2W and 3W fleets achieve ROI > 50% with payback of 1.6–1.8 years, while 4W fleets exceed 80% ROI with payback near 1.3 years. Customer–operator analysis reveals that pay per swap (PPS) pricing model is optimal for low-usage fleets (e.g., < 29 swaps/month for 2W), whereas subscription (SUB) benefits high-mileage fleets (e.g., > 36 swaps/month for 4W), while ensuring operator profitability above their own break-even points. The proposed model provides an overall techno-economic framework aligning LCC recovery with equitable BaaS pricing, bridging a critical gap in sustainable EV fleet adoption.