An optimization model evaluating the simultaneous deployment of battery electric multiple units and charging installations indicates that rail systems achieve optimal trade-offs by targeting low electrification costs rather than focusing primarily on reducing train fleet expenses. In a case study examining a non-electrified mountain line, Pareto-optimal fleet annuities for battery trains remained tightly clustered between 1.27 and 1.31 million euros per year, while the corresponding electrification infrastructure annuities varied much more widely, ranging from 0.50 to 1.36 million euros per year.
The operational scope of the investigation addresses the expanding integration between the energy and transport sectors, where finding cost-efficient pathways to electrify rail networks has become a central challenge. Battery electric multiple units, known as BEMUs, provide an alternative to traditional, continuous overhead line electrification across entire track lengths. Nevertheless, operating these battery-powered trains presents practical challenges, as their operational driving range remains fundamentally constrained by the current capabilities of battery technology and the availability of wayside recharging infrastructure along the routes.
To overcome these limitations, the authors developed a mathematical optimization framework capable of simultaneously dimensioning both the stationary recharging infrastructure and the onboard traction batteries. Instead of sizing trains and fixed electrical installations in isolation, the computational model coordinates the two components by deriving a complete system configuration directly from operational timetable data and calculated BEMU power demand. This dual approach accounts for operational duty cycles, allowing planners to identify balanced configurations where power supply, charging intervals, and energy storage complement one another across the transit network.
The system model incorporates an extensive array of route-specific and physical parameters to ensure realistic engineering outcomes. Key modeled variables include the route topology, scheduled timetable constraints, the geographic placement and operational capabilities of charging infrastructure, and the chemical aging behavior of the traction batteries over time. In addition, the modeling framework accounts for passenger comfort loads through heating, ventilation, and air conditioning demand, while also factoring in end-of-life battery capacity thresholds, discrete power ratings of installed charging stations, and periodic battery replacement cycles throughout the operational lifespan of the rolling stock.
The investigators evaluated the flexibility and general applicability of their optimization model using two distinct rail operating environments. The first scenario focused on a non-electrified mountain railway line evaluated under two alternative combinations of optimization targets. The second operational scenario assessed a long-distance rail corridor characterized by partial electrification. Applying the framework to these divergent operational contexts demonstrated that the model can be tailored to contrasting operating profiles, topographic challenges, and infrastructure baselines across different rail network optimization assignments.
System configurations along the Pareto front were evaluated by mapping conflicting stakeholder annuities, contrasting the financial commitments of BEMU fleet procurement and maintenance against fixed line electrification expenditures. Furthermore, the model analyzed the relationship between total stakeholder annuities and the minimal capacity buffer maintained in the traction batteries. The findings revealed that a substantial gain in battery resilience can be achieved for a modest financial outlay: expanding the traction battery energy buffer by 37.2 percent required an increase in total annuity of only 1.5 percent. This outcome illustrates that operational safety margins can be strengthened without inducing substantial cost escalation across the combined system.
To evaluate how underlying modeling assumptions influence system performance, the authors conducted a sensitivity analysis targeting the governing parameters that define the Pareto front. The evaluation demonstrated observable shifts in the optimal trade-off curves as these sensitive parameters changed, confirming that local infrastructure limitations, cost structures, and technical parameters alter the optimal balance between battery capacity and wayside charging installations.
Subject of Research: Optimization of battery electric multiple unit traction batteries and recharging infrastructure
Article Title: Pareto optimization of BEMU traction battery capacity and recharging infrastructure
Article References: Arens, S., Streuling, C., Schenker, M., Schork, L., Selbig, J., Dushina, A., & Klement, P. (2026). Pareto optimization of BEMU traction battery capacity and recharging infrastructure. Energy Informatics. https://doi.org/10.1186/s42162-026-00709-7
Image Credits: AI Generated
DOI: 10.1186/s42162-026-00709-7
Keywords: battery electric multiple units, BEMUs, traction batteries, recharging infrastructure, Pareto optimization, rail network electrification, route topology, timetable data, battery aging, heating ventilation and air conditioning, HVAC demand, end-of-life capacity, discrete charging power, battery replacement, stakeholder annuities, capacity buffer, mountain line case study, partial electrification
News Source: Faith Mcneil. (November 14, 2026). Battery Train Modeling Emphasizes Lower Electrification Costs Over Traction Fleet Annuities. Scienmag.
