Life-Cycle Powertrain Sizing of Diesel–Battery Hybrid Trains via Configuration-Specific Joint Optimization of Driving Strategy and Energy Management

Conventional powertrain sizing of diesel–battery hybrid trains evaluates candidate component sizes on a common prescribed driving cycle, although each design changes the fuel-efficient speed trajectory and power split. This study develops a configuration-specific bilevel framework in which every candidate diesel-generator–battery design is evaluated after jointly optimizing its driving strategy and energy management over the multi-station route. At the upper level, particle swarm optimization selects the diesel generator set (DGS) maximum output power and battery series and parallel cell numbers to minimize life-cycle cost, including acquisition, fuel, maintenance, and battery replacement costs. At the lower level, joint optimization of driving strategy and energy management is reformulated as a second-order cone program that minimizes fuel consumption under timetable, dynamics, power, and battery-energy constraints. Lithium titanate (LTO), lithium iron phosphate (LFP), and nickel manganese cobalt oxide (NMC) batteries are evaluated under identical route, timetable, and economic conditions. For the 320 km, 11-station case, the best-found designs pair 700, 1500, and 1900 kW DGSs with 316.0, 391.3, and 336.0 kWh batteries, respectively. The best-found LTO design achieves the lowest life-cycle cost, CNY 61.087 million, which is 7.66% and 9.26% lower than those of the best-found LFP and NMC designs. Compared with a sizing method using a fixed driving strategy, the proposed framework reduces life-cycle cost by 17.00–18.39% and changes the selected component sizes for all three chemistries. These results show that configuration-specific operation affects both the sizing decision and the economic comparison.