Diesel-electric hybrid trains (DEHT) equipped with battery systems can effectively reduce fuel consumption and greenhouse gas emissions. For chasing the best fuel consumption efficiency, the coupled optimization problem of driving strategy and energy management for DEHT is addressed in this paper. We propose a bi-level optimization method that jointly designs driving strategy and energy management for DEHT. The optimal driving strategy is obtained by modeling a particle swarm optimization problem in fuel consumption reduction at the top level. The power allocation for the diesel generator set and the battery is described as a convex optimization problem and solved at the bottom level. The proposed method can converge to an optimal value after cyclic iterations. In each iteration, the top-level outputs the current optimal driving strategy to the bottom problem. Based on this current optimal driving strategy, the bottom level calculates the current best fuel consumption and energy management, then feeds back to the top level to start a new iteration. To verify our introduced method, the proposed optimization is compared with the common problems, such as single energy management optimization, single-speed optimization, and sequence optimization. Results show that the maximum and minimum fuel consumption reduction by the introduced optimization method is 20.0% and 2.7%, respectively.

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.

Increment Capacity Analysis method has been widely concerned by experts and scholars because it can analyze the battery chemical reaction without destroying the physical structure of the battery. However the inevitable noise on the capacity increment curve brings huge damage to the analysis of the battery reaction process. In this paper, the influence of different voltage intervals on the ICA curve is analyzed firstly, then a certain voltage interval is selected, and the capacity increment curve is filtered by Gaussian process regression and wavelet transform. Compared with the original unfiltered ICA curve, the proposed method is verified feasible.

The sizing optimization for wayside energy storage systems (ESS) is considered an effective way to reduce energy consumption in the metro traction power supply system and improve the utilization rate of regenerative braking energy (RBE). However, existing research primarily focuses on the bilevel optimization approach of capacity sizing and energy management, or convex programming simplified by dividing the system into separate power supply zones. These algorithms neglect the complex coupling relationships between subsystems, thereby compromising model fidelity and overall optimality. Therefore, this paper formulates an optimization of ESS sizing to minimize the system’s lifecycle cost, which considers the spatiotemporal coupling characteristics between the train system, traction power supply system, and ESS, transforming the energy management problem into an optimal power flow calculation problem. Then, by introducing convex relaxation and linearization, we propose a second-order cone programming (SOCP) to reformulate the original non-convex problem into a convex problem that can be solved quickly. Case studies comparing multiple methods validate the effectiveness of the proposed method. The results demonstrate that the proposed method reduces the lifecycle cost by 27.28% compared to the unoptimized method, and by 4.47% relative to the traditional rule-based bi-level optimization method. Moreover, the computation time is reduced from ”hours” to ”seconds”, which significantly improves computational efficiency.