Extended-range hybrid electric vehicles (E-RHEVs) require optimized parking charging strategies that consider both charging time and battery health. Existing research often neglects the crucial impact of ambient temperature and long-term cycling on battery degradation. This study addresses this gap by developing a novel parking charging strategy for E-RHEVs that leverages a temperature-dependent battery aging model and a Multi-Objective Mantis Search Algorithm (MOMSA)—a metaheuristic optimization algorithm designed to solve multi-objective problems by efficiently exploring trade-offs between conflicting objectives. The MOMSA optimizes a five-stage State-of-Charge-based Multi-stage Constant Current (SMCC) charging profile—a dynamic current adjustment strategy that minimizes battery capacity degradation by dividing the charging process into sequential phases. The MOMSA-based SMCC strategy achieves an optimal balance between charging time and battery capacity degradation across a range of ambient temperatures (5 °C to 35 °C). Compared to a conventional 0.5C CC-CV charging strategy, the MOMSA-based SMCC strategy demonstrably reduces battery degradation with a moderate increase in charging time. Furthermore, the MOMSA-based charging strategy outperforms a Multi-Objective Particle Swarm Optimization (MOPSO)-based approach, achieving comparable degradation mitigation while significantly reducing charging time. One-week cycling simulations under realistic driving conditions further validate the MOMSA-based charging strategy’s superior long-term performance in delaying battery degradation across various temperatures. This strategy extends E-RHEV battery lifespan while maintaining operational efficiency.

To address the prominent fatigue failure risk of planetary gears in power-split hybrid buses and the lack of quantitative damage analysis across various operating modes in existing studies, this paper focuses on the front planetary gear set of a power-split hybrid bus. Based on a full-vehicle co-simulation model, loads under full operating conditions are decomposed into 11 operating modes, mode-switching loads are analyzed and extracted, and mode-decomposed and mode-switching fatigue loading spectra are compiled. Fatigue simulation is then conducted using Miner’s linear damage accumulation rule. Results show that the sun gear directly coupled to motor is the system’s most fatigue-susceptible component, exhibiting significant asymmetric unilateral tooth flank damage. The hybrid electric vehicle (HEV) mode contributes approximately 88% of total damage to the sun gear’s right flank, dominating system fatigue damage. Transient mode-switching conditions account for approximately 60% of total damage to the sun gear’s left flank, serving as the core damage source. Compared with the traditional full-condition merging method, the proposed mode-decomposed method improves the conservatism of life prediction. This work provides methodological support for refined strength design and targeted optimization of power-split hybrid transmission systems.