Effective energy management techniques are essential for the full utilization of energy in the field of extended-range electric vehicle research, with the goals of lowering energy consumption and exhaust emissions, enhancing driving comfort, and extending battery life. To achieve optimal comprehensive usage costs, this article uses bargaining game theory to design an adaptive energy management strategy (EMS_ad-bg) that focuses on battery life. In the study, a power system model was first built based on AVL/Cruise software and MATLAB/Simulink software. The impact of discount factors on strategy results was analyzed through simulation experiments. The results showed that the discount factor for auxiliary power unit (APU) focused more on energy optimization, while the discount factor for battery focused more on optimizing the degradation of battery life. When the initial state of charge (SoC) is high, the specific value of the discount factor also has a significant impact on the battery SoC value at the end of the trip. To improve the strategy’s adaptability to various initial SoC values, a fuzzy controller was created that can adaptively modify the discount factor based on the battery SoC. The results of the simulation experiment demonstrate that the bargaining game strategy taking SoC into account has more pronounced advantages in terms of overall usage cost when compared to the strategy of the fixed discount factor. The creation of an EMS_ad-bg that takes battery life into account based on a bargaining game can serve as a helpful model for the creation of a clever EMS that lowers the total cost of operating a vehicle.

To address energy management challenges for intelligent connected automated range-extended electric vehicles under vehicle-road cooperative environments, a hierarchical energy management strategy (EMS) based on speed prediction is proposed from the perspective of multi-objective optimization (MOO), with comprehensive system performance being significantly enhanced. Focusing on connected car-following scenarios, acceleration sequence prediction is performed based on Kalman filtering and preceding vehicle acceleration. A dual-layer optimization strategy is subsequently developed: in the upper layer, optimal speed curves are planned based on road network topology and preceding vehicle trajectories, while in the lower layer, coordinated multi-power source allocation is achieved through EMS_MPC-P, a Bayesian-optimized model predictive EMS based on Pontryagin’ s minimum principle (PMP). A MOO model is ultimately formulated to enhance comprehensive system performance. Simulation and bench test results demonstrate that with SoC₀ = 0.4, 7.69% and 5.13% improvement in fuel economy is achieved by EMS_MPC-P compared to the charge depleting-charge sustaining (CD-CS) method and the charge depleting-blend (CD-Blend) method. Travel time reductions of 62.2% and 58.7% are observed versus CD-CS and CD-Blend. Battery lifespan degradation is mitigated by 16.18% and 5.89% relative to CD-CS and CD-Blend, demonstrating the method’s marked advantages in improving traffic efficiency, safety, battery life maintenance, and fuel economy. This study not only establishes a technical paradigm with theoretical depth and engineering applicability for EMS, but also quantitatively reveals intrinsic mechanisms underlying long-term prediction accuracy enhancement through data analysis, providing critical guidance for future vehicle–road–cloud collaborative system development.