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• TIP-EMS enhances EV energy efficiency by integrated prediction and energy management. • Significant driving economy boost; 1.6%-4.2% better than state-of-the-art PEMS. • TIP-EMS shows robust performance with varying prediction weight adjustments. To enhance the energy efficiency of electrified vehicles (EVs), developing effective energy management strategies (EMS) for hybrid storage systems is essential. Predictive EMS (PEMS) that foresee future vehicle speeds have demonstrated substantial potential in boosting EMS performance. However, traditional PEMS models, employing a sequential approach of speed prediction followed by energy allocation, are hampered by cumulative errors. These errors from the initial speed predss this issue, this paper introduces a novel solution: the trainable integrated preiction negatively impact the efficiency of subsequent energy management. To addrediction and energy management strategy (TIP-EMS). Contrasting with conventional sequential PEMS, TIP-EMS features a dual-branch, integrated neural network, which is fully trainable. This network processes driving status inputs via attention layers, with one branch dedicated to energy management objectives using a reinforcement learning (RL) algorithm, and the other to vehicle speed prediction. Both branches are trained simultaneously, but post-training, only the RL branch is activated for energy management. Implemented with a soft actor-critic RL algorithm, TIP-EMS is applied to a fuel cell EV for optimized energy management. The validation involved training TIP-EMS using 27 driving profiles, which developed its prediction and energy management capabilities, followed by tests in untrained scenarios. The results show that TIP-EMS surpasses conventional sequential PEMS by up to 4.2% in scenarios where prediction accuracies are comparable, highlighting the efficacy of the trainable integrated mechanism. In addition, TIP-EMS exhibits superior energy conservation compared to non-predictive RL strategies. Lastly, TIP-EMS exhibits robustness to adjustments in the weight given to the prediction objective, further confirming its practical applicability. [ABSTRACT FROM AUTHOR]

The startup-shutdown behavior of the gas foil bearings-rotor system is vital for proton exchange membrane fuel cells due to the inherently frequent start-stop of hydrogen fuel cell vehicles, which significantly affects the sustainable power generation and efficiency of the fuel cells. In this paper, the effects of three systematic factors including bump foil structure, nominal clearance, and coating on the dynamic response of the bearings-rotor system are first investigated based on startup-shutdown experiments. The mathematical model considering both gas film and thermo-hydrodynamic characteristics is presented to investigate the startup-shutdown behavior further. The influencing mechanisms of the factors on performance are analyzed. The energetic results demonstrate that the bearing structure parameters have a significant influence on the startup-shutdown performance and energy consumption. Considering optimal startup-shutdown performance, a better bearings-rotor system is proposed. Compared to the average values in all cases, the take-off speed, temperature rise, and average energy consumption of the system are decreased by 51.3%, 16.2%, and 60.7%, respectively. This indicates that the startup-shutdown improvement of the bearing-rotor system has great significance for enhancing the energy efficiency of the fuel cell. [ABSTRACT FROM AUTHOR]