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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]

[Display omitted] • Feasibility of using organic wastewater as renewable energy source is verified. • ＞99% COD, TOC removal rate in the experimental platform. • Up to 78.75% of the energy recovery efficiency of the SCWO system. The degradation of high concentration organic wastewater and the recovery of energy using supercritical water oxidation (SCWO) technology have the potential to become viable in the future. High concentration organic wastewater contains a significant amount of energy. This paper focuses on utilizing high concentrated wastewater as a renewable energy source, from an energy system perspective, an experimental rig was designed to determine the optimal working conditions. The experiments were conducted with an oxidation coefficient of 1.1, a flow rate of high concentration organic wastewater of 9 ml/min, a heating temperature of 400 °C, and a reaction pressure of 25 MPa. Under these conditions, the COD and TOC removal rates of the high concentration organic wastewater were found to be 99.383 % and 99.876 % respectively. Additionally, the phenomenon of "Abnormalities in pressure sensitivity" observed during high concentration organic wastewater the experiment was further explained through additional experiments. Finally, by comparing the experimental energy recovery with the simulation results, an energy recovery efficiency of 78.95 % was achieved. The feasibility of the simulated energy recovery in practice was verified, confirming the potential to convert the previous energy-consuming wastewater treatment process into a productive one. This study provides a theoretical and experimental foundation for further related research. [ABSTRACT FROM AUTHOR]