The implementation of an energy management strategy plays a key role in improving the fuel economy of plug-in hybrid electric vehicles (PHEVs). In this article, a bi-level energy management strategy with a novel speed prediction method leveraged by reinforcement learning is proposed to construct the optimization scheme for the inner energy allocation of PHEVs. First, the powertrain transmission model of the PHEV in a power-split type is analyzed in detail to obtain the energy routing and its crucial characteristics. Second, a Q-learning (QL) algorithm is applied to establish the speed predictor. Third, the double QL algorithm is introduced to train an effective controller offline that realizes the optimal power distribution. Finally, given a reference battery's state of charge (SOC), a model predictive control framework solved by the reinforcement learning agent with a novel speed predictor is proposed to build the bi-level energy management strategy. The simulation results show that the proposed method performs with a satisfying fuel economy in different driving scenarios while tracking the corresponding SOC references. Moreover, the calculation performance also implies the potential online capability of the proposed method.

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The accumulation of original phosphogypsum (OPG) has occupied considerable land resources, which have induced significant environmental problems worldwide. The OPG-based cemented paste backfill (OCPB) has been introduced as a promising solution. In this study, a water-washing pre-treatment was used to purify OPG, aiming to optimize the transport performance and mechanical properties of backfills. The overall results proved that in treated phosphogypsum-based cemented paste backfill (TCPB), the altered particle size distribution can alleviate the shear-thinning characteristic. The mechanical properties were significantly optimized, of which a maximum increase of 183% of stress value was observed. With more pronounced AE signals, the TCPB samples demonstrated better residual structures after the ultimate strength values but with more unstable cracks with high amplitude generated during loading. Principal component analysis confirmed the adverse effects of fluorine and phosphorus on the damage fractal dimensions. The most voluminous hydration products observed were amorphous CSH and ettringite. The interlocked stellate clusters may be associated with the residual structure and the after-peak AE events evident in TCPB, indicate that more significant stress should be applied to break the closely interlocked stitches. Ultimately, the essential findings in this experimental work can provide a scientific reference for efficient OPG recycling.

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