The fuel economy of plug-in hybrid electric vehicles (PHEVs) is strongly affected by the battery state of charge (SOC) depletion pattern. This paper proposes and studies a real-time traffic-based SOC reference planning method. The method uses a dataset to collect and capture real traffic information and then enriches the dataset using a data augmentation method developed in this paper. The augmented dataset is optimized by dynamic programing (DP) algorithm to obtain the optimal reference SOC for model training. The traffic information and optimal reference SOC are processed and used to train a long-short term memory (LSTM) neural network, which is used for online reference SOC planning. Finally, a predictive energy management (PEM) strategy is adopted to follow the SOC reference by optimizing instantaneous power allocation with the predicted velocities. Simulation results show that the proposed method outperforms the linear reference SOC planning method in both smooth and congested traffic scenarios.

This paper proposes LHFNet, an improved object detection method based on CenterNet, which achieves a better speed-accuracy trade-off. The main contributions of this paper are as follows: We design a lightweight Hourglass network to reduce the number of parameters and computations of CenterNet, thus improving the detection speed. We introduce an intermediate feature map fusion module to enhance the feature extraction capability of Hourglass, which compensates for the possible performance degradation caused by the network simplification. We apply an attention module to the final heat map to improve the image feature representation. We design and optimize a series of loss functions to train LHFNet effectively. Through the research in this paper, we demonstrate that the feature map fusion and the attention module greatly improve the performance of LHFNet. We evaluate our method on the COCO dataset and achieve a high accuracy at a fast speed (Tesla V100), which outperforms other object detection methods in terms of both accuracy and efficiency.

In this paper, in view of the insufficiency of the CenterNet detector’s inability to achieve high real-time performance with high accuracy when performing object detection, we designed a new detector called A_CenterNet. In the detector, we use our newly designed lightweight Hourglass-256 model, and we also use the feature map fusion method we designed, as well as our improved attention mechanism. Through the experimental results on multiple datasets, it can be known that the A_CenterNet proposed in this paper has a competitive advantage compared with some existing classic detectors. A_CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 44.6 AP at 36 FPS. Compared with CenterNet, A_CenterNet greatly improves the detection speed without loss of detection accuracy.

Although the CenterNet object detection method can be used to achieve high accuracy, it cannot be used to detect objects with overlapping or almost overlapping centers. However, a good trade-off between detection speed and accuracy cannot be achieved when utilizing this method. We improved a series of backbones using CenterNet, output the fusion of multiple feature maps in backbone, and used the feature pyramid network (FPN) mechanism for multiscale object detection. Additionally, we improved the head of the detector and considered adding intersection over union (IoU) branches. Finally, the loss function was improved to improve detection accuracy. Based on the above research, the CenterNet Plus object detection method is proposed in this paper. Through experiments on the COCO dataset, it can be seen that the use of a multiscale FPN mechanism not only solves the problem of center point overlap but also helps improve the accuracy of detection. CenterNet Plus can be used to greatly improve the detection accuracy on the premise of having a higher detection speed.

In the original CenterNet algorithms, the object detection model with Hourglass as Backbone has a higher mean Average Precision(mAP) than other one-stage algorithms, but it is limited by the low detection speed.To address the problem, a new model named Hourglass-208 is proposed by using the original CenterNet object detection algorithm to improve the Hourglass-104 model.Additionally, a feature map fusion method for Twin Feature Pyramid Networks(TFPN) is given.On this basis, smooth L1 is used for the loss function of the object size to establish a new object detection algorithm, T_CenterNet, which can perform end-to-end training.Experimental results on the MS COCO data set show that the target detection evaluation index AP50, APS, APM of the proposed algorithm are 63.6%, 31.6%, 45.8%, respectively, and the detection speed of the algorithm reaches 36 frame/s.The comprehensive performance of the proposed algorithm is better than that of the original CenterNet algorithm.

分析了第四代清洁制冷剂二氧化碳、HFO-1234yf和R152a的各种性质,并讨论了各新型制冷剂替换现有空调制冷剂的可行性,指出新型制冷剂作为空调替换制冷剂的趋势.重点介绍了极具潜力的二氧化碳制冷剂的发展阶段和完善过程,并指出未来二氧化碳跨临界制冷循环的研究方向.