The braking system is the key part of trains, and its full life-cycle of health status is essential to ensure the safety of trains. How to accurately assess real-time health status throughout the full life-cycle of the train braking system is a challenge. In this paper, a digital twin-driven hybrid estimate method for health status of the braking system is proposed. Firstly, an equivalent model of the braking system is built in the digital twin platform. Then, a hybrid method of fusing model and data is proposed to assess the health status. Finally, a cloud digital twin experimental platform for health status assessment of the braking system is built, and the health status is shown by visualization framework. The experiments verify the effectiveness and practicality of the proposed scheme.

The driving performance of electric vehicles seriously degrades due to the deterioration of lithium-ion batteries at low temperatures. Preheating lithium-ion batteries can effectively improve the driving range of electric vehicles at subzero temperatures. In this paper, an optimal pulse heating strategy is proposed for low-temperature heating of lithiumion battery. Firstly, this paper establishes a coupling model to describe the electro-thermal-aging behavior of battery. Secondly, the heating time and capacity loss jointly form a multi-objective optimization problem with the current constraint. The optimization problem is solved by using the particle swarm optimization(PSO) algorithm and the effect of weighting coefficient on heating performance is discussed to obtain the optimal pulse current. The results show that the proposed strategy can effectively reduce heating time without causing serious capacity reduction.

Supercapacitors have recieved increasing attentions in emerging portable power applications. The charging process of supercapacitors significantly affects the performance of both supercapacitors and chargers. Considering the charging time of supercapacitors is typically limited in practical applications, in this paper, we propose an optimal charging method for supercapacits with the limited charging time. Firstly, we analyze existing cell balancing and charging circuits, and adopt the switched resistor circuit. Then, we design a user-interactive optimal charging method for supercapacitors where the charging time can be specified by the users. The energy efficiency maximization of the proposed charging method is proved rigorously. A simulation charging platform has been established to verify the effectiveness of the proposed charging method. The simulation results show that the proposed charging method can effectively improve the energy efficiency under charging time constraints when compared with existing methods.

The safety of railway crossings are of great important for rail and road transportation, because serious accidents occur in this area. Therefore, it is necessary to carry out foreign objects detection on railway crossings in order to improve the safety. Traditionally, video surveillance is one such solution, but it suffer from weather and illumination conditions. Under the hard environment conditions, the image of railway crossings is failed to capture by the camera. We propose a foreign objects detection system based on millimeter wave radar which has a higher detection accuracy, without the limitation of weather and light. Unlike vision-based approach, it can operate in darkness, high or low light intensity environment. With a millimeter wave radar, we first obtain the reflected signal from objects or ground and perform signal processing algorithm to extract the targets and suppress the clutter from received signal. We evaluate the detection capabilities of the millimeter wave radar in level crossings of railway.

Logistics distribution is an important link in logistics. Whether the logistics distribution path can be effectively optimized will directly affect the efficiency of the logistics distribution system. To plan the logistics distribution path reasonably, to reduce the cost of logistics management, for the multi-object path planning problem in logistics distribution, the fireworks differential evolution algorithm is used to design an optimization scheme. To achieve the overall goal of saving logistics and distribution costs, real number coding is used for each distribution point, and actual road information is obtained through the Gaode API. Aiming at the defects of the standard fireworks algorithm, the differential evolution algorithm is introduced based on the fireworks algorithm to plan the distribution route. The simulation results show that the firework differential evolution algorithm can effectively plan the optimal distribution path, and compared with the original firework algorithm, the ant colony algorithm and particle swarm optimization algorithm have a better improvement in the optimization accuracy.

Home energy management plays a key role in demand response for residential customers to reduce the total cost via scheduling household loads energy consumption. However, excessive energy consumption by customers will bring a great challenge to the stability of the grid. To address the challenge, a day-ahead multi-agent reinforcement learning method is proposed for home energy management under a peak power-limiting. We first formulate the total cost minimization problem as a Markov game, and then a novel household loads energy consumption scheduling algorithm is proposed based on Mutil-agent Deep Deterministic Policy Gradient (MADDPG). It is worth mentioning that the proposed algorithm can achieve cooperation between agents so that it can meet the peak power-limiting constraint. Simulation results are provided in this paper to show the effectiveness of the proposed method.

The high-speed solenoid valve is a crucial module in train braking system, which is an essential factor to ensure the safe operation of trains. How to estimate the health status of the high-speed solenoid valve accurately to improve the reliability of train braking system is a challenging issue. Most related work relies on accurate physical models or large amounts of historical data. To address this challenge, this paper proposes a hybrid data-fusion estimate method for the health status of train braking system. Firstly, the physical model of the high-speed solenoid valve is established, and physical indicators which represent the working performance are extracted. Then, the dynamic driving current is processed by ensemble empirical mode decomposition (EEMD) to calculate the information entropy. Physical indicators and information entropy indicators are combined into a feature vector, which can be reduced the dimension by the t-distributed stochastic neighbor embedding (T-SNE) algorithm. Finally, the feature vector is input into the probabilistic neural network (PNN) to estimate the health status of train braking system. The proposed method is implemented in the high-speed solenoid valve degradation dataset, which collected by the train brake system experiment platform. The result shows that it is better than other methods in the accuracy and calculation efficiency.

Different from previous data-driven methods for lithium-ion battery State-of-Charge (SoC) estimation, this paper aims to develop a hierarchical SoC estimation method to address the data dependency issue and measurement noise interferences. In the off-line training layer, aging-aware features are extracted to improve SoC estimation accuracy throughout the entire battery life cycle. Extreme gradient boosting (XGBoost) is introduced to map the relationship between the extracted features and SoC for its strong nonlinear fitting ability. In the on-line estimation layer, Ampere-hour integral method is utilized to provide SoC reference to guarantee the stability of the proposed method. Meanwhile, to suppress the measurement noise, we adopt Kalman filter to correct the SoC value estimated by XGBoost. The superiority of the proposed method is proved under the random walk discharging experiment by comparing with the results of XGBoost, i.e., without Kalman filter. The proposed method improved the accuracy of lithium-ion battery SoC by 4% to 10%.