Abstract: Truck platooning refers to a convoy of digitally connected automated trucks traveling safely with a small inter-vehicle gap. It has been identified as one of the most promising and applicable technologies towards automated and sustainable freight transportation. Although truck platooning delivers significant energy-saving benefits, it cannot be realized without good coordination of drivers’ shifts to lead the platoons subject to their mandatory breaks. Therefore, this study aims to route a fleet of trucks to their destinations using the least amount of fuel by maximizing platoon opportunities under the regulations of drivers’ mandatory breaks. We formulate this platoon coordination problem as a mixed-integer linear programming problem and solve it by CPLEX. Numerical experiments are conducted to demonstrate the effectiveness and efficiency of our proposed model. In addition, we also explore the impacts of drivers’ compulsory breaks on the fuel-savings performance. The results show a slight increase in the total fuel costs in the presence of drivers’ compulsory breaks, thanks to driving-while-resting benefit provided for the trailing trucks. This study may serve as a guide for the operators of automated freight transportation.
Abstract: This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.
Abstract: Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.