Truck platooning has been touted as one of the most promising technologies to improve fuel efficiency thanks to the reduced air drag of the digitally connected truck’s slipstream in a trainlike convoy. The benefits of truck platooning lie in the reduced fuel consumption and labor cost, and the enhanced driving experience. However, these benefits cannot be realized without an appropriate routing, scheduling, and platooning plan of trucks subject to practical constraints, especially the drivers’ break requirement for long-haul journeys. This study addresses the truck routing and platooning problem considering drivers’ mandatory breaks as well as other characteristics such as the state-and-position-dependent fuel-saving rates of platooning, trucks’ designated intermediate relays, and platoon size limit. The problem is to route the trucks to their respective destinations on time using the least amount of fuel by maximizing the formations of fuel-saving platoons over entire trips while satisfying the break time requirement of drivers. A mixed-integer linear programming (MILP) model is first developed for the proposed problem. A hybrid algorithm integrating the partial-MILP approach and iterated neighborhood search with tailored search operators is proposed to address the problem. Various randomly generated networks are used in numerical experiments to examine the effectiveness and efficiency of our proposed model and solution method. An extensive sensitivity analysis is also conducted to explore the impacts of several major influential factors, i.e., the drivers’ mandatory break times, the fuel reduction rate of the leading truck of platoons, as well as the width of the service time window, on the system performance and derive the managerial insights. 

Truck routing and platooning optimization considering drivers’ mandatory breaks

AbstractSemi-autonomous truck platooning, a futuristic and promising traveling mode of trucks on highways, has received extensive attention as autonomous driving gains more maturity. Under the semi-autonomous platooning mode, rather than requiring a human driver for each truck in the platoon, only one human driver is needed for the leading truck, and all following trucks can be controlled by autonomous driving and wireless communication technology. Therefore, in addition to saving energy, such a technology can help reduce labor costs by using fewer drivers to fulfill the same demand. Compared to traditional truck platooning, the schedules of drivers under semi-autonomous truck platooning may not be exactly the same as trucks, which are usually neglected in recent studies. Therefore, in this paper, we make the first attempt to develop a mathematical modeling framework to optimise the operation plan of drivers and trucks interdependently. To be mentioned, the truck fleet is equipped with the semi-autonomous platooning function and time windows. Specifically, the operation plan will dictate the number of drivers to be dispatched, and traveling routes and time schedules of drivers and trucks interdependently with the objective to minimise the total operation cost, including drivers' fixed dispatch cost, on-road labor cost, and energy consumption cost while fulfilling all delivery demands. To tackle the large-scale problem in a timely manner, a tailored Lagrangian Relaxation approach is proposed to solve the model. Numerical experiments are conducted to demonstrate the performance of the proposed modeling framework and validate the feasibility and efficiency of the proposed solution algorithm.Keywords: Semi-autonomous truck platooningvehicle routing and schedulingdriver dispatchinglabor and energy costlagrangian relaxation Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partially supported by the National Natural Science Foundation of China (72101153, 72061127003, 72001021), Shanghai Chenguang Program (21CGA72), Shanghai Eastern Scholar Program (QD2020057), the Preparation Fund of Shanghai Key Laboratory of Urban Design and Urban Science (Grant No. 10407_Key Lab_Preparation Fund), the NYU Shanghai Boost Fund, and the NYU Shanghai Doctoral Fellowships. 

Joint operation planning of drivers and trucks for semi-autonomous truck platooning

Energy regenerative shock absorber based on a slotted link conversion mechanism for application in the electrical bus to power the low wattages devices

Managing the energy supply of electric buses (EBs) is critical to balance operational reliability and cost reduction. However, ambient temperature significantly affects energy consumption, posing challenges in providing adequate energy supply, especially in cold regions. In this study, we propose a double-battery configuration approach for EBs operating in cold regions with substantial temperature variations between seasons. The method involves utilizing a higher-capacity battery during winter months and a lower-capacity battery for summer operations. To determine the optimal fleet size, battery capacities, and EB scheduling plans in both winter and summer seasons, we formulate an integer programming model, focusing on minimizing the fleet's average annual operating costs (OC) and carbon emissions (CE). The model is solved using the branch and bound method. A case study is conducted, examining EB routes in 11 cold cities. Results reveal that the double-battery configuration method effectively reduces the fleet's average annual OC and CE. Specifically, the reduction ratio range for OC is from 3.34% to 5.18%, while the reduction ratio for CE ranges from 4.06% to 5.16%. 

Double-battery configuration method for electric bus operation in cold regions

Battery-electric buses (BEBs) are a promising technology for replacing diesel buses and reducing their environmental burden. However, their charging process can take several hours, depending on the charging technique and strategy, making them susceptible to schedule disruptions. Furthermore, the selection of the charging strategy and battery size can increase the total capital investment and operational expenses of a BEB fleet, which is a major obstacle to its adoption. Therefore, to minimize the total cost of ownership (TCO) and prevent schedule disruptions, it is essential to establish a well-defined approach to determine an appropriate battery size and charging strategy for BEB fleets. This paper presents a method for reducing the TCO of BEB by determining the optimal battery size and charging strategy for each bus while satisfying operating constraints. The method involves a two-step optimization algorithm that uses Dynamic Programming and Genetic Algorithm. The study applies this approach to the bus fleet serving line 21 in Paris and generates the optimal battery sizing, charging strategy, and required charging infrastructure. The results indicate that 100 kWh batteries offer the best trade-off between capital and operational expenditure for the fleet deployment if used with 65–85 kW chargers at bus terminals. 

A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris

An optimal charging scheduling model and algorithm for electric buses

Truck platooning has been identified as a promising technology to save labor force for the local container drayage problem (LCDP). However, existing studies for the platoon-based LCDP assumed that each driver was attached to respective leading truck throughout the service process, and the driverless following trucks dropped off at customer sites cannot further serve the drayage requests until regaining guidance, resulting in low efficiency and utilization of drivers and trucks. To overcome this issue, this paper examines the LCDP under a novel improved platooning operation mode (IPOM), where the drivers are not necessarily attached to their respective leading trucks but can move by alternative transport modes to perform subsequent tasks. The objective is to determine the optimal numbers, routes, and schedules of the drivers and trucks that minimize the total operational cost to complete all the delivery and pickup requests of customers. A mixed-integer linear programming (MILP) model is developed to capture the features pertaining to the new platooning mode. A heuristic construction method incorporating simulated annealing is proposed to solve the problem. Numerical experiments are carried out to evaluate the proposed model and solution method and quantify the advantages of the improved platooning mode. Sensitivity analysis is also conducted to explore the impacts of some major influential factors on the system performance and derive managerial insights. 

Local container drayage problem with improved truck platooning operations

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An optimal charging scheduling model and algorithm for electric buses

Local container drayage problem with improved truck platooning operations