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The recovery of rare earth elements from ores is crucial because of their applications in modern technology. Bastnaesite (La/Ce(CO3)F) is typically found in deposits with other gangue minerals but can be purified by flotation. Accordingly, we investigated the interactions of the collector nonyl hydroxamic acid (NHA) with bastnaesite using density functional theory (DFT) calculations. In addition, we replaced Ce sites on the bastnaesite (100) surface with La and investigated the effect on NHA adsorption. Finally, we examined the effects of co-present aluminum species, which are frequently used inhibitors for associated gangue minerals during bastnaesite flotation, on NHA adsorption and, thus, the flotation efficiency. We found that doping with La increased the strength of adsorption between NHA and the bastnaesite (100) surface. In addition, we found that Al(OH)3(s) was adsorbed more strongly than NHA. Consequently, when Al(OH)3(s) is present in the flotation pulp, it is preferentially adsorbed, which reduces the number of sites for NHA adsorption and its flotation efficiency. These findings suggest that La doping can enhance the recovery of bastnaesite and indicate that the presence of Al minerals should be minimized. [ABSTRACT FROM AUTHOR]

Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) has emerged to significantly advance the perception of automated driving. However, current cooperative object detection methods mainly focus on ego-vehicle efficiency without considering the practical issues of system-wide costs. In this paper, we introduce VINet, a unified deep learning-based CP network for scalable, lightweight, and heterogeneous cooperative 3D object detection. VINet is the first CP method designed from the standpoint of large-scale system-level implementation and can be divided into three main phases: (1) Global Pre-Processing and Lightweight Feature Extraction which prepare the data into global style and extract features for cooperation in a lightweight manner; (2) Two-Stream Fusion which fuses the features from scalable and heterogeneous perception nodes; and (3) Central Feature Backbone and 3D Detection Head which further process the fused features and generate cooperative detection results. An open-source data experimental platform is designed and developed for CP dataset acquisition and model evaluation. The experimental analysis shows that VINet can reduce 84% system-level computational cost and 94% system-level communication cost while improving the 3D detection accuracy. [ABSTRACT FROM AUTHOR]

In natural kaolinite lattices, A l 3 + can potentially be substituted by cations such as M g 2 + , C a 2 + , and F e 3 + , thereby influencing its adsorption characteristics towards rare earth elements like S c 3 + . Density functional theory (DFT) has emerged as a crucial tool in the study of adsorption phenomena, particularly for understanding the complex interactions of rare earth elements with clay minerals. This study employed DFT to investigate the impact of these three dopant elements on the adsorption of hydrated S c 3 + on the kaolinite (001) Al-OH surface. We discerned that the optimal adsorption configuration for hydrated S c 3 + is S c (H 2 O) 8 3 + , with a preference for adsorption at the deprotonated Ou sites. Among the dopants, Mg doping exhibited superior stability with a binding energy of −4.311 eV and the most negative adsorption energy of −1104.16 kJ/mol. Both Mg and Ca doping enhanced the covalency of the Al-O bond, leading to a subtle shift in the overall density of states towards higher energies, thereby augmenting the reactivity of the O atoms. In contrast, Fe doping caused a pronounced shift in the density of states towards lower energies. Compared to the undoped kaolinite, Mg and Ca doping further diminished the adsorption energy of hydrated S c 3 + and increased its coordination number, while Fe doping elevated the adsorption energy. This study offers profound insights into understanding the role of dopant elements in the adsorption of hydrated S c 3 + on kaolinite. [ABSTRACT FROM AUTHOR]

Highlights • NKE is defined and used as a variable for capturing the regenerative braking effect in EV energy consumption estimation. • A systematic data-driven EV energy consumption decomposition analysis is conducted. • A novel link-level EV energy consumption estimation model is built upon the decomposition analysis. • A "W"-shaped relationship between link-level EV energy consumption rate and average speed is discovered and explained. Abstract Electric vehicles (EVs) have great potential to reduce transportation-related fossil fuel consumption as well as pollutant and greenhouse gas (GHG) emissions, due to their use of renewable electricity as the sole energy source. Therefore, the wide-spread deployment of EVs is regarded as an attractive means to mitigate the environmental problems (e.g., air pollution and climate change) resulting from transportation activities. Government agencies are trying to promote EV deployment by allocating considerable funding as well as promulgating supportive policies. However, the mass adoption of EVs is still impeded by the limited charging infrastructure and all-electric-range (AER). All these lead to a critical research topic: the EV energy consumption analysis and estimation under real-world traffic conditions, which is fundamental to various types of EV-centred applications that aim at improving the EV energy efficiency and extending the AER. For example, eco-routing systems for EVs rely on accurate link-level energy consumption estimation to calculate the EV energy consumption costs of the different route options. In this work, to obtain an accurate link-level energy consumption estimation model for EVs, the energy consumption under real-world traffic congestion is decomposed based on two proposed impact factors: positive kinetic energy (PKE) and negative kinetic energy (NKE). Upon this decomposition, a data-driven model is built to estimate EV energy consumption on each roadway link considering real-world traffic conditions. Finally, the model performance is evaluated by comparing with the performance of baseline model adapted from existing models. The results show that the proposed EV link-level energy consumption estimation model outperforms the existing models in terms of accuracy, implying that it is quite promising in various on-board EV applications. [ABSTRACT FROM AUTHOR]

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial. [ABSTRACT FROM AUTHOR]

Timely and reliable accident detection provides a foundation for traffic accident management (TIM), which is critical functionality for traffic management agencies. Effective TIM strategies mitigate negative impacts caused by non-recurrent events, improve quality of service and traveler satisfaction, and enhance transportation resilience. Most existing studies focus on traffic accident detection and system mobility. Very few systems attempted to quantify the environmental impacts of accidents. We examine a cloud-based data platform that fuses information from real-world traffic, probe vehicle data, and road weather. Moreover, we developed a data-driven approach to estimate the impacts of accidents using Otsu's method, morphological operation, time-series prediction, and emissions simulator model, allowing us to quantify the benefits of advanced accident detection. The proposed method was evaluated with a real-world scenario, showing that the studied accident may cause additional energy waste by 38% and CO emissions by 36%. [ABSTRACT FROM AUTHOR]

The adsorption behavior of Sc on the surface of kaolinite (001) was investigated using the density functional theory via the generalized gradient approximation plane-wave pseudopotential method. The highest coordination numbers of hydrated Sc 3 + ,   Sc OH 2 + , and   Sc OH 2   + species are eight, six, and five, respectively. The adsorption model was based on Sc OH 2 H 2 O 5 + , which has the most stable ionic configuration in the liquid phase. According to the adsorption energy and bonding mechanism, the adsorption of Sc ionic species can be categorized into outer layer and inner layer adsorptions. We found that the hydrated Sc ions were mainly adsorbed on the outer layer of the kaolinite (001)Al-OH and (00−1)Si-O surfaces through hydrogen bonding while also being adsorbed on the inner layer of the deprotonated kaolinite (001)Al-OH surface through coordination bonding. The inner layer adsorption has three adsorption configurations, with the lying hydroxyl group (Ol) position having the lowest adsorption energy (−653.32 KJ/mol). The adsorption energy for the inner layer is lower compared to the outer layer, while the extent of deprotonation is limited. This is because the deprotonation of the inner adsorption layer is energetically unfavorable. We speculate that Sc ions species predominantly adsorb onto the surface of kaolinite (001) in an outer layer configuration. [ABSTRACT FROM AUTHOR]

On-road vehicles have been considered as one of the major contributors to energy consumption and air pollutant emissions. In order to quantify the corresponding environmental impacts, great efforts have been dedicated to the microscopic and macroscopic modeling for vehicle energy consumption and emissions. However, the mesoscopic modeling research that is focused on estimating trip-based energy consumption and is critical to some ITS applications (e.g., environmentally-friendly navigation), is relatively deficient. This study aims to investigate the effects of different data segregation methods on the mesoscopic modeling for vehicle energy consumption. A variety of novel methods, including the so-called conditional operating mode based method, have been proposed and evaluated using field data. Based on real-world data, statistical analyses have demonstrated the superior performance of enhanced models (i.e., conditional operating mode/VSP based models) in estimating vehicle energy consumption on a trip basis, compared to the other four models (velocity binning, time snipping, distance snipping and VSP based models) tested in this study. [ABSTRACT FROM AUTHOR]

In urban areas, conventional transit priority systems (e.g., passive priority) present some problems, such as causing too many traffic delays and degradation of on line performance due to the lack of real-time data. The purpose of this study is to develop an on line adaptive priority strategy to optimize traffic operation at a series of consecutive grade crossings in urban areas. The proposed strategy takes into account the impact on cross-street traffic as well. A mathematical model of non linear programming with linear constraints is formulated to minimize overall passenger delays at grade crossings for light rail transit and to minimize the impact on cross-street traffic. The optimization is enabled by real-time arrival-time prediction with GPS-equipped vehicles. To exhibit the anticipatory performance of the proposed strategy, both a numerical analysis and a simulation test are conducted with trolley operation along a stretch of C Street in San Diego. Compared with current signal timings, those obtained from the proposed strategy demonstrate promising results. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the environmental impacts of signal timing on vehicle emissions at a pre-timed signalized intersection. We use a two-stage approach: the first consists of developing optimization models that examine the trade-off between vehicle delays and the number of stops; and, based on the outcomes of the first stage, the second of estimating vehicle emissions using microscopic emission estimation models. A dynamic programming algorithm is developed to solve the optimization problems appearing in the first stage. A vehicle specific power approach is used to estimate the vehicle emissions near the intersection based on the results from the first stage. [Copyright &y& Elsevier]

• Real-world survey to understand consumers' response to Connected Vehicle technology. • Both general CV and specific efficiency- and safety- oriented applications involved. • Complimentary analyses from multi-perspectives with different statistical methods. • Significance of association and influence patterns of significant factors assessed. This research is aimed at investigating drivers' attitudes towards connected vehicle technology in general and two connected vehicle applications in particular—Lane Speed Monitoring and High Speed Differential Warning—which have been demonstrated via simulation to be effective in enhancing traffic mobility and safety, respectively. An online survey was sent to customers of an automobile manufacturer in the United States. Out of the 1453 survey responses that were received, 650 complete and valid responses were used to analyze the respondents' stated acceptance of and expected behavioral responses to the two connected vehicle applications under a variety of scenarios. Statistical analyses were conducted to examine the influence of demographic and socioeconomic factors. The results reveal that the respondents express high willingness to use connected vehicle technology and the two applications under various circumstances, and the willingness is strongly associated with age, gender, education level, and income. Higher levels of acceptance are observed in older, male, higher-educated, or higher-income respondents, while the patterns of conditional acceptance and expected behavioral responses vary with specific scenarios. These results provide useful information for application developers, traffic operators, and policy makers to steer connected vehicle technology development and deployment in the direction that will benefit both the users and the society. [ABSTRACT FROM AUTHOR]

Many studies have simulated traffic behavior at signalized intersections using various Car-Following (CF) models. However, the performance of which CF Model is superior at signalized intersections has not been thoroughly analyzed and evaluated. In this study, two novel Artificial Neural Network (ANN) CF models, the Convolutional Neural Network—Long Short-term Memory (CNN-LSTM) and the Convolution-LSTM (Conv-LSTM)—are first applied to predict CF behaviors at signalized intersections. Both models can extract spatial and temporal information to address the long-term dependency problem more effectively. Based on the filtered NGSIM dataset, we conduct a comparative empirical study of three conventional CF models and five ANN CF models. The dataset is divided into two categories based on the characteristics of CF behavior at signalized intersections: continuous and discontinuous. The experiments demonstrated that ANN CF models outperformed conventional CF models when the output was the velocity in two categories of traffic flow but only failed to do so when the output was acceleration in discontinuous traffic flow. The proposed models were capable of accurately predicting acceleration, but the traffic fluctuations also existed as time passed. Additionally, it was discovered that while the ANN CF model is preferable for traffic flow simulation, the conventional CF model still cannot be ignored for discontinuous traffic flow simulation, particularly when acceleration is required. [ABSTRACT FROM AUTHOR]

In order to track the research progress of AEB-related technologies, this paper makes a systematic analysis and research on the impact factors, key technologies, and effect evaluation of AEB. First, the paper deeply analyzes the three levels of factors affecting the performance of AEB, which are vehicle factors, driver factors, and environmental factors. Second, the paper deeply studies the technical status of the three subsystems of environment perception, decision-making, and control execution. Particularly, the performance of Mazda, Honda, NHTSA, Berkeley, and Seungwuk Moon are compared and analyzed based on MATLAB. Third, the paper summarizes the current AEB virtual test methods, closed field test methods, and its test sites. Three classic evaluation methods in the world, including the AEB test evaluation standards of ENCAP, IIHS, and i-Vista are analyzed. Finally, the paper prospects the specific research directions, including the protection of vulnerable road users, target detection method, collision avoidance strategy, complex scenarios application, and application of emerging technologies. [ABSTRACT FROM AUTHOR]

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation. • Estimation of fuel consumption and emissions is closer to the real values than other methods. • Reconstructing second-by-second vehicle trajectories based on macroscopic traffic data. • Introduces CAVs trajectory data as a reference in the process of estimating the road spatio-temporal speed evolution. • Proposes a spatio-temporal consistency validation framework by using macro and micro data. • Experimental tests the effect of cell size and probe vehicle penetration on reconstruction accuracy. [ABSTRACT FROM AUTHOR]

Transportation is a key driver of development, enabling people to have access to jobs, education, health, and goods. It is then expected that CAVs will gradually appear in the market, increasing the complexity of the current transportation system as vehicles with different levels of connectivity and automation will start interacting with manually driven vehicles. Depending on the driving system of consecutive vehicles, a vehicle with an automated driving system (ADS) may decide either to use adaptive cruise control (ACC) or a cooperative adaptive cruise control (CACC) scheme for driving. [Extracted from the article]

Highlights • CAVs are capable of learning the optimal control strategy that reduce fuel consumption. • More than 16% fuel efficiency improvement can be achieved by a deep-reinforcement learning. • A dueling neural network structure (DDQN) can further increase the learning speed of the self-cleaning control model. Abstract To address the air pollution problems and reduce greenhouse gas emissions (GHG), plug-in hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The Energy Management System (EMS) is a very important component of a PHEV in achieving better fuel economy and it is a very active research area. So far, most of the existing EMS strategies are either just simply following predefined rules that are not adaptive to changing driving conditions; or heavily relying on accurate prediction of future traffic conditions. Deep learning algorithms have been successfully applied to many complex problems and proved to even outperform human beings in some tasks (e.g., play chess) in recent years, which shows the great potential of such methods in practical engineering problems. In this study, a deep reinforcement learning (Deep Q-network or DQN) based PHEV energy management system is designed to autonomously learn the optimal fuel/electricity splits from interactions between the vehicle and the traffic environment. It is a fully data-driven and self-learning model that does not rely on any prediction, predefined rules or even prior human knowledge. The experiment results show that the proposed model is capable of achieving 16.3% energy savings (with the designed PHEV simulation model) on a typical commute trip, compared to conventional binary control strategies. In addition, a dueling Deep Q-network with dueling structure (DDQN) is also implemented and compared with single DQN in particular with respect to the convergence rate in the training process. [ABSTRACT FROM AUTHOR]

Deploying the dynamic wireless power transfer (DWPT) equipment at intersections can improve the transportation efficiency and decrease the energy consumption. It can easily turn the disadvantages of waiting time for red phase into the advantages of charging benefits for electric vehicles (EVs). This study develops a multiobjective speed planning model with differentiated charging strategy to optimize the EV's trajectory at signalized intersections with DWPT. To mitigate the negative impact of DWPT, the proposed model is divided into charging priority driving (C-eco-driving) mode and time priority driving (T-eco-driving) mode for connected and automated electric vehicles (CAEVs) to apply different scenarios. Meanwhile, a widely applied car-following model, i.e., the Intelligent Driver Model (IDM), has been calibrated with the ground-truth dataset to meet the intersection characteristic for EVs in mixed traffic. The efficiency and robustness of two eco-driving modes were validated in single-vehicle and mixed traffic based on MATLAB simulation. For single vehicle, the electricity benefits of the C-eco-driving mode and T-eco-driving mode increase about 0.0867 kWh and 0.0532 kWh, respectively. T-eco-driving mode reduces 2.5 s of travel time. For mixed traffic, the C-eco-driving mode provides more charging benefits about 51~73% than no-control strategy. In contrast, the T-eco-driving mode provides significant time benefits of 2.6 s and slight electricity benefits of 12~30% than no-control strategy. The increment of market penetration rate and wireless charging length can significantly improve the charging benefits. When charging length has the same value, the closer the DWPT facilities to the intersection, the more obvious the charging benefits. [ABSTRACT FROM AUTHOR]

Ramp merging is considered to be one of the major causes of traffic accidents and congestion due to its inherent chaotic nature. With the development of the connected and automated vehicle (CAV) technology, CAVs can conduct cooperative merging using communication, and can also handle complicated situations even with legacy vehicles. In this article, a game theory-based ramp merging strategy has been developed for the optimal merging coordination of CAVs in mixed traffic, which can determine the dynamic merging sequence and corresponding longitudinal/lateral control. This strategy improves the safety and efficiency of the merging process by ensuring a safe intervehicle distance and harmonizing the speeds of CAVs in the traffic stream. To verify the proposed strategy, mixed traffic simulation runs under different penetration rates and different congestion levels have been carried out on an innovative Unity-SUMO integrated platform, which connects a game engine-based driving simulator with a state-of-the-art microscopic traffic simulator. The results show that the average speed of traffic flow can be increased up to 210%, while the fuel consumption can be reduced up to 53.9%. In addition, the driving volatility can be stabilized to a level with 0% extreme values. [ABSTRACT FROM AUTHOR]

In the urban corridor with a mixed traffic composition of connected and automated vehicles (CAVs) alongside human-driven vehicles (HDVs), vehicle operations are intricately influenced by both individual driving behaviors and the presence of signalized intersections. Therefore, the development of a coordinated control strategy that effectively accommodates these dual factors becomes imperative to enhance the overall quality of traffic flow. This study proposes a bi-level structure crafted to decouple the joint effects of the vehicular driving behaviors and corridor signal offsets setting. The objective of this structure is to optimize both the average travel time (ATT) and fuel consumption (AFC). At the lower-level, three types of car-following models while considering driving modes are presented to illustrate the desired driving behaviors of HDVs and CAVs. Moreover, a trigonometry function method combined with a rolling horizon scheme is proposed to generate the eco-trajectory of CAVs in the mixed traffic flow. At the upper-level, a multi-objective optimization model for corridor signal offsets is formulated to minimize ATT and AFC based on the lower-level simulation outputs. Additionally, a revised Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to identify the set of Pareto-optimal solutions for corridor signal offsets under different CAV penetration rates (CAV PRs). Numerical experiments are conducted within a corridor that encompasses three signalized intersections. The performance of our proposed eco-driving strategy is validated in comparison to the intelligent driver model (IDM) and green light optimal speed advisory (GLOSA) algorithm in single-vehicle simulation. Results show that our proposed strategy yields reduced travel time and fuel consumption to both IDM and GLOSA. Subsequently, the effectiveness of our proposed coordinated control strategy is validated across various CAV PRs. Results indicated that the optimal AFC can be reduced by 4.1%–32.2% with CAV PRs varying from 0.2 to 1, and the optimal ATT can be saved by 2.3% maximum. Furthermore, sensitivity analysis is conducted to evaluate the impact of CAV PRs and V/C ratios on the optimal ATT and AFC. • A bi-level structure was designed for coordinated control. • An eco-driving model was proposed to regulate CAV's trajectory. [ABSTRACT FROM AUTHOR]

Abstract Battery electric vehicles (BEVs) are a critical pathway towards achieving energy independence and meeting greenhouse and criteria pollutant gas reduction goals in the current and future transportation sector. Emerging connected and automated vehicle (CAV) technologies further open the door for developing innovative applications and systems to leverage vehicle efficiency and substantially transform transportation systems. Therefore, we present a simulation study of various BEV types and compare the performance when driving on real-road drive cycles to highly optimized eco-driving cycles using advanced CAV technologies. The results demonstrate that eco-driving has a high potential to reduce energy consumption for all types of BEVs considered. The investigated BEVs include a compact vehicle, a transit city bus, and a Class 7 delivery truck. The impact of eco-driving on conventional vehicles was also compared to comparable BEVs. Compared to the BEVs, eco-driving provides a larger reduction in the conventional vehicle's braking energy loss, and also provides conventional vehicles with greater reductions in the engine mechanical energy output but the fuel savings did not show a consistent trend among all the conventional vehicle types. As part of the study, a comprehensive EV powertrain model was developed to account for key EV components and powertrain configurations. Highlights • The developed BEV model enables comparisons between BEV technology options. • BEV key losses are aero drag, rolling resist, motor loss, drivetrain loss, and bus accessory. • Eco-driving boosts efficiency by 27%, 22% and 8% for the passenger BEV, E-bus and E-truck, respectively. • Low rolling resistance tires have a significant impact on the BEVs' efficiency under eco-driving. • The impact of eco-driving on comparable conventional vehicles was compared to the BEVs. [ABSTRACT FROM AUTHOR]

Abstract Most special-use freeway lanes, such as High Occupancy Vehicle (HOV) lanes, have traditionally been designed with either limited access or continuous access control from the adjacent general-purposed (GP) lanes. Studies have shown the advantages and disadvantages of each design in terms of safety, mobility, environment, and enforcement, among other factors. With a focus on improving the operational performance of HOV facilities, this paper proposes a new design called partially limited access control where the continuous access is mostly designated along the freeway to achieve higher travel speed while buffers between the HOV lane(s) and the adjacent GP lanes are strategically placed on selected freeway segments to accommodate higher throughput on those segments. The placement of buffers primarily aims to reduce the impact of HOV cross-weave flow on the capacity of GP lanes. In this research paper, a methodology for determining the location and length of buffers in the partially limited access control has been developed. A case study is performed along a 13-mile section of HOV facility on SR-210 E in Southern California, which is coded and evaluated in traffic microsimulation. The results show that the partially limited access control increases the throughput (represented by total vehicle miles traveled or VMT) and decreases the delay (represented by total vehicle hours traveled or VHT) of the freeway as compared with either the limited access or continuous access control. As a result, the overall efficiency (represented by average travel speed calculated as VMT/VHT) of the freeway with partially limited access HOV facility is 21% and 6% higher than that of the freeway with limited access and continuous access HOV facility, respectively, under the baseline traffic demand. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comprehensive model is developed to optimize PHEB powertrain efficiency. • The powertrain control algorithm includes charge- and discharge-dominant controls. • A CEED database enables rapid selection of an optimal powertrain operating state. • Energy savings by the control strategy were 10%–30% compared with the baseline HEV. • The powertrain control algorithm has been implemented into a prototype hybrid bus. A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results. [ABSTRACT FROM AUTHOR]

• Able to overtake slowly-moving vehicles for ecological purpose. • Able to optimize the travel duration approaching a signalized intersection. • Balancing fuel saving and vehicle's mobility. • Considering stochasticity of surrounding traffic. • Functional under partially connected and automated environment. This research presents an enhanced eco-approach controller with overtaking capability. The proposed controller overcomes the shortcomings of the conventional eco approach and is able to: i) overtake slowly-moving vehicles for the ecological purpose; ii) optimize the travel duration approaching an intersection; iii) guarantee both fuel saving and vehicle's mobility; iv) consider stochasticity of surrounding traffic; v) functional under partially connected and automated environment. It takes full advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input. The problem is formulated as an optimal control problem and is solved by GPOPS. The nonlinear bicycle model is adopted as the system dynamics to realize CAV's longitudinal and lateral coupling control, and linearized to reduce the computational burden. The stochasticity of surrounding traffic is considered as a probability distribution that is transformed into a linear chance constraint. Quantitative evaluation is conducted to compare the proposed controller against human drivers and the conventional eco approach which only has longitudinal automation. The evaluation results demonstrate that the proposed controller improves the fuel efficiency by 4.13–70.12%, and outperforms two baseline controllers by 6.06–36.73% in terms of fuel saving. The range is caused by the different arrival time of the ego CAV. In addition, the simulation experiment in VISSIM is conducted to analyze how background traffic flow influences the performance of the proposed controller. [ABSTRACT FROM AUTHOR]