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The development of connected and automated vehicle technologies allows for cooperative control of vehicles and traffic signals at intersections. This study aims at exploring the cooperation between traffic signal control and eco‐driving control for a connected hybrid electric vehicle (HEV) system. A two‐level cooperative control method for integrat...
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With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control strategy with efficient computation capacity is proposed for plug-in hybrid electric vehicles to achieve approximate optimal...
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... Similar work has been presented in [10,18]. Moreover, the authors investigate the potential values in a connected hybrid electric vehicle (HEV) system [19], a mix of connected automated vehicles (CAVs) and human-driven vehicles (HVs) environment [20], and urban street networks [21] caused by traffic signal control and vehicle speed control. ...
Transit operation efficiency and service quality can be enhanced through the implementation of signal and speed control. Previous studies prefer to change driving speed in priority to alleviate the adverse effects of signal timing adjustment on social vehicles. The driving safety and fuel consumption of transit are ignored. To this end, a cooperative control method consisting of three models is proposed. The cooperative control strategy model provides optimal schemes for allocating transit priority time. Based on this, the adjustment of phase time and the transit speed trajectory with the lower fuel consumption are calculated by signal control model and speed control model, respectively. Especially, the signal control model is established in the background of green wave coordinated control to further protect the travelling benefits of social vehicles. The simulation is performed in SUMO to demonstrate the effectiveness of the proposed method. The results show that the cooperative control method improves the crossing efficiency and enhances the fuel economy of transit under different arrival speeds and lengths of control area. Compared with the general signal control, the proposed method can minimize traffic interference, which is particularly obvious in a higher degree of saturation.
... The work focuses on the series HEV (sHEV) architecture, which is a common arrangement for modern HEVs and involves a number of products in the market, such as the Nissan Note e-Power, and numerous other extended-range electric vehicles Chen et al. (2019). Some recent literature that considers sHEVs as a CAV option for urban signalized intersection crossing problems is presented in Tang et al. (2021); Jian et al. (2021). In Tang et al. (2021), a multi-objective hierarchical optimal strategy is proposed to optimize fuel consumption and riding comfort in signalized intersections. ...
... In Tang et al. (2021), a multi-objective hierarchical optimal strategy is proposed to optimize fuel consumption and riding comfort in signalized intersections. A two-level cooperative control method is designed in Jian et al. (2021) to improve the travel time and energy consumption of sHEV in a signalized intersection. However, to the best knowledge of the authors, existing research lacks investigating control policies designed for sHEVs regarding the signal-free intersection crossing problem. ...
The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, providing convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains. This paper proposes a decentralized coordination strategy for a group of connected and autonomous vehicles (CAVs) with a series hybrid electric (sHEV) powertrain at urban signal-free intersections. The problem is formulated as a convex form with suitable relaxation and approximation of the powertrain model and solved by decentralized model predictive control (DMPC), which enables rapid search and a unique solution in real-time. Numerical examples validate the effectiveness of the proposed methods concerning physical and safety constraints. By utilizing the petrol fuel and battery charging prices over the last year, the performance of the proposed approach is evaluated against the optimal results produced by two benchmark solutions, conventional vehicles (CVs) and battery electric vehicles (BEVs). The comparison results demonstrate that the traveling cost of sHEVs approaches and, even under some circumstances, reaches the same level as for BEVs, which indicates the importance of hybridization, particularly under the current rising electricity price situation.
... Short-term control strategies consider the immediate future's planning and control for the system considering uncertainties. A two-level hierarchal cooperative control technique that fixed phase duration traffic signal and HEV energy management was used to reduce energy consumption [6]. An overview of various intelligent controls of vehicles to achieve energy savings is shared in [7]. ...
Traffic intersections throughout the United States combine fixed, semi-actuated, and fully actuated intersections. In the case of the semi-actuated and actuated intersections, uncertainties are considered in phase duration. These uncertainties are due to car waiting queues and pedestrian crossing. Intelligent transportation systems deployed in traffic infrastructure can communicate Signal and Phase Timing messages (SPaT) to vehicles approaching intersections. In the connected and automated vehicle ecosystem, the fuel savings potential has been explored. Prior studies have predominantly focused on fixed time control for the driver. However, in the case of actuated signals, there is a different and significant challenge due to the randomness caused by uncertainties. We have developed a predictive control using the SPaT information communicated from the actuated traffic intersections. The developed MPC-based algorithm was validated using model-based design platforms such as AMBER®, Autonomie®, MATLAB®, and SIMULINK®. It was observed that the proposed algorithm can save energy in a single phase, in multiple phase scenarios, and in compelled stopping at stop signs when employed considering communications.
... Energy management is a critical technology in HEV, which can determine the optimal power split control for the energy source subsystems. Generally, the existing energy management strategies (EMSs) of HEVs are grouped into heuristic and optimization-based strategies [6][7]. The heuristic strategies include the rule-based [8] and the fuzzy logic-based [9] while the optimization-based strategies include dynamic programming (DP) [10]. ...
The uncertainties and disturbances in the actual driving conditions of hybrid electric vehicles (HEVs) complicate the design of energy management strategy (EMS). To achieve better EMS performance for a battery-supercapacitor HEV, this paper proposes an improved and adaptive deep learning-based velocity prediction control EMS that can prolong the battery lifetime through efficient utilization of both the battery and supercapacitor. First, feature engineering techniques are used to extract and increase the key features from the historical driving cycle data of known driving conditions. With the extracted features, an improved long short-term memory (LSTM) velocity predictor was developed to predict future driving cycles for a real-time EMS under an unknown driving condition. Second, a real-time EMS based on the rule-based framework optimized with a neural network is proposed to optimize the power allocation online. Simulation results show that the proposed strategy smoothens battery peak power (i.e. prolongs battery life span) by approximately 26.85% on average and increases supercapacitor participation in the EMS, as evidenced by its increased energy throughput. Furthermore, compared with other EMS approaches, the proposed strategy improved the efficiency by significantly reducing total energy losses by approximately 22.25%. These results validate the reliability and robustness of the proposed strategy.
... The work focuses on the series HEV (sHEV) architecture, which is a common arrangement for modern HEVs and involves a number of products in the market, such as the Nissan Note e-Power, and numerous other extended-range electric vehicles Chen et al. (2019). Some recent literature that considers sHEVs as a CAV option for urban signalized intersection crossing problems is presented in Tang et al. (2021); Jian et al. (2021). In Tang et al. (2021), a multi-objective hierarchical optimal strategy is proposed to optimize fuel consumption and riding comfort in signalized intersections. ...
... In Tang et al. (2021), a multi-objective hierarchical optimal strategy is proposed to optimize fuel consumption and riding comfort in signalized intersections. A two-level cooperative control method is designed in Jian et al. (2021) to improve the travel time and energy consumption of sHEV in a signalized intersection. However, to the best knowledge of the authors, existing researches lack investigating convex optimization-based autonomous intersection management approaches, particularly for sHEVs. ...
The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, with convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains. This paper provides a decentralized coordination strategy for a group of connected and autonomous vehicles (CAVs) with a series hybrid electric (sHEV) powertrain at signal-free intersections. The problem is formulated as a convex form with suitable relaxation and approximation of the powertrain model and solved by decentralized model predictive control, which is able to ensure a rapid search and unique solution in real time. Numerical examples validate the effectiveness of the proposed methods concerning physical and safety constraints. By utilizing the petrol fuel and battery charging prices over the last year, the performance of the proposed approach is evaluated against the optimal results produced by two benchmark solutions, conventional vehicles (CVs) and battery electric vehicles (BEVs). The comparison results show that the traveling cost of sHEVs approaches and even under some circumstances reaches the same level as for BEVs, which indicates the importance of hybridization, particularly under the current rising electricity price situation.
The eco‐driving strategy is of great significance in driving cost for plug‐in hybrid electric vehicles in driving trips, especially at signalized intersections. To address the issue of further energy saving, this study proposes an ecological approach and departure‐driving strategy by using syncretic learning with trapezoidal collocation algorithm. First, a syncretic learning‐based speed predictor is built by merging back propagation neural networks and radial basis function neural networks. Second, the syncretic learning‐based speed predictor and trapezoidal collocation algorithm are combined to optimize the speed trajectory. Third, the torque between the engine and the motor is distributed by the dynamic programming algorithm. Then, model predictive control optimizes torque output in the control time domain. Finally, the driving interval optimization method is designed to avoid mixed‐integer programming problems and redundant constraints, which make vehicles cross intersections without stopping. The numerical verification results show that the trapezoidal collocation algorithm with syncretic learning has more advantages than other methods in speed trajectory planning. Compared with the original trajectory, the driving time through the intersection is reduced and the total driving cost is lowered by 19.82%. Validation results confirm the effectiveness of the proposed strategy in energy consumption management at signalized intersections.
The congestion caused by ramp overflow has an essential impact on the energy consumption of autonomous vehicles. It is urgent to solve the energy waste of ramp overflow on connected and autonomous vehicles and improve the energy utilization rate. Aiming at the traffic congestion caused by ramp overflow, this paper obtains the data of autonomous vehicles and traffic flow based on vehicle road cooperative perception, analyzes the impact mechanism of vehicle energy consumption, and summarizes the energy consumption modes of autonomous vehicles into three categories. Second, an energy consumption evaluation framework is proposed based on the CSANS strategy. This strategy can make up for the deficiency of constructing neighborhoods within European distance, find important influencing variables on connected and autonomous vehicles’ energy consumption, and accurately capture the manifold characteristics of energy consumption data. Finally, flow control is carried out from the macro perspective of traffic engineering to optimize ramp overflow’s impact on autonomous vehicles’ energy consumption. Through multiple groups of experiments, it has been found that it can effectively reduce the energy consumption of autonomous vehicles.
The current signal control systems are limited by the data acquisition of traditional sensors, which can only acquire limited vehicle information. However, connected vehicles in intelligent transportation systems can provide richer vehicle information, which provides opportunities to improve traffic flow and reduce delays at intersections. This paper proposes a new traffic signal control algorithm called the predictive numerical simulation algorithm (PNSA), which combines vehicles’ positions, headings, accelerations, and speeds information from connected vehicles with the modified car-following model to determine phasing and timings. The PNSA has the ability to respond to various traffic volumes without retiming automatically. The PNSA is compared with the actuated signal control algorithm and an adaptive signal control algorithm proposed by the previous study on a single intersection. The results show that the PNSA offers reductions in average delay and average numbers of stops of up to 38% and 22% respectively over the actuated signal control algorithm, and reductions in average delay and average numbers of stops of up to 65% and 65% respectively over the previous algorithm, for networks with 25–100% connected vehicle presence. Furthermore, the PNSA maintains or improves performance compared to the other algorithms under various traffic low levels with the high level of CV penetration, while performance becomes worse during saturated and oversaturated conditions with low levels of CV penetration.KeywordsIntelligent transport systemsConnected vehiclesTraffic signal controlAdaptive signal controlTraffic simulationTraffic delay
