An Advanced Simulation Framework of an Integrated Vehicle-Powertrain Eco-Operation System for Electric Buses

vities of transit buses traveling along arterial roads and city streets consist of frequent stops and idling events at many predictable occasions, e.g., loading/unloading passengers at bus stops, approaching traffic signals or stop signs, and going through recurrent traffic congestion, etc. Besides designing transit buses with electric powertrain systems that can save a noticeable amount of energy thanks to regenerative breaking, this urban traffic environment also unfolds a number of opportunities to further improve their energy efficiency via vehicle connectivity and autonomy. Therefore, this paper proposes a complete and novel simulation framework of integrated vehicle/powertrain eco-operation system for electric buses (Eco-bus) by co-optimizing the vehicle dynamics and powertrain (VD&PT) controls. A comprehensive evaluation of the proposed system on mobility benefits and energy savings has been conducted over various traffic conditions. Simulation results are presented to showcase the superiority of the proposed simulation framework of the Eco-bus compared to the conventional bus, particularly in terms of mobility and energy efficiency aspects

Enhanced Eco-Approach Control of Connected Electric Vehicles at Signalized Intersection with Queue Discharge Prediction

Long queues of vehicles are often found at signalized intersections, which increases the energy consumption of all the vehicles involved. This paper proposes an enhanced eco-approach control (EEAC) strategy with consideration of the queue ahead for connected electric vehicles (EVs) at a signalized intersection. The discharge movement of the vehicle queue is predicted by an improved queue discharge prediction method (IQDP), which takes both vehicle and driver dynamics into account. Based on the prediction of the queue, the EEAC strategy is designed with a hierarchical framework: the upper-stage uses dynamic programming to find the general trend of the energy-efficient speed profile, which is followed by the lower-stage model predictive controller to computes the explicit solution for a short horizon with guaranteed safe inter-vehicular distance. Finally, numerical simulations are conducted to demonstrate the energy efficiency improvement of the EEAC strategy. Besides, the effects of the queue prediction accuracy on the performance of the EEAC strategy are also investigated

Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles

With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag

Оптимізація горизонту прогнозування швидкості транспортного засобу на міжміській магістралі

The movement of the car in the traffic on intercity routes was investigated. Traffic should be energy efficient, safe and comply with the desired schedule. A method for analyzing the input data flow based on a simulation model has been developed. The proposed simulation algorithm is based on the use of available information resources for driving a car. Traffic control involves choosing a speed with known road and traffic restrictions. The presented algorithm allows to consider the expediency of each of speed increase opportunities over the forecast horizon. The content of the algorithm is the optimal redistribution of time resources. Indicators of control quality are absolute deviations from the optimal energy-saving program of free movement and from the planned schedule. The movement of a freight road train on the long-distance highway E−371 was performed. It was found that the total amount of information increases with increasing distance of scanned traffic. However, the share of reliable information is reduced. It was found that the dependence of the quality of vehicle traffic control on the size of the forecast horizon is piecewise-continuous. The dependence has an extreme value of the horizon in each continuous section, at which the deviation from the optimal program is minimal. The obtained results can be applied in modern intelligent transport systems. The research results make it possible to develop and adhere to optimal long-term traffic programs on highways. It solves the problem of managing large data streams. Large amounts of information for forecasting can be submitted in parts with reasonable frequency using the developed methodologyИсследуется движение автомобиля в магистральном транспортном потоке на междугородных маршрутах. Движение должно быть энергосберегающим, безопасным и давать возможность соблюдения желаемого расписания. Разработана методика анализа входящего потока данных на основе имитационной модели. Предложенный алгоритм имитационного моделирования базируется на использовании доступных информационных ресурсов управления автомобилем. Управление движением связано с выбором скорости при известных дорожных и транспортных ограничениях. Представленный алгоритм позволяет взвесить целесообразность использования каждой из возможностей увеличения скорости, в течение прогнозируемого горизонта. Содержание алгоритма заключается в оптимальном перераспределении ресурсов времени. Показателями качества контроля являются абсолютные отклонения от оптимальной энергосберегающей программы свободного движения и запланированного расписания движения. Выполнено моделирование движения грузового автопоезда на междугородной магистрали Е-371. Выявлено, что общий объем информации возрастает при увеличении дистанции сканируемого трафика. Однако доля достоверной информации при этом уменьшается. Установлено, что зависимость показателей качества контроля движения транспортного средства от размера горизонта прогнозирования является кусочно-непрерывной. На каждом непрерывном участке зависимость имеет экстремальное значение горизонта, при котором отклонение от оптимальной программы является минимальным. Полученные результаты можно применить в современных интеллектуальных транспортных системах. Результаты исследований позволяют разрабатывать и соблюдать оптимальные долгосрочные программы движения на магистральных дорогах. При этом решается проблема управления большими потоками данных. Пользуясь разработанной методикой, большие объемы информации для прогнозирования можно подавать частями, с обоснованной периодичностьюДосліджується рух автомобіля в магістральному транспортному потоці на міжміських маршрутах. Рух має бути енергоощадним, безпечним і уможливлювати дотримання бажаного розкладу. Розроблена методика аналізу вхідного потоку даних на основі імітаційної моделі. Запропонований алгоритм імітаційного моделювання базується на використанні доступних інформаційних ресурсів керування автомобілем. Керування рухом пов’язане з вибором швидкості при відомих дорожніх і транспортних обмеженнях. Представлений алгоритм дає змогу зважити доцільність використання кожної з можливостей збільшення швидкості, впродовж прогнозованого горизонту. Зміст алгоритму полягає в оптимальному перерозподілі ресурсів часу. Показниками якості контролю є абсолютні відхилення від оптимальної енергоощадної програми вільного руху і від запланованого розкладу руху. Виконано моделювання руху вантажного автопоїзда на міжміській магістралі Е-371. Виявлено, що загальний обсяг інформації зростає при збільшенні дистанції сканованого трафіку. Однак частка достовірної інформації при цьому зменшується. З’ясовано, що залежність показників якості контролю руху транспортного засобу від розміру горизонту прогнозування є кусково-неперервною. На кожній неперервній ділянці залежність має екстремальне значення горизонту, при якому відхилення від оптимальної програми є мінімальним. Отримані результати можна застосувати в сучасних інтелектуальних транспортних системах. Результати досліджень дають змогу розробляти і дотримуватись оптимальних довготермінових програм руху на магістральних дорогах. При цьому розв’язується проблема керування великими потоками даних. Користуючись розробленою методикою, великі об’єми інформації для прогнозування можна подавати частинами, з обґрунтованою періодичніст

Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions

The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpag

Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications

USDOT Grant 69A3551747114Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV\u2019s) is near at hand due to advances in sensor and computing technology. These capabilities in conjunction with high-definition maps enable lane determination, per lane queue determination, and enhanced performance in applications. This project investigated, analyzed, and demonstrated these related technologies. Project contributions include: (1) Experimental analysis demonstrating that the USDOT Mapping tool achieves internal horizontal accuracy better than 0.2 meters (standard deviation); (2) Theoretical analysis of lane determination accuracy as a function of both distance from the lane centerline and positioning accuracy; (3) Experimental demonstration and analysis of lane determination along the Riverside Innovation Corridor showing that for a vehicle driven within 0.9 meters of the lane centerline, the correct lane is determined for over 90% of the samples; (4) Development of a VISSIM position error module to enable simulation analysis of lane determination and lane queue estimation as a function of positioning error; (5) Development of a lane-level intersection queue prediction algorithm; Simulation evaluation of lane determination accuracy which matched the theoretical analysis; and (6) Simulation evaluation of lane queue prediction accuracy as a function of both CAV penetration rate and positioning accuracy. Conclusions of the simulation analysis in item (6) are the following: First, when the penetration rate is fixed, higher queue length estimation error occurs as the position error increases. However, the disparity across different position error levels diminishes with the decrease of penetration rate. Second, as the penetration rate decreases, the queue length estimation error significantly increases under the same GNSS error level. The current methods that exist for queue length prediction only utilize vehicle position and a penetration rate estimate. These results motivate the need for new methods that more fully utilize the information available on CAVs (e.g., distance to vehicles in front, back, left, and right) to decrease the sensitivity to penetration rate