Impact of different spacing policies for adaptive cruise control on traffic and energy consumption of electric vehicles

This paper assesses the impact of different spacing policies for Adaptive Cruise Control (ACC) systems on traffic and environment. The largest deal of existing studies focus on assessing the performance in terms of safety, while only few deal with the effect of ACC on the traffic flow and the environment. In particular, very little is know on traffic stability and energy consumption. In this study, the vehicles equipped with ACC are modelled and controlled by two different spacing policies. Besides, Human Driving Behavior (HDB) is modelled by using Gipps model for comparison and for simulating different penetration rates. As distinguished from other studies, vehicle dynamics and energy consumption of an electric car is formulated, which has completely different characteristics and limitations than combustion engine cars. Hence the study aims at providing additional understanding of how ACC-equipped electric vehicles will behave in dense traffic conditions. HDB and ACC vehicles are placed in a roundabout at different penetration rates. String stability and energy consumption are investigated by giving a shock wave to a stable traffic condition. It is found that ACC with quadratic spacing policy has significantly positive effects on string stability and energy consumption

Cooperative Adaptive Cruise Control Based on Reinforcement Learning for Heavy-Duty BEVs

Advanced driver assistance systems (ADAS) are playing an increasingly important role in supporting the driver to create safer and more efficient driving conditions. Among all ADAS, adaptive cruise control (ACC) is a system that provides consistent aid, especially in highway mobility, guaranteeing safety by minimizing the possible risk of collision due to variations in the speed of the vehicle in front, automatically adjusting the vehicle velocity and maintaining the correct spacing. Theoretically, this type of system also makes it possible to optimize road throughput, increasing its capacity and reducing traffic congestion. However, it was found in practice that the current generation of ACC systems does not guarantee the so-called string stability of a vehicle platoon and can therefore lead to an actual decrease in traffic capacity. To overcome these issues, new cooperative adaptive cruise control (CACC) systems are being proposed that exploit vehicle-to-vehicle (V2V) connectivity, which can provide additional safety and robustness guarantees and introduce the possibility of concretely improving traffic flow stability

Intelligent Transportation Systems, Hybrid Electric Vehicles, Powertrain Control, Cooperative Adaptive Cruise Control, Model Predictive Control

Information obtainable from Intelligent Transportation Systems (ITS) provides the possibility of improving the safety and efficiency of vehicles at different levels. In particular, such information has the potential to be utilized for prediction of driving conditions and traffic flow, which allows us to improve the performance of the control systems in different vehicular applications, such as Hybrid Electric Vehicles (HEVs) powertrain control and Cooperative Adaptive Cruise Control (CACC). In the first part of this work, we study the design of an MPC controller for a Cooperative Adaptive Cruise Control (CACC) system, which is an automated application that provides the drivers with extra benefits, such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as interfering vehicles cutting-into the CACC platoons or hard braking by leading cars. To address this problem, we first propose a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme. Then, the predicted trajectory of each vehicle in the adjacent lanes is used to estimate the probability of that vehicle cutting-into the CACC platoon. To consider the calculated probability in control system decisions, a Stochastic Model Predictive Controller (SMPC) needs to be designed which incorporates this cut-in probability, and enhances the reaction against the detected dangerous cut-in maneuver. However, in this work, we propose an alternative way of solving this problem. We convert the SMPC problem into modeling the CACC as a Stochastic Hybrid System (SHS) while we still use a deterministic MPC controller running in the only state of the SHS model. Finally, we find the conditions under which the designed deterministic controller is stable and feasible for the proposed SHS model of the CACC platoon. In the second part of this work, we propose to improve the performance of one of the most promising realtime powertrain control strategies, called Adaptive Equivalent Consumption Minimization Strategy (AECMS), using predicted driving conditions. In this part, two different real-time powertrain control strategies are proposed for HEVs. The first proposed method, including three different variations, introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. The factor is proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy by 4\%. Finally, we integrate the recent development in reinforcement learning to design a novel multi-level power distribution control. The proposed controller reacts in two levels, namely high-level and low-level control. The high-level control decision estimates the most probable driving profile matched to the current (and near future) state of the vehicle. Then, the corresponding low-level controller of the selected profile is utilized to distribute the requested power between Electric Motor (EM) and Internal Combustion Engine (ICE). This is important because there is no other prior work addressing this problem using a controller which can adjust its decision to the driving pattern. We proposed to use two reinforcement learning agents in two levels of abstraction. The first agent, selects the most optimal low-level controller (second agent) based on the overall pattern of the drive cycle in the near past and future, i.e., urban, highway and harsh. Then, the selected agent by the high-level controller (first agent) decides how to distribute the demanded power between the EM and ICE. We found that by carefully designing a training scheme, it is possible to effectively improve the performance of this data-driven controller. Simulation results show up to 6\% improvement in fuel economy compared to the AECMS

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Developing and Evaluating the driving and powertrain systems of automated and electrified vehicles (AEVs) for sustainable transport

In the transition towards sustainable transport, automated and electrified vehicles (AEVs) play a key role in overcoming challenges such as fuel consumption, emissions, safety, and congestion. The development and assessment of AEVs require bringing together insights from multiple disciplines such as vehicle studies to design and control AEVs and traffic flow studies to describe and evaluate their driving behaviours. This thesis, therefore, addresses the needs of automotive and civil engineers, and investigates three classes of problems: optimizing the driving and powertrain systems of AEVs, modelling their driving behaviours in microscopic traffic simulation, and evaluating their performance in real-world driving conditions. The first part of this thesis proposes Pareto-based multi-objective optimization (MOO) frameworks for the optimal sizing of powertrain components, e.g., battery and ultracapacitor, and for the integrated calibration of control systems including adaptive cruise control (ACC) and energy management strategy (EMS). We demonstrate that these frameworks can bring collective improvements in energy efficiency, greenhouse gas (GHG) emissions, ride comfort, safety, and cost-effectiveness. The second part of this thesis develops microscopic free-flow or car-following models for reproducing longitudinal driving behaviours of AEVs in traffic simulation, which can support the needs to predict the impact of AEVs on traffic flow and maximize their benefits to the road network. The proposed models can account for electrified vehicle dynamics, road geometric characteristics, and sensing/perception delay, which have significant effects on driving behaviours of AEVs but are largely ignored in traffic flow studies. Finally, we systematically evaluate the energy and safety performances of AEVs in real-world driving conditions. A series of vehicle platoon experiments are carried out on public roads and test tracks, to identify the difference in driving behaviours between ACC-equipped vehicles and human-driven vehicles (HDVs) and to examine the impact of ACC time-gap settings on energy consumption

Fuel-efficient driving strategies

This thesis is concerned with fuel-efficient driving strategies for vehicles driving on roads with varying topography, as well as estimation of road grade\ua0and vehicle mass for vehicles utilizing such strategies. A framework referred\ua0to as speed profile optimization (SPO), is introduced for reducing the fuel\ua0or energy consumption of single vehicles (equipped with either combustion\ua0or electric engines) and platoons of several vehicles. Using the SPO-based\ua0methods, average reductions of 11.5% in fuel consumption for single trucks,\ua07.5 to 12.6% energy savings in electric vehicles, and 15.8 to 17.4% average\ua0fuel consumption reductions for platoons of trucks were obtained. Moreover,\ua0SPO-based methods were shown to achieve higher savings compared to\ua0the commonly used methods for fuel-efficient driving. Furthermore, it was\ua0demonstrated that the simulations are sufficiently accurate to be transferred\ua0to real trucks. In the SPO-based methods, the optimized speed profiles were\ua0generated using a genetic algorithm for which it was demonstrated, in a\ua0discretized case, that it is able to produce speed profiles whose fuel consumption\ua0is within 2% of the theoretical optimum.A feedforward neural network (FFNN) approach, with a simple feedback\ua0mechanism, is introduced and evaluated in simulations, for simultaneous estimation of the road grade and vehicle mass. The FFNN provided road grade\ua0estimates with root mean square (RMS) error of around 0.10 to 0.14 degrees,\ua0as well as vehicle mass estimates with an average RMS error of 1%, relative\ua0to the actual value. The estimates obtained with the FFNN outperform road\ua0grade and mass estimates obtained with other approaches

A self-learning intersection control system for connected and automated vehicles

This study proposes a Decentralized Sparse Coordination Learning System (DSCLS) based on Deep Reinforcement Learning (DRL) to control intersections under the Connected and Automated Vehicles (CAVs) environment. In this approach, roadway sections are divided into small areas; vehicles try to reserve their desired area ahead of time, based on having a common desired area with other CAVs; the vehicles would be in an independent or coordinated state. Individual CAVs are set accountable for decision-making at each step in both coordinated and independent states. In the training process, CAVs learn to minimize the overall delay at the intersection. Due to the chain impact of taking random actions in the training course, the trained model can deal with unprecedented volume circumstances, the main challenge in intersection management. Application of the model to a single-lane intersection with no turning movement as a proof-of-concept test reveals noticeable improvements in traffic measures compared to three other intersection control systems. A Spring Mass Damper (SMD) model is developed to control platooning behavior of CAVs. In the SMD model, each vehicle is assumed as a mass, coupled with its preceding vehicle with a spring and a damper. The spring constant and damper coefficient control the interaction between vehicles. Limitations on communication range and the number of vehicles in each platoon are applied in this model, and the SMD model controls intra-platoon and inter-platoon interactions. The simulation result for a regular highway section reveals that the proposed platooning algorithm increases the maximum throughput by 29% and 63% under 50% and 100% market penetration rate of CAVs. A merging section with different volume combinations on the main section and merging section and different market penetration rates of CAVs is also modeled to test inter-platoon spacing performance in accommodating merging vehicles. Noticeable travel time reduction is observed in both mainline and merging lanes and under all volume combinations in 80% and higher MPR of CAVs. For a more reliable assessment of the DSCLS, the model is applied to a more realistic intersection, including three approaching lanes in each direction and turning movements. The proposed algorithm decreases delay by 58%, 19%, and 13% in moderate, high, and extreme volume regimes, improving travel time accordingly. Comparison of safety measures reveals 28% improvement in Post Encroachment Time (PET) in the extreme volume regime and minor improvements in high and moderate volume regimes. Due to the limited acceleration and deceleration rates, the proposed model does not show a better performance in environmental measures, including fuel consumption and CO2 emission, compared to the conventional control systems. However, the DSCLS noticeably outperforms the other pixel-reservation counterpart control system, with limited acceleration and deceleration rates. The application of the model to a corridor of four interactions shows the same trends in traffic, safety, and environmental measures as the single intersection experiment. An automated intersection control system for platooning CAVs is developed by combining the two proposed models, which remarkably improves traffic and safety measures, specifically in extreme volume regimes compared to the regular DSCLS model

Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles

The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred