Related papers: Real-time Eco-Driving Control in Electrified Conne…
In this paper we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy management problem considering both power split and engine on/off decisions. The solution of a…
Equipping approximate dynamic programming (ADP) with inputconstraints has a tremendous significance. This enables ADP to be applied tothe systems with actuator limitations, which is quite common for dynamicalsystems. In a conventional…
The automated vehicle (AV) equipped with the Adaptive Cruise Control (ACC) system is expected to reduce the fuel consumption for the intelligent transportation system. This paper presents the Advanced ACC-Micro (AA-Micro) model, a new…
In this paper, near optimal tracking of a class of nonlinear systems is addressed. Adaptive (approximate) dynamic programming approach is used to calculate the optimal control in closed form. ADP (Adaptive (approximate) dynamic programming)…
In this paper, a model predictive mixed integer control method for BYD Qin Plus DM-i (Dual Model intelligent) plug-in hybrid electric vehicle (PHEV) is proposed for co-optimization to reduce fuel consumption during car following. First, the…
Autonomous driving technology pledges safety, convenience, and energy efficiency. Challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to…
This paper investigates the cooperative planning and control problem for multiple connected autonomous vehicles (CAVs) in different scenarios. In the existing literature, most of the methods suffer from significant problems in computational…
Vehicle control algorithms exploiting connectivity and automation, such as Connected and Automated Vehicles (CAVs) or Advanced Driver Assistance Systems (ADAS), have the opportunity to improve energy savings. However, lower levels of…
The effective and safe management of traffic is a key issue due to the rapid advancement of the urban transportation system. Connected autonomous vehicles (CAVs) possess the capability to connect with each other and adjacent infrastructure,…
This paper details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric…
In this paper, we present an optimal control framework to address motion coordination of connected automated vehicles (CAVs) in the presence of human-driven vehicles (HDVs) in merging scenarios. Our framework combines an unconstrained…
This paper addresses the problem of finding the optimal Eco-Driving (ED) speed profile of an electric Connected and Automated Vehicle (CAV) in an isolated urban un-signalized intersection. The problem is formulated as a single-level…
In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an…
This paper proposes an ecological adaptive cruise control (EACC) concept with the primary goal to minimize the fuel consumption in a city bus with an internal combustion engine (ICE). A hybrid model predictive control (HMPC) is implemented…
To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization…
By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffic congestion. With the rapid development of the technology…
This paper presents the development and implementation of a Model Predictive Control (MPC) framework for trajectory tracking in autonomous vehicles under diverse driving conditions. The proposed approach incorporates a modular architecture…
Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world,…
Connectivity and automation in vehicles provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy…
Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizing both the vehicle's continuous dynamics and discrete gear…