Predictive velocity control in a hilly terrain over a long look-ahead horizon

Hamednia, Ahad; Murgovski, Nikolce; Fredriksson, Jonas

doi:10.1016/j.ifacol.2018.10.107

Cited by 21 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rated power of the actuator and road/traffic limits on speed are taken into consideration in the cruise speed filtering [23], [24], [34]. The cruising speed is assumed to be set manually by the driver or automatically by a telemetry system.…”

Section: A Computing Upper Bound On Travel Timementioning

confidence: 99%

“…Within the multi-level control architecture mentioned earlier, these problems are delegated to the highest supervisory level, generating reference travel time and battery state of charge trajectories over hundreds of kilometres. Early results on developing online implementable controllers that operate over long horizons, hereafter referred to as the mission managers, have been published in our previous work for the case of CV, see [34].…”

Section: Introductionmentioning

confidence: 99%

“…The goal of this paper is to generalise the mission manager developed in [34] to both CVs and EVs. The purpose of the mission manager is to generate optimal reference trajectories for the entire route, or for lookahead horizons that may stretch over hundreds of kilometres.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computationally efficient algorithm for eco-driving over long look-ahead horizons

Hamednia¹,

Sharma²,

Murgovski³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

This paper presents a computationally efficient algorithm for eco-driving over long prediction horizons. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimising gear as a function of longitudinal velocity and acceleration. The top level is solved online, optimising a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables.To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin's Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to standard cruise control, the energy savings of using the proposed algorithm is up to 15.71 %.

show abstract

Section: A Computing Upper Bound On Travel Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computationally efficient algorithm for eco-driving over long look-ahead horizons

Hamednia¹,

Sharma²,

Murgovski³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Factors such as transport demand and unsafe operation during unpredictable weather conditions can be the causes of road crashes in mountainous roadway areas road networks (Jawi et al, 2009). Other than that, road networks in mountainous roadway areas also faced problems such as difficulties in upgrading road networks and higher cost of road rehabilitation and maintenance (Hamednia et al, 2018;Zhang et al, 2020). Concerning road crash issues, few studies were conducted using secondary data focusing on the mountainous road network (e.g.…”

Section: Introductionmentioning

confidence: 99%

Road Crash Data Visualisation and Analytics Using Tableau for Mountainous Roadway Areas in Cameron Highlands, Malaysia

Mohd Nusa,

Ishak,

Rusli

et al. 2023

View full text Add to dashboard Cite

Despite government efforts and enforcement, alarming statistics in road crashes with high fatality rates in mountainous roadways are a significant concern to the authorities and community. This research has aimed to produce road crash profiling of Cameron Highlands based on secondary data using Tableau software. Results show that 66 cases involving motorcycles less than 251cc are the most dominant type of vehicle involved in road crashes, followed by tour or excursion vehicles, lorry trailers, four-wheel drive, and cars. The most frequent crash locations in Cameron Highlands are at the boundary of Jalan Keramat Pulai (45 cases) and Jalan Ringlet – Kg. Raja – Blue Valley (50 cases). These cases involved hitting pedestrians, sideswipe collisions, forced collisions, hitting the roadside animal, head-on collisions, vehicles out of control, and right-angle side, which lead to fatal injuries. These findings may assist in identifying intervention planning to control and manage road crashes in mountainous areas.

show abstract

“…The control methods of the predictive cruise control algorithm with the use of slope information can be divided into three categories: optimal control [16][17][18], model predictive control (MPC) [19][20][21] and instantaneous control [22,23]. Dynamic programming (DP) is based on optimal control theory and can always generate the most fuel-efficient results in handling PCC planning problems.…”

Section: Introductionmentioning

confidence: 99%

Predictive cruise control for heavy trucks based on slope information under cloud control system

Wan

Gao

et al. 2022

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

With the advantage of fast calculation and map resources on cloud control system (CCS), cloud-based predictive cruise control (CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control (PCC) system, lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method (RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also, compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity. Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

show abstract

Predictive velocity control in a hilly terrain over a long look-ahead horizon

Cited by 21 publications

References 19 publications

Computationally efficient algorithm for eco-driving over long look-ahead horizons

Computationally efficient algorithm for eco-driving over long look-ahead horizons

Road Crash Data Visualisation and Analytics Using Tableau for Mountainous Roadway Areas in Cameron Highlands, Malaysia

Predictive cruise control for heavy trucks based on slope information under cloud control system

Contact Info

Product

Resources

About