Fuel Efficient Control Algorithms for Connected and Automated Line-Haul Trucks

Liu, Jingxuan; Pattel, Bibin; Desai, Akash S; Hodzen, Edmund; Borhan, Hoseinali

doi:10.1109/ccta.2019.8920650

Cited by 12 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 3-truck platoon is then simulated on the 70 km stretch of U.S. highway travelling from Lanesville, IN to Siberia, IN for a nominal model of road grade conditions for heavy-duty long-haul truck applications [21]. A batch of scenarios is generated where each truck is loaded with a mass sampled from m ∈ {14, 22, 30, 38}t and all possible permutations of truck orderings with these loadings are simulated (24…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Considerate and Cooperative Model Predictive Control for Energy-Efficient Truck Platooning of Heterogeneous Fleets

Ard

Pattel

Vahidi

et al. 2022

2022 American Control Conference (ACC)

Self Cite

View full text Add to dashboard Cite

Connectivity-enabled automation of distributed control systems allow for better anticipation of system disturbances and better prediction of the effects of actuator limitations on individual agents when incorporating a model. Automated convoy of heavy-duty trucks in the form of platooning is one such application designed to maintain close gaps between trucks to exploit drafting benefits and improve fuel economy, and has traditionally been handled with classically-designed connected and adaptive cruise control (CACC). This paper is motivated by demonstrated limitations of such a control strategy, in which a classical CACC was unable to efficiently handle real-world road grade and velocity transient disturbances without the assistance of fleet operator intervention, and is non-adaptive to varied hardware and loading conditions of the operating truck. This automation strategy is addressed by forming a cooperative model predictive control (MPC) for eco-platooning that considers interactions with trailing trucks to incentivize platoon harmonization under road disturbances, velocity transients, and engine limitations, and further improves energy economy by reducing unnecessary engine effort. This is accomplished for each truck by sharing load, maximum engine power, transmission ratios, control states, and intended trajectories with its nearest neighbors. The performance of the considerate and cooperative strategy was demonstrated on a real-world driving scenario against a similar non-considerate control strategy, and overall it was found that the considerate strategy significantly improved harmonization between the platooned trucks in a real-time implementable manner.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Fig.6: Excerpt of Lanesville-Siberia route[21]. Considerate MPC (blue, solid) improved harmonization between the trucks and reduced transients compared to the anticipative MPC (red, dashed).…”

mentioning

confidence: 99%

Considerate and Cooperative Model Predictive Control for Energy-Efficient Truck Platooning of Heterogeneous Fleets

Ard

Pattel

Vahidi

et al. 2022

2022 American Control Conference (ACC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, considerations to isolate the traffic effects due to the optimal controller are made, so: travel times are normalized among simulations, and only flat road is considered. It should be noted that separate optimal controllers and planners can further improve fuel economy over human drivers with these effects considered [28]. In addition, simulations are run with a timestep of 100 ms [11], while MPC is discretized with a timestep of 1000 ms and run every simulation timestep.…”

Section: Simulation Environmentmentioning

confidence: 99%

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

Ard¹,

Dollar²,

Vahidi³

et al. 2019

Preprint

View full text Add to dashboard Cite

This paper studies the energy and traffic impact of a proposed Cooperative and Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the anticipative mode, more immediately beneficial when automated vehicle fleet penetration is low, and 2. the connected mode, beneficial in coordinated platooning scenarios and high automated vehicle penetrations appropriate for autonomous vehicle specific applications. Inhorizon and terminal constraints handle safety considerations, and vehicle constraints for acceleration capabilities are implicitly understood through the use of powertrain maps. Real traffic scenarios are then modeled using time headway distributions from traffic data. To study impact over a range of demands, we vary input vehicle volume from low to high and then vary CAV penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we find that our vehicles perform at a 10% -20% higher fuel efficiency over human drivers. Due to secondary effects of smoothing traffic flow, fuel benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more fuel-efficiently than they did in the baseline all-human scenario.

show abstract

“…Several drawbacks contribute to these limitations. Firstly, AODV employs a reactive routing approach, which leads to increased control message exchange [4] and longer route setup times in dense networks [5,6]. Secondly, periodic route maintenance in AODV consumes network resources despite no active data transmission, resulting in unnecessary control message exchanges and increased network overhead [7].…”

Section: Introductionmentioning

confidence: 99%

Learning Automata-Based AODV to Improve V2V Communication in A Dynamic Traffic Simulation

2024

IJIES

View full text Add to dashboard Cite

The study introduces the learning automata-based AODV (LA-AODV) protocol to enhance vehicle-tovehicle (V2V) communication in dynamic vehicular Ad-hoc networks (VANETs). Existing routing protocols, such as Ad-hoc on-demand distance vector (AODV) protocols, face significant challenges, including low data transfer rates, higher delay times, lower throughput, and data congestion resulting from rapidly changing network topologies. LA-AODV addresses these issues by optimizing the quality of service (QoS) through the real-time selection of relay nodes based on vehicle speed, distance, and actual position parameters. Simulations were conducted at the Gadjah Mada university (UGM) roundabout in Yogyakarta, Indonesia, using SUMO and NS3 simulators. LA-AODV outperforms AODV with Packet Delivery Ratios ranging from 95% to 99% and Average Throughputs between 36.90 Kbps and 56.50 Kbps. Although LA-AODV exhibits slightly higher End-to-End Delays, it effectively mitigates Packet Loss Ratios ranging from 1% to 4%. These enhancements optimize routing decisions, reduce communication overhead, and enhance network resource utilization.

show abstract

Fuel Efficient Control Algorithms for Connected and Automated Line-Haul Trucks

Cited by 12 publications

References 12 publications

Considerate and Cooperative Model Predictive Control for Energy-Efficient Truck Platooning of Heterogeneous Fleets

Considerate and Cooperative Model Predictive Control for Energy-Efficient Truck Platooning of Heterogeneous Fleets

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

Learning Automata-Based AODV to Improve V2V Communication in A Dynamic Traffic Simulation

Contact Info

Product

Resources

About