Energy-Efficient Driving in Connected Corridors via Minimum Principle Control: Vehicle-in-the-Loop Experimental Verification in Mixed Fleets

Ard, Tyler; Guo, Longxiang; Han, Jeongwoo; Jia, Yunyi; Vahidi, Ardalan; Karbowski, Dominik

doi:10.1109/tiv.2023.3234261

Cited by 21 publications

(12 citation statements)

References 36 publications

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“…Then, based on the system represented by (10), the auxiliary term of the controller can be designed as follows:…”

Section: Derivation Of the Controllermentioning

confidence: 99%

“…First, applying the error dynamics in (6) and eliminating the disturbance terms, the nominal system (10) combined with the dynamics of the leader can be rewritten as .…”

Section: Proofmentioning

confidence: 99%

“…This gap is typically smaller than the distance between independently controlled vehicles, reducing air drag and improving fuel economy [7]. Aiming to leverage the promising benefits of vehicle platoons, different countries and institutes have performed relevant research and even on-road experiments with regard to vehicle platoons [8][9][10].…”

Section: Introduction 1background Of This Researchmentioning

confidence: 99%

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State-Feedback and Nonsmooth Controller Design for Truck Platoon Subject to Uncertainties and Disturbances

Feng,

Gao,

Guo

2024

WEVJ

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Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a linearized expression method of platoon error dynamics based on state measurement, the state feedback mechanism combined with a nonsmooth controller for a truck platoon is proposed in the development of the distributed control method. The state-feedback controller can drive the nominal platoon system to the state of second-order consensus, and the nonsmooth controller counterparts the uncertainties and disturbances. The convergence and string stability of the proposed control algorithm are demonstrated both theoretically and experimentally, and the effectiveness and robustness are also verified by simulation tests.

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“…Then, based on the system represented by (10), the auxiliary term of the controller can be designed as follows:…”

Section: Derivation Of the Controllermentioning

confidence: 99%

“…First, applying the error dynamics in (6) and eliminating the disturbance terms, the nominal system (10) combined with the dynamics of the leader can be rewritten as .…”

Section: Proofmentioning

confidence: 99%

Section: Introduction 1background Of This Researchmentioning

confidence: 99%

See 1 more Smart Citation

State-Feedback and Nonsmooth Controller Design for Truck Platoon Subject to Uncertainties and Disturbances

Feng,

Gao,

Guo

2024

WEVJ

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show abstract

“…[1] VIL testing is generally performed in an enclosed open test field or on an indoor testbench. [16] Testbench-based VIL is more controllable, cost-efficient, and safer, which is applied more extensively. [17] As the testbench-based VIL is performed in a laboratory environment, and the whole AVUT is integrated into DOI: 10.1002/aisy.202300778 Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Cheng,

Wang,

Zhao

et al. 2024

Advanced Intelligent Systems

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Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing. Simulation‐based testing has gained widespread recognition as the essential approach for AV testing by providing sufficient testing scenarios in the virtual environment. Vehicle‐in‐the‐loop (VIL) simulation has the ability to perform comprehensive tests and validations for the AVs’ overall behaviors while keeping significant testing accuracy and efficiency through the combination of the virtual scenarios and the physical AV. This article provides an overview of representative studies on testbench‐based VIL simulation testing for AVs, mainly focusing on utilizing testbenches to simulate realistic road conditions, and using physical signal stimulation methods and related equipment to generate sensors’ physical signals. This article first summarizes current AV testing studies, identifying existing issues and flaws of the state‐of‐the‐art methods and tools. Afterward, the testbench‐based VIL is addressed around architecture, principles, advantages, and characteristics. Then, the road condition simulation and the sensor physical signal generation in VIL are discussed in depth from structure, principle, and corresponding advanced equipment. Finally, research gaps between cutting‐edge technologies and AV testing applications in industrialization are identified to facilitate future research in this direction.

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“…On the other hand, model predictive control (MPC) has been gaining popularity for EV controllers, especially with the advancement of connected and autonomous vehicle (CAV) technology enabled by vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications [7], [8]. With these communications, the ego vehicle is aware of the driving environment and the travel plans of other vehicles on the road.…”

mentioning

confidence: 99%

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Chen,

Kwak,

Kim

et al. 2024

IEEE Access

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The thermal management system (TMS) in electric vehicles (EVs), including climate control and battery thermal regulation, consumes more energy than any other auxiliary components. Therefore, optimizing TMS control is crucial for enhancing EV driving range. However, the complexity of the TMS, described by a differential algebraic system, poses challenges for real-time optimal control. This study proposes model predictive control (MPC)-based solutions for integrated TMS operation in EVs. An optimal thermal management problem is formulated using economic nonlinear MPC (NMPC), and its performance is evaluated. To reduce computational load, an approximated value function (VF) is introduced based on the economic NMPC results. A linear-time-varying MPC (LTV-MPC) with the approximated VF is proposed for real-time implementation using quadratic programming, and through simulations it is compared with the baseline NMPC controller and a rule-based (RB) controller. Results reveal that the LTV-MPC with an approximated VF performs similarly to NMPC while offering slightly compromised cooling performance. It also significantly reduces the computational time by a factor of 10 4 compared with NMPC owing to the short prediction horizon enabled by the approximated VF. Furthermore, when compared with the RB controller, the proposed LTV-MPC achieves energy savings in the range of 22.3% to 29.8%.

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Energy-Efficient Driving in Connected Corridors via Minimum Principle Control: Vehicle-in-the-Loop Experimental Verification in Mixed Fleets

Cited by 21 publications

References 36 publications

State-Feedback and Nonsmooth Controller Design for Truck Platoon Subject to Uncertainties and Disturbances

State-Feedback and Nonsmooth Controller Design for Truck Platoon Subject to Uncertainties and Disturbances

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

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