Control Design for an Articulated Truck With Autonomous Driving in an Electrified Highway

Félez, Jesús; García-Sánchez, Carlos; Lozano, José A.

doi:10.1109/access.2018.2875904

Cited by 18 publications

(11 citation statements)

References 22 publications

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“…Pilot projects have already been implemented on public highways in Sweden and Germany, with Siemens referring to this technology as the most cost-effective solution regarding electric trucks despite the required investment. In [299] is proposed a conceptual model for enhancing the eHighway system, aiming to endow it with autonomous driving. In [300] is proposed an energy management strategy for hybrid trucks, aiming to maximize the batteries lifetime while also reducing emissions.…”

Section: Electric Trucksmentioning

confidence: 99%

A Review on Power Electronics Technologies for Electric Mobility

et al. 2020

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Concerns about greenhouse gas emissions are a key topic addressed by modern societies worldwide. As a contribution to mitigate such effects caused by the transportation sector, the full adoption of electric mobility is increasingly being seen as the main alternative to conventional internal combustion engine (ICE) vehicles, which is supported by positive industry indicators, despite some identified hurdles. For such objective, power electronics technologies play an essential role and can be contextualized in different purposes to support the full adoption of electric mobility, including on-board and off-board battery charging systems, inductive wireless charging systems, unified traction and charging systems, new topologies with innovative operation modes for supporting the electrical power grid, and innovative solutions for electrified railways. Embracing all of these aspects, this paper presents a review on power electronics technologies for electric mobility where some of the main technologies and power electronics topologies are presented and explained. In order to address a broad scope of technologies, this paper covers road vehicles, lightweight vehicles and railway vehicles, among other electric vehicles.

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Section: Electric Trucksmentioning

confidence: 99%

A Review on Power Electronics Technologies for Electric Mobility

et al. 2020

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“…4,5 Better understanding human driver behaviors can more realistically simulate some situations in actual driving, which is very essential for the development of chassis control system and autonomous system of AHVs. 6…”

Section: Introductionmentioning

confidence: 99%

Dual predictive model adaptive switching control for directional control of tractor semitrailer combinations

Chen

Yao

2023

Advances in Mechanical Engineering

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In order to ensure that safety and reliability of tractor semitrailer combinations (TSCs) on the road, the human drivers’ steering decisions need to comprehensively consider the trajectories and states of the tractor and semitrailer. For this purpose, a dual predictive model adaptive switching control decision for directional control is proposed. Firstly, a multi-point preview algorithm and a general regression neural network are designed to percept the current local target paths for the tractor and semitrailer. Then, a kinematic predictive model control algorithm for low-speed path tracking control and a dynamic predictive model control algorithm for high-speed path following and lateral stability control are established respectively. In addition, an S-type switching function is introduced to realize smooth switching between the two control algorithms. Finally, the directional control decision in this study is validated by the numerical simulations under different conditions and compared with single-point preview driver and the MPC driver without considering semitrailer. The results show that the proposed approach can accurately track the target path and effectively improve the high-speed lateral stability.

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“…Model predictive control is also widely used in different automotive applications, especially for fuel consumption reduction [21]- [24], path tracking [25], [26] and cruise control [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

Glos

Otava

Václavek

2021

IEEE Trans. Veh. Technol.

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This article describes an application of Non-linear Model Predictive Control algorithms on energy efficient control of fully electric vehicle cabin temperature and air quality. Since fully electric vehicles can not utilize waste heat from a powertrain (or there is not enough waste heat) as ICE vehicles do, it is necessary to employ advanced control approaches (especially for cabin heating) due to the possible mileage lost by using energy from the batteries for cabin conditioning. The basic idea behind this is to avoid the heat losses caused by excessive air exchange and to ensure a satisfactory air quality in combination with a user defined temperature. The Non-linear Model Predictive control algorithms were successfully implemented into an Infineon AURIX Tricore microcontroller and tested within a Processor in the Loop simulation. Index Terms-non-linear model predictive control, fully electric vehicle, battery electric vehicle, vehicle cabin model, Extended Kalman filter, air quality control, temperature control NOMENCLATURE COP Coefficient

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Control Design for an Articulated Truck With Autonomous Driving in an Electrified Highway

Cited by 18 publications

References 22 publications

A Review on Power Electronics Technologies for Electric Mobility

A Review on Power Electronics Technologies for Electric Mobility

Dual predictive model adaptive switching control for directional control of tractor semitrailer combinations

Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

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