Modelling and control for economy‐oriented car‐following problem of hybrid electric vehicle

Sun, Yu; Wang, Xiangyu; Zhang, Lipeng; Shi, Jialei; An, Quan

doi:10.1049/iet-its.2018.5421

Cited by 17 publications

(7 citation statements)

References 36 publications

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“…The finally obtained Markov chain model is a trustworthy representation of the speed distributions of all vehicles running on the road. An instance of FIGURE 12 The sequences of errors Finally, the 6 weeks speed trajectories are used to validate the correctness of the optimal road by comparing energy consumption with two reference road profiles illustrated in Figure 14(b). The first one is the actual road and the second road is a straight line between the starting and terminal points.…”

Section: 23mentioning

confidence: 99%

Optimal road grade design based on stochastic speed trajectories for minimising transportation energy consumption

Liu

Feng

2021

IET Intelligent Trans Sys

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Reducing energy consumption, regardless of fuel or electricity, of ground vehicles is a paramount pursuit in academia and industry. Current research concentrates exclusively on the improvement of the vehicle, but accepts the road conditions as external influences. The innovation of this article is to consider the road conditions, particularly the grade angle, as design variables. It is assumed that the stochastic speed trajectories of all vehicles on the road can be modelled by a Markov chain. The expected value of the average energy consumptions of all vehicles running on the road is defined as the objective function. The optimisation problem is solved by dynamic programming with Markov model. Evaluations have been made on both simulated and measured speed trajectories. For the simulated speed trajectories, the optimal road grade profile designed by the method saves up to 22% energy compared with a flat road. For the measured speed trajectories, the optimal road grade profile saves up to 2.7% transportation energy compared with the actual road profile. Application of this method on building road could lead to considerable energy saving.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: 23mentioning

confidence: 99%

Optimal road grade design based on stochastic speed trajectories for minimising transportation energy consumption

Liu

Feng

2021

IET Intelligent Trans Sys

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“…The vehicle dynamic, mainly including gear, differential, wheels and brakes, and EV body, is shown in Figure 1. Since the mechanical transmission system of the vehicle is not the main focus of this paper, the modeling of the vehicle dynamic is not discussed in detail and the related details can be found in References [38][39][40].…”

Section: Structure Of the Fcev And Its Power Systemmentioning

confidence: 99%

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Yao

et al. 2019

Energies

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In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles' hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle's running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV's hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.

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“…Intelligent Vehicles (IV) and Intelligent Transportation System (ITS) have been hot topics during recent years [1]. They integrate information technology, sensor technology, and automatic control technology, and provide large potential to reduce pollutant emission [2,3]. As a pivotal component of ITS, Advanced Driver Assistance System (ADAS) which contains Adaptive Cruise Control (ACC) plays an important role in modern passenger cars and commercial vehicles [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation

et al. 2021

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Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.

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Modelling and control for economy‐oriented car‐following problem of hybrid electric vehicle

Cited by 17 publications

References 36 publications

Optimal road grade design based on stochastic speed trajectories for minimising transportation energy consumption

Optimal road grade design based on stochastic speed trajectories for minimising transportation energy consumption

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation

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