Closed‐loop hierarchical control strategies for connected and autonomous hybrid electric vehicles with random errors

Qiu, Shaolin; Qiu, Lihong; Qian, Lijun; Abdollahi, Zoleikha; Pisu, Pierluigi

doi:10.1049/iet-its.2018.5046

Cited by 10 publications

(12 citation statements)

References 24 publications

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“…The proposed prediction‐based strategy is suitable for real traffic conditions. To evaluate the performance of the optimisation strategy, the proposed GP‐RHC strategy is compared with an existing EMS proposed by Zhang et al [25]. The compared strategy is a prediction‐based optimal EMS, where ELM algorithm is exploited to provide the driver torque demand prediction for realising the receding horizon optimisation.…”

Section: Validationmentioning

confidence: 99%

“…Specifically, the velocity at the intersection usually varies sharply due to traffic signals, which leads to uncertainty in the forecast. Studies showed that the co‐optimisation methods for HEVs with the signal information considered can save fuel in an urban environment [24, 25]. In [26], a predictive energy management framework was proposed to incorporate dynamic traffic flow data into the EMS.…”

Section: Introductionmentioning

confidence: 99%

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Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

Zhang

Shen

2020

IET intell. transp. syst

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Section: Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

Zhang

Shen

2020

IET intell. transp. syst

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“…In this paper, the HEV is modelled in the Autonomie software which is a widely used powerful simulation tool based on MATLAB/Simulink for calculating vehicle energy consumption. Table 1 shows the components and parameters of the HEV obtained from our previous work [22]. The recuperation efficiency is defined as the energy recovering ratio from wheel to battery during braking and is calculated by using (41) according to the postprocessing files in the Autonomie software.…”

Section: Methodsmentioning

confidence: 99%

“…Since we simplify the intersection, only the longitudinal motion of the vehicle is considered in the upper layer control. The longitudinal vehicle dynamics are given by [10]:

(][, \begin{matrix} 1em4pt \\ \dot{s} \\ \dot{v} \end{matrix}) = (][, \begin{matrix} 1em4pt \\ v \\ - \frac{1}{2 M} C_{normalD} ρ_{normala} A v^{2} - (f + θ) g + a \end{matrix})

The objective of the upper layer control aims at minimising the HEV's fuel consumption, which can be expressed as [20, 22]:

min_{a_{L}^{i} false(t false)} \frac{1}{s_{L}^{i} ()(, t_{f})} \sum_{t = 0}^{t_{normalf}} {\dot{m}}_{L, fuel}^{i} (t) Δ t

{\dot{m}}_{L, normalfuel}^{i} false(t false) = \frac{1}{η_{L, normaleff}^{i} H_{L}^{i}} P_{L}^{i} (t)

v_{min} \leq v_{L}^{i} false(t false) \leq v_{max}

a_{min} \leq a_{L}^{i} false(t false) \leq a_{max}

where

{\dot{m}}_{L, normalfuel}^{i}

is the fu...…”

Section: Problem Formulationmentioning

confidence: 99%

“…The simulation results showed that energy efficiency was improved by the proposed method. Qiu et al [22] constructed a closed‐loop hierarchical controller for connected and autonomous HEVs considering the random errors of acceleration. In the high‐level controller, the velocity profile of each vehicle was optimised with stochastic model predictive control (SMPC) in order to reduce fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative control of connected hybrid electric vehicles and traffic signals at isolated intersections

Chen

Qian

Xuan

2020

IET intell. transp. syst

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The development of connected and automated vehicle technologies allows for cooperative control of vehicles and traffic signals at intersections. This study aims at exploring the cooperation between traffic signal control and eco‐driving control for a connected hybrid electric vehicle (HEV) system. A two‐level cooperative control method for integrating traffic signal control, vehicle speed control, and energy management is proposed with the objective of improving both traffic and fuel efficiency for HEVs at isolated intersections. In view of the energy management and recuperation system of HEV, the vehicle energy consumption characteristic is considered in the proposed method. More specifically, at the traffic level, a traffic signal control strategy is designed to minimise the total travel time and fuel consumption of all HEVs using dynamic programming, which explicitly considers the arrival time and recuperation information of vehicles. At the vehicle level, a hierarchical control architecture is applied to optimise the speed trajectories and powertrain of each HEV using model predictive control and adaptive equivalent consumption minimisation strategy. Simulation results show that compared with the fixed‐time and cycle‐based signal control strategies with eco‐driving, the proposed method can significantly reduce the travel time and fuel consumption by up to 27 and 24%, respectively.

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Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning

Kolmanovsky

et al. 2022

AI-enabled Technologies for Autonomous and Connected Vehicles

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Closed‐loop hierarchical control strategies for connected and autonomous hybrid electric vehicles with random errors

Cited by 10 publications

References 24 publications

Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

Cooperative control of connected hybrid electric vehicles and traffic signals at isolated intersections

Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning

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