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

Zhang, Bo; Xu, Fuguo; Shen, Tielong

doi:10.1049/iet-its.2020.0245

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…The prediction has been based on the most similar past trajectories to the given route and this information has been exploited for forecasting up to 20 min; then, traffic velocity predicted has been adopted for an MPC control approach. In V2X context, the study proposed in [31] and consistent with the attempt of our work, has developed a novel real-time EMS based on an improved torque demand predictor. A Gaussian process (GP)based predictor is employed aiming to forecast torque demand at 2-s and 4-s horizon.…”

Section: Introductionsupporting

confidence: 62%

A comparative study of driver torque demand prediction methods

Cavanini

Ciabattoni

Ferracuti

et al. 2022

IET Intelligent Trans Sys

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The performances of energy management systems or electric vehicles and hybrid electric vehicles are highly dependent on the forecast of future driver torque/power request sequence that affects vehicle efficiency and economy. Since the behaviour of the driver is challenging to model/predict by first‐principles models, modern artificial intelligence algorithms would represent feasible methods for approaching this problem in real‐world automotive systems. This work provides a comparative study and analysis of performances of different data‐driven torque prediction strategies. The studied and compared torque demand prediction techniques are exponentially varying model, linear regression, shallow and deep neural networks, and least square support vector machine‐based approaches. The prediction performance and computational cost of these techniques are evaluated and reported, and the possibility of exploiting these techniques in real‐world scenarios is also discussed.

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Section: Introductionsupporting

confidence: 62%

A comparative study of driver torque demand prediction methods

Cavanini

Ciabattoni

Ferracuti

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Moreover, a particle swarm optimization approach with numerous dynamic populations increases computational efficiency. To improve the fuel economy of a parallel hybrid electric vehicle (HEV), Bo Zhang et al [25] formulated a receding horizon control problem based on a cost function of energy consumption and optimized it with a sequential quadratic programming algorithm. The results of the simulation platform validate that the pro-posed strategy can improve fuel economy.…”

Section: Related Workmentioning

confidence: 99%

Leader-follower Optimal Control Method for Vehicle Platoons to Improve Fuel Eﬃciency

Li¹,

Guo²,

Wang³

et al. 2023

IJACSA

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The automotive industry has experienced swift development with the rapid growth of the economy. The increasing number of vehicles has led to deteriorating road traffic conditions, increased consumption of nonrenewable energy, and excessive vehicle emissions. To tackle the problems of fuel efficiency and safety control for vehicle platoons, this study suggests a novel leader-follower optimal control method for vehicle platoons to improve fuel efficiency. Depending on where vehicles are in a platoon, they are classified into two categories: the leader vehicle and follower vehicles. The differing driving circumstances of these two types of vehicles are considered in this paper by using various control methods. On the one hand, the leader vehicle uses an approximate fuel consumption model to improve computational efficiency. At the same time, speed and acceleration are constrained to obtain the speed optimization curve. The follower vehicle uses a distributed receding horizon control method, which calculates the vehicle's speed optimization profile online. On the other hand, a linear following model is used to prevent collisions between vehicles and ensure the safety of the vehicle platoon. The simulation experiment has demonstrated that this speed optimization control method can reduce the fuel consumption of the vehicle platoon and ensure the safety of the vehicles.

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On-board torque management approach to the E-COSM benchmark problem with a prediction-based engine assignment

Zhang

2022

Control Theory Technol.

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

Cited by 9 publications

References 33 publications

A comparative study of driver torque demand prediction methods

A comparative study of driver torque demand prediction methods

Leader-follower Optimal Control Method for Vehicle Platoons to Improve Fuel Eﬃciency

On-board torque management approach to the E-COSM benchmark problem with a prediction-based engine assignment

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