Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

[EN] In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. 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Intelligent energy management in hybrid electric vehicles

The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.<br /

A study on look-ahead control and energy management strategies in hybrid electric vehicles

Fuel efficiency in a hybrid electric vehicle requires a fine balance between usage of combustion engine and battery power. Information about the geometry of the road and traffic ahead can have a great impact on optimized control and the power split between the main parts of a hybrid electric vehicle. This paper provides a survey on the existing methods of control and energy management emphasizing on those that consider the look-ahead road situation and trajectory information. Then it presents the future trends in the control and energy management of hybrid electric vehicles.<br /

Eras of electric vehicles: electric mobility on the Verge. Focus Attention Scale

Daily or casual passenger vehicles in cities have negative burden on our finite world. Transport sector has been one of the main contributors to air pollution and energy depletion. Providing alternative means of transport is a promising strategy perceived by motor manufacturers and researchers. The paper presents the battery electric vehicles-BEVs bibliography that starts with the early eras of invention up till 2015 outlook. It gives a broad overview of BEV market and its technology in a chronological classification while sheds light on the stakeholders’ focus attentions in each stage, the so called, Focus-Attention-Scale-FAS. The attention given in each era is projected and parsed in a scale graph, which varies between micro, meso, and macro-scale. BEV-system is on the verge of experiencing massive growth; however, the system entails a variety of substantial challenges. Observations show the main issues of BEVsystem that require more attention followed by the authors’ recommendations towards an emerging market

Impact of Intelligent Transportation Systems on Parallel Hybrid Electric Heavy Duty Vehicles

A hybrid electric vehicle uses multiple sources of energy that can be independently or all together used to propel the wheels. In the presented work, the vehicle propulsion controller (VPC) for a parallel heavy duty hybrid electric vehicle (HEV) model has been modified to manage the alternative power source in advance based on the forthcoming traffic information. The goal is to prepare the powertrain for the next power event by making more energy storage capacity to capture free energy via regenerative braking or store more energy for expected need. The method of preparation will be by managing the battery state of charge (SOC), which is a metal hydride battery for this study, to take advantage of opportunistic regeneration. Autonomie software was used to simulate parallel HEV models.;The results revealed that the proposed looking-ahead control strategy for a class 8 parallel hybrid heavy duty vehicle with an engine power of 410 kW had a substantial contribution in preparing the system for forthcoming power demand. The looking-ahead strategy employed in this study improved fuel economy from 0.5% on flat terrain to about 3% on mountain terrain. Moreover, a looking-ahead strategy can contribute significantly to maintaining adequate power for the vehicle on different terrain types. The engine power can be downsized (with looking-ahead strategy) therefore improving fuel economy up to 13% while maintaining adequate power over different terrain types. The battery energy capacity can be downsized (with looking-ahead strategy) by half while maintaining nearly the same benefits (i.e. fuel economy and adequate power) compared to the hybridization system without looking-ahead strategy. Since different routes types (i.e. flat, hilly and mountain terrains) were used to investigate the impact of the looking-ahead strategy on heavy duty parallel HEV, these results can generally be applied to many terrain and traffic situations

A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Optimal Predictive Eco-Driving Cycles for Conventional, Electric, and Hybrid Electric Cars

International audienceIn this paper, the computation of eco-driving cycles for electric, conventional and hybrid vehicles using receding horizon and optimal control is studied. The problem is formulated as consecutive-optimization problems aiming at minimizing the vehicle energy consumption under traffic and speed constraints. The impact of the look-ahead distance and the optimization frequency on the optimal speed computation is studied to find a trade-off between the optimality and the computation time of the algorithm. For the three architectures considered, simulation results show that in urban driving conditions, a look-ahead distance of 300m to 500m leads to a sub-optimality less than 1% in the energy consumption compared to the global solution. For highway driving conditions, a look-ahead distance of 1km to 1.5km leads to a sub-optimality less than 2% compared to the global solution

Modelling and control of hybrid electric vehicles (a comprehensive review)

The gradual decline in global oil reserves and presence of ever so stringent emissions rules around the world, have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guarantying improved fuel economy and reduced emissions.The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilized. The challenge in developing an HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle.To this effect, a number of power management strategies have been proposed in literature. This paper presents a comprehensive review of these literatures, focusing primarily on contributions in the aspect of parallel hybrid electric vehicle modelling and control. As part of this treatise, exploitable research gaps are also identified. This paper prides itself as a comprehensive reference for researchers in the field of hybrid electric vehicle development, control and optimization