Adaptive control of a hybrid electric vehicle

Saeks, R.; Cox, C.; Neidhoefer, J.; Mays, P.; Murray, J.

doi:10.1109/tits.2002.804750

Cited by 58 publications

(11 citation statements)

References 42 publications

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“…The development and implementation of a power management control algorithm online constitutes a challenging control problem and has been the subject of intense study for the last two decades [17]- [20]. Several research efforts have focused on different heuristic approaches to optimized the power management control in PHEVs [21], [22]. The latter, however, cannot completely encompass all the potential fuel economy and emission benefits of the various HEV architectures.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Vehicle Dynamics and Powertrain Control for Connected and Automated Vehicles

Zhao

Mahbub

Malikopoulos

2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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The implementation of connected and automated vehicle technologies enables opportunities for a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. In this paper, we present a two-level control architecture for a connected and automated plug-in hybrid electric vehicle to optimize simultaneously its speed profile and powertrain efficiency. We evaluate the proposed architecture through simulation in a network of vehicles. I. INTRODUCTIONIn this paper, we are interested in investigating the opportunities to improve the efficiency of hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) when these vehicles are connected and automated. In an earlier work, we discussed the potential benefits of optimally coordinated connected and automated vehicles (CAVs) in a corridor using a traffic microsimulation environment without considering powertrain optimization [1]. In this paper, we apply a two-level supervisory control architecture for connected and automated PHEVs (CA-PHEVs) that consists of a vehicle dynamics (VD) controller and a powertrain (PT) controller. The supervisory controller oversees the VD and PT controllers and communicates the endogenous and exogenous information appropriately. The VD controller optimizes online the vehicle acceleration/deceleration profile to avoid stop-and-go driving in situations where there is a potential conflict with other vehicles, e.g., ramps, intersections, stop signs, roundabouts, etc. The PT controller computes the optimal nominal operation (set-points) for the engine and motor corresponding to the optimal solution derived from the VD controller. The complexity of the problem dimensionality can be managed by establishing two parallel and appropriately interacting computational levels, namely a cloud-based level, and a vehicle-based level.The objectives of this paper are to (1) optimize vehicle speed profile in terms of energy consumption and compare

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Vehicle Dynamics and Powertrain Control for Connected and Automated Vehicles

Zhao

Mahbub

Malikopoulos

2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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“…The empirical HEV model and control schemes were capable of realtime evaluation of a wide range of parameters. Saeks et al [27] proposed a decentralized adaptive control system for a motor/generator four-wheel-drive HEV designed to address unknown tire dynamics, changing road surfaces, and vehicle loading. Schouten et al [28] presented a fuzzy logic controller to determine the split of the driver's power demand between the engine and the motor and used the powertrain system analysis toolkit (PSAT), which is known today as Autonomie 1 [29], to validate the effectiveness of the controller.…”

Section: A Rule-based Power Management Control Algorithmsmentioning

confidence: 99%

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

Malikopoulos

2014

IEEE Trans. Intell. Transport. Syst.

196

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The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.Index Terms-Hybrid electric vehicles (HEVs), plug-in HEVs (PHEVs), supervisory power management control algorithms.

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“…Concerning the increasing long-term energy cost and attention on environmental impact, the application of Electric Vehicles (EVs) [1] is promising compared to that of traditional petrol based vehicles in many developed countries. Different from previous works which extensively investigate charging scheduling [2] for EVs already parking at home/Charging Stations (CSs), our research interest addresses another branch which has not received much attention, to manage the charging for on-the-move EVs by selecting appropriate CSs to travel for charging.…”

Section: Introductionmentioning

confidence: 99%

A reservation based charging management for on-the-move EV under mobility uncertainty

Cao

Wang

Kim³

et al. 2015

2015 IEEE Online Conference on Green Communications (OnlineGreenComm)

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With the continually increased attention on Electric Vehicles (EVs) due to environment impact, public Charging Stations (CSs) for EVs will become common. However, due to the limited electricity of battery, the driver may experience discomfort for long charging waiting time, if travelling towards a CS which is heavily loaded for charging. With this concern, we manage on-the-move EV charging by coordinating which CS to select for charging, and propose a CS-selection scheme considering EVs anticipated charging reservations generally including arrival time and expected charging time, in particular the parking duration is further considered in this paper. Upon this, by addressing mobility uncertainty that EVs may not reach their selected CSs on time due to uncertain traffic condition on the road, a periodical reservation updating for requesting the change of CS-selection decision is proposed to further coordinate charging management. The motivation for this is mainly due to that the mobility uncertainty affects the accuracy of reported reservation information, concerning the arrival time at selected CSs and expected charging time upon arrival. Evaluation results show the effectiveness of our proposal when considering realistic EV and CS characteristics.

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Adaptive control of a hybrid electric vehicle

Cited by 58 publications

References 42 publications

Optimal Vehicle Dynamics and Powertrain Control for Connected and Automated Vehicles

Optimal Vehicle Dynamics and Powertrain Control for Connected and Automated Vehicles

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

A reservation based charging management for on-the-move EV under mobility uncertainty

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