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Commercial Realization of Optimal Energy Management in Hybrid Electric Vehicles and Supporting Technologies

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (25 February 2022) | Viewed by 7511

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Special Issue Editors

Dr. Zachary D. Asher

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Guest Editor

Mechanical and Aerospace Engineering, Western Michigan University, 1903 W Michigan Ave, Kalamazoo, MI 49008-5343, USA
Interests: controls; optimization; energy systems; autonomous vehicles

Prof. Dr. Thomas H. Bradley

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Guest Editor

Department of Systems Engineering, Colorado State University, Engineering Building A202B, Fort Collins, CO 80523-1374, USA
Interests: mechanical engineering; energy systems; systems engineering

Special Issue Information

Dear Colleagues,

Optimal energy management of hybrid electric vehicles has been an active subject of research for nearly twenty years, yet commercial realization remains limited. Because of this long and storied history, there are many experienced researchers each with their own experiences and interpretations of the field. The goal of this Special Issue is to collect a series of papers that will establish a new and common understanding of the field in 2020. We have published our interpretation of the field recently in “Identification and Review of the Research Gaps Preventing a Realization of Optimal Energy Management Strategies in Vehicles” published in the SAE International Journal of Alternative Powertrains in 2019. However, it is critical to showcase the breadth and depth of recent research projects focused on this excited technology to establish a common understanding. We are looking for your help to shape the future of the field of optimal energy management, a technology that will likely be critical for achieving eventual transportation sustainability. We sincerely hope that you will consider being a part of this new movement.

Submit your paper and select the Journal “Energies” and the Special Issue “Commercial Realization of Optimal Energy Management in Hybrid Electric Vehicles and Supporting Technologies” via: MDPI submission system. Please contact the special issue editor ([email protected]) for any queries. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Dr. Zachary D. Asher
Prof. Dr. Thomas H. Bradley
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Optimal energy management
Hybrid electric vehicles
Optimal control
Globally optimal control
Stochastic control
Model predictive control
Perception
Planning
Disturbances
Prediction error
Hardware-in-the-loop
Vehicle-in-the-loop

Published Papers (3 papers)

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Research

20 pages, 3035 KiB

Open AccessArticle

Optimal Calibration of an Adaptive and Predictive Energy Management Strategy for Fuel Cell Electric Trucks

by Alessandro Ferrara, Saeid Zendegan, Hans-Michael Koegeler, Sajin Gopi, Martin Huber, Johannes Pell and Christoph Hametner

Energies 2022, 15(7), 2394; https://doi.org/10.3390/en15072394 - 24 Mar 2022

Cited by 10 | Viewed by 2635

Abstract

Energy management strategies have a significant impact on the hydrogen economy of fuel cell trucks and the lifetime of battery and fuel cell systems. This contribution presents the design and optimal calibration of an energy management strategy that is adaptive to the battery and ambient temperatures. Indeed, fuel cell trucks face critical operating conditions due to high ambient temperatures or high loads on long uphill roads. However, the presented adaptive energy management strategy shifts the electric loads to the fuel cell system to limit the battery usage, avoiding accelerated degradation due to battery temperature peaks without hindering the hydrogen economy. The strategy design and calibration involves a multi-objective optimization of performance indicators related to hydrogen consumption, fuel cell degradation, battery thermal state, equivalent charge/discharge cycles, and charge control. This work uses AVL CAMEO to systematically vary the adaptive curve parameters to explore the trade-off between the key performance indicators. The calibration considers real-world driving cycles of road freight vehicles, including measured speed, road elevation, and variable vehicle mass. Moreover, the energy management design is robust because the performance indicators are evaluated over 8935 km, covering an extensive range of real-world driving scenarios. Eventually, the adaptive and predictive energy management strategy proposed in this work can meet all the performance targets thanks to the optimal calibration, and it is particularly effective in avoiding battery temperature peaks. Full article

(This article belongs to the Special Issue Commercial Realization of Optimal Energy Management in Hybrid Electric Vehicles and Supporting Technologies)

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20 pages, 691 KiB

Open AccessArticle

Switched Optimal Control of a Heavy-Duty Hybrid Vehicle

by Muataz Abotabik and Richard T. Meyer

Energies 2021, 14(20), 6736; https://doi.org/10.3390/en14206736 - 16 Oct 2021

Cited by 2 | Viewed by 1807

Abstract

This work investigates the fuel energy and emission reductions possible with the hybridization of a Class 8 tractor-trailer. The truck tractor has two drive axles: one powered by an internal-combustion-engine-based powertrain (CP) and the other powered by an electric powertrain (EP) consisting of an electric drive system supplied by a battery pack, resulting in a through-the-road hybrid. The EP has two modes of operation depending on the direction of power flow: motoring/battery discharging and generating/battery recharging. Switched optimal control is used to select between the two modes of EP operation, and a recently developed distributed switched optimal control is applied. The control is distributed between the CP, the EP, and the vehicle motion operation components. Control-oriented, component-specific power flow models are set forth to describe the dynamics and algebraic relationships. Four different tractor-trailers are simulated: the original CP and three hybrids with engine sizes of 15 L, 11 L, and 7 L. Simulations are performed over a short test cycle and two regulatory driving cycles to compare the fuel use, total energy, and emissions. Results show that the hybrids have reduced fuel use, total energy, and emissions compared to the original CP; the reductions and reference velocity tracking error increases as the engine size is decreased. Particularly, fuel use is reduced by at least

4.1

% under a charge sustaining operation and by

9.8

% when the battery energy can be restored with an off-board charger at the end of the cycle. Full article

(This article belongs to the Special Issue Commercial Realization of Optimal Energy Management in Hybrid Electric Vehicles and Supporting Technologies)

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22 pages, 2799 KiB

Open AccessFeature PaperArticle

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

by Aaron Rabinowitz, Farhang Motallebi Araghi, Tushar Gaikwad, Zachary D. Asher and Thomas H. Bradley

Energies 2021, 14(18), 5713; https://doi.org/10.3390/en14185713 - 10 Sep 2021

Cited by 16 | Viewed by 2493

Abstract

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC. Full article

(This article belongs to the Special Issue Commercial Realization of Optimal Energy Management in Hybrid Electric Vehicles and Supporting Technologies)

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Commercial Realization of Optimal Energy Management in Hybrid Electric Vehicles and Supporting Technologies

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