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Improving Fuel Efficiency of Commercial Vehicles through Optimal Control of Energy Buffers
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.ORCID iD: 0000-0002-3626-6367
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption.

Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption.

The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications.

The contribution of the thesis can be summarized as follows:

• Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust.

• Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs.

• Developing a one-step prediction control method for improving the gearchanging decision.

• Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus.

• Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod.

• Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. , xviii, 71 p.
Series
TRITA-MMK, ISSN 1400-1179 ; 2016:01
Keyword [en]
Energy buffer, Optimal control, Hybrid electric vehicle, Engine cooling system, Equivalent consumption minimization strategy, Model predictive control
National Category
Mechanical Engineering Control Engineering Energy Engineering
Research subject
Machine Design
Identifiers
URN: urn:nbn:se:kth:diva-181071ISBN: 978-91-7595-850-7 (print)OAI: oai:DiVA.org:kth-181071DiVA: diva2:898419
Public defence
2016-02-18, F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20160128

Available from: 2016-01-28 Created: 2016-01-28 Last updated: 2016-01-28Bibliographically approved
List of papers
1. Reducing Auxiliary Energy Consumption of Heavy Trucks by Onboard Prediction and Real-time Optimization
Open this publication in new window or tab >>Reducing Auxiliary Energy Consumption of Heavy Trucks by Onboard Prediction and Real-time Optimization
Show others...
2017 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 188, 652-671 p.Article in journal (Refereed) Published
Abstract [en]

The electric engine cooling system, where the coolant pump and the radiator fan are driven by electric motors, admits advanced control methods to decrease auxiliary energy consumption. Recent publications show the fuel saving potential of optimal control strategies for the electric cooling system through offline simulations. These strategies often assume full knowledge of the drive cycle and compute the optimal control sequence by expensive global optimization methods. In reality, the full drive cycle is unknown during driving and global optimization not directly applicable on resource-constrained truck electronic control units. This paper reports state-of-the-art engineering achievements of exploiting vehicular onboard prediction for a limited time horizon and minimizing the auxiliary energy consumption of the electric cooling system through real-time optimization. The prediction and optimization are integrated into a model predictive controller (MPC), which is implemented on a dSPACE MicroAutoBox and tested on a truck on a public road. Systematic simulations show that the new method reduces fuel consumption of a 40-tonne truck by 0.36% and a 60-tonne truck by 0.69% in a real drive cycle compared to a base-line controller. The reductions on auxiliary fuel consumption for the 40-tonne and 60-tonne trucks are about 26% and 38%, respectively. Truck experiments validate the consistency between simulations and experiments and confirm the real-time feasibility of the MPC controller. © 2016 Elsevier Ltd

Place, publisher, year, edition, pages
Elsevier, 2017
Keyword
Engine cooling system; Model predictive control (MPC); Parasitic load reduction; Quadratic programming (QP)
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-181077 (URN)10.1016/j.apenergy.2016.11.118 (DOI)000393003100053 ()2-s2.0-85007038221 (Scopus ID)
Projects
CONVENIENT
Funder
EU, FP7, Seventh Framework Programme, 312314
Note

QC 20170111

Available from: 2016-01-28 Created: 2016-01-28 Last updated: 2017-05-22Bibliographically approved
2. Fuel Saving Potential of Optimal Engine Cooling System
Open this publication in new window or tab >>Fuel Saving Potential of Optimal Engine Cooling System
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The engine cooling system in trucks is one of the main sources of parasite load. Thus optimal control of the engine thermal management system with the objective of minimizing energy consumption can substantially improve fuel efficiency. Existing methods on the engine thermal control system concentrate mainly on regulating the engine coolant temperature within a safety range. This paper explicitly calculates the energy consumption of the cooling system using the optimal control methods to decide the trajectories of the control values of the cooling system. During the optimal operation, the engine cooling system serves as another energy buffer to balance the engine workload in conventional trucks. To expose the maximal fuel saving potential of the optimal engine thermal control system, we apply dynamic programming in the investigation and the results are compared with a simple state feedback controller.

Place, publisher, year, edition, pages
Society of Automotive Engineers, 2014
Keyword
Energy efficient vehicles
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-170378 (URN)
Conference
AVEC 2014 International symposium on advanced vehicle control, September 22-26, 2014
Note

QC 20150715

Available from: 2015-06-29 Created: 2015-06-29 Last updated: 2016-01-28Bibliographically approved
3. Predictive control of the engine cooling system for fuel efficiency improvement
Open this publication in new window or tab >>Predictive control of the engine cooling system for fuel efficiency improvement
2014 (English)In: Automation Science and Engineering (CASE), 2014 IEEE International Conference on, IEEE conference proceedings, 2014, 61-66 p.Conference paper, Published paper (Refereed)
Abstract [en]

The engine cooling system in trucks is one of the main sources of parasite load. Thus fuel efficiency can be improved by optimal control of engine thermal management system considering fuel consumption minimization as the objective. Although several optimal control methods have been proposed for the engine cooling system, their main emphasize is on regulating engine and coolant temperature in an acceptable range rather than minimizing fuel consumption. In contrast, this paper investigates the fuel saving potential of predictive optimal control methods for the engine cooling system of conventional trucks. Our method exploits the idea of energy buffers in the automotive system, where the engine cooling system and the battery serve as energy buffers. The advantages of this approach are the recovery of brake energy and the balance of energy sources so that the total energy loss is minimized. A model predictive controller is used as the real time controller, and the results are compared with a simple state feedback controller and a global optimal solution obtained by dynamic programming. The results show limited but notable improvement in fuel efficiency. The results also construct a base for ongoing research on energy buffer control in conventional heavy trucks.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
National Category
Mechanical Engineering
Research subject
Vehicle and Maritime Engineering; Energy Technology
Identifiers
urn:nbn:se:kth:diva-170377 (URN)10.1109/CoASE.2014.6899305 (DOI)
Conference
IEEE International Conference on Automation Science and Engineering (CASE), 18-22 Aug. 2014,Taipei
Note

QC 20150716

Available from: 2015-06-29 Created: 2015-06-29 Last updated: 2016-01-28Bibliographically approved
4. Optimization of gear shifting and torque split for improved fuel efficiency and drivability of HEVs
Open this publication in new window or tab >>Optimization of gear shifting and torque split for improved fuel efficiency and drivability of HEVs
2013 (English)In: SAE Technical Papers: Volume 2, 2013, S A E Inc , 2013, Vol. 2, 2013-01-1461- p.Conference paper, Published paper (Refereed)
Abstract [en]

Decreasing fuel consumption and emissions in automobiles has been an active research topic in recent years. Vehicles with alternative powertrain systems, especially hybrid-electric vehicles (HEVs), have shown significant reduction in fuel consumption and emissions, and therefore have attracted many researchers to this field. The focus is usually on the development of optimal power management control methods. For parallel HEVs, the primary control variable is the torque split between the internal combustion engine and the electric motor. More advanced approaches also simultaneously search for the optimal gear number and engine on/off state, which can further reduce the fuel consumption but also complicate the problem. In the literature on HEVs, the emphasis is typically only on fuel efficiency and sometimes the emissions. The drivability of the vehicle is usually not considered during the optimization process. Furthermore, gearbox models do not usually reflect the real behavior of vehicle due to over simplification in vehicle models. This paper studies the energy management problem of parallel HEVs. Fuel consumption and drivability are optimized through an integrated optimization process by searching optimal torque split and gear number simultaneously. Intelligent filters are designed to stabilize the values of gear number to avoid frequent oscillation. The method is suitable for real-time implementation and has been tested in the simulation software Autonomie.

Place, publisher, year, edition, pages
S A E Inc, 2013
Keyword
Fuel efficiency, Integrated optimization, Management problems, Optimization process, Power-train systems, Primary control, Real-time implementations, Simulation software
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-134173 (URN)10.4271/2013-01-1461 (DOI)2-s2.0-84881217834 (Scopus ID)
Conference
SAE 2013 World Congress and Exhibition; Detroit, MI; United States; 16 April 2013 through 18 April 2013
Note

QC 20131120

Available from: 2013-11-20 Created: 2013-11-18 Last updated: 2016-01-28Bibliographically approved
5. Improving Fuel Economy and Robustness of an Improved ECMS Method
Open this publication in new window or tab >>Improving Fuel Economy and Robustness of an Improved ECMS Method
2013 (English)In: 2013 10th IEEE International Conference on Control and Automation  (ICCA), IEEE , 2013, 598-603 p.Conference paper, Published paper (Refereed)
Abstract [en]

Hybrid electric vehicles have shown significant improvement for both fuel efficiency and emission reduction, and attracted many researchers. Paramount for the fuel efficiency of HEVs is the energy management control strategies. ECMS (equivalent consumption minimization strategy) is one of the well-known real time power management strategies and has been used extensively in different works; however, its intrinsic difficulty is to find the optimal equivalent factor, which in theory is determined by the a priori knowledge of the complete driving cycle. Different methods have been proposed to solve this issue, but each one has its own pros. and cons. Especially, the applicability of each method for different cycles as well as the computation overhead are two main concerns in the methods presented so far. In this paper, a new method is presented for calculating equivalent factor in the ECMS method. The method does not rely on any prediction nor the a priori knowledge of driving cycles. Its robustness is demonstrated through different driving cycles with distinct characteristics. Our new method will improve the effectiveness and robustness of the ECMS method.

Place, publisher, year, edition, pages
IEEE, 2013
Series
IEEE International Conference on Control and Automation ICCA, ISSN 1948-3449
Keyword
Computation overheads, Driving cycle, Emission reduction, Energy management control strategies, Fuel efficiency, Priori knowledge, Real-time power managements
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-132220 (URN)10.1109/ICCA.2013.6564946 (DOI)000324335200108 ()2-s2.0-84882308395 (Scopus ID)978-1-4673-4708-2 (ISBN)
Conference
2013 10th IEEE International Conference on Control and Automation, ICCA 2013; Hangzhou; China; 12 June 2013 through 14 June 2013
Note

QC 20131024

Available from: 2013-10-24 Created: 2013-10-24 Last updated: 2016-01-28Bibliographically approved
6. One-step prediction for improving gear changing control of HEVs
Open this publication in new window or tab >>One-step prediction for improving gear changing control of HEVs
2014 (English)In: Journal of Robotics and Mechatronics, ISSN 0915-3942, Vol. 26, no 6, 799-807 p.Article in journal (Refereed) Published
Abstract [en]

Decreasing fuel consumption and emissions in automobiles continues to be an active research problem. A promising technology is powertrain hybridization. Study in this area usually focuses on the development of optimal power management control methods. The equivalent consumption minimization strategy (ECMS) is a widely used real-time control method used for determining the optimal trajectory of the power split between the engine and motor. Reports also cover applying ECMS to find an optimal gear changing strategy, but results are not always satisfactory in fuel economy and drivability. One possible reason for this is that gearbox dynamics are slow, but ECMS is based on instant optimization and neglects this time delay. This paper proposes a simple prediction strategy for improving ECMS performance used with gear changing control. The proposed controller improves fuel efficiency and drivability without the need of adding extra sensors to the automobile. The proposed method’s simplicity makes it suitable for implementation.

Place, publisher, year, edition, pages
Fuji Technology Press, 2014
Keyword
Drivability, ECMS, Fuel efficiency, Hybrid electric vehicle, Prediction
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-161050 (URN)2-s2.0-84919427220 (Scopus ID)
Projects
CONENIENT
Funder
EU, FP7, Seventh Framework Programme, 312314
Note

QC 20150309

Available from: 2015-03-09 Created: 2015-03-06 Last updated: 2017-01-11Bibliographically approved
7. Fuel efficiency improvement in HEVs using electromechanical brake system
Open this publication in new window or tab >>Fuel efficiency improvement in HEVs using electromechanical brake system
2013 (English)In: 2013 IEEE Intelligent Vehicles Symposium (IV), IEEE , 2013, 322-327 p.Conference paper, Published paper (Refereed)
Abstract [en]

Today, two of the main concerns in transportation industry are reducing fuel consumption and emissions, and tough regulations are put on the vehicle manufacturers in these regards. One of the main approaches towards reducing CO2 emissions is hybridization of the powertrain system. Substantial R&D in this area over the last couple of years has resulted in rather optimal components and control strategies, and hence that further substantial improvements are difficult. This motivates research on other energy consuming vehicle subsystems, e.g. pneumatic and hydraulic systems. In this paper, the brake system of a hybrid city bus is studied. A complete electrification of the primary brake system would eliminate the use of low efficiency pneumatics for braking. It is therefore interesting to investigate how much energy can be saved by using electrically actuated and controlled primary brakes. The study is based on simulations in Autonomie which is a MATLAB/SIMULINK based vehicle simulation software package. Different representative driving cycles are studied. It is shown that fuel consumption can be reduced in the range of 0.5 to 1.5% by substituting the pneumatic brake system with a mechatronic one. This may seem limited, but can, combined with substitution of also other less efficient subsystems with their mechatronic counterparts, result in a substantial environmental and economic improvement.

Place, publisher, year, edition, pages
IEEE, 2013
Series
IEEE Intelligent Vehicles Symposium, Proceedings, ISSN 1931-0587
Keyword
Control strategies, Economic improvements, Electromechanical brake, Fuel efficiency improvement, Pneumatic brake system, Power-train systems, Transportation industry, Vehicle manufacturers
National Category
Control Engineering Vehicle Engineering
Identifiers
urn:nbn:se:kth:diva-143142 (URN)10.1109/IVS.2013.6629489 (DOI)000339402900027 ()2-s2.0-84892385920 (Scopus ID)978-146732755-8 (ISBN)
Conference
2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013; Gold Coast, QLD; Australia; 23 June 2013 through 26 June 2013
Note

QC 20140319

Available from: 2014-03-19 Created: 2014-03-17 Last updated: 2016-01-28Bibliographically approved

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