
Dr Ahu Ece Hartavi Karci
About
Biography
Dr. Hartavi Karci is an Associate Professor in the Center of the Automotive Engineering. Her current research interests are connected autonomous vehicle trajectory planner and controller, advanced driver assistance systems, electric/hybrid electric vehicles technology, modelling, and control methodology, active magnetic bearings and their applications. She is the author of over 40 publications and inventor of the 3 patents in the fields of electric vehicles. She is the Scientific Coordinator of the H2020 TRUSTVEHICLE project and the PI/co-PI of the HADRIAN, OWHEEL, OBELICS, SYS2WHEEL, ADVICE, EVE-RISE, TELL and EVC1000 projects. She recently received the awards for the Mercedes AMG: Best High Voltage Powertrain for Electrics Vehicles, and the IMechE: Most Efficient Electric car globally and Best UK Electric Car 2019 Awards.
News
In the media
ResearchResearch interests
- Electric and hybrid vehicle technology, modelling, design and control
- Connected autonomous vehicles
- Intelligent ground vehicles
- Software safety
- Electrical machines
- Active magnetic bearing systems and applications.
Research interests
- Electric and hybrid vehicle technology, modelling, design and control
- Connected autonomous vehicles
- Intelligent ground vehicles
- Software safety
- Electrical machines
- Active magnetic bearing systems and applications.
Publications
Catalytic converters mitigate carbon monoxide, hydrocarbon, nitrogen oxides and particulate matter emissions from internal combustion engines, and allow meeting the increasingly stringent emission regulations. However, catalytic converters experience light-off issues during cold start and warm up. This paper reviews the literature on the thermal management of catalysts, which aims to significantly reduce the light-off time and emission concentrations through appropriate heating methods. In particular, methods based on the control of engine parameters are easily implementable, as they do not require extra heating devices. They present good performance in terms of catalyst light-off time reduction, but bring high fuel penalties, caused by the heat loss and unburnt fuel. Other thermal management methods, such as those based on burners, reformers and electrically heated catalysts, involve the installation of additional devices, but allow flexibility in the location and intensity of the heat injection, which can effectively reduce the heat loss in the tailpipe. Heat storage materials decrease catalyst light-off time, emission concentrations and fuel consumption, but they are not effective if the engine remains switched off for long periods of time. The main recommendation of this survey is that integrated and more advanced thermal management control strategies should be developed to reduce light-off time without significant energy penalty.
The market penetration of electric vehicles (EVs) is going to significantly increase in the next years and decades. However, EVs still present significant practical limitations in terms of mileage. Hence, the automotive industry is making important research efforts towards the progressive increase of battery energy density, reduction of battery charging time, and enhancement of electric powertrain efficiency. The electric machine is the main power loss contributor of an electric powertrain. This literature survey reviews the design and control methods to improve the energy efficiency of electric machines for EVs. The motor design requirements and specifications are described in terms of power density, efficiency along driving cycles, and cost, according to the targets set by the roadmaps of the main governmental agencies. The review discusses the stator and rotor design parameters, winding configurations, novel materials, construction technologies as well as control methods that are most influential on the power loss characteristics of typical traction machines. Moreover, the paper covers: i) driving cycle based design methods of traction motors, for energy consumption reduction in real operating conditions; and ii) novel machine topologies providing potential efficiency benefits.
The main benefits of dual clutch transmissions (DCTs) are: (i) a higher energy efficiency than automatic transmission systems with torque converters; and (ii) the capability to fill the torque gap during gear shifts to allow seamless longitudinal acceleration profiles. Therefore, DCTs are viable alternatives to automated manual transmissions (AMTs). For vehicles equipped with engines that can generate considerable torque, large clutch-slip energy losses occur during power-on gear shifts and, as a result, DCTs need wet clutches for effective heat dissipation. This requirement substantially reduces DCT efficiency because of the churning and ancillary power dissipations associated with the wet clutch pack. To the knowledge of the authors, this study is the first to analyse the detailed power loss contributions of a DCT with wet clutches, and their relative significance along a set of driving cycles. Based on these results, a novel hybridised AMT (HAMT) with a single dry clutch and an electric motor is proposed for the same vehicle. The HAMT architecture combines the high mechanical efficiency typical of AMTs with a single dry clutch, with the torque-fill capability and operational flexibility allowed by the electric motor. The measured efficiency maps of a case study DCT and HAMT are compared. This is then complemented by the analysis of the respective fuel consumption along the driving cycles, which is simulated with an experimentally validated vehicle model. In its internal combustion engine mode, the HAMT reduces fuel consumption by >9% with respect to the DCT.
The main question in eco-driving is – what speed or torque profile should the vehicle follow to minimize its energy consumption over a certain distance within a desired trip time? Various techniques to obtain globally optimal energy-efficient driving profiles have been proposed in the literature, involving optimization algorithms such as dynamic programming (DP) or sequential quadratic programming. However, these methods are difficult to implement on real vehicles due to their significant computational requirements and the need for precise a-priori knowledge of the scenario ahead. Although many predictions state that electric vehicles (EVs) represent the future of mobility, the literature lacks a realistic analysis of optimal driving profiles for EVs. This paper attempts to address the gap by providing optimal solutions obtained from DP for a variety of trip times, which are compared with simple intuitive speed profiles. For a case study EV, the results show that the DP solutions involve forms of Pulse-and-Glide (PnG) at high frequency. Hence, detailed investigations are performed to: i) prove the optimality conditions of PnG for EVs; ii) show its practical use, based on realistic electric powertrain efficiency maps; iii) propose rules for lower frequency PnG operation; and iv) use PnG to track generic speed profiles.
This study addresses the development and Hardware-in-the-Loop (HiL) testing of an explicit nonlinear model predictive controller (eNMPC) for an anti-lock braking system (ABS) for passenger cars, actuated through an electro-hydraulic braking (EHB) unit. The control structure includes a compensation strategy to guard against performance degradation due to actuation dead times, identified through experimental tests. The eNMPC is run on an automotive rapid control prototyping unit, which shows its real-time capability with comfortable margin. A validated high-fidelity vehicle simulation model is used for the assessment of the ABS on a HiL rig equipped with the braking system hardware. The eNMPC is tested in 7 emergency braking scenarios, and its performance is benchmarked against a proportional integral derivative (PID) controller. The eNMPC results show: i) the control system robustness with respect to variations of tire-road friction condition and initial vehicle speed; and ii) a consistent and significant improvement of the stopping distance and wheel slip reference tracking, with respect to the vehicle with the PID ABS.
This paper presents the cooperative adaptive cruise control implementation of Team Mekar at the Grand Cooperative Driving Challenge (GCDC). The Team Mekar vehicle used a dSpace microautobox for access to the vehicle controller area network bus and for control of the autonomous throttle intervention and the electric-motor-operated brake pedal. The vehicle was equipped with real-time kinematic Global Positioning System (RTK GPS) and an IEEE 802.11p modem installed in an onboard computer for vehicle-to-vehicle (V2V) communication. The Team Mekar vehicle did not have an original-equipment-manufacturer-supplied adaptive cruise control (ACC). ACC/Cooperative adaptive cruise control (CACC) based on V2V-communicated GPS position/velocity and preceding vehicle acceleration feedforward were implemented in the Team Mekar vehicle. This paper presents experimental and simulation results of the Team Mekar CACC implementation, along with a discussion of the problems encountered during the GCDC cooperative mobility runs.
In car–trailer combinations, the hitch angle is the relative yaw angle between towing car and trailer. The literature has shown that the inclusion of the hitch angle measurement for the feedback control of trailer oscillations can bring safety benefits, compared with conventional trailer sway mitigation algorithms based on the yaw rate of the car. Given the nonlinearity of the vehicle system in the typical conditions requiring the hitch angle control function intervention, nonlinear model-based controllers could be an effective solution. This paper presents four real-time implementable nonlinear model predictive control (NMPC) formulations, using the hitch angle measurement for the torque-vectoring (TV) control of an electric frontwheel drive car towing a trailer. The simulation results show that: (i) the active safety is enhanced by the proposed NMPC TV formulations, with respect to a benchmarking NMPC TV controller only based on the control of the towing car; (ii) the NMPC formulations that directly constrain the hitch angle error, or perform continuous hitch angle tracking, outperform those that modify the reference yaw rate or yaw rate error based on the hitch angle error; and (iii) the NMPC approaches including a dynamic model of the trailer are robust with respect to variations of trailer parameters.
Additional publications
Book Chapters
Hartavi, A.E., Shah Alias Sangani, A., Kabbani, T., Sarhadi, P., Zohan, J., Kasıkcı, K., Maroun, S., Salem, M., Shanmugam, S., Krishna, A., Ou, J., Sozen, E., Aydemir, E. and Ahiad, S., n.d. Enhanced Trustworthiness and End User Acceptance of Conditionally Automated Vehicles in the Transition Period, Chapter 4: Reliable Sense-Plan-Act Approaches For Autonomous Vehicles. ISBN 978-3-030-60860-6, 1st ed. Springer, 2020. DOI: 10.1007/978-3-030-60861-3
Schiker, L., Watzeing, D., Hartavi, A. E. and Troglia, M. Enhanced Trustworthiness and End User Acceptance of Conditionally Automated Vehicles in the Transition Period, Chapter 1: Trustworthiness, User Acceptance And Expectations. ISBN 978-3-030-60860-6, 1st ed. Springer, 2020. DOI: 10.1007/978-3-030-60861-3
Hillbrand, B., Innerwinkler, P., Stettinger, G., Hartavi, A.E., Rodopl, K., Buyukakin, T., Sözen, E., Aydemir, E., Clement, P., Zaya, J., Sahimäki, S. and Tarkiainen, M. Enhanced Trustworthiness and End User Acceptance of Conditionally Automated Vehicles in the Transition Period, Chapter 5: Trustvehicle Verification Procedure. ISBN 978-3-030-60860-6, 1st ed. Springer, 2020. DOI: 10.1007/978-3-030-60861-3
Philipp, C., Hartavi, A.E., Bernhard, H., Philipp, Q., Herbert, D. and Kasikci, K., n.d. Enhanced Trustworthiness and End User Acceptance of Conditionally Automated Vehicles in the Transition Period, Chapter 6: Assessment Concept for Trust(ed)Vehicle(S). ISBN 978-3-030-60860-6, 1st ed. Springer, 2020. DOI: 10.1007/978-3-030-60861-3
Advancements in Steering Systems, Braking Systems, and Advanced Chassis Control and Rollover Stability, Chapter 7: Electric Regenerative Power Assisted Brake Algorithm for a Front and Rear Wheel Drive Parallel Hybrid Electric Commercial Van, ISBN: 978-0-7680-2092-2, SAE International, 2008.
Simulation of Commercial Vehicles 2008, Chapter 7: Maximizing Overall Efficiency Strategy for Power Split Control of a Parallel Hybrid Electric Vehicle, ISBN: 978-0-7680-2094-6, SAE International, 2008.
Journal Articles
Shao, Lingyun & Hartavi, Ahu Ece & Tavernini, Davide & Sorniotti, Aldo & Cheng, Ming. (2020). Design Approaches and Control Strategies for Energy-Efficient Electric Machines for Electric Vehicles – A Review. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.2993235.
Tavernini, Davide & Vacca, Fabio & Metzler, Mathias & Savitski, Dzmitry & Ivanov, Valentin & Gruber, Patrick & Hartavi, Ahu Ece & Dhaens, Miguel & Sorniotti, Aldo. (2019). An explicit nonlinear model predictive ABS controller for electro-hydraulic braking systems. IEEE Transactions on Industrial Electronics. PP. 1-1. 10.1109/TIE.2019.2916387.
Güvenc L, Uygan I.M.C., Kahraman K., Karaahmetoğlu R., Senturk M., Hartavi A.E., Emirler T. M., Altay I., Altug E., Turan M. C., Tas O. S., Bozkurt E., Ozguner U., (2012)“Cooperative Adaptive Cruise Control Implementation of Team Mekar at the Grand Cooperative Driving Challenge”, Special Issue of the IEEE Transactions on Intelligent Transportation Systems.
Hartavi A.E., Uygan I.M.C., Sezer V., Acarman T., Güvenc L. (2010), “Propulsion System Design of a Hybrid Electric Vehicle”, International Journal on Vehicle Design.
Sahinkaya M. N, Hartavi A.E. (2007), “Variable Bias Current in Magnetic Bearings for Energy Optimization”, IEEE Transactions on Magnetics.
Conference Papers
Shah Alias Sangani, A., Hartavi, A.E. Commercial Viability Assessment of Safety-Critical Embedded Software of Electrified Road Vehicles via detailed-COCOMO Approach. SAE Technical Paper, 2021
Rini, G., Bernardis, M. D., Shah Alias Sangani, A., Sorniotti, A., Hartavi, A.E., Nonlinear model predictive torque-vectoring control for a commercial electric vehicle. SAE Technical Paper, 2021
Kabbani, T., Sarhadi, P., Sozen, E., Kinavb, E., Hartavi, A.E., Level 3 Autonomous Fully Reverse-Parking System for a Heavy-Duty Vehicle. ITS European Congress, 2021
Hartavi A. E, Gol M., Akyuz B.,“A Comparative Study of Different Electric Drive Systems and Their Effects on Drive Cycle Performance of an Electric City Bus”, EVS28, May 3-6, 2015, Kintex, Korea.
Gol M., Inal T. T., Hartavi A. E, “An Effectıve Tool for Evaluating the Impact of E-powertrain on Energy Consumption and Performance, Otomotiv Teknolojileri Kongresi, May 26-27, 2014, Bursa, Turkey.
Inal T. T., Ustoglu I., Hartavi A. E, “Energy Benefit Analysis of Centre and Wheel-Hub Drives for Electric Vehicles” Automotive Technologies Conference, 2013, Istanbul, Turkey
Turan M. C., Hartavi A.E., Altug E., “Development of Rule Based Upper Level Control Algorithm for an Intelligent Vehicle in Automated Highway System” 2012 IEEE International Conference on Vehicular Electronics and Safety, July 24-27, 2012, Istanbul, Turkey.
Hartavi A.E., Uygan I.M.C., Turan M. C., Karaahmetoğlu R., Senturk M., Tas O. S., Kahraman K., Güvenc L, Guvenc B.A, Ozguner U., Altug E., Efendioglu B., “Design and Control Basics of a Cooperative Vehicle”, Otomotiv Teknolojileri Kongresi, June 4-5, 2012, Bursa, Turkey.
Hartavi A.E., Uygan I.M.C., Güvenc L, “Bypass Based Rapid Control Prototyping of Hybrid Brake Control System” Otomotiv Teknolojileri Kongresi, June 7-8, 2010, Bursa, Turkey.
Hartavi A.E. , Sahinkaya M. N, Tunçay R. N., “ The Effect of Current Control Strategies on Power Consumption of Magnetically Levitated Turbomolecular Pump”, International Power Electronics and Motion Control Conference, August 13-16, 2006, China.
Sahinkaya M. N, Hartavi A. E., Burrows C. R. and Tunçay R. N., “Bias Current Optimization and Fuzzy Controller for Magnetic Bearings In Turbomoleculer Pumps”, Proceedings of The Ninth International Symposium on Magnetic Bearings, 2004, Lexington, USA.