Areas of expertise
- Electric and Hybrid Vehicles
- Mechatronics & Advanced Controls
- On-Road Vehicle Dynamics
- Low Carbon Technology
- Vehicle Engineering & Mobility
Dr Abbas Fotouhi is a Senior Lecturer in Advanced Vehicle Engineering Centre at Cranfield University. He has more than fifteen years research experience in dynamical systems modelling, simulation, optimization, and control. He has also extensive practical and algorithmic experience of applying Artificial Intelligence and Machine Learning techniques in engineering problems. Before joining Cranfield, he was with the Centre for Artificial Intelligence and Robotics (CAIRO) at University Technology Malaysia. His current research is more focused on vehicular systems including hybrid and electric vehicle powertrain systems, energy storage technologies and autonomous cars. He is also interested in applications of AI and machine learning in Intelligent Transportation Systems.
Dr Fotouhi is part of the academic panel who lead MSc courses in Connected and Autonomous Vehicles, Automotive/Motorsport Mechatronics and Automotive Engineering at Cranfield University. He has supervised more than 40 MSc and PhD students and his total writing portfolio lists over 50 publications. After joining Cranfield University, Dr Fotouhi has been also heavily involved in research bid development with a total projects’ cost of over £20m, where he was successful to contribute to more than £1.9m income generation as an investigator (PI or Co-I). He is an editorial board member of Neural Computing and Applications Journal, editorial board member of the Journal of Smart Science, associate editor of International Journal of Strategic Engineering and guest editor of International Journal of Powertrain (IJPT) and journal of Energies. Dr Fotouhi is a Fellow of the UK Higher Education Academy, Fellow of the Faraday Institution in UK, and Senior Member of IEEE.
MSc by research and PhD opportunities are available in the following main areas:
* Electric and Hybrid Electric Vehicles
* Vehicle Autonomy and ADAS
* Intelligent Transportation Systems
* Applied AI and Machine Learning
If you are interested in any of those topics or you may have your own idea, please first email me to discuss it. To apply for a PhD position, fill in our online application form at https://tinyurl.com/PhD-in-Transport-Systems, mentioning your preferred supervisor's name on the form. Advanced Vehicle Engineering is part of our Transport Systems 'theme', so don't worry if the application form has this as the subject area.
Students are normally expected to identify their own source of funding, e.g. an employer or a nationally-funded scholarship. From time to time, we have funding for PhD scholarships - we always advertise these at https://www.cranfield.ac.uk/research/phd?Keyword=&Theme=transport+systems
MSc modules in Automotive Engineering and Motorsport.
Transport systems optimisation
Vehicle battery technology
Introduction to AI and machine learning
Black-box modelling technique
Introduction to MATLAB programming
Energy storage systems - battery HIL test, battery modelling and state estimation, lithium-sulfur battery.
Advanced vehicular technologies - EV and HEV powertrain design, optimization and control, automated driving and autonomous cars.
Intelligent transportation systems - vehicle-terrain interactions, fleet management systems, driving cycle development.
Artificial intelligence and machine learning - applications of AI and ML in transport systems, intelligent mobility, Motorsport.
- Innovate UK Project, NGB – Next Generation Battery, 2021-2023.
- Faraday Institution Fellowship project (FIIF-003), AI-BTMS - Artificial Intelligence for Battery Thermal Management System, 2020-2021.
- EPSRC Project (EP/T006382/1): Novel Unsteady Conjugate Cooling Mechanism, 2020-2022.
- Innovate UK Project (Reference No. 48727), CHIMERA – an intelligent battery management architecture for next generation electric vehicle batteries using multiple cell chemistries, 2019-2021.
- Innovate UK Project (Reference No. 105297), ICP – Developing the Isothermal Control Platform as the Basis of New Proposed Standards for the Testing of Lithium Batteries for Use in EVs, 2019-2021.
- Horizon 2020 Project (grant agreement ID 814471), LISA: Lithium Sulfur for Safe Road Electrification, 2019-2022.
- Innovate UK Project (TS/R013780/1) LiS:FAB - Lithium Sulfur: Future Automotive Battery (Advanced State Estimation and Management Algorithms, 2018-2021.
- Horizon 2020 Project (grant agreement ID 666157) ALISE: Advanced Lithium Sulfur Battery for xEV, 2018-2019.
- EPSRC IAA, Commercial deployment of model and estimator calibration techniques for Li-S battery management algorithms.
- ATI Project (TS/P003818/1) Zephyr Innovation Programme (ZIP) - development and integration of novel lithium-sulfur battery management and state estimation algorithms for UAVs, 2017-2019.
- EPSRC Project (EP/L505286/1) Revolutionary Electric Vehicle Battery (REVB) - design and integration of novel state estimation/control algorithms & system optimisation techniques, 2014-2017.
Dr Fotouhi’s team:
- Zihao (Oscar) Bai (PhD candidate in Automated Decision Making for Autonomous Vehicles at Roundabouts)
- Xuze (Lewis) Liu (PhD candidate in Formula-E Racing Strategy Development Using Machine Learning)
- Hanwen Zhang (PhD candidate in Battery Thermal Management System Using Artificial Intelligence)
- Ibrahim Kasar (PhD candidate in Intelligent Control of High-Temperature Solid Oxide Fuel Cells)
- Nicolas Valencia Contecha (Research Assistant in in Battery Management Algorithms)
Research Fellows & Research Assistants:
- Dr Z. Wang (2018-2020, Research Fellow in Li-S battery state estimation – ALISE H2020 Project and ICP Innovate UK Project)
- Dr Mehdi Soleymani (2018-2021, Research Fellow in Electric Vehicle Battery Management Algorithms – LiSFAB Innovate UK Project)
- Dr Chun-wei Chang (2017-2020, PhD title: Human-like Motorway Lane Change Control for AVs)
- Dr Zhaozhong Zhang (2016-2020, PhD title: Driver Distraction Detection using Machine Learning)
Irmiya Inniyaka (2015-16), Lu Zhang (2016-17), Rahul Khatry (2016-17), Salih Yousif (2017-18), Victor Calvo Serra (2017-18), Javier Biera (2017-18), Momen Abdallatif (2017-18), Zihao Bai (2017-18), Yan Cai (2017-18), Lichao Yang (2017-18), Ayush Maheswari (2017-18), Sayi Kumar (2017-18), Xuze Liu (2017-18), Zhiyu Sun (2018-19), Fanghao Xu (2018-19), Apurv Sharma (2018-19), Varun Pai (2018-19), Hanwen Zhang (2018-19), Ilyes Miri (2018-19), Pradhan Neelakandan (2018-19), Haolin Zheng (2018-19), Alberto Borges Salas (2018-19), Anthony VAN WAMBEKE (2018-19), Markos Papadimitriou (2018-19), Nicolas Valencia Contecha (2019-20), Jiadi (Ed) Yang (2019-20), Antoine Charlet (2019-20), Harshan Ravikumar (2019-20), Alberto Gonzalez (2019-20), Alessandro Schiraldi (2019-20), Lang Mao (2019-20), Marios Papadopoulos (2019-20), Kenneth Fernandes (2020-21), Muralidharan Jayaram (2020-21), Zhongkai Liu (2020-21), Sean Appleton (2020-21), Merzak Ouldali (2020-21).
Samriddh Lakhmani (2018), Nikunj Bangad (2018).
Articles In Journals
- Shateri N, Auger DJ, Fotouhi A & Brighton J (2021) An Experimental Study on Prototype Lithium–Sulfur Cells for Aging Analysis and State-of-Health Estimation, IEEE Transactions on Transportation Electrification, 7 (3) 1324-1338. Dataset/s: 10.17862/cranfield.rd.12562361
- Mao L, Fotouhi A, Shateri N & Ewin N (2021) A multi-mode electric vehicle range estimator based on driving pattern recognition, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Available online 20 July 2021.
- Zhang Z, Velenis E, Fotouhi A, Auger D & Cao D (2021) Driver distraction detection using machine learning algorithms: an experimental approach, International Journal of Vehicle Design, 83 (2/3/4) 122-139.
- Shateri N, Shi Z, Auger DJ & Fotouhi A (2021) Lithium-sulfur cell state of charge estimation using a classification technique, IEEE Transactions on Vehicular Technology, 70 (1) 212-224. Dataset/s: 10.17862/cranfield.rd.12011220
- Miri I, Fotouhi A & Ewin N (2021) Electric vehicle energy consumption modelling and estimation – a case study, International Journal of Energy Research, 45 (1) 501-520.
- Liu X, Fotouhi A & Auger DJ (2021) Formula-E race strategy development using distributed policy gradient reinforcement learning, Knowledge-Based Systems, Available online 20 January 2021, Article No. 106781.
- Calvo-Serra VC, Fotouhi A, Soleymani M & Auger DJ (2020) How suitable is lithium-sulphur battery for electric city bus application?, International Journal of Powertrains, 9 (4) 265-288.
- Liu X, Fotouhi A & Auger DJ (2020) Optimal energy management for formula-E cars with regulatory limits and thermal constraints, Applied Energy, 279 (December) Article No. 115805.
- Dörfler S, Walus S, Locke J, Fotouhi A, Auger DJ, Shateri N, Abendroth T, Härtel P, Althues H & Kaskel S (2020) Recent progress and emerging application areas for lithium-sulfur battery technology, Energy Technology: Generation, Coversion, Storage, Distribution, Available online 07 October 2020 (1).
- Liu X & Fotouhi A (2020) Formula-E race strategy development using artificial neural networks and Monte Carlo Tree Search, Neural Computing and Applications, Available online 30 March 2020 (18).
- Biera Muriel J & Fotouhi A (2020) Electric vehicle fleet management using ant colony optimisation, International Journal of Strategic Engineering, 3 (1) 1-6.
- Fotouhi A, Shateri N, Laila DS & Auger DJ (2019) Electric vehicle energy consumption estimation for a fleet management system, International Journal of Sustainable Transportation, 15 (1) 40-54.
- Propp K, Auger DJ, Fotouhi A, Marinescu M, Knap V & Longo S (2019) Improved state of charge estimation for lithium-sulfur batteries, Journal of Energy Storage, 26 (December) Article No. 100943. Dataset/s: 10.17862/cranﬁeld.rd.c.3723934
- Fotouhi A, Propp K, Samaranayake L, Auger D J & Longo S (2018) A hardware-in-the-loop test rig for development of electric vehicle battery identification and state estimation algorithms, International Journal of Powertrains, 7 (1-3) 227-248. Dataset/s: 10.17862/cranfield.rd.5687983
- V. Knap, D-I. Stroe, R. Purkayastha, S. Walus, D. J. Auger, A. Fotouhi & K. Propp (2018) Reference performance test methodology for degradation assessment of lithium-sulfur batteries, Journal of The Electrochemical Society, 165 (9) 1601-1609.
- Knap V, Auger DJ, Propp K, Fotouhi A & Stroe D-I (2018) Concurrent real-time estimation of state of health and maximum available power in lithium-sulfur batteries, Energies, 11 (8) Article No. 2133.
- Knap V, Stroe D-I, Purkayastha R, Walus S, Auger DJ, Fotouhi A & Propp K (2018) Methodology for assessing the lithium-sulfur battery degradation for practical applications, ECS Transactions, 77 (11) 479-490.
- Fotouhi A, Auger D, Propp K & Longo S (2018) Lithium-sulfur battery state-of-charge observability analysis and estimation, IEEE Transactions on Power Electronics, 33 (7) 5847-5859.
- Propp K, Auger DJ, Fotouhi A, Longo S & Knap V (2017) Kalman-variant estimators for state of charge in lithium-sulfur batteries, Journal of Power Sources, 343 (March) 254-267. Dataset/s: 10.17862/cranfield.rd.3834057
- Knap V, Stroe D-I, Christensen AE, Propp K, Fotouhi A, Auger DJ, Schaltz E & Teodorescu R (2017) Self-balancing feature of Lithium-Sulfur batteries, Journal of Power Sources, 372 (December) 245-251.
- Fotouhi A, Auger DJ, Propp K & Longo S (2017) Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies, IET Power Electronics, 10 (11) 1289-1297.
- Fotouhi A, Auger DJ, Propp K, Longo S, Purkayastha R, O'Neill L & Walus S (2017) Lithium-Sulfur cell equivalent circuit network model parameterization and sensitivity analysis, IEEE Transactions on Vehicular Technology, 66 (9) 7711-7721.
- Yousif SEA, Fotouhi A, Auger DJ & Propp K (2017) Self-discharge effects in lithium-sulfur equivalent circuit networks for state estimation, Journal of The Electrochemical Society, 165 (1) A6081-A6090. Dataset/s: 10.17862/cranfield.rd.c.3858703
- Fotouhi A, Auger D, O'Neill L, Cleaver T & Walus S (2017) Lithium-Sulfur battery technology readiness and applications – a review, Energies, 10 (12) Article No. 1937.
- Propp K, Marinescu M, Auger DJ, O'Neill L, Fotouhi A, Somasundaram K, Offer GJ, Minton G, Longo S, Wild M & Knap V (2016) Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries, Journal of Power Sources, 328 (October) 289-299. Dataset/s: 10.17862/cranfield.rd.c.3292031
- Fotouhi A, Auger DJ, Propp K & Longo S (2016) Accuracy versus simplicity in online battery model identification, IEEE Transactions on Systems Man and Cybernetics: Systems, 48 (2) 195-206. Dataset/s: 10.17862/cranfield.rd.3545847.v1
- Propp K, Fotouhi A, Knap V & Auger DJ (2016) Design, build and validation of a low-cost programmable battery cycler, ECS Transactions, 74 (1) 101-111.
- Fotouhi A, Auger DJ, Propp K, Longo S & Wild M (2016) A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur, Renewable and Sustainable Energy Reviews, 56 (April) 1008-1021.
- Fotouhi A, Auger DJ, Propp K & Longo S (2016) A study on battery model parametrisation problem: application-oriented trade-offs between accuracy and simplicity, IFAC-PapersOnLine, 49 (11) 48-53.
- Fotouhi A, Yusof R, Rahmani R, Mekhilef S & Shateri N (2014) A review on the applications of driving data and trafﬁc information for vehicles' energy conservation, Renewable and Sustainable Energy Reviews, 37 822-833.
- Fotouhi A & Montazeri-Gh M (2013) Tehran driving cycle development using the k-means clustering method, Scientia Iranica, 20 (2) 286-293.
- Montazeri M, Fotouhi A & Naderpour A (2012) Driving segment simulation for determination of the most effective driving features for HEV intelligent control, Vehicle System Dynamics, 50 (2) 229-246.
- Fotouhi A & Montazeri Gh M (2012) An investigation on vehicle's fuel consumption and exhaust emissions in different driving conditions, International Journal of Environmental Research, 6 (1) 61-70.
- Montazeri-Gh M & Fotouhi A (2011) Traffic condition recognition using the k-means clustering method, Scientia Iranica, 18 (4) 930-937.
- Montazeri-Gh M, Fotouhi A & Naderpour A (2011) Driving patterns clustering based on driving feature analysis, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 225 (6) 1301-1317.
- Munisamy S, Auger DJ, Fotouhi A, Hawkes B & Kappos E (2020) State of energy estimation in electric propulsion systems with lithium-sulfur batteries. In: 10th IET International Conference on Power Electronics, Machines and Drives (PEMD2020), Online, 1-3 December 2020.
- Wang Z, Fotouhi A & Auger DJ (2020) State of charge estimation in lithium-sulfur cells Using LSTM recurrent neural networks. In: 2020 European Control Conference (ECC), Saint Petersburg, 12-15 May 2020. Dataset/s: 10.17862/cranfield.rd.11843430
- Fotouhi A, Propp K, Samaranayake L, Auger DJ & Longo S. (2016) Electric vehicle battery management algorithm development using a HIL simulator incorporating three-phase machines and power electronics. In: 3rd Biennial International Conference on Powertrain Modelling and Control: Testing, Mapping and Calibration (PMC 2016), Loughborough, 7-9 September 2016.
- A Fotouhi, DJ Auger, K Propp & S Longo (2016) Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies. In: 8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016), Glasgow, 19-21 April 2016.
- Fotouhi A, Shateri N, Auger DJ, Longo S, Propp K, Purkayastha R & Wild M (2016) A MATLAB graphical user interface for battery design and simulation; from cell test data to real-world automotive simulation. In: 2016 13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), Lisbon, 27-30 June 2016.
- Fotouhi A, Auger DJ, Cleaver T, Shateri N, Propp K & Longo S (2016) Influence of battery capacity on performance of an electric vehicle fleet. In: 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, 20-23 November 2016.
- Fotouhi A, Propp K & Auger DJ (2015) Electric vehicle battery model identification and state of charge estimation in real world driving cycles. In: 7th computer science and electronic engineering conference (CEEC 2015), Colchester, 24-25 September 2015.
- Propp K, Fotouhi A & Auger DJ (2015) Low-cost programmable battery dischargers and application in battery model identification. In: 7th computer science and electronic engineering conference (CEEC 2015), Colchester, 24-25 September 2015.
- Fotouhi A, Auger DJ, Propp K & Longo S (2014) Simulation for prediction of vehicle efficiency, performance, range and lifetime: a review of current techniques and their applicability to current and future testing standards. In: 5th IET Hybrid and Electric Vehicles Conference (HEVC 2014), London, 5-6 November 2014.
- Fotouhi A & Yousefi-Koma A (2006) Improve hunting of a railway vehicle using semi-active primary suspension. In: ASME 8th Biennial Conference on Engineering Systems Design and Analysis, 2006, Torino, 4-7 July 2006.
- Fotouhi A, Yousefi-Koma A & Yasrebi N (2006) Active control of train bogies with MR dampers - art. no. 61710J. In: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2006, San Diego, CA, 26 February - 2 March 2006.
- Auger DJ, Fotouhi A, Propp K & Longo S (2019) Battery management systems – state estimation for lithium-sulfur batteries. In: Lithium-sulfur batteries, Wiley, p. 249-272.
- Fotouhi A, Propp K, Auger DJ & Longe S (2018) State of charge and state of health estimation over the battery lifespan. In: Behaviour of Lithium-Ion Batteries in Electric Vehicles, Springer International Publishing, p. 267-288, ed. 1.