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 Vehicle Engineering and Transport Systems at Cranfield University. He has 20 years of research experience in dynamic systems modelling, simulation, optimization, and control. He has also extensive practical and algorithmic experience in applying Artificial Intelligence and Machine Learning techniques to engineering problems. Before joining Cranfield University, he was with the Centre for Artificial Intelligence and Robotics (CAIRO) at University Technology Malaysia (UTM). His current research is focused on Electrified Transport Systems, Batteries for Electric Vehicles, Fleet Management Optimisation, and Autonomous Vehicles.
Dr Fotouhi is part of the academic panel that leads MSc courses in Connected and Autonomous Vehicles, Automotive/Motorsport Mechatronics, and Automotive Engineering at Cranfield University. He has supervised more than 50 MSc students and 7 PhD students, and his total writing portfolio lists over 65 publications. After joining Cranfield University, Dr Fotouhi has been also heavily involved in research bid development with a total project cost of over £20m, where he was successful to contribute to more than £2.1m income generation as an investigator (Co-I or PI). Dr Fotouhi is an Associate Editor-in-Chief of the Automotive Innovation Journal, and an editorial board member of Neural Computing and Applications Journal. He is a Fellow of the UK Higher Education Academy, a Fellow of the Faraday Institution in the UK, and a Senior Member of IEEE.
MSc by research and PhD opportunities in the following main areas:
· Electrified Transportation Systems
· 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, simply 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://tinyurl.com/Transport-Systems-PhD-Funding.
· Transport systems optimisation
· Batteries for electrified transportation
· Introduction to AI and machine learning
· Black-box modelling technique
· Introduction to MATLAB programming
· Electrified Transportation Systems – electric vehicles, charging infrastructure, “well-to-wheel" energy efficiency analysis, EV market penetration challenges, etc.
· Batteries - 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, NEXLFP – Next Generation LFP Batteries - Feb 2023 to Jan 2025.
· UKRI, Faraday Institution Fellowship: Artificial Intelligence for Battery Thermal Management System – Phase 2 (AI-BTMS 2) - Collaboration between Delta-Cosworth and Cranfield University, January 2022 to December 2022.
· Innovate UK Project, Bellerophon Rapid Assembly and Disassembly – February 2022 to May 2023.
· Innovate UK Project, NGB – Next Generation Battery - January 2022 to December 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)
· Hanwen Zhang (PhD candidate in Battery Thermal Management System Using Artificial Intelligence)
· You Gong (PhD candidate in Hybrid Battery Systems Using Low-Frequency Discrete Cell Switching)
· Ibrahim Kasar (PhD candidate in Intelligent Control of High-Temperature Solid Oxide Fuel Cells)
· Chenhui Yin (PhD candidate in trajectory prediction of road users and human-like navigation of autonomous vehicles in urban driving scenarios)
· Rusen Alp Akpinar (PhD candidate in Battery Temperature Prediction Using Machine Learning)
Research Fellows & Research Assistants:
· Nicolas Valencia Contecha (2020-2022, Research Assistant in Battery Management Algorithms – LiSFAB Innovate UK Project and LISA H2020 project)
· 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 Xuze (Lewis) Liu (2019-2022, PhD title: Formula-E Racing Strategy Development Using Machine Learning)
· 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), Werner Tapissier (2021-22), Chi Sun (2021-22), Po-Wei (2021-22), Rohithraj Nagarajan (2021-22), Jaime Ribelles Fayos (2021-22), Siva C M Pandian (2021-22), Yash Yardi (2021-22), Jibin Baby (2021-22), Ben Joseph (2021-22), Gbenga E Adesanya (2021-22), Venkatesh Ragunathan (2021-22), Kesava Kumar Yadala (2021-22), Abhijith Sreekumar (2021-22).
Samriddh Lakhmani (2018), Nikunj Bangad (2018).
Articles In Journals
- Shateri N, Auger DJ, Fotouhi A & Brighton J (2023) Charging characterization of a high‐capacity lithium‐sulfur pouch cell for state estimation–an experimental approach, Energy Storage, 5 (3) Article No. e412. Dataset/s: 10.17862/cranfield.rd.16482957
- Li J, Liu Y, Fotouhi A, Wang X, Chen Z, Zhang Y & Li L (2023) Cooperative ecological adaptive cruise control for plug-in hybrid electric vehicle based on approximate dynamic programming, IEEE Transactions on Vehicular Technology, 72 (3) 3132-3145.
- Valencia N, Fotouhi A, Shateri N & Auger D (2023) Development of a hybrid adaptive neuro-fuzzy inference system with coulomb-counting state-of-charge estimator for lithium–sulphur battery, International Journal of Fuzzy Systems, 25 (2) 407-422.
- Shateri N, Auger D, Fotouhi A, Brighton J, Du W, Owen RE, Brett DJL & Shearing PR (2022) Investigation of the effect of temperature on lithium-sulfur cell cycle life performance using system identification and x-ray tomography, Batteries and Supercaps, 5 (8) Article No. e202200035. Dataset/s: 10.17862/cranfield.rd.17163545
- Liu X, Fotouhi A & Auger D (2022) Application of advanced tree search and proximal policy optimization on formula-E race strategy development, Expert Systems with Applications, 197 (July) Article No. 116718.
- Gong Y, Auger DJ, Fotouhi A & Hale CJ (2022) A new topology for electric all-terrain vehicle hybrid battery systems using low-frequency discrete cell switching, IEEE Transactions on Transportation Electrification, Available online 12 July 2022. Dataset/s: 10.17862/cranfield.rd.19375928
- Liu X, Fotouhi A & Auger DJ (2022) Energy-optimal overtaking manoeuvres of Formula-E cars, Vehicle System Dynamics, Available online 11 July 2022.
- Mao L, Fotouhi A, Shateri N & Ewin N (2022) 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, 236 (6) 2677-2697.
- 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.
- 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
- 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.
- Liu X, Fotouhi A & Auger DJ (2021) Formula-E race strategy development using distributed policy gradient reinforcement learning, Knowledge-Based Systems, 216 (March) Article No. 106781.
- 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
- Biera Muriel J & Fotouhi A (2020) Electric vehicle fleet management using ant colony optimisation, International Journal of Strategic Engineering, 3 (1) Article No. 1.
- Liu X & Fotouhi A (2020) Formula-E race strategy development using artificial neural networks and Monte Carlo Tree Search, Neural Computing and Applications, 32 (18) 15191-15207.
- 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.
- 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.
- 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, Conversion, Storage, Distribution, Available online 07 October 2020 (1).
- 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
- Knap V, Stroe D-I, Purkayastha R, Walus S, Auger DJ, Fotouhi A & Propp K (2018) Reference performance test methodology for degradation assessment of lithium-sulfur batteries, Journal of The Electrochemical Society, 165 (9) 1601-1609.
- 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.
- 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
- 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.
- 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 (2021) State of energy estimation in electric propulsion systems with lithium-sulfur batteries. In: 10th IET International Conference on Power Electronics, Machines and Drives (PEMD2020), Virtual Event, 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.
- Ayadi F, Auger DJ, Fotouhi A & Shateri N (2022) State estimation methodologies for lithium-sulfur battery management systems. In: Lithium-Sulfur Batteries: Advances in High-Energy Density Batteries, Elsevier, p. 491-529.
- 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.