Dr Kai Li Lim

St Baker Fellow in E-Mobility

School of Chemical Engineering
Faculty of Engineering, Architecture and Information Technology

Overview

Dr Kai Li Lim is the inaugural St Baker Postdoctoral Research Fellow in Electromobility at The UQ Dow Centre for Sustainable Engineering Innovation. As a trained computer engineer with more than nine years of experience developing mobility and navigation frameworks, his early forays saw him designing navigational algorithms for mobile robots and indoor pedestrians. More recently, his applications employ techniques relating to data engineering, the Internet of Things, cloud computing, computer vision and deep learning, resulting in tangible products for real-time vehicle and infrastructural telematics and computer vision-based autonomous driving. His thesis, “Connected Autonomous Electromobility”, document some of these works with top recommendations from its examiners.

Before joining UQ, Kai Li oversees and manages The University of Western Australia’s Renewable Energy Vehicle (REV) Project EV charging station network, the first EV charging network in Australia and the sole network run by an academic institution. As a current adjunct fellow at UWA, he is also a mentor for autonomous driving and connected mobility students. In addition, his industrial experiences include sustainable engineering design projects in Singapore and Jakarta, and more notably, the design of an end-to-end telematics platform for electric propulsion aircraft, watercraft and their infrastructures for Perth-based engineering start-up Electro.Aero.

Here at UQ, he expands on his expertise by working on a data platform for connected vehicles and infrastructures, along with supporting the data processing design for the UQ Mobility-as-a-Service (MaaS) trial.

Kai Li received the BEng (Hons) degree in electronic and computer engineering from the University of Nottingham in 2012, the MSc degree in computer science from Lancaster University in 2014 and the PhD degree from The University of Western Australia in 2020, where he was fully supported by the Australian Government under the Research Training Program.

Research Interests

  • Electric vehicles
    Vehicle electronics, system design, telematics, data management
  • Autonomous vehicles
    Visual navigation, machine learning, sensor fusion, sensor integration, path planning, software architecture design

Research Impacts

Kai Li’s research focuses on developing innovative platforms for collecting electromobility (e-mobility) data. With the availability of high-speed internet connectivity, vehicles and their infrastructures are transmitting massive amounts of data in real-time, with high precision and with greater interoperability with other data. However, many data platforms are device-specific, forming manufacturer-restricted data silos. Through the fellowship, Kai Li is designing and developing a cohesive data platform that will consolidate information across a broad range of connected mobility devices.

Kai Li commenced as the Fellow in June 2021, and through this role, plans to develop the platform on cloud computing, with initial data coming from EVs (particularly Tesla vehicles). Results will be used to better understand EV computing behaviours on a spatial level, and by combining data analytics with machine learning, provide future predictions for these behaviours. Kai Li plans to publish these findings in a series of academic publications and green papers to better inform the industry and government collaborators about upcoming EV behaviours.

Qualifications

  • Bachelor of Engineering (Honours), University of Nottingham
  • Master of Science in Computer Science, The University of Lancaster
  • Doctor of Philosophy, The University of Western Australia

Publications

View all Publications

Publications

Book Chapter

  • Lim, Kai Li, Speidel, Stuart and Bräunl, Thomas (2020). REView: A unified telemetry platform for electric vehicles and charging infrastructure. Connected Vehicles in the Internet of Things: Concepts, Technologies and Frameworks for the IoV. (pp. 167-219) Springer International Publishing. doi: 10.1007/978-3-030-36167-9_8

  • Reid, Robert G., Lim, Kai Li and Bräunl, Thomas (2020). Cooperative multi-robot navigation–SLAM, visual odometry and semantic segmentation. Cooperative localization and navigation: theory, research, and practice. (pp. 181-198) edited by Chao Gao, Guorong Zhao and Hassen Fourati. Boca Raton, FL, United States: CRC Press. doi: 10.1201/9780429507229-10

  • Lim, Kai Li, Seng, Kah Phooi, Yeong, Lee Seng and Ang, Li-Minn (2017). RFID and dead-reckoning- based indoor navigation for visually impaired pedestrians. Smart Technologies: Breakthroughs in Research and Practice. (pp. 1-16) Taylor and Francis Inc.. doi: 10.4018/978-1-5225-2589-9.ch001

  • Lim, Kai Li, Seng, Kah Phooi, Yeong, Lee Seng and Ang, Li-Minn (2016). RFID and dead-reckoning-based indoor navigation for visually impaired pedestrians. Handbook of Research on Recent Developments in Intelligent Communication Application. (pp. 380-396) IGI Global. doi: 10.4018/978-1-5225-1785-6.ch015

  • Lim, Kai Li, Yeong, Lee Seng, Seng, Kah Phooi and Ang, Li-Minn (2015). Assistive navigation systems for the visually impaired. Encyclopedia of Information Science and Technology, Third Edition. (pp. 315-327) Hershey, PA, United States: IGI Global. doi: 10.4018/978-1-4666-5888-2.ch030

Journal Article

Conference Publication

  • Drage, Thomas, Lim, Kai Li, Hai Koh, Joey En, Gregory, David, Brogle, Craig and Braunl, Thomas (2021). Integrated modular safety system design for intelligent autonomous vehicles. 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan, 11-17 July 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IV48863.2021.9575662

  • Lim, Kai Li, Drage, Thomas, Podolski, Roman, Meyer-Lee, Gabriel, Evans-Thompson, Samuel, Lin, Jason Yao-Tsu, Channon, Geoffrey, Poole, Mitchell and Bräunl, Thomas (2018). A Modular Software Framework for Autonomous Vehicles. 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 26-30 June 2018. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IVS.2018.8500474

  • Lim, Kai Li, Drage, Thomas and Bra, Thomas (2017). Implementation of semantic segmentation for road and lane detection on an autonomous ground vehicle with LIDAR. 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Daegu, Korea, 16-18 November 2017. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/MFI.2017.8170358

  • Lim, Kai Li, Yeong, Lee Seng, Ch'Ng, Sue Inn, Seng, Kah Phooi and Ang, Li-Minn (2014). Uninformed multigoal pathfinding on grid maps. Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/InfoSEEE.2014.6946181