Sui, Xin

Sui, Xin

PROJECT TITLE: Accurate and Computation Efficient Algorithms for SOC and SOH Estimation of Lithium-ion Batteries in Electrical Vehicle Applications

PhD period: 2018.11.01 - 2021.10.31. 
Section: Power Electronic Systems
Research Programme: Battery Storage Systems
Supervisor: Remus Teodorescu
Co-Supervisor: Daniel-Ion Stroe
Contact information

Collaborator: To be announced later.
Funding: Self-financing and Department of Energy Technology.


Today, energy shortage is one of the biggest problems around the world. In order to deal with it, many countries vigorously develop new energy vehicles (NEV).  Electric Vehicles (EVs) are emerging because of their advantages on energy conservation and environmental protection. However, the problem is that EVs cost and driving range are still limited by the battery technology and management. Moreover, the main concern of drivers is how far can I drive before pack energy is depleted? Therefore, range anxiety is one of the most discouraging factors for potential EV customers and also makes the current EVs drivers frustrated. Furthermore, the penetration of RESS is limited by high costs of the battery pack. The key to solving this problem is to develop intelligent BMS guaranteeing safe and reliable operation of battery packs, prolonging battery life, increasing the utilization efficiency of energy/capacity, and observing states of battery especially the SOC, SOH and RR.

The purpose of this project is to develop online states estimation algorithms for accurate state of charge (SOC), state of health (SOH), capacity, remaining useful life (RUL), and remaining range (RR), which aims to increase the performance of batteries in Electrical Vehicles (EVs) and renewable energy storage systems (RESS) applications. Achieving the RR of EV from SOC and SOH is one of the most efficient way to eliminate or mitigate range anxiety of the EVs drivers. The battery states are also significantly important for eventually achieving the actual driving range longer. The presented methods should guarantee SOC estimation accuracy, robustness, and effectiveness. Moreover, with the simplification of the estimation process, the proposed algorithms are also able to be validated and implemented in low-cost hardware. For further reducing the cost of EVs, the proposed algorithms also are expected to work in distributed form to estimate the states of Smart Battery Cell (SBC) in the intelligent battery management system (BMS) with wireless communication technology.


Publications in journals and conference papers may be found at VBN.