System Modelling and State Estimation of Cell Core Temperature

Background of thesis project


Energy storage system (ESS) based on lithium-ion batteries is one of the most important but expensive and safety-critical components in the electrified powertrain. These batteries have complex nonlinear dynamics and need a battery management system (BMS) with advanced estimation and control algorithms to ensure their optimal performance and long lifetime. In this regard, the systems and control community have shown a lot of research interest in recent years. The overall goal is to develop a knowledgebase to design battery health-conscious BMS for optimal utilization of currently available cells to guarantee their long lifetime. One of the core BMS functions is to estimate battery internal state (state-of-charge [SOC], dynamic polarization, internal State-of-Temperature [SOT] etc.) and parameters (state-of-resistance [SOR], state-of-capacity [SOQ] etc.) using voltage, current, and temperature measurements. These estimates are used to provide critical predictions about maximum available battery energy and power (i.e., state-of-energy [SOE] and state-of-power [SOP]) during driving or charging. These predictions are then used to decide the maximum battery load to guarantee optimal, reliable, and safe operation (i.e., to respect voltage, current, and temperature limits).




Suitable background
Of students System Control and Mechatronics or Thermal simulation relevant Master

Description of thesis work


In BMS, the cell core temperature is important, because it provides information of the state of the cell. The information is a critical index for health and safety of cell usage, and it is also used for other SOX estimations. Setting a sensor in the cell and then directly measuring cell core temperature increases the structural complexity and risks the safety of the cell, so a better way is to accurately estimate it using an electrical-thermal model. To accurately estimate the cell core temperature, an accurate model and an accurate model-based estimator are needed. How to obtain this model and the estimation in a cost-effective and computationally efficient manner is still an open research problem. This thesis deals with a part of this puzzle with the scope confined to the following particular research tasks:
Development of a state-space model for the cell core temperature. The model in state-space representation should describe the relation among core temperature, surface temperature, internal resistance, etc. The key coefficients in the model should be identified for a specific cell.
Design a suitable estimator for the cell core temperature, based on the state-space model. We need to consider both computation efficiency and estimation accuracy.
Analyze and verify the proposed model and estimation scheme thoroughly using simulation or lab testing. The main purpose is to thoroughly evaluate the estimation accuracy of the proposed approach against core temperature simulation or measurements.

Thesis Level: Master

Language: English


Starting date: 2023


Number of students: Two


Tutor
Ashkan Pirooz, Control Engineer, ashkan.pirooz@volvo.com 073 902 77 35