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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 function is to estimate battery internal state (state-of-charge [SOC], dynamic polarization, internal State-of-Temperature [SoT] etc.) and parameters (impedance, capacity etc.) using voltage, current, and temperature measurements. These estimates are used to provide critical predictions about maximum available battery energy and power (i.e., SoE and SoP) during driving or charging. These predictions are then used to decide maximum battery load to guarantee optimal, reliable, and safe operation (i.e., to respect voltage, current, and temperature limits). In addition, the BMS performs several other important functions like cell balancing, thermal management, and fault detection and diagnosis etc.
Description of Thesis Work
The conventional ESS is a large network of multiple parallel-connected battery units. These units may exhibit different dynamic behaviours, due to inevitable variations/imbalance in their internal parameters and operating conditions leading to state of charge, power, and energy imbalance among them. A typical approach is to simply utilize these units based on constraints dictated by a weakest link in the network. However, this control approach is quite conservative in terms of utilizing the full potential service and capacity of ESS.
To improve quality of service and utilization of this heterogeneous network of complex dynamic systems, we may need higher controllability (active control using power converters) on pack level. This s witched multi-battery system (so-called reconfigurable ESS) enables optimal load sharing among battery units to maximize overall utility. How to achieve this optimal load sharing in a cost-effective and computationally efficient manner is still an open research problem. The main idea is to use battery units in the reconfigurable ESS according to their internal state. In nutshell, the load of the weakest link in the whole chain is shared by other units.
This thesis deals with a part of this puzzle with the scope confined to the following particular research tasks:
· Development of computationally efficient state-space model for this switched multi-battery system. The model must be scalable and configurable for any number and type of batteries
· Formulate the load sharing as an optimal control problem with the main objective to maximize SoP of ESS while respecting various physical constraints (dynamics, safety, health etc.). Solve this problem in a receding horizon fashion using a model predictive framework
· Analyze and verify the proposed control scheme thoroughly in comparison with the one used in conventional multi-battery system under different load cycles and operating conditions Thesis Title: On Modelling and Optimal Control of Reconfigurable Multi-Battery Systems
Thesis Level: Master
Starting date: 2020-01-13
Number of students: 2
Qualifications and Required Documents
· Must have strong educational background in electrical engineering, engineering physics, or mechatronics with very good grades in master level courses like nonlinear filtering/estimation, linear control systems, nonlinear and adaptive control systems, model predictive control etc.
· Must have high proficiency in Matlab and Simulink
· You must be self-motivated and meticulous in your problem solving approach.
· Familiarity with electro-thermal dynamics of lithium-ion batteries and some experience with dSpace embedded control software development tools will be considered as merit Please send your application including CV, Cover Letter, and Transcript of grades.
Contact [Supervisor] :
Principal Research Engineer & Project Leader, +46 31 323 5834
ESS Software and Control Design, Department of Electromobility
CampX, Gothenburg, Sweden
Master Thesis: Optimal Control of Reconfigurable Multi-Battery Systems
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