Master Thesis: Control of Heterogeneous Multi-Battery Systems

Thesis Background
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
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 an intelligent predictive control scheme. The control problem in this scheme basically boils down to 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 topic is also very relevant for developing robust battery management functions and for understanding the performance limits of energy storage systems (ESS).
This thesis deals with a part of this puzzle with the scope confined to the following particular research tasks:
  • Improve computational efficiency and accuracy of an existing state-space model for prediction of power-split between parallel-connected batteries of ESS. Validate this control-oriented model against lab experimental data and multi-battery plant model
  • Model-based dynamic analysis of power distribution/split among parallel heterogeneous batteries in the ESS. The main purpose is to understand the dynamic interaction among the batteries and how various internal and external factors affect the power sharing among them. This will provide some guidelines about important considerations for model-based control design
  • Develop a model predictive scheme for SoP predictions for heterogeneous network of batteries under varying operating conditions. In particular, the optimal number of parameters for n differently aged battery units that need to be considered for ESS level power prediction with acceptable accuracy will be investigated. This will improve the computational efficiency leading to faster predictions in real-time
  • Analyze and verify the performance of the proposed control scheme thoroughly in comparison with existing standard methods for couple of lithium-ion chemistries (like LFP, NMC, LTO etc.) under different load cycles and operating conditions
Proposed Thesis Title: On Modelling and Control of Heterogeneous Multi-Battery Systems
Thesis Level: Master
Language: English
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 meritorious
Please send your application including CV, Cover Letter, and Transcript of grades.
Contact [Supervisor]:
Faisal Altaf
Principal Research Engineer & Project Leader, +46 31 323 5834
ESS Software and Control Design, Department of Electromobility
CampX, Volvo Group, Gothenburg, Sweden

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