Investigation of SoC and SoH estimator adaption to new generation Li batteries

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Supervisor:
Debreceni Tibor
Department of Automation and Applied Informatics

In the latest few years electric vehicles (EV) saw a rapid development in drive systems, opening to new ways of transport such as aviation. The most widely used electric energy storage in these safety critical applications are lithium-ion (Li-Ion) based batteries.

These energy storage systems reached their capabilities, which led to the development of new technologies and battery chemistries. These new type of cells shows greater performance then the traditional lithium-ion batteries.

One promising new technology is the lithium-sulfur (Li-S) batteries. These batteries utilizes metallic lithium with a protective sulfur layer, which makes them more resistant against mechanical impacts and damages. At the same time the realized energy density of the Li-S chemistry already reached more then twice the energy density of the Li-Ion batteries. These factors makes the usage of Li-S batteries a favorable choice for safety critical applications, such as the aforementioned electric aviation.

However this new battery technology brings in new problems in utilizing them in energy storage applications. One of the most significant difference between the Li-S and the Li-Ion technologies is the voltage characteristic of the cells. The biggest problem is that the discharge characteristic of the Li-S cell is such that the State-of-Charge (SoC) of a battery cell cannot be accurately determined at any point. The same voltage belongs to two or more SoC values. This behavior of the Li-S cell prevents it to be used with traditional SoC estimation algorithms.

The SoC estimators are one of the main component of a Battery Management System (BMS) besides the safety mechanisms. The SoC is the indicator of the usable charge left in the battery. In most non-critical applications, simple estimation methods are used. These methods do not take into account battery specific properties, which allows them to be used universally with several different battery technologies. These methods are however inaccurate for safety critical applications, where the accurate SoC estimation is essential.

In order to have an accurate SoC estimation method in the long term use of a battery energy storage, a State of Health (SoH) estimator implementation is also required. With each charge-discharge cycle, the internal chemistry of a battery cell changes, which results in less usable capacity. This quality degradation is represented with SoH.

In my master thesis I will investigate the difficulties of adapting existing SoC estimation methods to the new Li-S battery technology. I will present a possible solution of this problem. The proposed method is adaptable and easily implementable in most BMSs. I will introduce the most widely used SoH estimation methods, and present an implementation of a technology independent SoH estimation method.

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