Adaptive algorithms for monitoring of lithium-ion batteries in electric vehicles

Lithium-ion battery packs are always equipped with a battery management system (BMS). The BMS consists of hardware and software for battery management including, among others, algorithms determining battery states. The battery states of interest are state of charge (SoC), state of health (SoH), and some states of function (SoF). A state of function is a figure of merit that describes the capability of the battery to perform a certain task in a given application. The continuous determination of battery states during operation is called battery monitoring. The main objective of this thesis is to develop the core algorithms for the monitoring system. The algorithms have to represent the best compromise between the accuracy, reliability under real conditions, capability to adapt their parameterization to changing battery characteristics, and applicability on low-cost target hardware. This goal is achieved by developing algorithms in a consequent way beginning from the study of lithium-ion battery characteristics and analysis of requirements on monitoring algorithms, and proceeding right down to the verification of developed methods and their implementation on the target hardware. Another objective of this thesis is related to the task of verification of battery monitoring algorithms. This task might require performing an immense number of tests; therefore, it might take significant time and effort. To decrease the time and effort involved, various techniques, such as software-in-the-loop test benches or model-based verifications, can be applied. Thus, some important aspects have to be considered to make these verification tests as close as possible to real conditions. The discussion of these aspects forms another objective of this thesis.