Lityum tabanlı piller için şarj durumu kestirimi

This thesis proposes a new State of Charge (SOC) estimation method for lithium based batteries, which offers a good trade-off between convergence and computation times. Lithium-based battery packages are quite common in the automotive industry and beyond because of their high-power density and dynamic response capabilities. Per a given volume, lithium-based battery cells have much more capacity, higher C rates, and lower internal resistance than other cell chemistries. However, this comes at a cost because of lithium's reactive nature. It is hard to preserve, monitor, cool, and control lithium in a pack within a safe state. For these reasons, battery control, or in other words, Battery Management Systems (BMS) is a major topic in the literature, and estimation of SOC, State of Health (SOH), and State of Power (SOP) are considered as core subfunctions of BMS. This thesis focuses on improving SOC estimation for lithium-based batteries. SOC estimation determines the remaining charge level on the battery and is very critical for battery-powered devices. This process is relatively straightforward when the battery is in the resting state. However, it can be difficult while the battery-powered device is operating, due to process disturbances and model uncertainties. The best performing SOC estimation methods in the literature are based on Kalman Filtering, and they are specifically Extended Kalman Filter (EKF) and Adaptive Dual Extended Kalman Filter (ADEKF). While EKF offers the shortest computation time, it results in a long convergence time. On the other hand, ADEKF offers short convergence time and long computation time. We propose PID-controlled EKF, which offers a mid-point in terms of convergence and computation times. The importance of convergence characteristics are also articulated in this thesis, especially from an automotive perspective.