Real Time Condition Monitoring Of Chemical Batteries
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Li-Ion batteries are suitable rechargeable choices for electric and hybrid electric vehicle (EV/HEV) applications due to their relatively high level of energy and power density. One of the important issues in automotive batteries is to monitor their online state-of-charge (SOC) and state-of-health (SOH).Hence, development of an efficient method for monitoring their online state-of-charge (SOC) and state-of-health (SOH) is of high importance for vehicular applications. Open circuit voltage, as one parameter used for predicting the SOC of the battery, is not readily available during charge and discharge cycles. A wide variety of research has been done on SOC and SOH estimation techniques for the automotive batteries. Most of these researches rely upon open circuit voltage of the battery.This dissertation focuses on the state-of-charge and state-of-health monitoring of chemical batteries while the battery is under load. Also, a battery management system (BMS) to monitor the temperature, voltage and current of a battery pack has been developed. This BMS has been used to validate the proposed method of health monitoring. The method introduced in this dissertation is based on the impulse response of the battery. Initially the impulse response of the battery is captured using a series of diagnostic experiments and the System Identification Toolbox from MATLAB®. The impulse response includes the behavioral characteristics of the battery and varies based on the specific properties of the battery such as the amount of remained charge (SOC), the amount of available charge or in other words the health status of the battery (SOH) and test conditions like temperature and current magnitude.In order to detect the health status, various possible faults which can occur inside a battery have been identified. The behavior of the battery under different faults has been simulated and investigated using the Battery Design Studio® software. Using this software the access to the chemical characteristics of the battery is possible and internal changes corresponding to different faults can be modeled.The impulse responses corresponding to various SOCs and also different faults are calculated and stored in a look-up table. They are used to calculate the output voltage of the battery. By comparing the calculated and the measured voltage from terminals of the battery, the SOC and SOH of the battery are estimated.Although the focus of the research in this dissertation is on the Li-Ion batteries, the developed method can be used for other types of chemical batteries. The above mentioned steps have been supported by simulation and experimental results.