Novel Modeling Method For Battery Cell Balancing Performance Optimization In Battery Management System

There is rising attention and interest of electric vehicles(EVs)due to its advantages of zero green-house gaseous emissions and higher efficiency.The battery pack is utilized as the main energy storage mode in EVs.Therefore strict management of battery packs is very critical to ensure the safety and performance of the battery pack when subjected to various load and driving states.Therefore,an efficient and effective battery management system is required which is capable of performing the estimation of state-of-charge(SOC),predicting of battery remaining useful life(RUL),performing battery cell balancing,and controlling the temperature.Lithium-ion batteries(LIBs)for EVs can be affected by many factors,key among them being cell unbalancing.When the battery cells in the battery pack are not properly balanced,the individual cell voltage will differ over time,and this leads to quick battery aging.This will result into the ultimate failure of EVs and can lead to catastrophes.Proper cell balancing will pray an important role in the preservation of the battery life hence extending the battery life appropriately,making the use of LIBs in EVs to be more efficient.This thesis focuses on battery cell balancing optimization to improve battery efficiency and safety in three different approaches.Firstly aiming at the problems of battery management system(BMS)for EVs,for the view of optimizing battery performance and life cycles of the battery and to improve safety element to the battery,the accurate battery remaining useful life(RUL)prediction method based on the particle filter is proposed as it is important to the prognosis and health management(PHM)of BMS.Secondly various battery balancing schemes and topological structures based on MATLAB/Simulink are analyzed;a data analysis modelling method for battery balancing performance optimization of BMS is designed,developed and verified,which improves the reliability and system performance of BMS.Lastly the influence of temperature,vibration and temperature/vibration test coupling key parameters on battery balance is studied.A battery temperature/vibration coupling influencing factor model based on the Kalman filter and the extended Kalman filter is proposed,which can support battery and electric vehicle manufacturers to optimize the design of BMS system.This study will be helpful to the manufacturers of batteries and EVs to try to understand the dynamics that are caused by the effects of the temperature/vibration factors are coupled together.Thus this study will act as a guide to manufacturers to optimize and improve the cell design,and materials properties used for their products.