State Of Health Estimation For Power Lithium Ion Batteries And Safety Predictions In Its Power Supply

In modern society,lithium ion batteries are common.The high power and energy density of lithium-ion batteries makes them popular in a wide range of applications,especially electric vehicles(EVs)and portable devices such as mobile phones and laptops,compared to other forms of electrochemical energy storage.Despite the numerous advantages of lithium-ion batteries over other forms of energy sources,their performance and durability are still suffering from ageing and degradation.The goal of the research described in this study is to examine how the cycle life and ageing processes of lithium-ion cells affect different properties of the load cycle by estimation of state of health of the battery and using the results to predict its life cycle.The ternary lithium ion battery of chemical component of Li Fe P(50AH)was used for the research study.Various estimation approaches were used but the current innovative approach was the use of the dual Extended Kalman Filter algorithm for estimation of State of Health.This algorithm used an Equivalent Circuit model to define the battery and its dynamics.The model parameters were identified and verified.The identified parameters were used as inputs for the Extended Kalman Filter algorithm in estimating state of charge.After estimating State of Charge,the results were also applied in a time-based Kalman filter to predict the battery degradation in terms of the capacity.The State of Health of the lithium ion battery was defined in terms of the capacity of the batteries at various temperatures.The influence of temperature on state of health and degradation of the lithium ion battery was identified.The error range of the algorithm was determined to be lower because of the dual filtering iteration used in the algorithm.The inputs to the first extended kalman filter algorithm for state of charge estimation was corrected or filtered by the parameter identification algorithm,while the inputs to the second kalman filter time event-based algorithm for capacity health estimation was filtered by the extended kalman filter algorithm.The series of filtration of inputs into the system resulted in a more accurate output results.The estimation error for the algorithm was identified to be less than 0.03%.Some safety predictions were made from the observation of the estimation of the battery using the dual extended kalman filter algorithm for a health working condition.Some important measures like the ambient working condition of the battery,setting of cut-off voltages and the identification of battery capacity before and after use were proposed.Most of these parameters had great influence on the battery and its estimation of state as well.Different ambient temperatures of the battery were used and it was notice that the higher the ambient temperature,the higher the degradation rate and vice versa.The best or optimum working temperature range for the battery was considered to be from 10~oC to 30~oC.Higher temperatures above such range cause an increase degradation while lower temperatures beneath this range make battery operation less effective and behaves abnormal.