Research On State Of Charge And State Of Health Estimation Of Lithium-Ion Batteries For Vehicles Without Open Circuit Voltage Test

Following the worsening of the worldwide ecological crisis and the dryness of natural sources,finding new energy sources that are less harmful to the natural environment has become a global research hotspot.One of the new energy industries that has gained widespread attention is the electric vehicle.The driving forces of electrical automobiles originate from the battery.These batteries exhibit a number of vulnerabilities,and minor inconsiderate manipulations tend to have an impaired response to the emission of power and the efficiency of the battery.Consequently,it is exceedingly imperative for the Battery Management System(BMS)to uphold proficient operation and security of the battery.The precise evaluate of both the State of Charge(SOC)and State of Health(SOH)are pivotal undertakings of the BMS,and their reliability is of utmost importance in optimizing battery efficacy and ensuring the credibility of electric vehicles.Nevertheless,there are challenges in accurately estimating SOC and SOH since they cannot be directly measured by instruments and are easily influenced by factors such as the application environment.Thus,this paper explores SOC and SOH estimation with lithium-ion batteries as the subject of study.(1)The model building and parameter identification of lithium-ion batteries.Lithium-ion battery modeling and parameter identification.First,various typical models are investigated and their characteristics are described.A comparison of the sophistication and reliability of the model led to the Thevenin equivalent circuit model being applied to this article.The paragraph describes the use of two advanced approaches,namely Forgetting Factor Recursive Least Squares(FFRLS)and Adaptive Forgetting Factor Recursive Least Squares(AFFRLS),for enhancing the online recognition of the battery’s target elements.The accuracy of the battery model and parameter identification was subsequently validated under diverse experimental conditions.(2)The OCV estimation model is built.The Open Circuit Voltage(OCV)and SOC relation is major for the equivalent circuit model to calculate the battery status,which is usually obtained using Incremental OCV(IO)and Low-current OCV(LO)tests.However,the two OCV offline tests will take a long time and are affected by factors such as temperature and ageing.Therefore,this study proposes to achieve the estimation of the battery state without performing OCV tests,where OCV is achieved by means of establishing the OCV differential equation.Therefore,an estimation of the battery state without OCV tests is proposed in this study,and where OCV is achieved by establishing the OCV differential equation.After the feasibility of the OCV estimation model is verified,it is used in the online estimation of the battery state in follow up.Furthermore,the accuracy of OCV estimation online is verified by reconstructing the OCV curve in subsequent state estimation.(3)The SOC of lithium-ion batteries is estimated.Firstly,the Extended Kalman filter(EKF)and Unscented Kalman filter(UKF)algorithms were introduced,and based on this,they were combined with noise adaptive algorithms,which eventually resulted in the Adaptive Extended Kalman filter(AEKF)and Adaptive Unscented Kalman filter(AUKF)for SOC estimation.After that,the proposed approaches are tested for precision with variable operating conditions.The experimental measurements demonstrate that the suggested methods deliver an excellent estimation accuracy and exhibit an acceptable robustness to misleading SOC initial values at various temperatures.Furthermore,the above two algorithms are achieved without OCV experiments,and their primary difference is that whether the available capacity of the battery is taken into account in the SOC estimate.The final results show that considering the available battery capacity into the SOC estimation has better accuracy.(4)The SOH of lithium battery is estimated.To tackle the issue that the battery usable capacity can have an impact on the SOC estimation,an online evaluation framework for battery operational capacity estimation without OCV experiments is proposed.Initially,the battery’s model parameters were ascertained using the FFRLS approach in real-time.Subsequently,the AUKF technique was implemented for evaluating the battery’s potential capacity by leveraging the online model parameters.Finally,under the accelerated ageing and normal ageing conditions,the proposed framework was validated.The outcomes revealed that the suggested approach could have reasonable precision and reliability for original values at various aging status.