Lithium-Ion Batteries: Prognosis Algorithms, Challenges and Future Scenario

This study concentrates on different types of prognosis algorithms for forecasting lithium-ion battery parameters. Various SoC estimation techniques are examined and compared based on their SoC estimation performance indexes. SoC estimation methods are broadly classified as Kalman filter, particle filter, data-driven and hybrid methods. These types of filters are compared based on the complexity of the implementation on hardware as well as their performance parameters such as statistics (errors), driving test schedules, laboratory testing data, type of battery model used and different types of prognosis methods used in the SoC estimation. It helps in the proper selection of the hardware and methods for battery model parameter forecasting which is critical for electrical vehicle (EVs) battery management system coordination and control. This study also focuses on the challenges faced by the different SoC prognosis methods and their modern trends to improve the forecasting of important parameters of the batteries.