Abstract
Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.
Highlights
Two main industrial applications require high technology systems in energy storage: smart grids and electric vehicles (EVs) [1]
Due to the trend in State of charge (SoC) estimation techniques before 2013 presented in [4], and due to the fact that the trend over the last five years has been towards the inclusion of some probabilistic techniques or artificial intelligence to improve the performance of estimation algorithms, this paper presents an exhaustive review of the SoC estimation methods published since 2013 and gives a description of each of those methods according to a general classification, presenting the main drawbacks
In SoC estimation applications the process of iterative update of the position of the particles is continued until the stop criterion is met, and this criterion is given by an objective function that evaluates the relation open circuit voltage (OCV)–SoC in a direct or indirect way like in [78,149], where a Particle Swarm Optimization Algorithm-Based Estimation (PSO) method is used to determine the unknown parameters of a second-order electric circuit model (ECM) to obtain the OCV
Summary
Two main industrial applications require high technology systems in energy storage: smart grids and electric vehicles (EVs) [1]. An overview of online implementable SoC estimation methods for LiBs was published in [17] It only considers those techniques that can be implemented online and is limited to presenting the basic concepts related to Coulomb counting, open circuit voltage, and impedance spectroscopy. SoC estimation classification methods in this review are based on 145 references published in the last five years; 57% of those cited references correspond to journals and transactions, 39% to conferences, and the remaining 4% to other sources like magazines, books, theses, and proceedings. For EVs and HEVs the trend is towards the design of intelligent BMS ,which involves research areas in artificial intelligence applied for the battery state estimation [12].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
