Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles

By Rui Xiong, Jiayi Cao, Quanqing Yu, Hongwen He, Fengchun Sun. IEEE Access, 2018

829 citations recorded by the source corpus.

Battery technology is the bottleneck of the electric vehicles (EVs). It is important, both in theory and practical application, to do research on the modeling and state estimation of batteries, which is essential to optimizing energy management, extending the life cycle, reducing cost, and safeguarding the safe application of batteries in EVs. However, the batteries, with strong time-variables and nonlinear characteristics, are further influenced by such random factors such as driving loads, operational conditions, in the application of EVs. The real-time, accurate estimation of their state is challenging. The classification of the estimation methodologies for estimating state-of-charge (SoC) of battery focusing with the estimation method/algorithm, advantages, drawbacks, and estimation error are systematically and separately discussed. Especially for the battery packs existing of the inevitable inconsistency in cell capacity, resistance and voltage, the advanced characterizing monomer selection, and bias correction-based method has been described and discussed. The review also presents the key feedback factors that are indispensable for accurate estimation of battery SoC, it will be helpful for ensuring the SoC estimation accuracy. It will be very helpful for choosing an appropriate method to develop a reliable and safe battery management system and energy management strategy of the EVs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1f4afa1d44221218e3276dbfc0f6112d0397a18e

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Citations in the corpus

Review of Electric Vehicle Charging Technologies, Standards, Architectures, and Converter Configurations (2023, methodology): “estimation methods is presented in [91] by highlighting algorithm/control design, advantages, disadvantages, and challenges to selecting appropriate SoC methods for EVs.”
State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations (2018, background): “Overcharging might cause cell explosion, whereas undercharging might damage the chemical properties of the battery and shorten the life of the battery cells [93], [94].”
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “For example, if the prior knowledge is not correct, then the estimation process error may lead to divergence [77].”
Global Trends in High-Power On-Board Chargers for Electric Vehicles (2019, background): “A review on SoC estimation methods was recently published in [26], and battery voltage equalizer circuits are studied in [27].”