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

TLDR

Battery technology is the bottleneck of electric vehicles, and real‑time, accurate state‑of‑charge estimation is difficult due to the batteries’ nonlinear, time‑varying behavior and the influence of driving loads and operating conditions. This review investigates battery state‑of‑charge estimation methods to optimize energy management, extend battery life, reduce costs, and enhance safety in electric vehicles. The authors systematically classify SoC estimation algorithms, evaluating their advantages, drawbacks, and estimation errors, and discuss bias‑correction techniques for packs with cell capacity, resistance, and voltage inconsistencies. The review identifies key feedback factors essential for accurate SoC estimation, assists in selecting appropriate methods for reliable battery management systems, and offers recommendations for developing next‑generation smart SoC estimation and BMS solutions.

Abstract

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.