Methods for estimating lithium-ion battery state of charge for use in electric vehicles: a review

Abstract

In recent decades, electric vehicles (EVs) have been garnering tremendous popularity because of their improved performance and efficiency, as well as new concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels. Extensive use of electric vehicles has already been seen in the automotive industry, especially because of the CO2 emissions and global environmental challenges they help with. A lot of attention has been paid to lithium-ion batteries for their numerous benefits, including lightweight, fast charging, high energy density, extended lifespan, and low self-discharge. This study covers the state of charge (SOC) estimation and management of the lithium-ion battery for sustainable future electric vehicle applications. The importance of adopting a lithium-ion battery management system (BMS) is shown, which guarantees a stable and safe operation and assesses the battery state of charge (SOC). According to the review, the SOC is an important parameter as it denotes the battery’s remaining charge and influences charging and discharging tactics. Additionally, it is shown that existing lithium-ion battery SOC has a positive effect on ensuring the safe and efficient operation of electric vehicles with their charging and discharging capacities. Despite these hurdles, batteries still have certain limitations, such as complex electro-chemical reactions, decreased performance, and inaccuracies in enhancing battery performance and life. This paper thoroughly reviews the approaches used to estimate or capture (SOC) parameters by focusing on the calculation model or algorithm, advantages, disadvantages, and estimation error. It describes a number of aspects and obstacles that have been identified and suggestions for their use in the development of BMS and for estimating SOC in future EV applications are offered. The rising attempts to improve the high-tech future EV applications, SOC calculation method, and energy management system will be enhanced by this review’s highlight insights.

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

“…Improved Accuracy: Ensemble methods combine multiple models, reducing the risk of overfitting and improving overall prediction accuracy [102].…”

“…Fleet management is a complex task that involves overseeing and optimizing a group of vehicles, commonly found in commercial and industrial settings. Machine learning-based SoC estimation has significant applications in this domain: a) Energy-Efficient Routing Accurate SoC estimation enables the planning of routes that maximize energy efficiency and minimize the need for recharging or refueling [102]. For example, in the realm of delivery fleets, particularly in services such as postal deliveries and online shopping transportation, accurate SoC estimation proves to be a critical factor in enhancing overall operational efficiency.…”

Design and PIL Test of High Performance MPPT Controller Based on P&O-Backstepping Applied to DC-DC Converter

“…It is necessary to get 𝑉 ̇1 = −𝑘 1 𝑒 1 2 < 0 for this raison we obtain the (9), where 𝑘 1 is positive.…”