Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Abstract

Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation.

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

“…These approaches primarily depend on the quality of the collected datasets, as well as the selection and optimization of the model, employing extensive data to train models that discern the nonlinear mapping relationships between the input features and SOC, thus enabling more accurate SOC estimations. Machine-learning models possess formidable nonlinear mapping capacities, robust generalization, and adaptability [14]. Consequently, data-driven SOC estimation methods for lithium batteries have rapidly developed.…”

State-of-Charge Estimation of Lithium-Ion Battery Based on Convolutional Neural Network Combined with Unscented Kalman Filter

“…Therefore, the SOC varies between 0 and 1, the lower and higher limits corresponding to fully discharged and fully charged states [6]. Accurate estimation of the SOC is crucial for EVs, being used by the battery management system (BMS) to prevent over-discharging and over-charging and, ultimately, to guarantee the reliability and safe operation of EVs [7][8][9]. However, the SOC is not directly measurable, being indirectly determined through acquired sensory data, namely battery voltage, current and temperature, and some suitable data processing method [10].…”

Integrated Extended Kalman Filter and Deep Learning Platform for Electric Vehicle Battery Health Prediction

“…SoH refers to the overall condition and remaining capacity of the battery, while SoC indicates the current level of charge stored in the battery [4,5]. Developing precise and dependable models for predicting SoH and SoC is a significant research challenge in the automotive industry [6,7]. These models must account for various factors such as battery chemistry, temperature, charging cycles, and usage patterns to provide accurate assessments of battery health and charge status.…”