A Review on Different State of Battery Charge Estimation Techniques and Management Systems for EV Applications

Abstract

Electric vehicles (EVs) have acquired significant popularity in recent decades due to their performance and efficiency. EVs are already largely acknowledged as the most promising solutions to global environmental challenges and CO2 emissions. Li-ion batteries are most frequently employed in EVs due to their various benefits. An effective Battery Management System (BMS) is essential to improve the battery performance, including charging–discharging control, precise monitoring, heat management, battery safety, and protection, and also an accurate estimation of the State of Charge (SOC). The SOC is required to provide the driver with a precise indication of the remaining range. At present, different types of estimation algorithms are available, but they still have several challenges due to their performance degradation, complex electrochemical reactions, and inaccuracy. The estimating techniques, average error, advantages, and disadvantages were examined methodically and independently for this paper. The article presents advanced SOC estimating techniques, such as LSTM, GRU, and CNN-LSMT, and hybrid techniques to estimate the average error of the SOC. A detailed comparison is presented with merits and demerits, which helped the researchers in the implementation of EV applications. This research also identified several factors, challenges, and potential recommendations for an enhanced BMS and efficient estimating approaches for future sustainable EV applications.

A Data-Driven Digital Twin of Electric Vehicle Li-Ion Battery State-of-Charge Estimation Enabled by Driving Behavior Application Programming Interfaces

“…Various modeling and state estimation methods have been proposed to describe the nonlinear relationship between SOC and measured LIB variables. The SOC methods can be broadly divided into four categories [3,9,20]: direct estimation method, model-based method, artificial intelligence (AI)-based method, and hybrid method.…”

“…The MBSEM takes advantage of both the measured current, voltage, and temperature signals without relying on the prior knowledge of the initial SOC of the battery [6], and thus forms a close loop estimation method, leading to a more accurate estimation and at the expense of a high computational cost. Such MBSEMs could be the Kalman filter family [20], the sliding mode method [25], the Luenberger observer method [9], etc. As a model-based method, the accuracy and efficiency of SOC estimation are highly affected by the performance of the battery model [7].…”

“…Accurately estimating the SOC remains a challenging task, primarily due to the intricate nature of battery dynamics and the fluctuating operational conditions, which encompass factors like ambient temperature, self-discharge rate, hysteresis, and battery aging [1,3,5,7,8]. Although several methods have been introduced for estimating the SOC of a battery [3,9], many of the existing methods often disregard the influence of EV user behavior and environmental factors, as depicted in Figure 1, on the performance of electric drive vehicles, leading to an inadequate SOC estimation system. In this context, the advancements produced by Industry 4.0 concepts, including significant advancements in information technologies, have facilitated a critical role for digital twins (DTs), particularly in the transportation industry, enabled by sophisticated data analytics and connectivity through the Internet-of-Things (IoT) [10,11].…”

A Comprehensive Review of Various Topologies and Control Techniques for DC-DC Converter-Based Lithium-Ion Battery Charge Equalization

“…(i) Very fexible (ii) Easy implementation (i) Less accuracy (ii) Very difcult to observe when a slight change in internal resistance causes wide variation in SOC IS [95] To assess impedance, multiple frequencies are applied across the battery. SOC is calculated using this impedance.…”

Enhancing battery system identification: nonlinear autoregressive modeling for Li-ion batteries

“…Establishing mathematical battery models is a multifaceted problem, posing challenges in both academic and industrial realms. Measurable quantities in battery management systems (BMS) include current input, output observation, terminal voltage, and temperature [8], [9]. BMS, functioning as an electronic guardian, monitors battery operation, shielding it from damage during charging and discharging.…”