A review of battery state of charge estimation and management systems: Models and future prospective

Abstract

Batteries are considered critical elements in most applications nowadays due to their power and energy density features. However, uncontrolled charging and discharging will negatively affect their functions and might result in a catastrophic failure of their applications. Hence, a battery management system (BMS) is mandated for their proper operation. One of the critical elements of any BMS is the state of charge (SoC) estimation process, which highly determines the needed action to maintain the battery's health and efficiency. Several methods were used to estimate the Lithium‐ion batteries (LIBs) SoC, depending on the LIBs model or any other suitable technique. This article provides a critical review of the existing SoC estimation approaches and the main LIB models with their pros and cons, their possibility to integrate with each other's for precise estimation results, and the applicability of these techniques in electric vehicles and utility applications with the commonly used standards and codes in these sectors. Moreover, this study will also explore a future framework for integrating digital twins (DTs) with BMSs for improved and advanced management. Based on this comprehensive review, it can be concluded that merging the model‐based estimation techniques with the data‐driven approaches with their promising development to determine the dynamic patterns inside the battery can efficiently achieve precise estimation results while reducing the complexity of these models. This integration will align also with the integration of digital twin technology to provide complete and accurate supervision for the BMSs.This article is categorized under: Emerging Technologies > Energy Storage Emerging Technologies > Digitalization Energy and Power Systems > Distributed Generation

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

“…The DD model's capability to identify and track a battery's dynamics and accurately estimate the SoC has led to an increase in their utilization, since this is considered a significant challenge with the conventional white models. Further, data-driven algorithms can be adjusted to changing battery behavior over time by learning from big datasets [13,48,49].…”

“…However, notable limitations include their dependency on substantial training datasets for optimal performance, computational intensity, and susceptibility to overfitting. Therefore, the effectiveness of an NN is contingent on appropriate training data, architecture selection, and parameter tuning [13,84,85].…”

“…and 𝑔 (. ), respectively, will be modeled as follows [87]: 𝐸 = 𝑓(𝑤 , 𝑏 ; 𝑥) 𝐷 = 𝑔(𝑤 , 𝑏 ; 𝑧) (13) where 𝑤 and 𝑏 are the encoder parameters, while 𝑤 and 𝑏 are the parameters for the decoder. If the algorithm implementation is performed through an NN, 𝑤 and 𝑏 for both will be the weight matrices and bias vectors concerning encoder and decoder neural networks [87].…”

“…In addition to the advantage of DD models of not requiring deep knowledge of battery characteristics, they also play a crucial role in modern BMS systems with advanced technologies such as Digital Twin (DT) [12,13]. These features motivate researchers to investigate different architectures and models to enhance state estimation results and improve BMS response.…”

Hardware Implementation of a Resilient Energy Management System for Networked Microgrids

“…The Thevenin equivalent circuit model (ECM) is widely utilized to calculate the battery voltage in response to the current, as in Figure 2. According to [50], an ideal voltage source that is associated with the state of charge of the battery (SoC) represents the OCV. R 0 represents the battery's ohmic resistance, and the parallel RC network represents its transient behavior resulting from interfacial charge-transfer reactions at the electrode (R1 and C1).…”