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Exact state-of-charge estimation is necessary for every application related to energy storage systems to protect the battery from deep discharging and overcharging. This leads to an improvement in discharge efficiency and extends the battery lifecycle. Batteries are a main source of energy and are usually monitored by management systems to achieve optimal use and protection. Coming up with effective methods for battery management systems that can adequately estimate the state-of-charge of batteries has become a great challenge that has been studied in the literature for some time. Hence, this paper analyses the different energy storage technologies, highlighting their merits and demerits. The various estimation methods for state-of-charge are discussed, and their merits and demerits are compared, while possible applications are pointed out. Furthermore, factors affecting the battery state-of-charge and approaches to managing the same are discussed and analysed. The different modelling tools used to carry out simulations for energy storage experiments are analysed and discussed. Additionally, a quantitative comparison of different technical and economic modelling simulators for energy storage applications is presented. Previous research works have been found to lack accuracy under varying conditions and ageing effects; as such, integrating hybrid approaches for enhanced accuracy in state-of-charge estimations is advised. With regards to energy storage technologies, exploring alternative materials for improved energy density, safety and sustainability exists as a huge research gap. The development of effective battery management systems for optimisation and control is yet to be fully exploited. When it comes to state-of-the-art simulators, integrating multiscale models for comprehensive understanding is of utmost importance. Enhancing adaptability across diverse battery chemistries and rigorous validation with real-world data is essential. To sum up the paper, future research directions and a conclusion are given.

INTRODUCTION: In smart grid applications, energy storage systems (ESS) are critical for balancing power supply and demand, but they often suffer from performance degradation due to State of Charge (SOC) inconsistencies in series-configured lithium battery packs. These disparities can compromise grid stability and battery lifespan. OBJECTIVES: This study proposes an active equalization method based on a novel modular layered architecture for ESS in smart grids. The core innovation lies in the synergistic combination of a hierarchical bidirectional Buck-Boost topology and a multivariable fusion fuzzy logic control strategy, aiming to enhance battery consistency, efficiency, and reliability for grid support. METHODS: A hierarchical BUCK-BOOST-based circuit is designed to enable bidirectional energy transfer, incorporating a multivariable fuzzy controller for real-time regulation of balancing currents. This approach facilitates cooperative equalization within and between battery groups, optimizing energy flow. RESULTS: Simulations based on an eight-cell model in Matlab/Simulink demonstrate that the proposed hierarchical topology reduces equalization time by 11.53% compared to the conventional single-layer topology. Furthermore, with the proposed multivariable fusion fuzzy logic control algorithm, the equalization time is further reduced by 26%, significantly improving both the equalization speed and adaptability to dynamic grid conditions. CONCLUSION: The proposed strategy effectively mitigates battery inconsistencies, enhancing the overall performance and safety of energy storage systems in practical applications. It provides a reliable technical approach for battery management in smart grids.

Hybrid battery energy storage system (HBESS) consists of high power density battery and high energy density battery will have a bright future in special isolated DC microgrid conditions such as the all-electric ships and all-electric airplanes, which have strict limitation on storage capacity and size. In this study, a new decentralised control strategy based on mixed droop is proposed to HBESSs with considering the batteries. In decentralised control strategy, conventional V–I droop controller is utilised to high energy density battery to mainly supply the steady power, I–V droop controller is utilised to high power density battery to respond to power change and supply a few steady power. In addition, dynamical state-of-charge (SoC) regulation algorithm is utilised to reassign the battery power according to their own SoC. The power coordination of the high energy density batteries and high discharge rate batteries is achieved by adjusting the values virtual impedance and reference input voltage. Case study shows that the proposed control strategy is flexible and efficient.

Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations.

Battery storage systems are subject to frequent charging/discharging cycles, which reduce the operational life of the battery and reduce system reliability in the long run. As such, several Battery Management Systems (BMS) have been developed to maintain system reliability and extend the battery's operative life. Accurate estimation of the battery's State of Charge (SOC) is a key challenge in the BMS due to its non-linear characteristics. This paper presents a comprehensive review on the most recent classifications and mathematical models for SOC estimation. Future trends for SOC estimation methods are also presented.

The widespread adoption of lithium batteries has raised concerns regarding safety due to the risk of thermal runaway. The mechanisms by which State of Charge (SOC) and internal heat generation influence thermal runaway in these batteries remain unclear. In this study, we utilize a three-dimensional electrochemical-thermal coupled model of lithium batteries to investigate the evolution of thermal runaway with increasing temperature by examining heat generation and temperature growth rate. Our findings indicate that: (1) both heat generation and temperature growth rate in lithium batteries during thermal runaway initially increase before eventually decreasing; (2) an increase in SOC leads to a reduction in lithium intercalation concentration at the negative electrode, resulting in decreased heat generation and lower peak temperatures during thermal runaway; (3) internal heat generation significantly impacts the critical time of thermal runaway, with higher heat generation associated with higher peak temperatures and increased risk. These results provide theoretical support for optimizing lithium battery safety and preventing associated fires and explosions.

Electric vehicles (EVs) have emerged as a promising solution for sustainable transportation. The high energy density, long cycle life, and low self-discharge rate of lithium-ion batteries make them an ideal choice for EVs. Recently, these batteries have been prone to faster decay in life span, leading to sudden failure of the battery. To avoid uncertainty among EV users with sudden battery failures, a robust health monitoring and prediction scheme is required for the EV battery management system. In this regard, the Unscented Kalman Filter (UKF)-based technique has been developed for accurate and reliable prediction of battery health status. The UKF approximates nonlinearity using a set of sigma points and propagates them via the nonlinear function to enhance battery health estimation accuracy. Furthermore, the UKF-based health estimation scheme considers the state of charge (SOC) and internal resistance of the battery. Here, the UKF-based health prediction technique is compared with the Extended Kalman filter (EKF) scheme. The robustness of the UKF and EKF-based health prognostic techniques were studied under varying initial SOC values. Under these abrupt changing conditions, the proposed UKF technique performed effectively in terms of state of health (SOH) prediction. Accurate SOH determination can help EV users to decide when the battery needs to be replaced or if adjustments need to be made to extend its life. Ultimately, accurate and reliable battery health estimation is essential in vehicular applications and plays a pivotal role in ensuring lithium-ion battery sustainability and minimizing environmental impacts.

Alleviating and restraining thermal runaway (TR) of lithium-ion batteries is a critical issue in developing new energy vehicles. The battery state of charge (SoC) influence on TR is significant. This paper performs comprehensive modeling and analysis with the non-uniform distribution of SoCs at the module level. First, a numerical model is established and validated with experimental data to calculate the TR of the cells with different SoCs. Then, the influence of uniform and non-uniform SoC distribution on TR propagation is studied. The results show that the battery temperature, TR propagation time, and range are significantly affected by the total SoC of the battery module. When the total SoC is reduced below 30%, the energy released by the battery is significantly reduced, which is not enough to trigger the TR of all battery cells, and the TR propagation can be interrupted. Furthermore, the analysis of TR propagation in a battery model with non-uniform SoC distribution indicates that the propagation can be mitigated by reducing the SoC of two adjacent batteries on the spreading path. When the total SoC of adjacent cells is less than 55%, the TR propagation will be successfully inhibited.

Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system (BMS), where building an accurate battery model is the first step in model-based estimation algorithms. To date, although the comparative studies on different battery models have been performed intensively, little attention is paid to the comparison among different online parameters identification methods regarding model accuracy, robustness ability, adaptability to the different battery operating conditions and computation cost. In this paper, based on the Thevenin model, the three most widely used online parameters identification methods, including extended Kalman filter (EKF), particle swarm optimization (PSO), and recursive least square (RLS), are evaluated comprehensively under static and dynamic tests. It is worth noting that, although the built model’s terminal voltage may well follow a measured curve, these identified model parameters may significantly out of reasonable range, which means that the error between measured and predicted terminal voltage cannot be seen as a gist to determine which model is the most accurate. To evaluate model accuracy more rigorously, battery state-of-charge (SOC) is further estimated based on identified model parameters under static and dynamic tests. The SOC prediction results show that EKF and RLS algorithms are more suitable to be used for online model parameters identification under static and dynamic tests, respectively. Moreover, the random offset is added into originally measured data to verify the robustness ability of different methods, whose results indicate EKF and RLS have more satisfactory ability against imprecisely sampled data under static and dynamic tests, respectively. Considering model accuracy, robustness ability, adaptability to the different battery operating conditions and computation cost simultaneously, EKF is recommended to be adopted to establish battery model in real application among these three most widely used methods.