Due to urbanization and the rapid growth of population, carbon emission is increasing, which leads to climate change and global warming. With an increased level of fossil fuel burning and scarcity of fossil fuel, the power industry is moving to alternative energy resources such as photovoltaic power (PV), wind power (WP), and battery energy-storage systems (BESS), among others. BESS has some advantages over conventional energy sources, which include fast and steady response, adaptability, controllability, environmental friendliness, and geographical independence, and it is considered as a potential solution to the global warming problem. This paper provides a comprehensive review of the battery energy-storage system concerning optimal sizing objectives, the system constraint, various optimization models, and approaches along with their advantages and weakness. Furthermore, for better understanding, the optimization objectives and methods have been classified into different categories. This paper also provides a detailed discussion on the BESS applications and explores the shortages of existing optimal BESS sizing algorithms to identify the gaps for future research. The issues and challenges are also highlighted to provide a clear idea to the researchers in the field of BESS. Overall, this paper conveys some significant recommendations that would be useful to the researchers and policymakers to structure a productive, powerful, efficient, and robust battery energy-storage system toward a future with a sustainable environment.

Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues

Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook

Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement

Lithium-ion batteries are promising energy storage devices for electric vehicles and renewable energy systems. However, due to complex electrochemical processes, potential safety issues, and inherent poor durability of lithium-ion batteries, it is essential to monitor and manage batteries safely and efficiently. This study reviews the development of battery management systems during the past periods and introduces a multilayer design architecture for advanced battery management, which consists of three progressive layers. The foundation layer focuses on the system physical basis and theoretical principle, the algorithm layer aims at providing a comprehensive understanding of battery, and the application layer ensures a safe and efficient battery system through sufficient management. A comprehensive overview of each layer is presented from both academic and engineering perspectives. Future trends in research and development of next-generation battery management are discussed. Based on data and intelligence, the next-generation battery management will achieve better safety, performance, and interconnectivity.

Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries

Digital twin and cloud-side-end collaboration for intelligent battery management system

Capacity decline is the focus of traditional battery health estimation as it is a significant external manifestation of battery aging. However, it is difficult to depict the internal aging information in depth. To achieve the goal of deeper online diagnosis and accurate prediction of battery aging, this paper proposes a data-driven battery aging mechanism analysis and degradation pathway prediction approach. Firstly, a non-destructive aging mechanism analysis method based on the open-circuit voltage model is proposed, where the internal aging modes are quantified through the marine predator algorithm. Secondly, through the design of multi-factor and multi-level orthogonal aging experiments, the dominant aging modes and critical aging factors affecting the battery capacity decay at different life phases are determined using statistical analysis methods. Thirdly, a data-driven multi-factor coupled battery aging mechanism prediction model is developed. Specifically, the Transformer network is designed to establish nonlinear relationships between factors and aging modes, and the regression-based data enhancement is performed to enhance the model generalization capability. To enhance the adaptability to variations in aging conditions, the model outputs are set to the increments of the aging modes. Finally, the experimental results verify that the proposed approach can achieve satisfactory performances under different aging conditions.

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Data-Driven Battery Aging Mechanism Analysis and Degradation Pathway Prediction

Precise health prognosis is essential to guarantee efficient and safe battery operation. However, the non-invasive analysis of the microscopic aging mechanism of batteries has always been a challenge. To address this problem, a macro–micro non-invasive health prognosis method for lithium-ion batteries is proposed based on aging mechanisms migration in this paper. Firstly, an improved reduced order physics-based model considering hysteresis is established. Besides, a multi-step full parameter identification method is designed based on the multi-verse optimizer. Secondly, the critical mechanism parameters are determined for model lightweighting, through the correlation and sensitivity analysis, which can dominate the electrical performance and capture the aging modes. Then, an aging mechanism migration method is designed for macro–micro state of health (SOH) estimation and remaining useful life (RUL) prediction. The superiority of the proposed method is that it can predict not only macroscopic capacity-defined SOH, but also microscopic aging mechanisms non-invasively, including the loss of lithium inventory, loss of positive/negative active material, and reaction kinetics decline. Finally, experiments are carried out to demonstrate the effectiveness of proposed model and method under different aging conditions. • An improved physics-based model with hysteresis correction is established. • Parameters are decoupled and identified step-by-step using multi-verse optimizer. • A non-invasive method for quantifying the aging mechanism is proposed. • The migration-based macro–micro health prognosis method is proposed. 

Ruilong Xu

Papers

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries

Digital twin and cloud-side-end collaboration for intelligent battery management system

Data-Driven Battery Aging Mechanism Analysis and Degradation Pathway Prediction

A migration-based method for non-invasive revelation of microscopic degradation mechanisms and health prognosis of lithium-ion batteries