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

The hybrid energy storage system is a kind of complex system including state coupling, input coupling, environmental sensitivity, life degradation, and other characteristics. How to accurately estimate the internal state of the system, delay the battery life degradation, realize the coordinated and optimized control of power and energy have become the focus and difficulty of the hybrid energy storage system. With the application and popularization of hybrid energy storage systems in electric vehicles and smart grids, relevant theoretical and technological breakthroughs become more and more urgent. Many new achievements, new theories, new methods and new technologies from the fields of materials, information, energy, control and artificial intelligence have been put into this field. This paper comprehensively reviewed the key issues for control and management in hybrid energy storage systems from the aspects of multi-scale state estimation, aging mechanism investigation, life prediction, and energy optimization control of the hybrid energy storage system. Through the in-depth review of key scientific issues, the latest theoretical techniques and application results are presented. Finally some future research challenges and outlooks are presented, in hopes to provide some new ideas and inspirations for the future investigation of the hybrid energy storage systems.

A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems

Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation

Sizing optimization and energy management strategy (EMS) are two key points for the application of the hybrid energy storage system (HESS) in electric vehicles. This article aims to conduct the sizing optimization of HESS and apply an adaptive real-time EMS for practice. First, considering the system cost and battery lifespan, the multiobjective grey wolf optimizer is used to obtain the Pareto front. Second, with optimal parameters, the offline optimal power splitting results by dynamic programming (DP) under different driving patterns are analyzed. Then, the random forests (RF) method is used to learn control rules from the DP results. Driving pattern recognition (DPR) is implemented by the support vector machine (SVM). The intelligent EMS is composed of RF to guide power distribution and SVM to realize DPR. Finally, a combined load cycle involving different driving patterns is used for verification. Results illustrate that the proposed adaptive RF-based EMS can demonstrate a notable superiority in terms of battery protection, ultra-capacitor utilization, and system efficiency. Compared with the ordinary RF-based EMS without DPR, the proposed method can reduce total energy loss by 0.74%–9.49%, and reduce the battery Ah-throughput by 0.5%–19.83% under unknown driving cycles.

Mince Li

Papers

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

A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems

Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation

Sizing Optimization and Energy Management Strategy for Hybrid Energy Storage System Using Multiobjective Optimization and Random Forests

An Energy Management Strategy for Hybrid Energy Storage Systems coordinate with state of thermal and power