As a new type of energy-storage device, supercapacitors are widely used in various energy storage fields because of their advantages such as fast charging and discharging, high power density, wide operating temperature range, and long cycle life. However, the degradation and failure of supercapacitors in large-scale applications will adversely affect the operation of the whole system. To maximize the efficiency of supercapacitors without damaging the equipment and to ensure timely replacement before reaching the end of their useful life, it is critical to accurately predict the remaining useful life of supercapacitors. This paper presents a comprehensive review of model-based and data-driven approaches to predict the remaining useful life of supercapacitors, introduces the characteristics of the various methods, and foresees future trends, with the expectation of providing a reference for further research in this field.

New energy storage devices such as batteries and supercapacitors are widely used in various fields because of their irreplaceable excellent characteristics. Because there are relatively few monitoring parameters and limited understanding of their operation, they present problems in accurately predicting their state and controlling operation, such as state of charge, state of health, and early failure indicators. Poor monitoring can seriously affect the performance of energy storage devices. Therefore, to maximize the efficiency of new energy storage devices without damaging the equipment, it is important to make full use of sensing systems to accurately monitor important parameters such as voltage, current, temperature, and strain. These are highly related to their states. Hence, this paper reviews the sensing methods and divides them into two categories: embedded and non-embedded sensors. A variety of measurement methods used to measure the above parameters of various new energy storage devices such as batteries and supercapacitors are systematically summarized. The methods with different innovative points are listed, their advantages and disadvantages are summarized, and the application of optical fiber sensors is emphasized. Finally, the challenges and prospects for these studies are described. The intent is to encourage researchers in relevant fields to study the early warning of safety accidents from the root causes.

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A battery energy storage system is one of the practical and effective ways to achieve carbon neutrality. Lithium-ion batteries are widely used because of their long cycle life, high energy density, high rated voltage, and low self-discharge rate, etc. Their large-scale application has put forward higher requirements for the battery management system (BMS) to ensure the reliable operation of batteries. However, due to the electrochemical reactions and complex operating conditions inside the battery, it is difficult to accurately assess the internal state of the battery. The state of charge (SOC) and state of health (SOH) are two important parameters in the battery management system. This paper systematically summarizes the current commonly used SOC and SOH estimation methods and analyzes the characteristics of each method. The problems and future research trends of battery SOC and SOH estimation techniques are also described, which are expected to provide a reference for further research in this field.

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