A review of supercapacitors modeling, SoH, and SoE estimation methods: Issues and challenges

Supercapacitors (SCs), or ultracapacitors, due to their attractive features, such as, high power density, long life cycle, etc., have received much attention from the transportation sector. SCs can be used as an additional energy storage system (ESS) in combination with lithium-ion batteries to enhance the performance of electric vehicles (EVs) in dynamic states, including acceleration and regenerative braking modes of operation. Online accurate estimation of SCs' state of health (SoH) and state of energy (SoE) is essential for an efficient energy management and real-time condition monitoring in EV applications. The accuracy of the estimation of the SoE and SoH is based on the model's efficiency, which ensures that in order to minimize the impact of aging, model parameters should be defined in real time. Nevertheless, because the SC model is obviously nonlinear and broad in scale, online identification of the parameters estimation is usually difficult. In this paper, a generalized SC model of high accuracy and good robustness is proposed. The classification of the estimation methodologies for SoH and SoE of SC will be very helpful in choosing the appropriate method for the development of reliable and secure ESS and an energy management strategy for EVs.

Automated summary

Supercapacitors are widely used in electric vehicles for enhanced performance and longevity, and accurate estimation of their health and energy states is crucial. A high-accuracy and robust SC model is proposed in the research, which will be helpful in choosing suitable methods for developing reliable energy storage systems and energy management strategies for EVs.