Real-time state-of-health (SoH) estimation is often difficult to obtain due to the unavailability of capacity measurements in real-time monitoring. The equivalent internal resistance (EIR), which is easily obtained and closely related to battery deterioration, is studied as a possible solution for achieving real-time and reliable SoH estimation for lithium-ion batteries. A novel real-time SoH estimation method based on the EIR is introduced for lithium-ion batteries. First, an experimental study of the relationship between the EIR and battery degradation is implemented, and this study is used to develop an empirical description of battery degradation using the EIR vector. Second, a fast extraction method for identifying the EIR in real time is proposed by leveraging the relationship between the EIR vector and state of charge (SoC). Third, a support vector regression (SVR)-based method for real-time SoH estimation is introduced by characterizing the hidden relationship between the EIR vector and battery SoH. The proposed method is demonstrated using laboratory test data. The results show that the proposed method can predict the battery SoH in real time with good accuracy and robustness.

The effects of substituent groups of TEMPO-based catholytes and supporting electrolytes on electrochemical properties, solubility and battery performance were examined systematically for aqueous redox flow batteries.

Existing health prognostics methods often omit the internal health recovery of proton exchange membrane fuel cells (PEMFCs), although this phenomenon commonly exists, especially in the long-term usage of PEMFCs for hydrogen fuel cell vehicles. To this end, a novel hybrid method for PEMFCs is proposed, and internal recovery effects and external health data are collaboratively leveraged to achieve high-accuracy health prognostics. Aiming at characterizing health degradation in detail, the health prognostics of PEMFCs is addressed as voltage prediction with recovery identification, trend prediction and fluctuation prediction. Notably, the internal impedance extracted from electrochemical impedance spectroscopy is used to identify the internal recovery effects. This model-based recovery identification is further incorporated with particle filter for the trend prediction and with random forest regression for the fluctuation prediction by using external health data. Equipped with this hybrid method, simultaneous long-term and short-term health assessment and prognostics are realized. Durability test data of two PEMFCs are used to demonstrate the proposed method. The RMSE of the proposed method can reach 0.0090, 0.0088, and 0.0094 for the long-term predictions at 550 h, 600 h, and 650 h, respectively, which are smaller than conventional model-based, data-driven and EKF-LSTM hybrid methods.

Mobile crowd sensing (MCS) is a new sensing paradigm that leverages participatory sensing data from mobile devices for accomplishing large-scale sensing tasks. Incentivizing device owners to contribute high-quality sensing data is a prerequisite for the success of MCS services. In this paper, we first propose a pre-contracting incentive mechanism that involves the participation of not only the device owners located in close proximity to Point of Interests (PoIs) but also the device owners that are going to pass through those locations. Furthermore, the quality of sensing data is guaranteed through the use of redundancy. In particular, sensing data from multiple device owners is processed and compared at an edge side (i.e., base station) so as to detect the measurement error at the proximity of data sources. Simulation results confirm that the proposed incentive mechanism is efficient in terms of improving the total utility.