The battery internal temperature estimation is important for the thermal safety in applications, because the internal temperature is hard to measure directly. In this work, an online internal temperature estimation method based on a simplified thermal model using a Kalman filter is proposed. As an improvement, the influences of entropy change and overpotential on heat generation are analyzed quantitatively. The model parameters are identified through a current pulse test. The charge/discharge experiments under different current rates are carried out on the same battery to verify the estimation results. The internal and surface temperatures are measured with thermocouples for result validation and model construction. The accuracy of the estimated result is validated with a maximum estimation error of around 1 K.

The open‐circuit voltage (OCV) of batteries is a crucial characteristic parameter that reflects many aspects of a battery's performance, such as capacity, state‐of‐charge (SOC) and state‐of‐health. OCV is most widely used to determine the SOC when the battery works in a charge‐depleting state. However, the application of the OCV to SOC estimation can be difficult because of the need for a long rest time for full relaxation. In this study, based on the analysis on the curve shape of battery voltage relaxation, a new adaptive model for simulating the voltage relaxation process is developed to predict the final static OCV in a few minutes instead of via the traditional long‐term rest method. Avoiding this disadvantage, the SOC can be deduced from the predicted OCV via the corresponding relationship obtained in a short amount of time. A working condition experiment is performed to validate the new methods and the results are very accurate.

In a parallel-connected battery group (PCBG), capacity degradation is usually caused by the inconsistency between a faulty cell and other normal cells, and the inconsistency occurs due to two potential causes: an aging inconsistency fault or a loose contacting fault. In this paper, a novel method is proposed to perform online and real-time capacity fault diagnosis for PCBGs. Firstly, based on the analysis of parameter variation characteristics of a PCBG with different fault causes, it is found that PCBG resistance can be taken as an indicator for both seeking the faulty PCBG and distinguishing the fault causes. On one hand, the faulty PCBG can be identified by comparing the PCBG resistance among PCBGs; on the other hand, two fault causes can be distinguished by comparing the variance of the PCBG resistances. Furthermore, for online applications, a novel recursive-least-squares algorithm with restricted memory and constraint (RLSRMC), in which the constraint is added to eliminate the “imaginary number” phenomena of parameters, is developed and used in PCBG resistance identification. Lastly, fault simulation and validation results demonstrate that the proposed methods have good accuracy and reliability.