Abstract: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 lithium-ion batteries retired from electric vehicles (EVs) and hybrid EVs have been exponentially utilized in battery energy storage systems (BESSs) for 2nd use due to their economic and environmentally friendly benefits. Therefore, research on their aging mechanism and state of health (SOH) has attracted increasing amounts of attention across the world. However, few studies focus on optimizing the economic operation of BESSs that are built by retired batteries with various SOHs. This paper proposes an economic operation optimization method for BESSs comprised of retired batteries with different SOHs, which provides a way for the BESS to operate with new and retired battery systems (BSs) together. An operation cost model is put forward that considers the cost increase caused by aging. This method aims to minimize the operating cost in a time step based on the particle swarm optimization method. To validate the feasibility of the economic operation optimization method, a case was studied using a BESS consisting of four BSs with different SOHs under peak load shifting. Compared with the traditional method, which allocated power according to the available peak power of each BS, the proposed method has advantages in the scheduled number and cost.

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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.