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.

To comprehensively investigate the thermal and energy characteristics of air-cooling battery thermal management systems (BTMSs) during fast charging, a battery pack with 32 lithium-ion batteries was studied at various charge rates and air velocities. The evaluation parameters of the thermal performance during fast charging were experimentally obtained, and the energy characteristics of active air-cooling BTMSs with external and internal air circulation were analyzed and compared by combining experimental results and numerical models. The results show that, compared with the external air circulation system, the active air-cooling BTMS with internal air circulation can save up to 69.4% of the total energy consumption in a 30°C environment and 83.6% at 40°C. In the internal air circulation BTMS, the refrigeration energy consumption can constitute between 90.2% at maximum and 46.5% at minimum of the total energy consumption, and the remaining energy is consumed in supplying airflow. The energy consumed for each 1°C reduction of the maximum temperature rise decreases with increasing charge rate.

It is vital to accurately estimate the state of health (SOH) of lithium-ion batteries of electrical vehicles. Despite the significant impact of temperature on the SOH, most conventional data-driven methods for SOH estimation neglect the influence of temperature-related characteristics. This paper proposes a novel SOH estimation method with attentional feature fusion (AFF) considering differential temperature (DT) features for lithium-ion batteries. Firstly, this paper extracts DT features from the differential temperature curves and employs the Pearson correlation coefficient (PCC) to select the health features (HFs) of the DT curve that exhibit the strongest correlation with SOH. Then, a novel SOH estimation model combining AFF and LSTM (AFF-LSTM) is proposed, which can automatically learn the influence weights of different types of HFs on SOH, thereby improving the accuracy of SOH estimation. The MAPE evaluation metric is approximately 0.126% on the Oxford dataset. Finally, various comparative analyses demonstrate that the proposed method can accurately estimate the SOH of lithium-ion under different operating temperatures.

To guarantee the safe and efficient operation of lithium-ion batteries, it is crucial to precisely estimate the state of health (SOH) of batteries. However, most of the existing studies have primarily focused on complete or large-range charging curves, which are highly challenging to acquire in practical applications. To this end, a novel SOH estimation method based on partial charging curve reconstruction is proposed in this article. First, a partial charging curve reconstruction model based on a convolutional neural network reconstructs the charging segments from voltage ranges with low SOH correlation to those with high SOH correlation. Second, health features are extracted from the reconstructed charging segments and used as inputs to estimate the SOH based on a Gaussian process regression model. Finally, validation experiments were conducted on two lithium-ion battery datasets to demonstrate the effectiveness and generalization of the proposed method. The proposed method enables precise SOH estimation using only a narrow charging segment (0.05 V), making it suitable for practical application scenarios.