Permanent magnet vernier machine has received more and more attention, due to its high torque density at low rotation speed. This paper proposes a dual-stator hybrid excited permanent magnet vernier machine with Halbach-array permanent magnets. The proposed machine can achieve high torque density with consequent-pole Halbach-array permanent magnets in the rotor, has good flux regulation capability and high power factor. Firstly, the basic principle of the proposed machine is discussed, and the proposed machine can be regarded as a combination of two machines. Finite Element Analysis simulations are performed to verify the performance. And the influence of different parameters on the torque density, flux regulation capability and power factor are analyzed. The results demonstrate the effectiveness of the proposed machine.

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.

It is essential to accurately estimate the state of health (SOH) for lithium-ion batteries from the perspectives of safety and reliability. However, most existing data-driven methods are based on charging or discharging data, which is relatively difficult to apply. This paper proposes a novel SOH estimation approach based on the relaxation voltage reconstruction and a long short-term memory network with an attention mechanism (AM-LSTM) method. First, based on the relaxation voltage data, a complete relaxation curve is reconstructed with a gated recurrent unit (GRU) neural network. Next, health feature (HF) extraction is carried out on the reconstructed data, and the correlation is analyzed based on Pearson correlation analysis. Then, the SOH estimation is performed based on the AM-LSTM model. Various comparative studies have been conducted to verify the effectiveness of the proposed method by comparing it with relaxation voltage reconstruction and different SOH estimation methods. The experimental results demonstrate that the proposed method can effectively reconstruct the relaxation voltage and have good accuracy in estimating the SOH with a partial relaxation voltage curve.

The permanent-magnet-biasing saturated core fault current limiter (PMFCL) is one of the most promising devices to limit the fault current. To improve the biasing ability of permanent-magnets (PMs) and reduce the cost of PMFCL, this paper proposes a novel compact permanent-magnet-biased fault current limiter (CPMFCL). The three-limb structure can reduce the size and cost; the optimal small-section structure can improve the biasing ability of PMs and reduce the usage amount of PMs. First, the principle, equivalent magnetic circuit, and small-section optimized structure are introduced. To validate the principle and performance of CPMFCL, various finite element analysis simulation and optimization study are performed in Maxwell-3D. A 220 V/10 A CPMFCL prototype is designed and tested. The simulation and experimental results demonstrate that, compared with traditional PMFCLs, the CPMFCL has the advantages of smaller size, better biasing capacity of PMs, lower possibility of demagnetization, and excellent fault clipping performance.

With the rapid development of machine learning and cloud computing, deep learning methods based on big data have been widely applied in the assessment of lithium-ion battery health status. However, enhancing the accuracy and robustness of assessment models remains a challenge. This study introduces an innovative T-LSTM prediction network. Initially, a one-dimensional convolutional neural network (1DCNN) is employed to effectively extract local and global features from raw battery data, providing enriched inputs for subsequent networks. Subsequently, LSTM and transformer models are ingeniously combined to fully utilize their unique advantages in sequence modeling, further enhancing the accurate prediction of battery health status. Experiments were conducted using both proprietary and open-source datasets, and the results validated the accuracy and robustness of the proposed method. The experimental results on the proprietary dataset show that the T-LSTM-based estimation method exhibits excellent performance in various evaluation metrics, with MSE, RMSE, MAE, MAPE, and MAXE values of 0.43, 0.66, 0.53, 0.58, and 1.65, respectively. The performance improved by 30–50% compared to that of the other models. The method demonstrated superior performance in comparative experiments, offering novel insights for optimizing intelligent battery management and maintenance strategies.