Liangliang Wei

This paper investigates the quantitative torque analysis and conducts various comparative analysis of different stator/rotor pole combinations of a double-stator hybrid excitation machine (DS-HEM) with DC bias current. Multiple working harmonics can be provided in DS-HEM. The DS-HEM can simultaneously render the abundant harmonics generated by consequent-pole PMs after the single flux modulation effect, facilitated by the rotor and DC bias current, via the dual flux modulation effect provided by the rotor and inner stator, to achieve high torque density. Most importantly, aiming at quantitatively comparing DS-HEMs with distinguishing slot/pole combinations in terms of torque characteristics, this paper analyzes the torque contribution based on a frozen permeability with the Maxwell stress tensor method (FP-MSTM). The results demonstrate that the FP-MSTM can be effectively implemented to analyze torque components with a small error in the range of approximately 3%-8%. Additionally, various performance comparisons among different stator/rotor pole combinations of the proposed machine were conducted. Compared with traditional machines, the proposed DS-HEM can achieve a torque improvement of 62.7%. Finally, the aforesaid analyses of the DS-HEM are thoroughly validated by various prototype experiments.

To ensure the safe and reliable application of lithium-ion batteries, accurate estimation of the state of health (SOH) is essential. However, most existing data-driven methods rely on charge/discharge data and lack interpretability due to their "black-box" nature. To address these challenges, this paper pro-poses an interpretable-algorithm-driven SOH estimation method based on relaxation voltage. To enhance the interpretability of the approach, we integrated the GradCAM++ algorithm into the con-volutional neural network (CNN). This integration not only im-proved the model’s estimation accuracy, with an average reduc-tion in root mean square error (RMSE) of 16.34%, but also gener-ated important class activation maps. Subsequently, these maps were correlated with a battery equivalent circuit model for aging analysis. The results reveal how the CNN focuses on either diffu-sion depolarization or activation depolarization across different battery lifecycle stages to perform SOH estimation. For the batter-ies exhibiting different aging trends, the model emphasizes distinct depolarization processes. Comprehensive validation experiments confirm that the proposed method achieves accurate SOH estima-tion with an average RMSE of 1.70% using only the first 6 minutes of relaxation voltage data, while providing insights for guiding SOH estimation strategies in practical applications.

This paper proposes and optimizes a novel dual stator U-type hybrid excitation machine with DC bias current (DSU-DCHEM), considering flux regulation capability. The proposed dual stator with U-type PMs can elevate the torque density, and the hybrid excitation with integrated winding can improve the efficiency and flux regulation capability. Firstly, the working principle of the proposed machine is introduced, and multi-objective optimization of the proposed machine is performed based on a genetic algorithm (GA). Additionally, various comparative analyses with different PM configurations, including the consequent-pole type, spoke-type, and U-type, are conducted, demonstrating that the proposed DSU-DCHEM has the merits of high torque density and high efficiency. Finally, a prototype is manufactured and tested to verify the performance of the proposed machine. Compared with conventional machines, the proposed DSU-DCHEM can improve the average torque and efficiency by 14.9% and 2.4%, respectively.

State of health (SOH) estimation is critical for the safe and efficient operation of lithium-ion batteries. However, obtaining complete charge-discharge data in real-world applications remains challenging. This article proposes an SOH estimation framework based on physical information constraints and partial charging data reconstruction. A convolutional neural network (CNN) is designed to reconstruct partial voltage curves into the peak region of the incremental capacity (IC) curve, capturing critical aging information. Features extracted from the reconstructed IC curve are then used to estimate SOH, with physics-based constraints from reconstructed IC peak incorporated into the loss function to enhance model interpretability and accuracy. Experimental validation on three different datasets demonstrates that the proposed method achieves high estimation accuracy, with errors under 2% using limited voltage segments. The results verify the effectiveness and generalization capability of the approach, providing a practical solution for the limited data availability in real scenarios.

A parameter estimation and flux weakening cooperative control strategy is proposed for a double stator hybrid excitation machine with dc biased current. In the flux weakening region, the proposed flux weakening cooperative control strategy considering three-dimensional armature currents utilizes the 0-axis current as an additional degree of freedom to regulate the air-gap flux. Compared with the traditional method, the proposed strategy can improve the flux weakening capability and efficiency. Besides, a new parameter estimation method for a hybrid excitation machine considering inductance nonlinearity is proposed. By employing the proposed parameter estimation method, the resistance, permanent magnet flux, and nonlinear inductance of the proposed machine can be obtained. Then, the optimal current combination for the flux weakening cooperative strategy can be calculated based on the Lagrange extreme function. Finally, the proposed parameter estimation and flux weakening cooperative control strategy are validated with various experiments. Compared with traditional methods, the output torque and efficiency are increased by 8.7% and 6.5%, respectively, which demonstrates the accuracy and effectiveness of the proposed method.

This article proposes a maximum torque per ampere (MTPA) cooperative control strategy for a double stator dc-bias hybrid excitation machine (DS-HEM) considering inductance nonlinearity. The proposed DS-HEM uses integrated windings and double stators to improve the torque density, efficiency, and flux regulation capability. Moreover, the proposed MTPA cooperative control strategy considering inductance nonlinearity can achieve high torque and lower copper loss. First, the configuration and drive system of the proposed machine is introduced and a mathematical model of the proposed machine is established, then the proposed MTPA cooperative control strategy considering two dimensions of the armature current and dc-bias current is calculated by using the Lagrange extremum method. Besides, the inductance nonlinearity is also considered by establishing the quadratic polynomial model. Finally, a prototype of the proposed machine is fabricated, and various experiments are conducted to validate the accuracy and effectiveness of the proposed method. Compared with conventional current control strategies, the proposed MTPA cooperative control strategy can generate a larger output torque and lower copper loss.

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.

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 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.