Accurate prediction of the battery state of charge (SOC) is of great significance to improve the utilization efficiency and safety performance of the battery, and the monitoring of the battery state of charge is very important to help prevent overcharge and overdischarge accidents. The traditional SOC prediction methods are highly dependent on the mechanism model and statistical model, and have problems such as sensitive outliers and limited practical accuracy. In this study, a CNN-LSTM-AM (convolutional neural network - long short term memory neural network - attention mechanism) model is proposed to predict SOC variation trend through battery measurable variables.

The model first uses a one-dimensional convolutional neural network to extract spatial features of measurable variables, including battery current, voltage, temperature and average voltage, and then sends them to bidirectional long and short time memory for time series analysis. Finally, the attention mechanism is introduced to screen key features, reduce the redundancy of feature data, and improve the accuracy and generalization of the model. In addition, CNN-LSTM-AM model adopts rime optimization algorithm to optimize the hyperparameters in the training process, which effectively improves the training efficiency and reduces the training cost.

The actual evaluation on CALCE (Center for Advanced Life Cycle Engineering) data set of lithium iron phosphate shows that the attention mechanism can effectively improve the training performance of the prediction model, and the rime optimization algorithm adopted can help reduce the model hyperparameters, so as to obtain higher prediction accuracy. The performance of CNN-LSTM-AM model was tested under different temperature conditions, and both RMSE and MAE were less than 1%, which was sufficient to confirm the feasibility of the model to predict SOC. In addition, even if the initial SOC is uncertain, the proposed CNN-LSTM-AM model can still accurately track SOC trend changes, and the overall prediction accuracy reaches RMSE<1.5% and MAE<1.5%. The RMSE and MAE results of the network proposed in this study are smaller than those of CNN-LSTM and CNN-LSTM-AM. It shows strong robustness and generalization ability. Finally, in order to comprehensively compare the performance of different SOC prediction methods, the CNN-LSTM-AM model proposed in this study is compared with other experimental results. It can be seen that the method proposed in this study has significantly lower RMSE compared with AT-CNN-LSTM. At the same time, considering that the proposed method uses less training set data, we can also see the advantages of the designed network. Compared with EI-LSTM-CO(extended input-LSTM-constrained output), it can be found that the error is close. In addition, EI-LSTM-CO performs some post-processing on the predicted SOC, which can also reflect the superiority of the proposed method.

The following conclusions are drawn from the simulation analysis: (1) A CNN-LSTM-AM model is proposed and applied to the SOC prediction task of battery, which can effectively capture important input features and improve the prediction accuracy. (2) Design a rime optimization algorithm, which can automatically search the optimal solution of CNN-LSTM-AM model, effectively reduce the time cost of hyperparameter optimization. (3) The influence of different ambient temperatures and initial SOC values on the prediction accuracy of CNN-LSTM-AM was studied, and the performance of CNN-LSTM-AM was compared with that of traditional prediction models to verify its strong robustness and high generalization ability.

With the continuous development of the new energy vehicle industry, lithium-ion batteries are used in large quantities as on-board power batteries. The battery management system (BMS) is responsible for monitoring, evaluating, maintaining, and optimizing the performance and life of Li-ion batteries, and the prediction of c is an important part of the BMS. Accurate prediction of a battery's RUL helps identify batteries that are nearing the end of their life to prevent potential safety risks such as overheating, combustion, or explosion, and allows O&M personnel to schedule battery maintenance and replacements based on the battery’s actual state of health, rather than on a pre-determined schedule, thereby reducing unnecessary O&M costs. However, lithium-ion batteries exhibit nonlinear aging trends due to their complex internal chemical reactions during use, and the aging process of batteries usually exhibits multi-stage degradation, which increases the difficulty of RUL prediction. In view of this, this paper proposes a RUL prediction method for lithium-ion batteries based on public battery data from the University of Maryland and lithium iron phosphate battery data collected by the group's own laboratory, and the main research contributions are as follows:

Aiming at the problem that battery capacity is difficult to be measured directly, and the poor ability of traditional network models to capture multi-feature input information, a method is proposed to optimize the bidirectional gated recurrent unit (BiGRU) network based on the fusion feature and the osprey optimization algorithm (OOA) for RUL prediction of lithium-ion batteries. Simple and easy-to-measure current, voltage and time data during battery aging are collected, from which the health factors that can reflect the aging trend of the battery are extracted. The Savitzky-Golay filtering method is selected to reduce the influence of noise on the prediction accuracy. A fusion feature screening strategy combining filter and wrapper is proposed to reduce the complexity of the model and prevent model overfitting. Considering the insufficient ability of the traditional model to capture battery aging information when dealing with multi-feature inputs, the GRU network, which predicts only based on historical information, is upgraded to the BiGRU network, which is capable of handling both forward and backward information of the sequences. The BiGRU network is able to understand the overall structure and dynamic properties of the sequences in a more in-depth manner, better integrate the multi-dimensional features, and adapt to dependencies in different time scales. OOA is used to effectively optimize the hyper parameters inside the BiGRU model, which improves the prediction accuracy of the model and also realizes the automatic configuration of the parameters. Different types of battery data are used to compare the proposed method with traditional network models to verify the reliability of the proposed OOA-BiGRU model. In addition, the effect of the proposed fusion feature prediction is compared with all feature prediction and filtered feature prediction, which proves that the fusion feature better represents the aging degree of the battery and improves the accuracy of the model prediction.

The research results of this paper provide a new method and idea for RUL prediction of lithium-ion power batteries, which can be applied to the BMS system of new energy vehicles and is of practical significance.

As an important parameter in power electronic converters, the leakage inductance of high-frequency transformers is of great significance in improving the operating mode and power transmission characteristics of isolated DC-DC converters. Compared with the solid round wire, the Litz wire can reduce eddy current losses in high-frequency magnetic components. However, the complicated structure of the Litz-wire windings poses a serious challenge to predicting leakage inductance in high-frequency transformers. On the one hand, it is difficult to precisely extract the magnetic field energy in various regions of the core window. On the other hand, it is hard to accurately characterize the multi-stranded and twisting structures of the Litz wires. Therefore, this paper presents a fast calculation method of leakage inductance in the high-frequency transformer with Litz-wire winding.

Firstly, a homogenized equivalent process for Litz wire is proposed to enhance the flexibility of modeling and the efficiency of computation. The magnetic field energy variation with frequency inside the Litz-wire conductors are analyzed. Then, the 2-D magnetic field energy inside the core window is extracted based on the method of images to eliminate the impact of the edge effect. The internal and external magnetic fields at different locations in the winding are accurately characterized by introducing the meshing into the method of images, and a coordinate transformation method is proposed to consider the twisting structure of the Litz wires. Finally, two high-frequency transformer prototypes with different structures are designed and fabricated. Compared with the measurement results and two existing methods, the accuracy and efficiency of the proposed approach are verified.

The following conclusions can be drawn. (1) A homogenized equivalent model of the Litz-wire twisting structure is developed by introducing the relative complex permeability, which simplifies the model building and reduces the computational cost. The variation of the magnetic field energy in the Litz wires with frequency is analyzed, and the magnetic field energy stored in the Litz wires gradually decreases with the frequency increase. (2) The meshing process is introduced into the method of images, and the coordinate transformation method is proposed to characterize the twisting structure of the Litz wire. It can counteract a part of the external magnetic field and reduce the magnetic field energy in the conductors. (3) Considering the twisting characteristics of the Litz wire and the high-frequency effect, a leakage inductance prediction model is developed based on the magnetic field energy variation with frequency. (4) The accuracy and efficiency of the proposed method are verified compared with the measurement and the current two analytical methods. The maximum error does not exceed 4% throughout the measurement frequency range, and the calculation time is about 20 seconds. Moreover, the proposed method can be effectively applied to fast iterative calculations in the optimal design of high-frequency transformers.

Fractional-order elements (FOEs) serve as fundamental components in fractional-order circuits, forming the cornerstone of research into fractional-order circuit systems. Unlike single-component counterparts, the multi-component method offers greater flexibility in selecting constituent elements, enabling the adjustment of order and impedance coefficients for enhanced practicality. This paper provides a comprehensive overview of prevailing construction methods to facilitate the selection of appropriate multi-component FOEs for specific application needs. These methods are classified into three categories based on the type of constituent devices: passive devices, operational amplifiers, and power electronic converters.

In the construction method based on passive devices, two approaches involving Foster RLC ladder circuits are discussed. Passive RLC networks are used to create circuits that match the impedance characteristics of the transfer function. Reducing phase error requires increasing the order and using numerous components, leading to complex structures and cumbersome calculations. When standard off-the-shelf components cannot be used, replacing analytical parameters with standard ones increases the phase angle deviation. Adjusting the FOE order or impedance coefficients requires replacing all circuit components. This method is most suitable for fixed FOE order and impedance coefficients, which are effective in medium to low-frequency scenarios.

Next, the paper introduces the construction method based on operational amplifiers. The method of constructing FOEs based on generalized impedance converter (GIC) circuits can realize FOEs with orders varying from -2 to 2. However, the limited open-loop gain and gain instability of operational amplifiers often result in significant deviations of the obtained FOE performance from its ideal characteristics. Therefore, exploring how to use other active devices, such as operational transconductance amplifiers (OTA) and current feedback operational amplifiers (CFOA), to construct FOEs is an exploration direction. GIC circuits offer great integration and functionality, making them suitable for applications where precise impedance and phase characteristics are crucial.

The construction method based on power electronic converters is also discussed in detail. Multi-component FOEs based on power electronic converters have wide applications because their power level depends on the inverter, and their order and impedance can be adjusted by changing the control parameters. However, different structures of filters affect the operating performance of fractional-order elements. Therefore, exploring the application of different filter structures on multi-component FOEs and optimizing the parameters of the filters become the direction of development for power electronic converter-based FOEs. Power electronic converters provide the advantage of handling higher power levels and dynamic adaptability. The ability to digitally control the fractional order and impedance in real-time makes these elements highly versatile.

Lastly, this paper proposes a three-phase fractional-order electrical spring (TPFES). TPFES controls the order of the equivalent fractional-order capacitance of each phase and the pseudo-capacitance. The effect of stabilizing the load voltage is realized, the power factor is improved, and the power is balanced. The application in grid power compensation demonstrates that multi-component FOEs can effectively enhance the performance of practical circuit systems. This work provides a reference for future applications of fractional order components in electrical engineering.

In wireless power transfer (WPT) systems, achieving accurate voltage regulation and efficient operation are critical. Current research achieves constant voltage output and zero voltage switch (ZVS) with additional DC-DC converters and variable resonant networks. However, these approaches increase system losses and costs. Therefore, this paper proposes a two-sided LCL phase-shifting control strategy. The internal phase shift angle of the inverter and active rectifier (AR) is used to achieve constant voltage output and maximum efficiency tracking, and the external phase shift angle between the two converters achieves ZVS of all switching tubes. By analyzing the power loss, the constraint condition between the internal phase shift angle is obtained. The minimum external phase shift angle δ_opt of ZVS is further determined, and the system’s high efficiency is realized. In addition, the power angle θ_power is introduced as the intermediate variable, and the frequency synchronization of the primary and secondary sides is realized using the voltage-controlled oscillator (VCO).

Firstly, utilizing the fundamental wave equivalent model, the constant voltage characteristics of the system and the constraint conditions of the inverter output voltage and AR input voltage pulse-width ratio D₁ and D₂ are analyzed. The results show that transmission efficiency peaks when the AC voltage ratio α =1. With load variations, achieving constant voltage output and maximum efficiency tracking is feasible by adjusting D₁ and D₂. Secondly, based on the time-domain harmonics model, the derivation and simplification of the time-domain expression of inductance current are conducted. The simplified model is then analyzed to determine the external phase shift angle δ. By comparing δ of the inverter and AR, the δ_opt is obtained. Thirdly, the overall control strategy is introduced. The constant voltage output is achieved by adjusting D₁ and D₂. The introduction of θ_power as an intermediate variable establishes the relationship between δ and θ_power, enabling indirect control of δ through the regulation of θ_power. Subsequently, the frequency synchronization of the primary and secondary sides is realized using a VCO, effectively solving the synchronization challenge associated with an active rectifier.

Finally, system simulations and experiments were conducted. The experimental results show that the system can achieve ZVS for all MOSFETs and maintain a constant voltage output regardless of load variations. Moreover, the proposed synchronous control strategy can effectively track the switching frequency of the inverter and precisely adjust the required δ_opt. As D₁ and D₂ consistently adhere to the maximum efficiency constraints during system adjustments, the system also achieves maximum efficiency tracking. When the coupling coefficient k is 0.31, the transmission efficiency of the system is the highest, and the maximum efficiency is 93.8%.

In order to realize the freedom of placing pots, the free zone induction cooker with multiple induction heating coils is gradually developing. It can perform user-customized heating of different pots, greatly improving the flexibility of using the induction cooker. In actual work, the induction heating coil in the corresponding area needs to be triggered based on the real-time position. Therefore, the correct identification of the pot position affects the working status and heating performance of the free zone induction cooker system. As a result, pot position identification has become a key factor that needs to be solved urgently. Traditional pot position identification methods are easily affected by fluctuations in external factors, require a large amount of sample data, and need to improve the accuracy and speed of pot position identification. This paper indicates that the misalignment of the pot affects the mutual inductance M between the pot and the induction heating coil, thereby affecting the impedance and current phase of the system. An identification method is proposed using the current phase to control the switch array, which achieves positioning and heating of the pot. The proposed pot position identification strategy requires a small amount of sample data, and the branch current phase is only related to the position of the pot. It is not easily affected by system voltage and current fluctuations, so the position of the pot can be judged in real-time.

Secondly, regarding the impact of pot misalignment on current and power, this paper introduces a power regulation strategy based on an adjustable capacitor circuit. Under the misaligned working condition of the pot, the disturbance observation method of maximum current search is used to adaptively adjust the adjustable capacitor. The maximum power output of the induction cooker is obtained, and the heating speed of the pot is improved. The pot position identification strategy and the power regulation strategy coordinate to ensure accurate identification of the pot position and rapid heating, which only needs to collect the current information of the system branch. Thus, the sampling circuit is simplified.

Finally, an experimental prototype of a free zone induction cooker based on three coils was built. Experimental results show that in the pot position identification strategy, the accuracy of the theoretical and experimental phases is over 96%, and the identification speed is 0.8 ms. The maximum temperature rise within three minutes is 17.3℃ using the power regulation strategy, higher than without the power regulation strategy. The proposed pot position identification strategy can correctly identify the exact position of the pot and achieve the maximum power output of the induction cooker after the power regulation strategy. The correctness of the pot position identification strategy and the power regulation strategy are verified.

The equivalent circuit is valuable for designing an induction machine and its driving system, and its parameters are essential for analyzing the electromagnetic performance and establishing the driving model, especially the magnetizing inductance that characterizes the main flux distribution in the machine. The finite element analysis can precisely determine the magnetizing inductance. However, it is unsuitable for the initial design stage when the design parameters need frequent adjustment. Traditional analytical calculations like the flux linkage method neglect the influence factors, such as core saturation, tooth-slot effect, and rotor movement in the actual operation, causing accuracy issues. The improved analytical calculation method can significantly enhance the accuracy. However, the magnetic circuit in each core segment still needs to be more accurate, and the local saturation points are easily ignored. Besides, the solving process contains nonlinear iterations of multi-segment of the magnetic circuit, which has repeating calculations under different slips.

This paper proposes an elementary layer method based on the main and leakage magnetic circuits to calculate the magnetizing inductance. Firstly, the magnetic voltage drop of each pole of the main magnetic circuit under different air-gap flux densities is calculated, and the magnetic voltage drop and the air-gap flux density are converted into the electromotive force and the magnetizing current to obtain the objective function. Secondly, the distribution of the leakage flux in the stator slot and the current induced in the rotor bar is analyzed to calculate the slot leakage inductance of the stator and rotor, together with the rotor AC resistance. The stator terminal voltage expression is constructed as the constraint condition. Finally, each set of the electromotive force and the magnetizing current on the objective function are substituted into the constraint condition to make the results equal to the rated phase voltage of the stator. The ratio of the set is the value of the magnetizing reactance, and therefore, the magnetizing inductance is obtained. In the main and leakage flux circuits, the irregular and nonlinear magnetic and electric circuits are regularized and linearized using the thin layer elements to substitute theoretical integral. Hence, the tooth-slot structure and the nonlinear material properties can be considered more accurately when calculating the magnetic voltage drop and leakage inductance.

A wet submersible induction machine is an example of the analytical calculation of the magnetizing inductance using the proposed elementary layer method and the traditional flux leakage method. Besides, the steady-state outputs of the equivalent circuit are obtained. The prototype test and the finite element simulation under the magnetic saturation of the stator teeth are conducted. The results show that the elementary layer method considers the distribution of the magnetic voltage drop in the core segment, which is more effective than the flux linkage method when the tooth magnetic circuit is saturated. Therefore, the calculation of the magnetizing inductance is more accurate, and in the steady-state outputs of the equivalent circuit, the stator current, input power, and power factor curves are consistent with the finite element analysis. The error is less than 0.5% when the finite element results are used as the reference. The proposed method links the design parameters and the magnetizing inductance, providing convenience for the initial design and optimization of the submersible induction machine and other types of machines.

The doubly salient electric-excited motor has many advantages such as a simple structure, low manufacturing cost and high reliability, making it a good candidate for applications in electric vehicles, aerospace, and other fields. However, its large torque ripple and low torque density limit its development and applications. This paper proposes a new topology structure for the doubly salient electromagnetic machine (DSEM).

The topology structure and working mechanism of the proposed DSEM are analyzed in detail. The combination of stator poles and rotor poles is elaborated, and the winding method of armature windings is described with the influence on the harmonics of EMF. The relationship between the pole-combination and harmonics of the magnetic field, together with the output torque, is investigated according to the magnetic field modulation mechanism, and the air-gap flux density harmonics of 18/10 and 18/11 DSEMs are obtained by finite element analysis. The influence of pole combination on motor characteristics is analyzed by finite element analysis, including the no-load electromagnetic performance, the torque features, and the loss characters, which shows the superiority of the DSEM with odd-number rotor poles. Finally, a prototype of the new 18/11 DSEM is manufactured and tested.

The results show that due to the new winding method, the flux in armature windings changes bipolar, resulting in high sinusoidal flux linkages and, thus, a high sinusoidal EMF. The DSEM with odd-number rotor poles has more effective space magnetic harmonics than that with even-number rotor poles, almost with odd orders, resulting in higher output torque. Besides, due to the offset of even-order time-harmonics, the DSEM with odd-number rotor poles has higher sinusoidal EMFs and lower torque ripples. In addition, different rotor poles show different characteristics, as seen from the simulation results.

The following conclusions can be drawn. (1) By adjusting the winding method of the field winding and the armature winding, the new type of DSEM realizes the bipolar change of the armature flux, and the back EMF has a high sinusoidal degree. (2) As can be seen from equations (21) and (26), the effective harmonics’ frequency of air-gap flux density in the odd-rotor pole motor is different from that in the even-rotor pole motor, resulting in different torque harmonics. (3) If the number of rotor poles is even, there are more even order harmonics in the motor back EMF, and the cogging torque and torque ripple are also large; if odd, the armature coils with opposite polarity are connected in series, and the even harmonics in the motor back EMF cancel each other, resulting in smaller harmonic content, cogging torque, and torque ripples. (4) The motor performance is optimal for the proposed DSEM with 18 stator poles when the rotor pole number is 11 or 13.

The demagnetization fault of permanent magnet synchronous motors (PMSM) reduces output performance and load capacity, seriously affecting the motor’s service life. Establishing an accurate fault motor analytical model, conducting rapid electromagnetic performance analysis, and obtaining operational data such as current and torque under fault conditions are beneficial for early prediction and diagnosis of demagnetization faults.

A parameter D is introduced for the partial demagnetization fault of the surface-mounted PMSM prototype, representing the spatial angle of the demagnetized region, defined as the ratio of the spatial angle occupied by the demagnetized region to that of one pole arc. The radial and tangential component equations of the residual magnetization Fourier coefficients as a function of parameter D are derived, which reflect the influence of the spatial angle of the demagnetized region on the magnitude and the waveform of the residual magnetization. An analytical model of PMSM under partial demagnetization is established.

In addition, regarding the control system’s circuit interface in practical applications, an analytical model of demagnetization faults in a PMSM driven by a voltage source inverter with magnetic flux linkage as the intermediate variable is established. This model is applied to the vector control circuit. Thus, a co-simulation model combining the analytical model and the control circuit is created.

The load performance of the prototype is calculated under normal conditions and partial demagnetization using the co-simulation model. Compared with the simulation results from the Ansys/Simplorer time-stepping finite element method and the measured results from the prototype on the experimental platform, the conclusions are as follows. (1) The proposed partial demagnetization analytical model reflects the influence of the demagnetized region on the magnitude and the waveform of the residual magnetization. This model is more consistent with actual conditions than the method of equating partial demagnetization to an overall reduction in magnetic flux linkage. (2) The calculation results of the co-simulation model are in good agreement with the time-stepping finite element simulation results, with the relative errors for the stator flux linkage, stator current, and electromagnetic torque less than 1.5% under normal and partial demagnetization conditions. Furthermore, the computation time of the co-simulation model is only 1/20 that of the finite element model, which greatly improves the operation efficiency. (3) The current waveforms of the prototype under the same control strategy are measured on the experimental platform and subjected to spectral analysis. The results are consistent with the co-simulation results, which validate the accuracy of the co-simulation model, combining the analytical model and the control circuit.

A Bi-stable permanent magnet actuator (BPMA) shares the same magnetic circuit as the breaking and closing coils, and the magnetic flux generated by any coil passes through the breaking and closing air gaps. The permanent magnet automatically distributes the permanent magnetic flux according to the dynamic reluctance of the air gaps. The electromagnetic flux and permanent magnetic flux in the upper and lower air gaps always cause the moving iron core to be coupled by two opposite magnetic forces. As the motion of the moving iron core and the change of coil current, the magnetic circuit quickly saturates, and the electromagnetic flux and permanent magnetic flux interact, exacerbating the complexity of nonlinear coupling in the breaking and closing air gaps. To flexibly control the action characteristics of permanent magnet switches, it is necessary to simultaneously control the air gap flux and the magnetic force pointing to the breaking and closing positions. Therefore, this paper proposes an air gap flux decoupling control method based on finite control set-model predictive control (FCS-MPC). Decoupling control can be achieved by rapidly weakening the magnetic force pointing to the non-excited coil and rapidly increasing the magnetic force pointing to the excited coil.

Firstly, according to the operating principle of BPMA, the vector magnetic force acting on the moving iron core depends on the “magnetic flux squared difference” of the breaking and closing air gaps. Therefore, only controlling this vector magnetic flux square difference in real-time can dynamically control BPMA. Secondly, a predictive model of the breaking and closing air gap magnetic flux is designed through discretization of the voltage balance equation, which can predict the magnetic flux at the next moment based on the voltage and current values collected at the current moment. Thirdly, the breaking and closing air gap magnetic flux and the mechanism drive circuit are regarded as a whole. A set of switching states is constructed through the excitation intensity analysis under different switching states. Predictive magnetic flux is obtained by traversing all switching state combinations. Finally, a decoupling control cost function is designed, the predictive magnetic flux under different switching combinations is input into the cost function, and the optimal control is selected for the next control period. In rolling optimization over multiple control periods, the breaking and closing air gap magnetic flux quickly approaches their respective reference values, achieving decoupling control.

A co-simulation platform for intelligent control is designed based on LabVIEW and Multisim, and hardware testing circuits are constructed. The simulation and experimental waveforms show that this proposed scheme can effectively control the breaking and closing air gap flux. As a result, the non-excited air gap flux to zero is quickly reduced, approaching the set reference value of the excited air gap flux and effectively weakening the coupling between the air gaps. Compared with the traditional current closed-loop control scheme, the proposed control scheme reduces the energy loss during the entire action process and improves the response and action time of the core action.