A flat bottom current control method is introduced in this article for torque tipple and power loss reduction in a switched reluctance motor (SRM). The proposed driving method operates on the basis of a ring structure driving topology, which drives an SRM by a full-bridge inverter instead of a conventional asymmetric H-bridge, to reduce the cost of the driving system and improve the performance of the SRM. The proposed driving method generates the winding currents with flat bottom waveform, by injecting an additional second and fourth harmonic in the sinusoidal waveform currents, the equations of the flat bottom winding currents are then obtained. Based on the theoretical analysis and calculation, the flat bottom driving method reduces the copper loss, compared with the space vector PWM (SVPWM) driving method and the direct torque control (DTC) method, and generates smoother current commutation than the conventional pulse current driving method. The proposed driving method is validated with a 6/4 SRM through simulation and experimental system, of which the results are compared to that of the SVPWM driving method, conventional pulse current driving method, and DTC method, under different speed and load conditions.

A novel buck-boost power converter is proposed to improve the performance of switched reluctance generator (SRG) system in an electric vehicle. In the proposed topology, the energy conversion part is formed by a buck-boost circuit and additional switches, with which, it is flexible to significantly boost the magnetization voltage and demagnetization voltage, thereby the output power range is improved and the power losses are reduced. The basic structure of the proposed converter is presented first and the attached operating modes are analyzed. The control strategy of the SRG system is then made to control the output voltage and the boost capacitor voltage. The simulation results show that compared to the conventional buck-boost converter, the proposed converter enhances the efficiency and reduces the power losses.

The power-voltage (P-V) characteristic curve of the centralized thermoelectric generation (TEG) system under nonuniform temperature distribution (NTD) exhibits multiple extreme point characteristics, and the traditional maximum power point tracking (MPPT) algorithm is prone to fall into the local maximum power point (LMPP) and takes a long time to track. This paper designs a BP-IPSO algorithm based on back propagation neural network (BPNN) and improved particle swarm optimization (IPSO) for MPPT. The algorithm firstly utilizes the good nonlinear function fitting ability of BPNN to obtain the fitting curve of the relationship between system control input and power output to establish the TEG array power prediction model. Then, the dynamic learning factor and weight coefficient are introduced into the traditional particle swarm optimization (PSO) algorithm to search the output power prediction model and realize MPPT control. MATLAB/Simulink experiment results show that BP-IPSO algorithm can effectively avoid falling into LMPP, quickly and accurately track the global maximum power point (GMPP), and effectively suppress the oscillation of voltage and power during the tracking process. Especially in the start-up test experiment, compared with perturb and observe (P&O), PSO, and grey wolf optimizer (GWO), the energy generated by BP-IPSO increased by 12.84%, 3.18%, and 4.75%, respectively, which improved the system power generation efficiency.

Since the thermoelectric generation (TEG) sheets will be placed in places with different temperature gradients, it leads to multiple peaks in the duty-power (D-P) characteristic curve of a centralized TEG system under non-uniform temperature distribution (NTD). For this reason, this paper proposes an ENN-ISSA control algorithm, which combines the Elman neural network (ENN) with the sparrow search algorithm (SSA) by adding firefly perturbation. The ENN obtains the centralized TEG system’s single-input and single-output fitting curves, after which the firefly perturbation is introduced into the SSA algorithm. Then the improved SSA algorithm is used to realize the maximum power point tracking (MPPT) control based on the fitted curves. Based on building a centralized TEG system Simulink model and analyzing the output characteristics of the TEG module, temperature constancy experiments, temperature change experiments, and accuracy analysis were conducted. The results of these simulation experiments all show that the algorithm can track the global maximum power point (GMPP) quickly and accurately in the duty-power (D-P) curve with multiple peaks compared with the perturbation observation method and particle swarm algorithm.