Search

This article presents a reinforcement learning (RL) controller based on the twin delayed deep deterministic (TD3) policy gradient algorithm for single-inductor multiple-input multiple-output (SI-MIMO) dc–dc converters. The controller aims to address the power allocation challenges arising from parallel input sources and mitigate cross-regulation among multiple output channels. The objective is to enable the converter to exhibit outstanding performance in both steady-state and dynamic regulation during operation. The proposed RL controller is trained using the TD3 algorithm. It directly generates duty cycle control signals based on the observed states from the controlled converter. After applying input-side power allocation modulation and output-side time-multiplexing modulation, the controller generates the switching signals for each switch, completing the closed-loop control of the SI-MIMO converter. To validate the effectiveness of the proposed controller, stability analysis of the RL controller is conducted, and an experimental platform is established. Experimental results demonstrate that the proposed controller exhibits superior control performance. It effectively addresses the challenges associated with input-side power allocation and output voltage cross-regulation in SI-MIMO dc–dc converters.

This article investigates a double-stator dual permanent magnet (dual-PM) hybrid excitation machine (DS-DHEM) with dc bias current. Integrated winding accommodated on the outer stator can produce the armature field and excitation field simultaneously with the dc bias current to improve the efficiency and flux regulation capability, and the dual consequent-pole PMs on the inner stator and rotor are proposed to enhance the torque density. The working principle of the proposed DS-DHEM is based on multiple working harmonics introduced by three excitation sources and flux modulation effect, which is demonstrated by developing magnetic motive force (MMF) and permeance models. The influence of key parameters is studied, after which the multiobjective genetic algorithm (MOGA) is exploited to optimize the global design parameters. In addition, the stator-PM is found to have the largest torque contribution for DS-DHEM with the frozen permeability (FP) method. The comparative studies between DS-DHEM and an existing dual-PM hybrid excitation machine (DP-HEM) are performed to convincingly verify the superiority of DS-DHEM, which exhibits larger torque density, higher efficiency, and better flux regulation capability. Finally, a prototype is manufactured to conduct the experimental tests to validate the proposed machine.

Electric vehicles (EVs) with hybrid energy storage systems (HESSs) are getting popular as HESS can improve the battery’s lifetime and thus reduce maintenance costs. In this article, an ultra-local model (ULM)-based method with the bus voltage supervisor (BVS) is proposed for the HESS in EVs with stable and quick dynamic performance. In the method, an integral sliding mode observer (ISMO) is designed for the disturbance term in ULM, and ULM generates the overall HESS current demand faster and more precisely than the conventional methods, which is then split into the transient and average components for driving the battery and supercapacitor (SC). Moreover, BVS generates extra current demand for SC during the transient process, realizing rapid recovery of bus voltage and decreasing the bus voltage spike. BVS will leave no degradation in steady-state performance with magnifying coefficients greater than one. Series simulations have been conducted to verify the dynamic performance and the effect of different curve functions on BVS. Consequently, a hardware-based experiment is conducted and the experimental results demonstrate the effectiveness of the proposed method with the 65%–69% faster dynamic performance and the 36%–41% lower voltage spike than the conventional PI methods.

Hybrid energy storage system (HESS) is effective to compensate for fluctuation power in renewables and fast fluctuation loads in DC microgrids. To regulate DC bus voltage, a power management strategy is an essential issue. In the meantime, the increasing integration of constant power loads (CPLs) in DC microgrids brings great challenges to stable operation due to their negative incremental impedance. In this paper, a fast composite backstepping control (FBC) method is proposed for the HESS to achieve faster dynamics, smaller voltage variations, and large-signal stabilization. In the FBC method, a higher order sliding mode observer (HOSMO) is adopted to estimate the coupled disturbances. Furthermore, the FBC method is integrated with the droop control; so that the FBC-based decentralized power allocation (FBC-DPA) strategy for HESS in DC microgrids is developed. The proposed FBC method is designed based on the Lyapunov function to ensure its stability. Moreover, the design guidelines are provided to facilitate the application of the proposed method. Both simulation and experimental studies under different operating scenarios show that the proposed method achieves faster voltage recovery and smaller voltage variations than the conventional backstepping control method.

This article proposes an event-triggered model predictive control (ET-MPC) method for inverters to connect dc microgrids with battery-supercapacitor hybrid energy storage systems (HESSs) to the grid. The procedure for creating the ET-MPC method is discussed in detail, including the system’s state-space model formulation, the condition of grid connection under unbalanced grid voltage, the triggering condition setting, and the design of the controller. Introducing an event-triggered control strategy into the conventional finite-control-set MPC (FCS-MPC) can significantly reduce the number of control actions by eliminating unnecessary operations. This solves FCS-MPC’s issue of a heavy computational burden while maintaining a satisfactory performance. The regulation performance verification of ET-MPC is conducted using digital simulation and hardware-in-loop (HIL) experiments. The comparison of the simulation and HIL results for ET-MPC and FCS-MPC shows the effectiveness of ET-MPC in connecting the dc microgrid to the grid; this new method has a similar performance to that of FCS-MPC but requires fewer switching actions and has a lower computational burden.