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This paper addresses challenges related to the short service life and low efficiency of hybrid energy storage systems. A semiactive hybrid energy storage system with an ultracapacitor and a direct current (DC) bus directly connected in parallel is constructed first, and then related models are established for the lithium-ion battery, system loss, and DC bus. Based on the multiobjective evaluation function, a hybrid energy storage system Model Predictive Control-Differential Evolution (MPC-DE) energy management method is proposed. Experiments were conducted under China Light-Duty Vehicle Test Cycle-Passenger Car (CLTC-P) and Highway Fuel Economy Test (HWFET) driving cycles. The experimental results show that compared with other methods, this strategy can effectively reduce the loss of lithium-ion battery and ultracapacitor hybrid energy storage system and improve the system efficiency.

Abstract The DC link single sensor phase current reconstruction method has brought several challenges, which consist of the unobservable area and current zero drift error, where the duration of the effective vector is too short to measure, and current zero drift error leads to reconstruction error. An error self‐correction complementary non‐zero vector pulse width modulation (ESC2N‐PWM) method is proposed. Through the analysis of the DC link sampling principle, the minimum sample time is defined. Then applying complementary non‐zero vectors to replace zero voltage vector, the current sampling window is extended, and the sector boundary unobservable area is eliminated. At the same time, through the dynamic double sampling of the complementary effective voltage vectors, the zero drift of current is self‐detected and self‐corrected. The experimental results show that the reconstruction error of the proposed method is lower than 2.84%, and the current THD is lower than 6.20%.

In the finite control set model predictive control (FCS-MPC) strategy of the grid-tied inverter, the current ripple (CR) affects the selection of optimal voltage vectors, which leads to the increase of output current ripples. In order to solve this problem, this paper proposes a CR reduction method based on reference current compensation (RCC) for the FCS-MPC strategy of grid-tied inverters. Firstly, the influence of the CR on optimal voltage vector selection is analyzed. The conventional CR prediction method is improved, which uses inverter output voltage and grid voltage to calculate current ripples based on the space state equation. It makes up for the shortcomings that the conventional CR prediction method cannot predict in some switching states. The improved CR method is more suitable for the FCS-MPC strategy. In addition, the differences between the two cost functions are compared through visual analysis. It is found that the sensitivity of the square cost function to small errors is better than that of the absolute value function. Finally, the predicted CR is used to compensate the reference current. The compensated reference current is substituted into the square cost function to reduce the CR. The experimental results show that the proposed method reduces the CR by 47.3%. The total harmonic distortion (THD) of output current is reduced from 3.86% to 2.96%.

Abstract Analysing the steady‐state operation characteristic of the modular multilevel converter (MMC) in time domain poses challenges due to the presence of harmonic components in the arm current and capacitor voltage. While ignoring the influence of the harmonic currents and harmonic voltages would reduce the accuracy of the analysis results for MMC. This paper investigates the circular interaction among the electrical quantities and different order harmonic components in MMC. A new steady‐state model is developed and the general matrix equation is established to accurately describe the explicit analytical expressions for electrical variables in MMC without specific assumptions. Based on the steady‐state operation characteristic analysis, the resonance phenomenon and characteristic are identified for MMC. Finally, the feasibility and accuracy of the proposed method are verified by comparing its analytical results with simulation and experimental results.

The essence of biomimetics in human–computer interaction (HCI) is the inspiration derived from natural systems to drive innovations in modern-day technologies. With this in mind, this paper introduces a biomimetic adaptive pure pursuit (A-PP) algorithm tailored for the four-wheel differential drive robot (FWDDR). Drawing inspiration from the intricate natural motions subjected to constraints, the FWDDR’s kinematic model mirrors non-holonomic constraints found in biological entities. Recognizing the limitations of traditional pure pursuit (PP) algorithms, which often mimic a static behavioral approach, our proposed A-PP algorithm infuses adaptive techniques observed in nature. Integrated with a quadratic polynomial, this algorithm introduces adaptability in both lateral and longitudinal dimensions. Experimental validations demonstrate that our biomimetically inspired A-PP approach achieves superior path-following accuracy, mirroring the efficiency and fluidity seen in natural organisms.

Abstract Focussing on the ineffective operating cycle and potential battery life degradation introduced by traditional energy converter‐based balancing techniques, a new distributed online active balancing scheme is proposed. In order to achieve a wider output voltage range, a new distributed converter is presented first, which can operate in boost or buck mode. Then, by regulating the discharge rate of each smart cell proportional to the deviation between its own state of charge (SOC) and the average SOC of all cells, an online linear SOC balancing scheme is presented. An experimental prototype consisting of eight smart cells is developed, experiments in constant resistance discharging mode and dynamic current discharging mode are carried out, and the results demonstrate the successful cell SOC balancing and DC bus adjustment capability of the proposed scheme.

Abstract A numerical modeling method is proposed for the melting process of Titanium metals of Titanium alloys powder preparation used for 3D printing. The melting process simulation, which involves the tight coupling between electromagnetic field, thermal field and fluid flow as well as deformation associated during the melting process, is conducted by adopting the finite element method. A two-way coupling strategy is used to include the interactions between these fields by incorporating the material properties dependent on temperature and the coupling terms. In addition, heat radiation and phase change are also considered in this paper. The arbitrary Lagrangian–Eulerian formulation is exploited to model the deformation of Titanium metal during the melting process. The distribution of electromagnetic flux density, eddy current density, temperature, and fluid flow velocity at different time can be determined by utilizing this numerical method. In a word, the method proposed in this paper provides a general way to predict the melting process of electrode induction melting gas atomization (EIGA) dynamically, and it also could be used as a reference for the design and optimization of EIGA.

In order to tap the advantages of Narrow Band-Internet of Things (NB-IoT) on the demand side of the smart grid, improve the security of information transmission, and make use of demand-side data, we focus on the secure transmission, trusted services, and intelligent analysis of “5G + Smart Grid,” and we construct a comprehensive solution which consists of encrypted data terminals, management systems, and intelligent analysis methods. A 5G NB-IoT framework proposed in this study can serve grid planning and demand response, and it can further promote the deep integration of 5G and the smart grid. Therefore, this research will contribute to the implementation of a new generation of information technology on the smart grid, build a deep integration application scenario of the “5G + Smart Grid,” improve the intelligence of the grids, and further promote the “dual carbon” goal of the power system.

To improve the balancing time of battery energy storage systems with “cells decoupled and converters serial-connected,” a new cell voltage adaptive balancing control method in both charging and discharging modes is proposed in this study. The overall system architecture and basic operating principle of the active balancing system with “cells decoupled and converters serial-connected” are presented first. Then, by dynamically regulating the balancing acceleration coefficient of each cell according to the cell voltage deviation, the adaptive balancing control of cell voltage in charging and discharging modes is analyzed. An experimental prototype consisting of six smart cells is developed, and experiments in charging and discharging modes were carried out. The experiment results demonstrate that compared with the balancing process with a static acceleration coefficient, the proposed adaptive balancing control of cell voltage shows significant improvement in balancing speed and smaller cell voltage discrepancy.

In order to overcome the limitations of the Pareto optimality in solving multi-objective optimization problems, a new optimality definition, fuzzy optimality is proposed, which considered both of the numbers of improved objectives and the extent of the improvements. Then, the fuzzy optimality-based multi-objective particle swarm optimization algorithm is presented. It inherits the basic structure of the particle swarm optimization and evaluates the particles by the fuzzy optimality. The numerical experiments are carried out on 6 representative test functions, and the results show that the proposed fuzzy optimality based multi-objective particle swarm optimization algorithm shows better performance on aspects of quality of solutions, robustness, and computational complexity, compared with the results of the NSGA-II and MOPSO. Finally, the efficacy and practicality of the proposed approach are validated in the APU fuel consumption and emissions multi-objective optimization problem.

This paper proposed a optimal strategy for coordinated operation of electric vehicles (EVs) charging and discharging with wind-thermal system. By aggregating a large number of EVs, the huge total battery capacity is sufficient to stabilize the disturbance of the transmission grid. Hence, a dynamic environmental dispatch model which coordinates a cluster of charging and discharging controllable EV units with wind farms and thermal plants is proposed. A multi-objective particle swarm optimization (MOPSO) algorithm and a fuzzy decision maker are put forward for the simultaneous optimization of grid operating cost, CO2 emissions, wind curtailment, and EV users’ cost. Simulations are done in a 30 node system containing three traditional thermal plants, two carbon capture and storage (CCS) thermal plants, two wind farms, and six EV aggregations. Contrast of strategies under different EV charging/discharging price is also discussed. The results are presented to prove the effectiveness of the proposed strategy.

Auxiliary power units (APUs) are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP) firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC) emissions, carbon monoxide (CO) emissions and nitric oxide (NO x ) emissions. Then, the multi-objective particle swarm optimization (MOPSO) algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC), Federal test procedure (FTP) and high way fuel economy test (HWFET) driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

The yaw stability of distributed drive electric vehicle (DDEV) can be guaranteed by the active steering and torque distribution systems. Conventional yaw stability control strategy mainly focuses on the study of yaw moment, while the tyre lateral saturation and excessive longitudinal skid problems are difficult to be solved efficiently. This may lead to the unstable yaw phenomenon of the vehicle such as the sideslip or tail flick. To improve the yaw stability of DDEV under the tyre lateral saturation and excessive longitudinal skid situations, a double-layer model predictive control system (DLMPCS) is presented in this work. The proposed DLMPCS consists of an anti-saturation yaw stability controller (AYSC) and an anti-slip torque distribution controller (ATDC). The AYSC is designed to guarantee the vehicle yaw stability under the tyre lateral saturation situation, through the active steering and yaw moment. The ATDC is designed to realise the torque distribution and keep the longitudinal slip ratio within the stable zone; then, the vehicle can be prevented from the excessive skid phenomenon. The simulation results confirm the improved yaw performance of the DLMPCS for DDEV under different situations. [ABSTRACT FROM AUTHOR]