This paper proposes an AC bus-based distributed battery management system, where the secondary windings of modular equalizers are series-connected. The proposed system enables rapid and flexible power balancing from any cells to any cells. The modular equalizers are designated as either active or inactive, depending on their involvement in power balancing. By employing loosely coupled transformers and resonant networks, it ensures decoupling between active and inactive equalizers, allowing balancing to occur exclusively among unbalanced cells and thereby improving efficiency. The distributed control strategy enables each equalizer to determine its power target based on SOC, improving balancing accuracy. This target is tracked via locally generated control signals, eliminating the need for external synchronization or global timing, thereby enhancing system scalability. Finally, simulation results from a four-equalizer prototype demonstrate the effectiveness of the theoretical analysis.

Talkative Power Conversion (TPC) is a cost-effective communication solution for DC microgrids, enabling power converters to transmit both power and data simultaneously by embedding data into their output voltage ripples. However, due to its broadcast nature, TPC is susceptible to eavesdropping. To address this security vulnerability, this paper proposes a novel physical-layer data carrier encryption strategy. The embedded data ripple generated by the transmitter is mixed and encrypted with a noise ripple generated by the receiver, making unauthorized interception infeasible. In addition, closed-loop impedance models are derived to construct and analyze the small signal model of the proposed system. Finally, a DC microgrid based on bidirectional buck-boost converters is implemented, and theoretical analysis is validated through simulation results.

In wireless power transfer (WPT) systems, effective communication between the transmitter and receiver is crucial for precise control and system monitoring. To address this need, simultaneous power and information transfer (SWPIT) has been extensively studied. However, SWPIT implementations often require additional coils or circuits, which complicates the overall system. This work presents the design and numerical experiments of a minimal SWPIT system. The auxiliary modulation circuits are simplified using phase shift control in the inverter, while multi-carrier modulation can be embedded to enhance frequency band utilization. Similarly, auxiliary demodulation circuits are simplified by leveraging the frequency-mixing effect in the rectifier. The proposed minimal SWPIT method is adaptable across diverse compensation networks. The key transfer characteristics of the compensation network are cloned in the information transmission, enabling load-independent communication. Simulation results confirm the theoretical analysis, demonstrating the transfer characteristics and elasticity of the minimal SWPIT system.

Transfer learning (TL) and generative adversarial networks (GANs) have been widely applied to intelligent fault diagnosis under imbalanced data and different working conditions. However, the existing data synthesis methods focus on the overall distribution alignment between the generated data and real data, and ignore the fault-sensitive features in the time domain, which results in losing convincing temporal information for the generated signal. For this reason, a novel gated recurrent generative TL network (GRGTLN) is proposed. First, a smooth conditional matrix-based gated recurrent generator is proposed to extend the imbalanced dataset. It can adaptively increase the attention of fault-sensitive features in the generated sequence. Wasserstein distance (WD) is introduced to enhance the construction of mapping relationships to promote data generation ability and transfer performance of the fault diagnosis model. Then, an iterative "generation-transfer" co-training strategy is developed for continuous parallel training of the model and the parameter optimization. Finally, comprehensive case studies demonstrate that GRGTLN can generate high-quality data and achieve satisfactory cross-domain diagnosis accuracy.

The communication of the Battery Management System (BMS) is crucial to the regular operation of the BMS. This paper proposes a communication-integrated battery equalization strategy based on bidirectional flyback converters. In comparison to the strategies currently in use, the proposed strategy sends data by modulating the drive signal of the switching devices in the bidirectional flyback converter rather than using a separate signal transmission circuit, which reduces hardware complexity and lowers cost. Additionally, the receiver can demodulate data by analyzing the voltage ripple of the input and output ports in the bidirectional flyback converter. In this paper, an experimental system prototype with three bidirectional flyback converters is built. The experiment achieves an 8.33kb/s communication rate and verifies the feasibility of the proposed strategy.

Power/signal dual modulation (PSDM) is a promising integrated communication solution for intelligent switched-mode power supply (SMPS) systems. A common strategy is to utilize input/output switching ripples as information carriers, which transmit along common power bus. As the switching harmonics peaks are located in the same frequency band with communication signals, they may lead serious noise interference. To deal with this issue, random PWM is a proper solution, as it scatters the spectrum of switching harmonic peaks to a much wider and flatter one. This paper proposes a novel chipped PWM strategy to shape the switching harmonics to semi-Gaussian white noise. It breaks the periodicity of PWM sequence and releases more control freedom of the gate signal. Conventional PWM strategy is not used, but independent chips of high voltage level state and low voltage level state are introduced. They are combined in a pseudo random way, and the numbers of “on” and “off” chips in each pseudo random encoding interval are constant to ensure a desired equivalent duty-cycle. Therefore, switching harmonic suppression is achieved for improved communication performance with PSDM. Simulation and experimental results are presented to validate the proposed strategy.

This article proposes a novel control method for vehicle active suspension systems in the presence of time-varying input delay and unknown nonlinearities. An unknown system dynamics estimator (USDE), which employs first-order low-pass filter operations and has only one tuning parameter, is constructed to deal with unknown nonlinearities. With this USDE, the widely used function approximators (e.g., neural networks and fuzzy-logic systems) are not needed, and the intermediate variables and observer used in the traditional estimators are not required. This estimator has a reduced computational burden, trivial parameter tuning and guaranteed convergence. Moreover, a predictor-based compensation strategy is developed to handle the time-varying input delay. Finally, we combine the suggested USDE and predictor to design a feedback controller to attenuate the vibrations of vehicle body and retain the required suspension performances. Theoretical analysis is carried out via the Lyapunov-Krasovkii functional to prove the stability of the closed-loop system. Simulation results based on professional vehicle simulation software Carsim are provided to show the efficiency of the proposed control scheme.

Simultaneous wireless power and data transmission is a novel topic in wireless electric vehicle (EV) charging. The data transfer sharing the existing power coils is an attractive method compared to the conventional far-field communication technologies. The frequency division multiplexing (FDM) technique can achieve the data transfer without interupting the power transfer. This paper proposes a general method to construct communication channels that fit the FDM technique on inductive power transfer (IPT) topologies. According to this method, nine kinds of communication channel structures can be derived for the four basic compensation topologies. The high transfer gain of data carrier is achieved without changing the power carrier transfer path. The communication interference from the power harmonics can be effectively suppressed. The proposed communication channel is simulated and verified by experiments.

In wireless electric vehicle charging, the relative position of the primary and secondary coils has a significant impact on the transfer power and efficiency. In this paper, a magnetic coupling positioning method using simultaneous power and data transfer is proposed. The proposed positioning method employs four auxiliary coils placed on the primary coil to position the secondary coil. The positioning process is presented, which can be conducted both online and offline. The optimum locations of auxiliary coils are analyzed and simulated. A prototype is built up to verify the proposed method, and the experimental results are presented and discussed.

The energy Internet concept has been considered as a new development stage of the smart grid, which aims to increase the energy transmission efficiency and optimize the energy dispatching in time and space. Energy router is a core device in the energy Internet and it connects all the devices together into a net structure and manages power flows among them. The research work presented in this paper described the energy router's structure and function expectations from the network perspective, and improved the existing energy router design. Open shortest path first (OSPF) protocol and virtual circuit switching mode are referenced from the Internet in the energy local area network (e-LAN) design. This paper proposed a design of an energy routing algorithm based on graph theory in an e-LAN. A lowest cost routing selection algorithm is designed according to the features of power transmission, and a source selection and routing design algorithm is proposed for very heavy load conditions. Both algorithms have been verified by case analyses.

Energy saving is critical to real-time embedded systems. In many embedded systems, some tasks contain conditional instructions or operations that could have different execution times for different inputs. Due to the uncertainties in execution time of these tasks, this paper models each varied execution time as a probabilistic random variable. We propose a practical algorithm to minimize the expected value of total energy consumption while satisfying the timing constraint with a guaranteed confidence probability for uniprocessor embedded systems with continuous probability distributions. The experimental results show that our approach achieves significant energy saving than previous work.

This paper studies the scheduling and assignment problem that minimizes the total energy including both dynamic and leakage energy for applications with loops on multi-voltage, multi-processor DSP. An algorithm, LSAMP (Loop Scheduling and Assignment to Minimize Power), is proposed. The algorithm attempts to minimize the total energy while satisfying timing constraint with guaranteed probability. We will perform scheduling and assignment simultaneously. Our approach shows better performance than the approach that considers scheduling and assignment at separate phases. Compared with previous work, our algorithm shows a significant improvement in total energy reduction.

Many high-performanceDSP processors employ multi-bank on-chip memory to improve performance and energy consumption. This architectural feature supports higher memory bandwidth by allowing multiple data memory accesses to be executed in parallel. This paper studies the scheduling and assignment problem on minimizing the total energy consumption while satisfying timing constraint with heterogeneous multi-bank memory for applications with loop. An algorithm, TASL (Type Assignment and Scheduling for Loops), is proposed. The algorithm uses loop scheduling and assignment with the consideration of variable partition to find the best configuration for both memory and ALU.

Many high-performance DSP processors employ multi-module on-chip memory to improve performance and power consumption. This architectural feature supports higher memory bandwidth by allowing multiple data memory accesses to be executed in parallel. However, making effective use of multiple memory modules remains difficult, considering the combined effect of performance and power requirement. This paper studies the scheduling and assignment problem that minimizes the total energy while satisfying erformance for applications with loops. An algorithm, LSAMEM (Loop Scheduling and Assignment to Minimize Energy for Memory), is proposed. The algorithm attempts to maximum energy saving while satisfying timing constraint with guaranteed probability. The experimental results show that the average improvement on energy-saving is significant by using LSAMEM.