Bing Ji

To meet higher power demands, multiple SiC MOSFET chips are often connected in parallel. However, non-uniform junction temperature distribution among these paralleled devices has emerged as a critical challenge, significantly impacting system reliability and lifetime. Despite various thermal management efforts, existing techniques lack an accurate, real-time method to monitor individual chip junction temperatures in paralleled configurations. In this article, a junction temperature monitoring method for paralleled SiC MOSFETs with discrete gate drive (DGD) control is proposed. And a PCB Rogowski coil is settled on the confluence of paralleled branches to capture dynamic current characteristics. By actively adjusting the turn-on delay among SiC MOSFETs, the junction temperature can be extracted by leveraging the temperature dependence of quasi-threshold voltage (V_th-qua) during a gate delay interval without intrusive sensing. Comprehensive analysis is conducted to evaluate the impact of device temperature imbalance and varying operating conditions on measurement accuracy. The proposed method is finally applied and verified online in a buck experiment platform to sensing junction temperature of paralleled SiC MOSFETs. Under three distinct uneven cooling scenarios, the maximum monitoring error is limited to just 2.9 °C, confirming the method's high accuracy and robustness. This work offers a practical and scalable approach for junction temperature monitoring in paralleled SiC MOSFETs, paving the way for more reliable thermal design and intelligent health monitoring in high-power, safety-critical applications.

Retired lithium-ion batteries retain considerable residual value for second-life energy storage applications, but significant variations in health conditions and complex operating scenarios make accurate remaining useful life (RUL) prediction challenging. To address the limited availability of capacity measurements and the poor adaptability of conventional models to dynamically fluctuating degradation trajectories, a hybrid RUL prediction framework integrating Wavelet Packet Energy Entropy (WPEE), a Fractional-Order Grey Model (FGM), and an Unscented Kalman Filter (UKF) is proposed. WPEE extracted from discharge voltage signals is employed as a degradation indicator, while a Box–Cox transformation enhances its correlation with capacity. An Adaptive Mutation Particle Swarm Optimization (AMPSO) algorithm is used to determine the optimal fractional-order parameter, and the optimized FGM is incorporated into the UKF state-transition process for recursive state correction. Validation was conducted using four retired lithium-ion cells and two series-connected battery packs with different health conditions at prediction starting points of 20, 25, and 30 cycles. The results show that the proposed method effectively tracks degradation evolution, with RUL prediction errors within 7 cycles for retired cells and within 6 cycles for battery packs. Across all 18 prediction cases, FGM–UKF achieved an overall mean AE of 3.111 cycles, lower than those of FGM (5.111 cycles), GM(1, 1) (4.000 cycles), and LR (4.278 cycles). These results demonstrate the effectiveness and robustness of the proposed framework for lifetime assessment in second-life battery energy storage systems.

Bond‐wire fatigue and gate oxide degradation are the main reliability issues for silicon carbide (SiC) MOSFETs. Accurately separating these two degradation modes and identifying the dominant failure mechanism in operation is crucial for improving device reliability. In this paper, a dynamic diagnosis method for separating bond‐wire‐fatigue and gate oxide degradation in SiC MOSFETs is proposed. A switching transient analytical model considering bond‐wires related parasitic parameters is established first. The analytical analysis reveals that the drain current slope during the turn‐on process can comprehensively characterise both bond wires and gate oxide degradation. Furthermore, numerical analysis is conducted to quantitatively investigate the dependence of the drain current slope on bond‐wire fatigue and gate oxide degradation. It is found that the relative change in drain current slope induced by bond‐wire fatigue decreases with increasing gate resistance, whereas that caused by gate oxide degradation decreases with increasing gate voltage. Based on this observation, a dynamic diagnostic method is proposed to separate the two degradation modes by obtaining the drain current slope under different driving parameters. Finally, equivalent degradation experiments are carried out to validate the proposed dynamic diagnosis method. This work provides a foundation for reliability monitoring and control of SiC MOSFETs in actual switching operation.

This paper presents a fundamental performance analysis of joint location and velocity estimation in a cell-free (CF) MIMO integrated sensing and communication (ISAC) system. Unlike prior studies that primarily rely on continuous-time signal models, we consider a more practical and challenging scenario in the discrete-time digital domain. Based on the discrete received signal, we first formulate a logarithmic likelihood function (LLF) for both single- and multiple-target sensing under deterministic, unknown, and spatially varying radar cross-section (RCS) models. Building upon the proposed LLF framework, closed-form Cramér-Rao lower bounds (CRLBs) for joint location and velocity estimation are derived. These CRLBs can serve as a fundamental performance metric to guide CF MIMO-ISAC system design. To enhance tractability, we also develop a class of simplified closed-form CRLBs, referred to as approximate CRLBs, along with a rigorous analysis of the conditions under which they remain accurate. Furthermore, we investigate how the sampling rate, squared effective bandwidth, and time width influence CRLB performance. For a multiple-target scenario, the concepts of safety distance and safety velocity are introduced to characterize the conditions under which the CRLBs converge to their single-target counterparts. Extensive simulations using orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM) validate the theoretical findings and provide practical insights for CF MIMO-ISAC system design.