arXiv subjects

Jianbiao Wang

Publications and source records attributed to Jianbiao Wang.

5 recordsLinked to original sources

arXiv2026

When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics

Battery management systems increasingly require accurate battery health prognostics under strict on-device constraints. This paper presents DLNet, a practical framework with dual-stage distillation of liquid neural networks that turns a high-capacity model into compact and edge-deployable models for battery health prediction. DLNet first applies Euler discretization to reformulate liquid dynamics for embedded compatibility. It then performs dual-stage knowledge distillation to transfer the teacher model's temporal behavior and recover it after further compression. Pareto-guided selection under joint error-cost objectives retains student models that balance accuracy and efficiency. We evaluate DLNet on a widely used dataset and validate real-device feasibility on an Arduino Nano 33 BLE Sense using int8 deployment. The final deployed student achieves a low error of 0.0066 when predicting battery health over the next 100 cycles, which is 15.4% lower than the teacher model. It reduces the model size from 616 kB to 94 kB with 84.7% reduction and takes 21 ms per inference on the device. These results support a practical smaller wins observation that a small model can match or exceed a large teacher for edge-based prognostics with proper supervision and selection. Beyond batteries, the DLNet framework can extend to other industrial analytics tasks with strict hardware constraints.

cs.LG↗

arXiv2026

Interpretable Battery Aging without Extra Tests via Neural-Assisted Physics-based Modelling

State of health (SoH) is widely used for battery management, but it is a single scalar and offers limited interpretability. Two batteries with similar SoH can exhibit very different degradation behaviors and the lack of interpretability hinders optimal battery operation. In this paper, we propose IBAM for interpretable battery aging modelling with a neural-assisted physics-based framework. IBAM outputs a 2-D aging fingerprint without extra diagnostic tests and uses only routine logs from the battery management system. The fingerprint offers great interpretability by capturing a battery's curve-wide polarization voltage loss and the tail loss near the end-of-discharge. IBAM first creates a physics-based battery model based on a fractional-order equivalent circuit model, and then extracts per-cycle fingerprints from the model using a two-stage least-squares method. IBAM further anchors fingerprints on the SoH axis with physics-guided regression, where the per-cycle SoH is estimated via a bidirectional gated recurrent unit with customized multi-channel voltage features. Across batteries with short-, medium-, and long-lifespans, IBAM consistently yields the best physics model fidelity at different aging stages, and provides clear interpretations of degradation mechanisms and fingerprint patterns about batteries of different lifespans. The resulting fingerprints support interpretable battery health assessment and can inform battery control choices.

eess.SP↗

arXiv2023

Jointly optimization of passenger-route assignment and transfer incentivization scheme for a customized modular bus system

As an emerging travel mode, the modular vehicle system (MVS) is receiving increasing attention. In particular, the operators could connect multiple modular vehicles as an assembled bus in response to the temporary demand varies. Therefore, in this study, the MVS is adopted in the context of customized bus design to satisfy passengers reserved travel demand. In addition, to increase the potential of the customized modular bus system, the transfer among buses can be considered to group the passengers with the same or close destinations. However, the passengers will not actively transfer as it is viewed as a disutility. Thus, the appropriate incentivization should be provided. To this end, we jointly optimize the passenger-route assignment and the transfer incentivization scheme, in which the transfer demand is considered elastically under different incentivization. Then, the linearization approaches are adopted to transform the original nonlinear model into a mixed integer linear programming model, which can be solved by state-of-the-art solvers. The experiment on the small network reveals that the performance of the bus system with an incentivization scheme is better than that without an incentivization scheme, and the extent of such superiority depends on the total demand level in the system.

math.OC↗

arXiv2020

Ergodicity breaking of an inorganic glass in aging near ${T_g}$ probed by elasticity relaxation

We performed a series of aging experiments of an inorganic glass (As${_2}$Se${_3}$) at a temperature ${T_2}$ near the glass transition point ${T_g}$ by first relaxing it at ${T_1}$. The relaxation of Young's modulus was monitored, which was(almost if not ideally) exponential with a ${T_1}$-dependent relaxation time ${τ(T_1)}$. We demostrate the Kovacs' paradox for the first time in an inorganic glasses. Associated with the divergence of $τ$, the quasi-equilibrated Young's modulus ${E_\infty}$ does not converge either. An elastic model of relaxation time and a Mori-Tanaka analysis of ${E_\infty}$ lead to a similar estimate of the persistent memory of the history, ergodicity breaking within the accessible experimental time. Experiments with different ${T_2}$ exhibits a critical temperature ${T_p \sim T_g}$, i.e., when ${T_2 > T_p}$, both $τ$ and ${E_\infty}$ converge.

cond-mat.dis-nn↗

arXiv2020

On the mechanical beta relaxation in glass and its relation to the double-peak phenomenon in impulse excited vibration at high temperatures

A viscoelastic model is established to reveal the relation between alpha-beta relaxation of glass and the double-peak phenomenon in the experiments of impulse excited vibration. In the modelling, the normal mode analysis (NMA) of potential energy landscape (PEL) picture is employed to describe mechanical alpha and beta relaxations in a glassy material. The model indicates that a small beta relaxation can lead to an apparent double-peak phenomenon resulted from the free vibration of a glass beam when the frequency of beta relaxation peak is close to the natural frequency of specimen. The theoretical prediction is validated by the acoustic spectrum of a fluorosilicate glass beam excited by a mid-span impulse. Furthermore, the experimental results indicate a negative temperature-dependence of the frequency of beta relaxation in the fluorosilicate glass S-FSL5 which can be explained based on the physical picture of fragmented oxide-network patches in liquid-like regions.

cond-mat.dis-nn↗