Image and Video Processing · Cross-listed from cs.AR
An Energy-Efficient RFET-Based Stochastic Computing Neural Network Accelerator
Sheng Lu, Qianhou Qu, Sungyong Jung et al. (+2 more)
Stochastic computing (SC) offers significant reductions in hardware complexity for traditional convolutional neural networks (CNNs). However, despite its advantages,
stochastic computing neural networks (SCNNs) often suffer from high resource consumption due to components such as
stochastic number generators (SNGs) and accumulative parallel counters (APCs), which limit overall performance. This paper proposes a novel SCNN accelerator based on reconfigurable field-effect transistors (RFETs). The inherent reconfigurability at the device level enables the design of highly efficient and compact SNGs, APCs, and other related essential components. To assess their system-level impact, a representative existing SCNN architecture is adopted as an evaluation framework. Based on accessible open-source standard
cell libraries, experimental results demonstrate that the proposed RFET-based SCNN accelerator achieves significant reductions in area, latency, and energy consumption compared to its FinFET-based counterpart at the same technology node.
Read the full abstract ▾
All authors Sheng Lu, Qianhou Qu, Sungyong Jung, Qilian Liang, Chenyun Pan
Image and Video Processing · Cross-listed from cs.AI
Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation
Moein Heidari, Ali Mehrabian, Mohammad Amin Roohi et al. (+5 more)
Echocardiography interpretation requires integrating multi-view temporal evidence with quantitative measurements and guideline-grounded
reasoning, yet existing foundation-model pipelines largely solve isolated subtasks and fail when tool outputs are noisy or values fall near clinical cutoffs. We propose Echo-CoPilot, an end-to-end agentic framework that combines a multi-perspective workflow with knowledge-graph-guided measurement selection. Echo-CoPilot runs three independent ReAct-style
agents, structural, pathological, and quantitative, that invoke specialized echocardiography tools to extract parameters while querying EchoKG to determine which measurements are required for the clinical question and which should be avoided. A self-contrast
language model then compares the evidence-grounded perspectives, generates a discrepancy checklist, and re-queries EchoKG to apply the appropriate guideline thresholds and resolve conflicts, reducing hallucinated measurement selection and borderline flip-flops. On MIMICEchoQA, Echo-CoPilot provides higher accuracy compared to SOTA baselines and, under a stochasticity stress test, achieves higher reliability through more consistent conclusions and fewer answer changes across repeated runs. Our code is publicly available at https://github.com/moeinheidari7829/Echo-CoPilot.
Read the full abstract ▾
All authors Moein Heidari, Ali Mehrabian, Mohammad Amin Roohi, River Jiang, Wenjin Chen, David J. Foran, Jasmine Grewal, Ilker Hacihaliloglu
Systems and Control
Learning Reachability of Energy Storage Arbitrage
Tomás Tapia, Agustin Castellano, Enrique Mallada et al. (+1 more)
Power systems face increasing weather-driven variability and, therefore, increasingly rely on flexible but energy-limited storage resources. Energy storage can buffer this variability, but its value depends on intertemporal decisions under uncertain prices. Without accounting for the future reliability value of stored energy, batteries may act myopically, discharging too early or failing to preserve reserves during critical hours. This paper introduces a stopping-time reward that, together with a state-of-charge (SoC) range target penalty, aligns arbitrage incentives with system reliability by rewarding storage that maintains sufficient SoC before critical hours. We formulate the problem as an online optimization with a chance-constrained terminal SoC and embed it in an end-to-end (E2E) learning framework, jointly training the price predictor and control policy. The proposed design enhances reachability of target SoC ranges, improves profit under volatile conditions, and reduces its standard deviation.
Read the full abstract ▾
All authors Tomás Tapia, Agustin Castellano, Enrique Mallada, Yury Dvorkin
Systems and Control
Deep Neural Network-Based Aerial Transport in the Presence of Cooperative and Uncooperative UAS
Muhammad Junayed Hasan Zahed and Hossein Rastgoftar
We present a resilient deep neural network (DNN) framework for decentralized transport and coverage using uncrewed aerial systems (UAS) operating in $\mathbb{R}^n$. The proposed DNN-based mass-transport architecture constructs a layered inter-UAS communication graph from an initial formation, assigns time-varying communication weights through a forward scheduling mechanism that guides the team from the initial to the final configuration, and ensures stability and convergence of the resulting multi-
agent transport dynamics. The framework is explicitly designed to remain robust in the presence of uncooperative
agents that deviate from or refuse to follow the prescribed protocol. Our method preserves a fixed feed-forward
topology but dynamically prunes edges to uncooperative
agents, maintains convex, feedforward mentoring among cooperative
agents, and computes global desired set points through a sparse linear relation consistent with leader references. The target set is abstracted by $N$ points that become final desired positions, enabling coverage-optimal transport while keeping computation low and guarantees intact. Extensive simulations demonstrate that, under full cooperation, all
agents converge rapidly to the target zone with a 10% boundary margin and under partial cooperation with uncooperative
agents, the system maintains high convergence among cooperative
agents with performance degradation localized near the disruptions, evidencing graceful resilience and scalability. These results confirm that forward-weight scheduling, hierarchical mentor–mentee coordination, and on-the-fly DNN restructuring yield robust, provably stable UAS transport in realistic fault scenarios.
Read the full abstract ▾
Image and Video Processing
Proof of Concept for Mammography Classification with Enhanced Compactness and Separability Modules
Fariza Dahes
This study presents a validation and extension of a recent methodological framework for medical image classification. While an improved ConvNeXt Tiny architecture, integrating Global Average and Max Pooling fusion (GAGM), lightweight channel
attention (SEVector), and Feature Smoothing Loss (FSL), demonstrated promising results on Alzheimer MRI under CPU friendly conditions, our work investigates its transposability to mammography classification. Using a Kaggle
dataset that consolidates INbreast, MIAS, and DDSM mammography collections, we compare a baseline CNN, ConvNeXt Tiny, and InceptionV3 backbones enriched with GAGM and SEVector modules. Results confirm the effectiveness of GAGM and SEVector in enhancing feature discriminability and reducing false negatives, particularly for malignant cases. In our experiments, however, the Feature Smoothing Loss did not yield measurable improvements under mammography classification conditions, suggesting that its effectiveness may depend on specific architectural and computational assumptions. Beyond validation, our contribution extends the original framework through multi metric evaluation (macro F1, per class recall variance, ROC/AUC), feature interpretability analysis (Grad CAM), and the development of an interactive dashboard for clinical exploration. As a perspective, we highlight the need to explore alternative approaches to improve intra class compactness and inter class separability, with the specific goal of enhancing the distinction between malignant and benign cases in mammography classification.
Read the full abstract ▾
Systems and Control · Cross-listed from math.OC
Switched Linear Ensemble Systems and Structural Controllability
Haoyu Yin, Yi Li, Ouyang Du et al. (+2 more)
This paper introduces and solves a structural controllability problem for ensembles of switched linear systems. All individual systems in the ensemble are sparse and governed by the same sparsity pattern, and undergo switching among subsystems by following the same switching sequence. The controllability of an ensemble system describes the ability to use a common control input to simultaneously steer every individual system. A sparsity pattern is called structurally controllable for pair \((k,q)\) if it admits a controllable ensemble of \(q\) individual systems with at most \(k\) subsystems. We derive a necessary and sufficient condition for a sparsity pattern to be structurally controllable for a given \((k,q)\), and characterize when a sparsity pattern admits a finite \(k\) that guarantees structural controllability for \((k,q)\) for arbitrary $q$. Compared with the linear time-invariant ensemble case, this second condition is strictly weaker. We further show that these conditions have natural connections with maximum flow, and hence can be checked by
polynomial algorithms. Specifically, the time complexity of deciding structural controllability is \(O(n^3)\) and the complexity of computing the smallest number of subsystems needed is \(O(n^3 \log n)\), with \(n\) the dimension of each individual system.
Read the full abstract ▾
All authors Haoyu Yin, Yi Li, Ouyang Du, Bruno Sinopoli, Xudong Chen
Signal Processing
Tiled Beamspace MVDR for 1024-element Wideband Radar
Oveys Delafrooz Noroozi, Jiyoon Han, Wei Tang et al. (+2 more)
We present a tiled architecture for computationally efficient digital beamforming for wideband massive
MIMO radar, using beamspace dimension reduction for each tile, and coordinated training of reduced-dimension MVDR beamformers across tiles. We illustrate the efficacy of our approach for a setting in which a 1024-element airborne radar platform beamforms towards airborne targets while suppressing strong interference from ground transmitters. The array is organized into eight 128-element tiles, each a 2D array with 4 (vertical) x 32 (horizontal) elements. Each tile applies a 2D spatial DFT to achieve energy concentration in beamspace, and a 1D temporal FFT to channelize the wideband signal into subbands for which narrowband array models apply. A small tile-level beamspace window is selected for each target (depending on its angle of arrival) in each subband, and coordinated training across tiles is used to compute reduced-dimension MVDR beamformers per-target, per-subband. While full-dimensional MVDR processing is infeasible for the system under consideration, we show that our proposed approach significantly outperforms beamspace MVDR beamforming for a single 128-element tile, where we set the dimensions of the spatial filter (and hence the complexity of MVDR training) to be equal in both systems.
Read the full abstract ▾
All authors Oveys Delafrooz Noroozi, Jiyoon Han, Wei Tang, Zhengya Zhang, Upamanyu Madhow
Systems and Control
The E-Rocket: Low-cost Testbed for TVC Rocket GNC Validation
Pedro Santos, André Fonte, Pedro Martins et al. (+1 more)
This paper presents the E-Rocket, an electric-powered, low-cost rocket prototype for validation of Guidance,
Navigation & Control (GNC)
algorithms based on Thrust Vector Control (TVC). Relying on commercially available components and 3D printed parts, a pair of contra-rotating DC brushless motors is assembled on a servo-actuated gimbal mechanism that provides thrust vectoring capability. A custom avionics hardware and software stack is developed considering a dual computer setup which leverages the capabilities of the PX4 autopilot and the modularity of ROS 2 to accommodate for tailored GNC
algorithms. The platform is validated in an indoor motion-capture arena using a baseline PID-based trajectory tracking controller. Results demonstrate accurate trajectory tracking and confirm the suitability of the E-Rocket as a versatile testbed for rocket GNC
algorithms.
Read the full abstract ▾
All authors Pedro Santos, André Fonte, Pedro Martins, Paulo Oliveira
Signal Processing
Scaling Wideband Hybrid Beamforming for sub-THz Communication
Amirmohammad Haddad, Oveys Delafrooz Noroozi, Canan Cebeci et al. (+2 more)
We investigate the capacity attainable for a multiuser
MIMO uplink as we scale both array size and bandwidth for regimes in which all-digital arrays incur excessive hardware complexity and power consumption. We consider a tiled hybrid beamforming architecture in which each tile, or subarray, is a phased array performing analog (or RF) beamforming, followed by DSP on the tile outputs. For parameters compatible with sub-THz fixed access links, we discuss hardware and power consumption considerations for choosing tile size and the number of tiles. Noting that the problem of optimal multiuser
MIMO in our wideband regime is open even for the simplest possible channel models, we compare the spectral efficiencies attainable by a number of reasonable strategies for tile-level RF beamforming, assuming flexibility in the digital signal processing (DSP) of the tile outputs. We consider a number of beam broadening approaches for addressing the “beam squint” incurred by RF beamforming in our wideband regime, along with strategies for sharing tiles among users. Information-theoretic
benchmarks are computed for an idealized
MIMO-OFDM system, with linear per-subcarrier multiuser detection compared against an unconstrained complexity receiver.
Read the full abstract ▾
All authors Amirmohammad Haddad, Oveys Delafrooz Noroozi, Canan Cebeci, Mark J. W. Rodwell, Upamanyu Madhow
Signal Processing
Increasing Data Rate through Shaping on Wireless Channels Subject to Mobility and Delay Spread
Sandesh Rao Mattu, Nishant Mehrotra and Robert Calderbank
This letter describes how to improve performance of cellular systems by combining non-equiprobable signaling (shaping) with low-density parity check (LDPC) coding for an orthogonal frequency division multiplexing system. We focus on improving performance at the
cell edge, where the 5G standard specifies a suite of LDPC codes with different rates that are applied to 4-QAM. We employ the method of shaping on rings which adds to the transmission rate as it shapes the input distribution. We double the size of the $4$-QAM constellation by introducing a second shell of signal points, and we implement non-equiprobable signaling through a shaping code which selects the high energy shell less frequently than the low energy shell. We describe how to combine coding and shaping by integrating shaping into the calculation of log-likelihood ratios (LLRs) necessary for decoding LDPC codes. We employ rate $1/2$ LDPC coding and select the rate of the shaping code to match that of rate $3/4$ LDPC coding using $4$-QAM. We present simulation results for a representative Veh-A channel showing gains of $4$ dB at a bit error rate (BER) of $10^{-3}$. When we choose an LDPC code from the 5G suite to match the BER performance of rate $1/2$ LDPC coding with shaping we show that transmission rate can be improved by $20 $%.
Read the full abstract ▾
Signal Processing · Cross-listed from cs.LG
BitStopper: An Efficient Transformer Attention Accelerator via Stage-fusion and Early Termination
Huizheng Wang, Hongbin Wang, Shaojun Wei et al. (+2 more)
Attention-based large
language models (LLMs) have transformed modern AI applications, but the quadratic cost of self-
attention imposes significant compute and
memory overhead. Dynamic sparsity (DS)
attention mitigates this, yet its hardware efficiency is limited by the added prediction stage and the heavy
memory traffic it entails. To address these limitations, this paper proposes BitStopper, a fine-grained
algorithm-architecture co-design that operates without a sparsity predictor. First, a bit-serial enable stage fusion (BESF) mechanism is proposed to reuse and minimize the
memory access by progressively terminating trivial
tokens and merging the prediction stage into the execution stage. Second, a lightweight and adaptive
token selection (LATS) strategy is developed to work in concert with the bit-level sparsity speculation. Third, a bit-level asynchronous processing (BAP) strategy is employed to improve compute utilization during the on-demand bit-grained
memory fetching. Finally, an elaborate architecture is designed to translate the theoretical complexity reduction into practical performance improvement. Extensive evaluations demonstrate that, compared to state-of-the-art (SOTA)
Transformer accelerators, BitStopper achieves 2.03x and 1.89x speedups over Sanger and SOFA, respectively, while delivering 2.4x and 2.1x improvements in energy efficiency.
Read the full abstract ▾
All authors Huizheng Wang, Hongbin Wang, Shaojun Wei, Yang Hu, Shouyi Yin
Systems and Control · Cross-listed from cs.RO
Leveraging Port-Hamiltonian Theory for Impedance Control Benchmarking
Leonardo F. Dos Santos, Elisa G. Vergamini, Cícero Zanette et al. (+2 more)
This work proposes PH-based metrics for benchmarking impedance control. A causality-consistent PH model is introduced for mass-spring-damper impedance in Cartesian space. Based on this model, a differentiable, force-torque sensing-independent, n-DoF passivity condition is derived, valid for time-varying references. An impedance fidelity metric is also defined from step-response power in free motion, capturing dynamic decoupling. The proposed metrics are validated in Gazebo simulations with a six-DoF manipulator and a quadruped leg. Results demonstrate the suitability of the PH framework for standardized impedance control benchmarking.
Read the full abstract ▾
All authors Leonardo F. Dos Santos, Elisa G. Vergamini, Cícero Zanette, Lucca Maitan, Thiago Boaventura
Systems and Control · Cross-listed from physics.soc-ph
Demographic Dependence of Vaccine Adoption under Opinion Persuasion
Alessandro Casu, Camilla Quaresmini, Robin Delabays et al. (+2 more)
Inspired by contagion models of social belief formation, we develop an epistemically-informed modeling framework, SIS-Vo, in which vaccine-related information propagates on a signed opinion network. Our model allows for heterogeneous treatment effects of policy messages across subpopulations through demographic-specific responses. We derive fixed-point characterizations of the healthy (disease-free) and endemic
equilibria of this model, and obtain conditions for local stability of the healthy state in terms of the contact network and opinion-dependent vaccination capacities. Using numerical simulations, we illustrate how suitably targeted policy interventions, acting through opinion dynamics, can stabilize the epidemic process by moving the system towards the healthy regime. The SIS-Vo framework thus provides a natural basis for control-theoretic analysis of vaccination policies that remain robust even when misinformation targets specific subgroups.
Read the full abstract ▾
All authors Alessandro Casu, Camilla Quaresmini, Robin Delabays, Lewis Mitchell, Philip E. Paré
Systems and Control
Data Generation for Stability Studies of Power Systems with High Penetration of Inverter-Based Resources
Francesca Rossi, Mauro Garcia Lorenzo, Eduardo Iraola de Acevedo et al. (+5 more)
The increasing penetration of inverter-based resources (IBRs) is fundamentally reshaping power system dynamics and creating new challenges for stability assessment. Data-driven approaches, and in particular machine learning models, require large and representative
datasets that capture how system stability varies across a wide range of operating conditions and control settings. This paper presents an open-source, high-performance computing framework for the systematic generation of such
datasets. The proposed tool defines a scalable operating space for large-scale power systems, explores it through an adaptive sampling strategy guided by sensitivity analysis, and performs small-signal stability assessments to populate a high-information-content
dataset. The framework efficiently targets regions near the stability margin while maintaining broad coverage of feasible operating conditions. The workflow is fully implemented in Python and designed for parallel execution. The resulting tool enables the creation of high-quality
datasets that support data-driven stability studies in modern power systems with high IBR penetration.
Read the full abstract ▾
All authors Francesca Rossi, Mauro Garcia Lorenzo, Eduardo Iraola de Acevedo, Elia Mateu Barriendos, Vinicius Albernaz Lacerda, Francesc Lordan-Gomis, Rosa Badia, Eduardo Prieto-Araujo
Systems and Control · Cross-listed from cs.CY
The Missing Variable: Socio-Technical Alignment in Risk Evaluation
Niclas Flehmig, Mary Ann Lundteigen and Shen Yin
This paper addresses a critical gap in the risk assessment of AI-enabled safety-critical systems. While these systems, where AI systems assist human operators, function as complex socio-technical systems, existing risk evaluation methods fail to account for the associated complex interaction between human, technical, and organizational components. Through a comparative analysis of system attributes from both socio-technical and AI-enabled systems and a review of current risk evaluation methods, we confirm the absence of explicit socio-technical considerations in standard risk expressions. To bridge this gap, we introduce a novel socio-technical
alignment ($STA$) variable designed to be integrated into the traditional risk equation. This variable estimates the degree of harmonious interaction between the AI systems, human operators, and organizational processes. A case study on an AI-enabled
liquid hydrogen ($LH_2$) bunkering system demonstrates the variable's relevance. By comparing a naive and a safeguarded system design, we illustrate how the $STA$-augmented expression captures socio-technical safety implications that traditional risk evaluation overlooks, providing a more system-theoretic basis for risk evaluation.
Read the full abstract ▾
Systems and Control · Cross-listed from math.OC
Stabilizing Rate of Stochastic Control Systems
Hui Jia, Yuan-Hua Ni and Guangchen Wang
This paper develops a quantitative framework for analyzing the mean-square exponential stabilization of
stochastic linear systems with multiplicative noise, focusing specifically on the optimal stabilizing rate, which characterizes the fastest exponential stabilization achievable under admissible control policies. The framework consists of two complementary developments. First, we extend the norm-based analysis from deterministic switched systems to the
stochastic setting and establish computable upper and lower bounds for the optimal stabilizing rate. Second, by restricting
attention to state-feedback policies, we introduce an optimal control formulation of the optimal stabilizing rate problem and derive a Bellman-type equation. Since this Bellman-type equation is not directly tractable, we recast it as a nonlinear
matrix eigenvalue problem whose valid solutions require strictly positive-definite
matrices. To overcome the possible absence of such solutions, we introduce a regularization scheme and develop a Regularized Normalized Value Iteration (RNVI)
algorithm, which in turn generates strictly positive-definite fixed points for a perturbed version of the original nonlinear
matrix eigenvalue problem while producing feedback controllers. Evaluating these regularized solutions further yields certified lower and upper bounds for the optimal stabilizing rate, providing a constructive procedure for estimating the fastest achievable mean-square decay rate. We also provide a sufficient condition for the certified gap to close and a necessary structural condition satisfied by each regular nonvanishing-gap fixed-point sequence. Numerical experiments further demonstrate the effectiveness of the proposed framework.
Read the full abstract ▾
Systems and Control · Cross-listed from cs.OS
Defending Event-Triggered Systems against Out-of-Envelope Environments
Marcus Völp, Mohammad Ibrahim Alkoudsi, Azin Bayrami Asl et al. (+3 more)
The design of real-time systems is based on assumptions about environmental conditions in which they will operate. We call this their safe operational envelope. Violation of these assumptions, i.e., out-of-envelope environments, can jeopardize timeliness and safety of real-time systems, e.g., by overwhelming them with interrupt storms. A long-lasting debate has been going on over which design paradigm, the time- or event-triggered, is more robust against such behavior. In this work, we investigate the claim that time-triggered systems are immune against out-of-envelope behavior and how event-triggered systems can be constructed to defend against being overwhelmed by interrupt showers. We introduce importance (independently of priority and criticality) as a means to express which tasks should still be scheduled in case environmental design assumptions cease to hold, draw parallels to mixed-criticality scheduling, and demonstrate how event-triggered systems can defend against out-of-envelope behavior.
Read the full abstract ▾
All authors Marcus Völp, Mohammad Ibrahim Alkoudsi, Azin Bayrami Asl, Kristin Krüger, Julio Rodrigues Mendonca da Neto, Gerhard Fohler
Systems and Control
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov et al. (+6 more)
We survey classical, machine learning, and data-driven system identification approaches to learn control-relevant and physics-informed models of dynamical systems. Recently, machine learning approaches have enabled system identification from noisy, high-dimensional, and complex data. However, their utility is limited by their ability to provide provable guarantees on control-relevant properties. Meanwhile, control theory has identified several properties that are useful in analysis and control synthesis, such as dissipativity, monotonicity, energy conservation, and
symmetry-preserving structures. We posit that merging system identification with such control-relevant or physics-informed properties can provide useful inductive bias, enhance explainability, enable control synthesis with provable guarantees, and improve sample complexity. We formulate system identification as an optimization problem where control-relevant properties can be enforced through direct parameterization (constraining the model structure to satisfy a desired property by construction), soft constraints (encouraging control-relevant properties through regularization or penalty terms), and hard constraints (imposing control-relevant properties as constraints in the optimization problem). Through this lens, we survey methods to learn physics-informed and control-relevant models spanning classical linear and nonlinear system identification, machine learning approaches, and direct identification through data-driven and behavioral representations. We also provide several expository examples that are accompanied by code and brief tutorials on a public Github repository. We also describe challenging directions for future research, including identification in networked, switched, and time-varying systems, experiment design, and bridging the gaps between data-driven, learning-based, and control-oriented approaches.
Read the full abstract ▾
All authors S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov, Thai Duong, Jie Feng, Tim Martin, Yuezhu Xu, Vijay Gupta, Frank Allgöwer
Audio and Speech Processing
Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation
Xining Song, Zhihua Wei, Rui Wang et al. (+3 more)
Identity, accent, style, and
emotions are essential components of human
speech. Voice conversion (VC) techniques process the
speech signals of two input speakers and other modalities of auxiliary information such as
prompts and
emotion tags. It changes para-linguistic features from one to another, while maintaining linguistic contents. Recently, VC models have made rapid advancements in both generation quality and personalization capabilities. These developments have attracted considerable
attention for diverse applications, including
privacy preservation, voice-print reproduction for the deceased, and dysarthric
speech recovery. However, these models only learn non-robust features due to the clean training data. Subsequently, it results in unsatisfactory performances when dealing with degraded input
speech in real-world scenarios, including additional noise, reverberation,
adversarial attacks, or even minor perturbation. Hence, it demands robust deployments, especially in real-world settings. Although latest researches attempt to find potential attacks and countermeasures for VC systems, there remains a significant gap in the comprehensive understanding of how robust the VC model is under input manipulation. here also raises many questions: For instance, to what extent do different forms of input degradation attacks alter the expected output of VC models? Is there potential for optimizing these attack and defense strategies? To answer these questions, we classify existing attack and defense methods from the perspective of input manipulation and evaluate the impact of degraded input
speech across four dimensions, including intelligibility, naturalness, timbre similarity, and subjective perception. Finally, we outline open issues and future directions.
Read the full abstract ▾
All authors Xining Song, Zhihua Wei, Rui Wang, Haixiao Hu, Yanxiang Chen, Meng Han
Systems and Control
A Hybrid Physics-Based and Reinforcement Learning Framework for Electric Vehicle Charging Time Prediction
Praharshitha Aryasomayajula, Ting Bai and Andreas A. Malikopoulos
In this paper, we develop a hybrid prediction framework for accurate electric vehicle (EV) charging time estimation, a capability that is critical for trip planning, user satisfaction, and efficient operation of charging infrastructure. We combine a physics-based analytical model with a reinforcement learning (RL) approach. The analytical component captures the nonlinear constant-current/constant-voltage (CC–CV) charging dynamics and explicitly models state-of-health (SoH)–dependent capacity and power fade, providing a reliable baseline when historical data are limited. Building on this foundation, we introduce an RL component that progressively refines charging-time predictions as operational data accumulate, enabling improved long-term adaptation. Both models incorporate SoH degradation to maintain predictive accuracy over the battery lifetime. We evaluate the framework using $5{,}000$ simulated charging sessions calibrated to manufacturer specifications and publicly available EV charging
datasets. Our results show that the analytical model achieves $R^{2}=98.5\%$ and $\mathrm{MAPE}=2.1\%$, while the RL model further improves performance to $R^{2}=99.2\%$ and $\mathrm{MAPE}=1.6\%$, corresponding to a $23\%$ accuracy gain and $35\%$ improved robustness to battery aging.
Read the full abstract ▾