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Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning

https://doi.org/10.3390/en14051361
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-58571-6_11openalex-snapshot
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
inferredcites →https://doi.org/10.1016/j.arcontrol.2018.09.005openalex-snapshot
Reinforcement learning for control: Performance, stability, and deep approximators
inferredcites →https://doi.org/10.1016/j.automatica.2021.109689openalex-snapshot
Reinforcement learning control of constrained dynamic systems with uniformly ultimate boundedness stability guarantee
inferredcites →https://doi.org/10.1016/j.egypro.2019.01.783openalex-snapshot
A Review of Lithium-Ion Battery for Electric Vehicle Applications and Beyond
inferredcites →https://doi.org/10.1016/j.electacta.2005.06.033openalex-snapshot
A generalized cycle life model of rechargeable Li-ion batteries
inferredcites →https://doi.org/10.1016/j.engappai.2012.09.013openalex-snapshot
Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicles
inferredcites →https://doi.org/10.1016/j.ifacol.2018.11.736openalex-snapshot
State of Charge Estimation for Lithium Ion Battery Based on Reinforcement Learning
inferredcites →https://doi.org/10.1061/(asce)0733-9496(2003)129:6(458)openalex-snapshot
Neural Networks and Reinforcement Learning in Control of Water Systems
inferredcites →https://doi.org/10.1109/aero.2007.352728openalex-snapshot
Lithium Ion Batteries for Space Applications
inferredcites →https://doi.org/10.1109/jproc.2003.823141openalex-snapshot
Unscented Filtering and Nonlinear Estimation
inferredcites →https://doi.org/10.1109/pes.2011.6039674openalex-snapshot
An overview of generic battery models
inferredcites →https://doi.org/10.1109/ssci47803.2020.9308468openalex-snapshot
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
inferredcites →https://doi.org/10.1109/tii.2020.3014599openalex-snapshot
Battery Thermal- and Health-Constrained Energy Management for Hybrid Electric Bus Based on Soft Actor-Critic DRL Algorithm
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1109/tsg.2020.2986333openalex-snapshot
Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model
inferredcites →https://doi.org/10.1145/3397271.3401238openalex-snapshot
Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic
inferredcites →https://doi.org/10.1149/1.1787631openalex-snapshot
Cycle Life Modeling of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1371/journal.pcbi.1007783openalex-snapshot
Deep reinforcement learning for the control of microbial co-cultures in bioreactors
inferredcites →https://doi.org/10.3390/app8050659openalex-snapshot
Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles
inferredcites →https://doi.org/10.3390/en10070987openalex-snapshot
State-of-Charge Estimation with State-of-Health Calibration for Lithium-Ion Batteries
inferredcites →https://doi.org/10.3390/en10122107openalex-snapshot
Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids
inferredcites →https://doi.org/10.3390/en12142784openalex-snapshot
Battery Remaining Useful Life Prediction with Inheritance Particle Filtering
inferredcites →https://doi.org/10.3390/inventions4030041openalex-snapshot
Comparison of Lithium-Ion Battery Models for Simulating Storage Systems in Distributed Power Generation
inferredcites →https://doi.org/10.3390/wevj9030039openalex-snapshot
Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems
inferredcites →https://doi.org/10.36001/phmconf.2013.v5i1.2252openalex-snapshot
Electrochemistry-based Battery Modeling for Prognostics
inferredcites →https://doi.org/10.36001/phmconf.2014.v6i1.2490openalex-snapshot
Adaptation of an Electrochemistry-based Li-Ion Battery Model to Account for Deterioration Observed Under Randomized Use
inferredcites →https://doi.org/10.48550/arxiv.1611.05095openalex-snapshot
Learning Dexterous Manipulation Policies from Experience and Imitation
inferredcites →https://doi.org/10.48550/arxiv.1801.01290openalex-snapshot
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
inferredcites →https://doi.org/10.48550/arxiv.1812.05905openalex-snapshot
Soft Actor-Critic Algorithms and Applications

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.ifacol.2024.12.031openalex-snapshot
Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization
inferred← citeshttps://doi.org/10.1016/j.jechem.2023.08.047openalex-snapshot
Specialized deep neural networks for battery health prognostics: Opportunities and challenges
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2023.233474openalex-snapshot
Battery prognostics and health management from a machine learning perspective
inferred← citeshttps://doi.org/10.1016/j.pecs.2023.101142openalex-snapshot
Battery safety: Machine learning-based prognostics
inferred← citeshttps://doi.org/10.1016/j.pmatsci.2025.101574openalex-snapshot
Advanced strategies to boost sustainable high-rate Ni-rich cathodes toward durable LIBs
inferred← citeshttps://doi.org/10.1016/j.procs.2026.02.245openalex-snapshot
Real-time Calibration of Li-Ion Battery Equivalent Circuit Model with Neural Networks and Reinforcement Learning for State of Charge Prediction
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Transformer-augmented deep Q-network-based risk-informed maintenance policy for partially observable systems under combined degradation and random shock effects
inferred← citeshttps://doi.org/10.1016/j.rser.2026.117225openalex-snapshot
Influence of different aging pathways on the thermal runaway characteristics of lithium-ion batteries: A review with gas evolution as the bridging perspective
inferred← citeshttps://doi.org/10.1016/j.ymssp.2026.114291openalex-snapshot
Online Adaptive Reinforcement Learning and Approximate Bayesian Computation (OARL-ABC) for real-time long-term calibration of rotordynamic models
inferred← citeshttps://doi.org/10.1039/d2dd00047dopenalex-snapshot
A review of reinforcement learning in chemistry
inferred← citeshttps://doi.org/10.1109/access.2022.3176446openalex-snapshot
Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals
inferred← citeshttps://doi.org/10.1109/iecon49645.2022.9968893openalex-snapshot
Robust Parameter Estimation and Tracking through Lyapunov-based Actor-Critic Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/iscsic60498.2023.00014openalex-snapshot
Model Calibration of Active Magnetic Bearing Using Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/itsc55140.2022.9922041openalex-snapshot
Dynamic Car-following Model Calibration with Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/naps66256.2025.11272294openalex-snapshot
Battery Parameter Calibration Using EKF and UKF for Equivalent Circuit Model
inferred← citeshttps://doi.org/10.1109/tte.2022.3186151openalex-snapshot
Improving Aging Identifiability of Lithium-Ion Batteries Using Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1142/s2737480723500176openalex-snapshot
A Novel Model Calibration Method for Active Magnetic Bearing Based on Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.21122/1029-7448-2022-65-6-481-498openalex-snapshot
Universal Simulation Model of Battery Degradation with Optimization of Parameters by Genetic Algorithm
inferred← citeshttps://doi.org/10.2139/ssrn.5211901openalex-snapshot
A Bayesian Ensemble Approach for Improved Sustainable Aviation Fuel Modeling
inferred← citeshttps://doi.org/10.32604/cmc.2022.029322openalex-snapshot
State of Health Estimation of LiFePO4 Batteries for Battery Management燬ystems
inferred← citeshttps://doi.org/10.3389/fcomp.2026.1884141openalex-snapshot
SAE-MRHL: a Self-Adaptive Explainable Meta-Reinforcement Hybrid Learner for remaining battery life prediction in IoE systems
inferred← citeshttps://doi.org/10.3390/a15110393openalex-snapshot
Lithium-Ion Battery Prognostics through Reinforcement Learning Based on Entropy Measures
inferred← citeshttps://doi.org/10.3390/en14123493openalex-snapshot
Accessible Battery Model with Aging Dependency
inferred← citeshttps://doi.org/10.3390/en15051695openalex-snapshot
Surrogate Model of the Optimum Global Battery Pack Thermal Management System Control
inferred← citeshttps://doi.org/10.3390/en17061320openalex-snapshot
A Review of Bidirectional Charging Grid Support Applications and Battery Degradation Considerations
inferred← citeshttps://doi.org/10.3390/en18040786openalex-snapshot
Li-Ion Batteries for Electric Vehicle Applications: An Overview of Accurate State of Charge/State of Health Estimation Methods
inferred← citeshttps://doi.org/10.36227/techrxiv.170846720.02245839/v1openalex-snapshot
Enhanced Resilience in Battery Charging through Co-Simulation with Reinforcement Learning
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