TY - JOUR
T1 - An Electrochemical Parameter Identification Method for Power Batteries Based on an Improved Physics-Informed Neural Network
AU - Li, Ziyu
AU - Li, Jiahao
AU - Luo, Zhaofeng
AU - Zhang, Yao
AU - Lin, Ni
AU - Zhang, Qiang
N1 - Publisher Copyright:
© 2026 The Electrochemical Society (“ECS”). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. This article is available under the terms of the https://publishingsupport.iopscience.iop.org/iop-standard/v1.
PY - 2026
Y1 - 2026
N2 - Accurate identification of internal electrochemical parameters is essential for state estimation, model-based prediction, and safety warning of lithium-ion batteries. Conventional teardown-based methods are destructive and costly, while data-driven metaheuristic algorithms often suffer from low efficiency and poor generalization. To address these issues, this paper proposes a robust parameter identification method based on a Sequence-to-Sequence Physics-Informed Neural Network (SS-PINN). The governing equations of a reduced-order pseudo-two-dimensional model are embedded as physical constraints within a recurrent neural network. A hybrid loss function integrates voltage measurements with internal electrochemical mechanisms, enabling non-destructive and accurate identification of key geometric, transport, and kinetic parameters. Validation using real-vehicle dynamic operating data shows that the SS-PINN method achieves higher accuracy and faster convergence than the particle swarm optimization algorithm. The maximum voltage simulation error remains below 13 mV. Moreover, voltage curves generated with identified parameters align closely with those from direct teardown measurements, confirming experimental-level accuracy without battery damage. This study offers a promising solution for real-time state perception and adaptive parameter updating in battery management systems.
AB - Accurate identification of internal electrochemical parameters is essential for state estimation, model-based prediction, and safety warning of lithium-ion batteries. Conventional teardown-based methods are destructive and costly, while data-driven metaheuristic algorithms often suffer from low efficiency and poor generalization. To address these issues, this paper proposes a robust parameter identification method based on a Sequence-to-Sequence Physics-Informed Neural Network (SS-PINN). The governing equations of a reduced-order pseudo-two-dimensional model are embedded as physical constraints within a recurrent neural network. A hybrid loss function integrates voltage measurements with internal electrochemical mechanisms, enabling non-destructive and accurate identification of key geometric, transport, and kinetic parameters. Validation using real-vehicle dynamic operating data shows that the SS-PINN method achieves higher accuracy and faster convergence than the particle swarm optimization algorithm. The maximum voltage simulation error remains below 13 mV. Moreover, voltage curves generated with identified parameters align closely with those from direct teardown measurements, confirming experimental-level accuracy without battery damage. This study offers a promising solution for real-time state perception and adaptive parameter updating in battery management systems.
KW - battery management system
KW - electrochemical model
KW - parameter identification
KW - physics-informed neural network
KW - power battery
UR - https://www.scopus.com/pages/publications/105042546525
U2 - 10.1149/1945-7111/ae6e21
DO - 10.1149/1945-7111/ae6e21
M3 - Comment/debate
AN - SCOPUS:105042546525
SN - 0013-4651
VL - 173
JO - Journal of the Electrochemical Society
JF - Journal of the Electrochemical Society
IS - 12
M1 - 120513
ER -