The thermal coupled equivalent circuit model provides a vital role not only in accurate and reliable state monitoring, but also in effective thermal management of lithium-ion batteries. However, it lacks appropriate modeling strategies for including both the temperature and state of charge effects into the thermal coupled equivalent circuit model. In this study, a unified artificial neural network based thermal coupled equivalent circuit model approach is proposed to accurately and reliably capture the electrical and thermal dynamics of lithium-ion batteries. Both reversible and irreversible heat generation mechanisms are introduced in the thermal model. The quantitative relationship between circuit parameters and temperature/state of charge in equivalent circuit model is modeled by artificial neural network. Both electrical and thermal related parameters are simultaneously identified by means of least square strategy with l(1)-norm, penalty on output weights in artificial neural network and positive constraints on circuit parameters. The effectiveness of the proposed artificial neural network based thermal coupled equivalent circuit model approach is validated by the experimental constant current discharge, pulse current discharge test and hybrid pulse power characterization test of a commercial large-format pouch-type lithium-ion battery. It implies that the proposed hybrid modeling strategy can provide a general framework for the inclusion of other effects such as health state and current into battery models and can be easily extended to more complicated models such as first-principle electrochemical-thermal model. (C) 2017 Elsevier Ltd. All rights reserved.