The　performance　of　lithium-ion　batteries　declines　rapidly　over　time,　inducing　anxiety　in　their　usage.　Ascertaining　the　capacity　of　these　batteries　is　difficult　to　measure　directly　during　online　remaining　useful　life　(RUL)　prediction,　and　a　single　deep　learning　model　falls　short　of　accuracy　and　applicability　in　RUL　predictive　analysis.　Hence,　this　study　proposes　a　lithium-ion　battery　RUL　indirect　prediction　model,　fusing　convolutional　neural　networks　and　bidirectional　gated　recurrent　units　(CNN-BiGRU).　The　analysis　of　characteristic　parameters　of　battery　life　status　reveals　the　selection　of　pressure　discharge　time,　average　discharge　voltage　and　average　temperature　as　health　factors　of　lithium-ion　batteries.　Following　this,　a　CNN-BiGRU　model　for　lithium-ion　battery　RUL　indirect　prediction　is　established,　and　the　Tree-structured　Parzen　Estimator　(TPE)　adaptive　hyperparameter　optimization　method　is　used　for　CNN-BiGRU　model　hyperparameter　optimization.　Overall,　comparison　experiments　on　single-model　and　other　fusion　models　demonstrate　our　proposed　model’s　superiority　in　the　prediction　of　RUL　in　terms　of　stability　and　accuracy.　©　2023　the　Author(s),　licensee　AIMS　Press.　This　is　an　open　access　article　distributed　under　the　terms　of　the　Creative　Commons　Attribution　License　(http://creativecommons.org/licenses/by/4.0)