Accurate　estimation　of　the　state-of-charge　(SOC)　of　lithium-ion　batteries　is　a　key　technique　for　automotive　battery　management　systems　to　overcome　the　non-linearity　and　complications　of　practical　applications.　The　data-driven　approach　for　estimating　SOC　requires　a　large　number　of　training　samples　and　costly　input.　To　this　end,　an　improved　gated　recurrent　unit　(GRU)-based　transfer　learning　SOC　estimation　is　proposed　for　small　target　sample　sets.　To　ensure　the　completeness　and　consistency　of　data　features,　Lagrangian　interpolations　and　standard　normalization　are　used　for　analyzing　the　open-source　battery　datasets.　The　source　domain　GRU　model　is　pre-trained　to　obtain　rich　battery　characteristics　with　the　preprocessed　datasets;　the　GRU　hidden　unit　structure　can　be　enhanced,　and　it　is　advantageously　used　in　conjunction　with　transfer　learning.　Moreover,　weight　parameters　of　the　source　domain　are　transferred　to　the　GRU　model　of　target　batteries.　The　experimental　results　show　that　the　proposed　improved　GRU-based　transfer　learning　can　use　small　target　samples　to　achieve　fast　and　accurate　SOC　estimations　by　ordinary　computing　hardware.　In　particular,　the　RMSEs　are　1.115%,　1.867%,　and　1.141%　under　dynamic　conditions,　32　°C-FUDS,　36　°C-US06,　and　50　°C-UDDS,　respectively.　The　proposed　method　demonstrates　the　potential　of　SOC　estimation　using　small　target　samples-based　big　data　techniques　in　practice.　©　2022　Elsevier　Ltd