Deep　learning　methods　have　been　widely　used　for　battery　aging　state　estimation　with　either　manual　or　automatic　features,　while　the　contribution　of　multi　-source　features　is　rarely　considered.　To　solve　this　problem,　a　hybrid　method　is　proposed　to　combine　the　manual　and　automatic　features　based　on　a　temporal　convolution　network　(TCN)　and　a　self　-attention　mechanism　(SA).　Specifically,　the　local　voltage,　capacity,　and　incremental　capacity　are　manually　extracted　as　battery　aging　features.　Then,　for　extracting　automatic　features,　TCN　employs　dilated　convolution　to　capture　the　capacity　regeneration　phenomenon　during　battery　degradation.　Considering　the　contribution　of　multi　-source　features,　we　use　SA　to　fuse　the　obtained　manual　and　automatic　features.　Finally,　the　available　capacity　and　remaining　useful　life　of　the　battery　are　predicted　using　a　fully　connected　neural　network　on　one　dataset　from　our　lab,　the　Oxford　University　dataset,　and　the　MIT　University　dataset.　The　experimental　results　show　that　the　proposed　method　exhibits　a　high　accuracy　of　aging　state　identification.