Multisensory　and　artificial　intelligence　approaches　are　key　tools　to　achieve　intelligent　management　of　future　battery　systems.　Strain　monitoring　using　optical　fiber　sensors　is　an　important　role　of　multi-sensing　in　batteries.　In　this　paper,　the　strain　of　batteries　is　monitored　by　fiber　Bragg　grating　sensors,　and　the　strain　data　are　used　to　estimate　the　state　of　charge　(SoC)　and　state　of　health　(SoH)　of　batteries.　A　Kalman　filtering　(KF)　model　is　proposed　for　SoC　estimation　based　on　strain　signal　of　cells.　Moreover,　this　work　employs　an　artificial　neural　network　(NN)　for　SoC　estimation　based　on　the　strain　data.　The　experimental　data　are　acquired　from　commercial　lithium-ion　cells　under　two　operating　conditions.　The　KF　model　is　established　based　on　multiple　regression　between　strain　and　SoC,　which　shows　good　performance　in　estimation　for　the　static　cycles.　For　NN　estimators,　input　variables　with　strain　parameter　can　enhance　the　accuracy　of　SoC　estimation.　A　KF　model　based　on　the　peak　strain　is　developed　to　estimate　the　capacity　degradation　of　battery,　and　the　results　show　that　strain　can　be　used　as　an　indicator　to　estimate　SoH.　The　results　present　an　encouraging　outcome　that　SoC　estimation　can　be　achieved　using　non-electrical　parameters　solely,　and　the　strain　signal　can　also　be　used　as　an　auxiliary　parameter　to　improve　the　accuracy　of　SoC　estimation.　This　new　exploration　provides　a　basis　for　multi-parameter　cooperative　estimation　of　battery　state　in　the　future　battery　system　with　a　multisensory　approach.　©　2022　Elsevier　Ltd