Accurate　measurement　information,　especially　precise　voltage,　is　essential　for　model-based　multi-state　estimation　algorithms　of　lithium-ion　battery.　Regarding　the　shortcomings　in　existing　diagnosis　methods,　such　as　the　difficulty　in　threshold　value　determination,　low　voltage　sensor　fault　detection　efficiency　and　the　assumption　of　no　multiple　faults　occur　simultaneously,　a　simple　and　practical　voltage　sensor　fault　detection,　isolation　and　estimation　method　is　proposed　in　this　paper.　Firstly,　the　widely　used　algorithm　by　fusion　of　recursive　least　square　and　extend　Kalman　filter　is　adopted　to　obtain　the　measurement　innovation　(MI)　between　measured　and　estimated　terminal　voltage　during　online　state-of-charge　estimation　process.　Subsequently,　focusing　on　the　MI　generated　under　faultless　and　faulty　voltage　sensors,　a　simple　feature　point　(FP)　identification-based　voltage　sensor　detection　and　isolation　method　is　further　designed.　Finally,　with　the　identified　FP＇s　y-coordinate,　a　three-step　based　voltage　sensor　fault　estimation　method　is　developed　to　determine　the　fault　mode　and　the　corresponding　fault　value.　Moreover,　the　proposed　voltage　sensor　fault　diagnosis　method　is　verified　by　Federal　Urban　Driving　Schedule　test,　whose　results　demonstrate　that　it　can　still　realize　immediate　voltage　sensor　fault　detection　(at　the　moment　of　sensor　fault　occurrence)　and　accurate　voltage　sensor　fault　estimation　even　though　there　exist　certain　current　sensor　fault.　©　2022