Considering　the　limitations　in　existing　correlation　coefficient-based,　entropy-based　and　big　data　analysis-based　voltage　sensor　fault　diagnosis　methods,　we　develop　a　novel　dual　time-scale　voltage　sensor　fault　detection　and　isolation　method　for　series-connected　lithium-ion　battery　pack　in　this　paper.　Firstly,　a　＇ohmic　resistance＇-based　selection　method　is　periodically　performed　to　artificially　divide　all　in-pack　cells　into　＇representative　cell＇　and　non-representative　cells.　Secondly,　during　the　＇representative　cell＇-based　battery　pack　state-of-charge　(SOC)　and　cell　SOC　inconsistence　estimation　process,　the　measurement　innovation　(MI)　between　measured　and　estimated　voltage　of　the　＇representative　cell＇　and　non-representative　cells　is　generated　in　micro　time-scale　and　macro　time-scale,　respectively.　Regarding　the　＇representative　cell＇,　the　faulty　voltage　sensor　is　immediately　detected　at　the　moment　of　the　voltage　sensor　fault　occurrence　by　catching　the　abnormal　MI.　As　for　the　non-representative　cells,　through　analyzing　the　discreteness　degree　of　generated　MI　under　faultless　and　faulty　voltage　sensors,　an　abnormal　variance-based　voltage　sensor　fault　diagnosis　method　and　an　abnormal　variance　contribution-based　fault　location　method　are　developed.　The　validation　results　through　three　sophisticated　cases　demonstrate　that　this　method　can　rapidly　catch　the　abnormal　features　for　further　voltage　sensor　fault　diagnosis　with　low　complexity　and　satisfactory　robustness　even　though　there　exist　certain　faulty　current.　©　2022　Elsevier　Ltd