With　the　rapid　development　of　urban　rail　transit　systems　and　the　consequent　sharp　increase　of　energy　consumption,　the　energy-saving　train　operation　problem　has　been　attracting　much　attention.　Extensive　studies　have　been　devoted　to　optimal　control　of　a　single　metro　train　in　an　inter-station　run　to　minimize　the　energy　consumption.　However,　most　of　the　existing　work　focuses　on　offline　optimization　of　the　energy-saving　driving　strategy,　which　still　needs　to　be　tracked　in　real　train　operation.　In　order　to　attain　better　performance　in　the　presence　of　disturbances,　this　paper　studies　the　online　optimization　problem　of　the　energy-saving　driving　strategy　for　a　single　metro　train,　by　employing　the　model　predictive　control　(MPC)　approach.　Firstly,　a　switched-mode　dynamical　system　model　is　introduced　to　describe　the　dynamics　of　a　metro　train.　Based　on　this　model,　an　MPC-based　online　optimization　problem　is　formulated　for　obtaining　the　optimal　mode　switching　times　with　minimal　energy　consumption　for　a　single　train　in　an　inter-station　run.　Then　we　propose　an　algorithm　to　solve　the　constrained　optimization　problem　at　each　time　step　by　utilizing　the　exterior　point　penalty　function　method.　The　proposed　online　optimal　train　control　algorithm　which　determines　the　mode　switching　times　can　not　only　improve　the　computational　efficiency　but　also　enhances　the　robustness　to　disturbances　in　real　scenarios.　Finally,　the　effectiveness　and　advantages　of　this　online　optimal　train　control　algorithm　are　illustrated　through　case　studies　of　a　single　train　in　an　inter-station　run.