Mobil­ity on-​​demand vehi­cle (MODV) ser­vices have grown explo­sively in recent years, threat­en­ing tar­gets for local air pol­lu­tion and global car­bon emis­sions. Despite evi­dence that on-​​demand auto­mo­tive fleets are ripe for elec­tri­fi­ca­tion, adop­tion of bat­tery elec­tric vehi­cles (BEVs) in fleet appli­ca­tions has been hin­dered by lack of charg­ing infra­struc­ture and long charg­ing times. Recent research on elec­tri­fi­ca­tion pro­grams in Chi­nese megac­i­ties sug­gests that top-​​down pol­icy tar­gets can spur invest­ment in charg­ing infra­struc­ture, while intel­li­gent charg­ing coor­di­na­tion can greatly reduce require­ments for bat­tery range and infra­struc­ture, as well as rev­enue losses due to time spent charg­ing. Such capa­bil­ity may require labor pol­icy reform to allow fleet oper­a­tors to man­age their dri­vers’ charg­ing behav­ior, along with col­lec­tion and inte­gra­tion of sev­eral key datasets includ­ing: 1) vehi­cle tra­jec­to­ries and energy con­sump­tion, 2) charg­ing infra­struc­ture instal­la­tion costs, and 3) real-​​time charg­ing sta­tion avail­abil­ity. In turn, dig­i­ti­za­tion enabled by fleet elec­tri­fi­ca­tion holds the poten­tial to enable a host of smart urban mobil­ity strate­gies, includ­ing inte­gra­tion of pub­lic tran­sit with inno­v­a­tive trans­porta­tion sys­tems and emission-​​based pric­ing policies.