The　hydrogen/electric　vehicle　charging　station　(HEVCS)　is　widely　regarded　as　a　highly　attractive　system　for　facilitating　the　popularity　of　hydrogen　and　electric　vehicles　in　the　future.　However,　conventional　optimal　dispatch　of　HEVCS　could　lead　to　poor　performance　due　to　the　lack　of　adequate　consideration　of　vehicle　charging　decision　behaviours　and　neglection　of　the　impacts　of　different　information　sources　on　it.　This　paper　investigates　a　charging　demand　prediction　method　that　considers　multi-source　information　and　proposes　a　multi-objective　optimal　dispatching　strategy　of　HEVCS.　First,　an　information　interaction　framework　of　integrated　road　network,　vehicles　and　HEVCS　is　introduced.　Road　network　model　and　HEVCS　model　are　established　based　on　the　proposed　framework.　To　improve　the　flexibility　of　dispatch,　two　charging　modes　are　designed,　which　are　intended　to　guide　drivers　to　adjust　their　consumption　behaviour　by　electricity　price　incentives.　Furthermore,　psychologically　based　hybrid　utility-regret　decision　model　and　WeberFechner　(W-F)　stimulus　model　are　developed　to　reasonably　predict　drivers＇　choice　of　charging　stations　and　charging　modes.　The　daily　revenue　of　HEVCS　and　the　total　queuing　time　of　drivers　are　the　objective　functions　considered　in　this　paper　simultaneously.　The　above　multi-objective　optimization　results　that　the　proposed　strategy　can　effectively　improve　the　benefits　of　HEVCS　and　reduce　energy　waste.　Additionally,　this　paper　discusses　the　results　of　a　sensitivity　analysis　conducted　by　varying　incentive　discount,　which　reveals　the　combined　benefits　of　the　HEVCS　and　the　vehicles　are　effectively　increased　by　setting　reasonable　incentive　discounts.&　COPY;　2023　Hydrogen　Energy　Publications　LLC.　Published　by　Elsevier　Ltd.　All　rights　reserved.