For　the　purpose　of　better　matching　the　performance　of　the　organic　Rankine　cycle　(ORC)　system　concerning　the　vehicle　engine　waste　heat　recovery,　this　paper　studies　the　output　performance　of　free　piston　expander-linear　generator　(FPE-LG).　A　test　bench　of　FPE-LG　is　established　for　small　scale　ORC　system,　and　timing　and　displacement　control　strategy　is　proposed.　Furthermore,　the　impact　of　the　intake　pressure　and　the　torque　on　motion　characteristics　and　output　performance　of　FPE-LG　are　analyzed.　According　to　evaluating　different　learning　rates,　number　of　hidden　artificial　neural　networks　and　training　functions,　a　prediction　model　of　FPE-LG　based　on　artificial　neural　network　is　established.　Genetic　algorithm　is　used　to　optimize　the　key　operating　parameters,　to　maximize　the　power　output　of　FPE-LG.　In　consideration　of　the　mean　square　error　and　determination　coefficient,　the　artificial　neural　network　model　is　verified　and　tested　by　experimental　data.　Finally,　combining　genetic　algorithm　with　artificial　neural　network　model,　the　maximum　power　output　of　FPE-LG　is　optimized　and　its　performance　is　predicted.　The　results　show　that　the　maximum　value　of　electric　current,　voltage　and　power　output　are　2.8　A,　14.75　V　and　28.5　W,　respectively.　Based　on　artificial　neural　network,　this　method　can　provide　useful　guidance　for　performance　prediction　and　coordinated　optimization,　with　advantages　of　minimum　deviation　and　high　precision.　(C)&　nbsp;2022　The　Authors.　Published　by　Elsevier　Ltd.&　nbsp;&　nbsp;