The　eco-driving　strategy　is　of　great　significance　in　driving　cost　for　plug-in　hybrid　electric　vehicles　in　driving　trips,　especially　at　signalized　intersections.　To　address　the　issue　of　further　energy　saving,　this　study　proposes　an　ecological　approach　and　departure-driving　strategy　by　using　syncretic　learning　with　trapezoidal　collocation　algorithm.　First,　a　syncretic　learning-based　speed　predictor　is　built　by　merging　back　propagation　neural　networks　and　radial　basis　function　neural　networks.　Second,　the　syncretic　learning-based　speed　predictor　and　trapezoidal　collocation　algorithm　are　combined　to　optimize　the　speed　trajectory.　Third,　the　torque　between　the　engine　and　the　motor　is　distributed　by　the　dynamic　programming　algorithm.　Then,　model　predictive　control　optimizes　torque　output　in　the　control　time　domain.　Finally,　the　driving　interval　optimization　method　is　designed　to　avoid　mixed-integer　programming　problems　and　redundant　constraints,　which　make　vehicles　cross　intersections　without　stopping.　The　numerical　verification　results　show　that　the　trapezoidal　collocation　algorithm　with　syncretic　learning　has　more　advantages　than　other　methods　in　speed　trajectory　planning.　Compared　with　the　original　trajectory,　the　driving　time　through　the　intersection　is　reduced　and　the　total　driving　cost　is　lowered　by　19.82%.　Validation　results　confirm　the　effectiveness　of　the　proposed　strategy　in　energy　consumption　management　at　signalized　intersections.　This　study　proposes　an　ecological　approach　and　departure-driving　strategy　by　using　syncretic　learning　with　trapezoidal　collocation　algorithm.　The　speed　predictor　is　combined　with　the　trapezoidal　collocation　algorithm　to　obtain　the　speed　trajectory　with　higher　accuracy.　The　driving　interval　optimization　method　is　proposed　to　make　vehicles　cross　the　intersections　without　stopping.image　(c)　2024　WILEY-VCH　GmbH