Fuel　cell　lifetime　is　strongly　affected　by　dynamic　conditions.　Most　existing　energy　management　works　only　focus　on　the　fuel　cell　durability　protection　from　the　perspective　of　output　power　slope,　without　deeply　considering　the　influence　of　the　important　parameters　inside　the　stack.　However,　considering　the　variation　of　stack　internal　parameters　(mechanism　analysis)　is　more　significant　for　fuel　cell　lifetime　evaluation.　In　this　paper,　a　health　-aware　model　predictive　control　(HA-MPC)　energy　management　strategy　is　proposed　for　fuel　cell　electric　vehicle.　A　fuel　cell　health　state　model　is　established　from　the　perspective　of　stack　hydrogen　excess　ratio　(HER),　oxygen　excess　ratio　(OER)　and　humidity　through　the　hybrid　modeling　method.　The　fuel　cell　mechanism　model　and　the　low-dimensional　data-driven　model　are　established　through　the　grey-box　model　estimation　method　and　genetic　algorithm-based　radial　basis　function　(GA-RBF)　neural　network.　Then　the　objective　function　of　energy　management　strategy　is　developed　considering　the　total　equivalent　hydrogen　consumption　and　stack　improper　parameter　changes　of　HER,　OER　and　humidity.　Comparing　with　model　predictive　control　strategy　based　on　the　typical　power　cost　function,　the　HA-MPC　can　effectively　reduce　the　steep　drop　of　HER　and　OER　in　low　power　region　by　3.58%　and　4.41%,　which　can　protect　the　fuel　cell　system　lifetime.