Aiming　at　finding　the　major　vibration　and　noise　sources　of　vehicles,　a　quantitative　estimation　method　for　source　contribution　using　the　kurtosis-based　constrained　independent　component　analysis　(cICA)　algorithm　is　proposed.　First,　the　similarity　between　the　ICs　and　the　reference　signals　with　given　characteristics　is　described　by　a　concise　and　effective　closeness　measurement　function.　Meanwhile,　how　to　choose　the　reference　signals　and　the　choice　of　some　other　closeness　measurements　is　discussed.　Then,　a　widely　used　contrast　function,　namely,　kurtosis,　is　modified　by　the　closeness　measurement　to　obtain　an　enhanced　contrast　function.　The　fixed-point　iteration　and　deflation　approach　are　employed　to　train　the　separating　matrix.　Then,　the　enhanced　contrast　function　is　therefore　maximized　and　the　kurtosis-based　cICA　algorithm　is　obtained.　After　that,　the　source　contribution　is　quantitatively　calculated　by　the　reduced　energy　of　the　mixed　signals　in　each　extraction:　the　reduction　of　the　energy　in　mixed　signals　corresponds　to　the　contribution　of　the　extracted　IC.　The　correspondence　relationship　between　the　ICs　and　source　signals　can　be　obtained　by　prior　knowledge.　Finally,　the　effectiveness　of　the　proposed　algorithm　is　verified　by　numerical　simulation　and　experiments.　The　results　show　that　the　proposed　method　has　high　accuracy　in　separating　sources　and　quantitatively　calculating　the　source　contribution.