Implementing　real-time　and　onboard　fault　diagnosis　on　electric　vehicles　can　effectively　avoid　potential　dangers.　However,　the　low　calculating　ability　and　limited　storage　capacity　of　electric　vehicles　hamper　the　development　of　real-time　and　onboard　fault　diagnosis.　To　address　the　issue,　combining　neural　network　and　fuzzy　logic,　we　propose　a　low　complexity　onboard　vehicle　fault　diagnosis　method　to　monitor　the　vehicle　status　and　give　early　warning　of　accidents.　In　twelve　months,　we　first　utilize　three　electric　vehicles　and　collect　6.　52GB　real　data　related　to　vehicle　components.　Motivated　by　those　data,　we　conducted　an　in-depth　research　on　the　major　vehicle　faults,　and　divided　them　into　four　types　which　are　no　fault,　battery　fault,　sensor　fault,　and　module　fault.　Furthermore,　we　propose　a　BP　neural　network　based　multiple　training　method　to　define　the　correlation　between　data　types　and　fault　types.　Then,　applying　the　correlation　and　data,　a　fuzzy　logic　based　classification　method　is　proposed　to　evaluate　the　vehicle　status　and　give　early　warning.　Finally,　a　comprehensive　simulation　is　conducted,　which　indicates　that　the　accuracy　is　88%.　©　2020　IEEE.