Problem. The increasing number of electric vehicles necessitates effective diagnostic methods, as traditional approaches developed for internal combustion engine (ICE) vehicles are not always suitable for electric transport. One of the key diagnostic tools is OBD-II, which allows for obtaining information about the state of a vehicle's electrical systems. However, the accuracy and limitations of this method require detailed analysis. Objective. To explore the possibilities of using OBD-II for diagnosing electric vehicles by analyzing their functional potential, features of data reading and interpretation, and assessing the limitations of this method in comparison with other diagnostic approaches. Methodology. An analysis of the functional capabilities of OBD-II for diagnosing electric vehicles was conducted. An experimental assessment of the accuracy of data obtained using OBD-II in real-time was performed. Diagnostic parameters of the high-voltage battery, inverter, and electric motor were analyzed, as well as the efficiency of energy consumption and regenerative braking. Results. The study confirmed that OBD-II allows obtaining important diagnostic data about an electric vehicle, particularly regarding the state of the battery, electric motor, and other components. However, limitations were identified related to the incompleteness of certain parameters, especially concerning battery cell balancing. This can affect the accuracy of assessing the condition of the high-voltage battery and its remaining capacity. Originality. The article provides a comprehensive analysis of the application of OBD-II for monitoring electric vehicles, differing from traditional approaches focused on ICE vehicles. The possibilities of diagnosing major electrical systems in real-time are evaluated. Practical Value. The results may be useful for electric vehicle owners who wish to independently monitor the technical condition of their vehicles, as well as for auto service centers that use OBD-II in their practice. Integrating OBD-II with more accurate data processing algorithms can improve the efficiency of electric vehicle diagnostics.
Hnatov et al. (Wed,) studied this question.