Energy consumption forecast and charging demand alert based on operation condition clustering and control variable method

Travel anxiety of automobile owners has been aggravated because of the difficulty in accurately controlling the operation energy consumption and imperfection in charging infrastructure construction and other problems. Relying on the massive historical operation data of automobiles, it acquired the powerconsumption increasing coefficient of speed and temperature by means of clustering and control variable methods. Furthermore, the map Application Programming Interface (API) was invoked to obtain the path planning results thus realizing prediction on power consumption. The historical charging data of current automobile was used to build the mapping relations of the state of charge (SOC) and the state of energy (SOE). Combining with the prediction value of energy consumption it calculated the needed charge capacity and judge whether to issue the charging demand alert. Indicated by the application results, the proposed algorithm of energy-consumption forecast is more accurate than traditional average energy-consumption forecast algorithm. Accordingly, the charging demand alert function can effectively relive the travel anxiety of automobile owners.