Prediction of Human Error Rates in Electric Vehicle Charging Scheduling Based on the Improved CREAM Extended Method

With the increasing popularity of electric vehicles and charging stations, and the gradual improvement of charging equipment and strategies, human factors have become a significant influence on the failure of charging tasks, affecting the scheduling of electric vehicles. To effectively assess the reliability of human factors in the charging tasks of electric vehicle scheduling, this study combines the Cognitive Reliability and Error Analysis Method (CREAM) with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to weight Common Performance Conditions (CPC). An improved formula for the total weighting factor of human error rate is used to quantitatively predict the human error rate. This study provides a reference for future quantitative studies on driver error rates and is of great significance for improving the success rate of electric vehicle charging scheduling.