Model-Based Range Prediction for Electric Cars and Trucks under Real-World Conditions

The further development of electric mobility requires major scientific efforts to obtain reliable data for vehicle and drive development. Practical experience has repeatedly shown that sheets do not contain realistic consumption range figures. Since the fear low is a significant obstacle acceptance mobility, database can provide developers with additional insights create confidence among users. This study presents detailed, yet easy-to-implement modular physical model both passenger commercial battery vehicles. takes consumption-relevant parameters, such as seasonal influences, terrain character, driving behavior, into account. Without any posteriori parameter adjustments, an excellent agreement known field other experimental observations achieved. validation conveys much credibility predictions regarding real-world impact on energy cruising in standardized cycles. Some conclusions, almost impossible experimentally, are winter conditions hilly each reduce by 7–9%, aggressive reduces up 20%. quantitative results also reveal important contributions recuperation rolling resistance towards overall budget.