Estimating an Electric Vehicle's "Distance to Empty" Using Both Past and Future Route Information

• Main Authors: Wilhelm, Erik (Author), Frey, Daniel (Contributor), Rodgers, Lennon Patrick (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
• Format: Article
• Language: English
• Published: ASME International, 2018-11-20T18:41:54Z.
• Subjects: Article
• Online Access: Get fulltext

 An electric vehicle's Distance to Empty (DTE) is defined as the actual distance the vehicle can be driven before recharging is required. A real-time estimate is commonly displayed on the vehicle's electronic instrument cluster and is used by the driver to plan their route. It is proved in this paper that the challenge for any DTE estimation algorithm is to accurately predict the future energy consumption of a vehicle. Future energy can be predicted reliably if either (i) future energy consumption is sufficiently similar to the past or (ii) applicable information about the future is known beforehand. A stochastic simulation was used to show that the average energy use (Wh/km) measured over the past ∼300 km often does an adequate job at predicting future energy use. A conventional DTE algorithm assumes this condition by "blending" both a long- and short-term average of past energy use. However, significant changes in driving conditions (e.g. traffic or auxiliary energy use) for sustained periods of time can cause large errors in DTE estimates. This paper showed that DTE error can be reduced if those future changes are detected beforehand by obtaining route information from the driver. For example, if the driver provides their destination(s) beforehand, a navigation system could obtain route, traffic and weather information via the internet and use this information to improve the DTE estimate. A multivariate linear regression model was derived that adjusts a historical average of energy consumption based on estimated changes in speeds, traffic and temperature. This method utilizes the measuring ability of the vehicle and thus does not require complex physics-based models. The algorithm could be implemented as a cloud-based mobile phone application since it is computationally light and the model is fitted using historical driving data. Finally, the algorithms were compared using a stochastic vehicle simulation and it was shown that incorporating future route information can significantly reduce DTE error.