Estimating an Electric Vehicle’s “Distance to Empty” Using Both Past and Future Route Information

Abstract

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.

The Effect of Driving Style on Battery Electric Vehicle Range

“…The tests simulated driving on a daily basis. During the trials we assumed the following: − The season was common to all tests; − The air cooling was always turned off [17][18][19]; − The vehicle followed traffic regulations; − The vehicle accelerated adequately according to the traffic conditions and driving style of the other drivers; − The vehicle did not move while idling.…”

“…Due to the inaccuracies of the range estimation algorithm [17][18][19] and simulation models [20][21][22][23], an upgrade of the driving technique and DtE experiments should be considered. Moreover, the results of DtE experiments are not presented in the literature [24][25][26][27][28][29].…”

“…Studies focused on improving an electric vehicle's distance to empty (DtE) estimation. Studies [17][18][19] confirm that estimating the theoretical range of electric vehicles is inaccurate. Factors, such as driving style or heat pump operation in the heating mode, are not included in the estimation algorithm.…”

“…), the more difficult it is to predict future EC. Accurate results will be displayed when the future use is similar to its past use or applicable information about its future use is known beforehand [19]. Secondly, energy measurements can be made in strictly defined locations.…”

Battery electric vehicle energy consumption modelling for range estimation

“…The energy consumption is dependent on vehicle characteristics and some characteristics are influenced by the weather conditions (Neaimeh et al, 2013;Nordelöf et al, 2014). There are two methodologies for determining the energy consumption: building statistical models based on real world driving measurements (Shankar and Marco, 2013;De Cauwer et al, 2015;Rodgers et al, 2013) and creating vehicle models based on physics (Dhand and Pullen, 2013;Wu et al, 2015). Using real-world measurements can result in more realistic values for the energy consumption calculation, but it relies on a high number of real-world measurements and cannot be directly coupled with vehicle parameters and environmental conditions.…”