Estimation of a battery electric vehicle output power and remaining driving range under subfreezing conditions

This manuscript proposes a hybrid machine learning approach to estimate the battery output power and remaining driving range in a battery electric vehicle (BEV). These two metrics are among the most significant factors in the market penetration of BEVs, specifically in countries with harsh winters. The proposed hybrid method comprises a recurrent dynamic network, nonlinear autoregressive with exogenous inputs (NARX), and a recursive algorithm, Kalman filter (KF). The NARX neural network utilizes the information from the weather, the vehicle's speed, and road conditions to predict the battery output power, and the remaining driving range. KF, unlike the other similar studies, is employed to account for the dynamic changes in different road conditions by providing the vehicle rolling resistance and aerodynamic drag coefficient estimates to the network. To validate the performance of the proposed method, several driving tests under different ambient conditions are conducted using a real BEV (Kia Soul 2017), and the performance of the suggested approach is compared with a conventional model-based method. The comparison between the measured battery output power and the estimated one shows that the proposed hybrid method provides more accurate estimates than a model-based approach with over 50 % of percentage change in terms of root mean squared error. Furthermore, through a systemic network optimization, it is shown that the data from two trips (out of 14 trips) are sufficient to successfully predict the real-time battery output power, energy consumption, and the remaining driving range in different weather conditions, including winter. Therefore, the proposed NARX network is particularly suitable to circumvent the range anxiety issues and hence predict the remaining range better than the traditional vehicle dynamics-based approach.

Automated summary

This study proposes a hybrid machine learning approach to estimate the battery output power and remaining driving range of a battery electric vehicle, which has been demonstrated to provide more accurate predictions compared to traditional model-based methods through real-world testing.