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Electric vehicle (EV) is a worldwide researching focus due to its environmental friendliness, but the inaccurate Remaining Driving Range (RDR) estimation hinders the EVs' popularity, and an accurate determination of the battery Residual Usable Energy (RUE) is the key factor to obtain a precise RDR value. A common RUE estimation method is based on State-of-Charge (SOC) estimation, in which the RUE is proportionally related to the current SOC. However, the battery voltage varies significantly under real-world conditions, and the traditional method results in certain estimation errors. An adaptive RUE prediction method (AEP) is introduced in this paper, in which the dynamic voltage is predicted based on the future discharge profile and a battery model, while the RUE is then calculated by the predicted voltage and current sequences.

In order to reduce driver's anxiety about range and energy, a direct and effective approach is to offer the remaining driving range based on the vehicle's states. Consequently, the estimation accuracy of the battery's remaining energy is very important. This paper introduces a experiment-based model for predicting the remaining energy, which considers many factors, such as current, temperature, difference between battery cells, and so on. This approach ensures the accuracy of the remaining driving range. Finally the method is validated through the environment space test. Validation results show that this method can offer exact remaining energy, which reduces the estimation error of the remaining range greatly.