IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. XX, NO. XX, XXX 2019 1 Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles Donkyu Baek, Member, IEEE, Yukai Chen, Member, IEEE, Alberto Bocca, Member, IEEE, Lorenzo Bottaccioli, Member, IEEE, Santa Di Cataldo, Member, IEEE, Valentina Gatteschi, Member, IEEE, Daniele Jahier Pagliari, Member, IEEE, Edoardo Patti, Member, IEEE, Gianvito Urgese, Member, IEEE, Naehyuck Chang, Fellow, IEEE Alberto Macii, Senior Member, IEEE, Enrico Macii*, Fellow, IEEE Paolo Montuschi, Fellow, IEEE, Massimo Poncino, Fellow, IEEE Abstract—The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user’s attitude towards them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one third of potential European users are deterred by this problem when considering the move to an EV. Similar consideration applies to aerial EVs for commercial use, where a careful planning of the flying range is essential to guarantee the service but also to avoid the loss of the EVs due to charge depletion during the flight. Therefore, route planning for EVs for different purposes (range estimation, route optimization) and/or application scenarios (ter- restrial, aerial EVs) is an essential element to foster the acceptance of EVs as a replacement of traditional vehicles. One essential element to enable such accurate planning is an accurate model of the actual power consumption. While very elaborate models for the electrical motors of EVs do exist, the motor power does not perfectly match the power drawn from the battery because of battery non-idealities. In this work we propose a general methodology that allows to predict and/or optimize the operation range of EVs, by allowing different accuracy/complexity tradeoffs for the models describing the route, the vehicle and the battery, and taking into account the decoupling between motor and battery power. We demonstrate our method on two use cases. The first one is a traditional driving range prediction for a terrestrial EV; the second one concerns an unmanned aerial vehicle, for which the methodology will be used to determine the energy-optimal flying speed for a set of parcel delivery tasks. Keywords—State of charge, Battery non-ideality, Energy-efficient scheduling, Electric vehicles, Operation range estimation I. I NTRODUCTION In the last few years, Electric Vehicles (EVs) have shifted from absolute novelty to ordinary in most sectors of public and private transportation. Thanks to government subsidies and increased environmental awareness of the people, the market demand for electric cars is rapidly growing, in addition to small vehicles such as electric hover-boards, skateboards and bikes. Unmanned aerial vehicles (UAVs) or drones are also becoming more and more popular, as a large number of logistics companies such as UPS, DHL and Amazon are heavily investing on drone package delivery. Nonetheless, the reduced energy storage capacity of the batteries translates Manuscript received XXX, XX, 2019; revised XXX, XX, 2019. *Corresponding author: E. Macii (email: enrico.macii@polito.it). into range limitation problems for EVs, acting as a barrier to widespread adoption compared to traditional vehicles based on internal combustion engines (ICEs). While accurately predicting the range of operations for ICEs has never been considered a significant problem, because re-fueling is usually fast and readily available but for very peculiar geographical contexts, range prediction for battery- operated vehicles (driving or flying range for terrestrial and aerial vehicles, respectively) is receiving ever-growing atten- tion. The reason of this interest is to be found in the increased diffusion and critical role of EVs in various application do- mains (personal and commercial transportation, surveillance, etc.) as well as in the technical and logistic difficulties involved with the re-charging. Compared to traditional ICEs, range estimation prediction is a very challenging multi-factorial problem, involving a large number of variables that are not always easy to estimate. Besides the technical characteristics of the vehicle, for on- road EVs (e.g. electric cars) the factors that need to be taken into account include road topology and grade, speed, acceleration/deceleration patterns, use of the in-board electric devices (e.g. A/C) as well as driving style (e.g. normal vs aggressive). For aerial EVs (e.g. drones), payload and delivery task characteristics (e.g. take-off, flying and landing speeds). On the other hand, additional challenges are posed by main- taining the range estimation computationally light, so that it can be easily executed by on-board computers. To achieve this purpose, some of the aforementioned factors are either simplified or neglected in the standard practice, imposing an accuracy/complexity trade-off. As a matter of fact, most of the available range estimators over-simplify the problem by considering in their computation the electrical energy/power drawn by the motor, which does not have a perfect 1:1 match with the energy/power that is actually drawn by the battery. The reason of this mismatch is mainly two-fold: (i) the power delivered by the battery is not constant, as it depends on its current state of charge (SOC). More specifically, the lower the SOC, the lower the efficiency of the battery. Hence, a partially charged battery will be depleted more than a fully-charged one for the same task and under the same conditions. On top of that, the efficiency drop is not linear [1]. (ii) the conversion process that delivers power from the battery to the motor is not ideal. The former aspect is particularly relevant, as it suggests that summing up