## 1 Introduction
[12] analysed the variables involved in range estimation, and found that the battery SoC accounted for 54%, ambient temperature for 25%, driver behaviour for 10%, route conditions for 6%, and traffic for 5%. Since the actual SoC values can be considered as a direct function of battery capacity and energy consumed/regenerated, the SoC variation at the beginning of any driving experience appears to be the most important variable to be analysed. Consequently, two aspects should be taken into account regarding the real estimation of the SoC variation through time: battery state of health (SoH) and end of life (EoL). The real capacity available to the battery is significantly affected by the SoH [13], therefore the ageing process of the on-board energy storage system becomes a fundamental sub-variable to be controlled. In fact, as the usage level of a battery increases, the number of maximum life-cycles reduces because of the reduction in the available battery capacity. Moreover, the battery EoL is typically determined when the residual capacity is reduced by 20% of its initial value [14]. [PERSON] et al. [15] showed that the number of life-cycles of a lithium-ion battery varies according to different depth of discharge (DoD) values, dropping from 1,000,000 cycles with DoD equal to 3%, down to 3000 cycles with DoD equal to 100%. Thus, premature ageing of the battery can be avoided by excluding deep discharge cycles. The second most influential variable on range estimation is ambient temperature. High ambient temperatures can produce a reduction in battery nominal life-cycles [16; 17] due to thermal degradation, while low ambient temperatures (e.g., zero and sub-zero Celsius) might result in capacity losses because of low battery conductivity, which affects its performance [18]. Therefore, battery thermal management is considered as a major concern both for high and low ambient temperatures [12]. To this end, battery pack temperatures, in addition to the efficiency of the battery thermal management system (BTMS) thermal control [16; 19], are additional prominent aspects to be considered. Moreover, the effect of ambient temperatures defined in [12] also considers heating, ventilation and air conditioning (HVAC) system demands for the cabin conditioning. [PERSON] et al. [20] calculated the range reductions in the New European Driving Cycle (NEDC) under different environmental conditions and showed a drop between 17.2% and 37.1% in the remaining range during summer, versus a 17.1% to 54.1% reduction in winter. Similarly, [PERSON] and [PERSON] [21] found that the residual range of a Nissan Leaf changed from more than 150 km at 20 \({}^{\circ}\)C, to nearly 85 km at 0 \({}^{\circ}\)C, with a decrease of approximately 43% in the federal urban driving cycle (FUDC) and NEDC driving cycles. As a result, the main draining source beyond the traction power required by the battery of a BEV is represented by the HVAC system [22]. An assessment of the effects produced by each above-mentioned variable on a BEV cannot explain the real driving range estimation if the entire set of variables is not considered simultaneously. To this end, a model considering all the different variables including battery SoC variations, ambient temperatures, driver behaviour, route, as well as traffic conditions, has not yet been developed and validated. In the present paper, a model with the capability of estimating the real residual range of a L-7 BEV quadricycle is presented. As a first step, the vehicle model was developed and validated based upon real-world data. The battery SoC variations were estimated through the enhanced coulomb counting (ECC) method, considering the effects of the ambient temperature and battery ageing level. Driver behaviour, route and traffic conditions have been implicitly considered by using the experimental driving missions conducted with an L-7 electric vehicle in the H2020 STEVE project [23].