Electric Vehicles (EVs) emerged new kinds of applications that strongly depend upon the energy information such as identifying the optimal path towards the vehicle’s destination where the EV maximizes the recovered electrical power, displaying the energetic map that provides an overview about the required energy consumption on each lane, etc. The quality of these applications relies on the reliability of the vehicle-related information (e.g. location, energy consumption). EVs may provide wrong energy information due to sensors’ failure, selfish or malicious reasons. To this aim, a fuzzy-based energy consumption trust (ECOTRUST) model is proposed herein to evaluate the quality of energy information based on two fuzzy inference systems: Instant Energy Trust (IEN-Trust) and Total Energy Trust (TEN-Trust) systems. IEN-TRUST relies on a series of plausibility checks to evaluate the coherence between the reported energy information and other parameters (slope degree, speed and acceleration rate) while TEN-TRUST relies on the similarity between neighbouring vehicles. The performance of the ECOTRUST model is evaluated in terms of the system’s robustness and accuracy under different traffic intensities. We varied the traffic volume and the percentage of malicious vehicles and their behaviours. Results show that IEN-TRUST is resilient to false messages with/without the collusion attack. However, it is unable to deal with complex behaviours of malicious vehicles (e.g. on-off attack, bush telegraph). TEN-TRUST was proposed to deal with the latter issue. Simulation results show that it can accurately deal with complex behaviours in different traffic volumes.