An Intelligent estimation of Dynamic Vehicle mass for Electric Vehicle

Dynamic Vehicle mass is one of the most critical parameters in the automotive controls such as battery management, transmission shift scheduling, distance-to-empty predictions and most importantly, various active and passive safety systems. This work aims to find out Dynamic Vehicle mass for Electric Vehicles in a real time transient driving conditions. The work proposes a real-time approach in finding Dynamic vehicle mass where accumulated Energy based vehicle performance, an improvement to the vehicle dynamics equation, has been employed for consistent and accurate results. Factors affecting vehicle mass such as road grade, dynamic friction coefficient, driving pattern, wheel slip etc. have been considered for model optimization. Here recursive Bayesian state estimator has been used for finding vehicle mass as a constant state variable while time varying forgetting factors are used to nullify the impact of major losses. Algorithm is auto tuned using Machine Learning techniques to first find out stable driving conditions and subsequently go for model application to converge towards the end results. The performance of the proposed vehicle mass estimator is validated against several groups of payload in varying surrounding conditions. The results demonstrate that the output of the model is well within 92-95% accuracy in all such cases and consistent results are obtained for more than 90% of the test scenarios.