A comparative study of driver torque demand prediction methods

The performances of energy management systems or electric vehicles and hybrid electric vehicles are highly dependent on the forecast of future driver torque/power request sequence that affects vehicle efficiency and economy. Since the behaviour of the driver is challenging to model/predict by first-principles models, modern artificial intelligence algorithms would represent feasible methods for approaching this problem in real-world automotive systems. This work provides a comparative study and analysis of performances of different data-driven torque prediction strategies. The studied and compared torque demand prediction techniques are exponentially varying model, linear regression, shallow and deep neural networks, and least square support vector machine-based approaches. The prediction performance and computational cost of these techniques are evaluated and reported, and the possibility of exploiting these techniques in real-world scenarios is also discussed.