Modelling of Energy Recovery in Electric Vehicles for Various Braking Scenarios on Changing Road Surfaces

Abstract

Due to great braking energy losses caused by traffic jams, changing velocity, and frequent start-stop modes, recovery of braking energy has become a top priority. In this paper, the universal braking system is described that operates at various driving scenarios including smooth braking and emergency antilock braking on different road surfaces and integrates both the friction and the electric braking strengths. The vehicle model reflects multiple factors, such as air resistance, road slope, and variable friction. The refined tire model recognizes changing road surfaces at different velocities. In the motor and battery model, the state of charge and electric current/voltage restrictions of the hybrid energy storage are taken into account. Braking torque generated by the Sugeno's fuzzy logic controller established in the Simulink environment is allocated between friction and electric brakes. Often cited torque oscillations at low vehicle velocities have found their description, being reduced and evenly distributed throughout the braking process with the help of torque stabilisation loop. The outcomes of this study can be considered in the design of braking systems for electric vehicles with superior energy recovery capacity.

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

“…Electrical current recharges the HES block from the EB while the pressure signal adjusts the FB. The physics-based vehicle model PBEV was established in [7] based on experimental data that describe the interacted single-wheel rotational properties and the EV physical features. It is made up of an adjustable electrical drive implementing regenerative EB, a friction drive integrated with FB, and a module simulating the gear and vehicle inertia.…”

“…The torque allocation module TA algorithmically distributes actuating braking torque T* generated on the MRC output between the front and rear wheels in a fixed ratio [25] and allocates it between FB and EB based on real-time SOC, voltage, and permissible EB current. In [7], an appropriate torque allocation algorithm is offered. In order to keep the HES battery and ultracapacitor within their safe operating areas, this algorithm checks if electrical current and actuating motor torque meet the real-time HES restrictions.…”

“…On the other side, very little researches are published about energy saving in intensive antilock braking systems (ABS), such as [6]. Even less attention is paid to energy recycling solutions that might be so universal that equally succeed in both braking scenarios [7].…”

“…However, there exist diverse views on how to apply the obtained friction-slip estimates to ensure both the requested stopping rate and maximal energy recovery taking into account that the friction peak optimality for a given road surface is variable. For example, in [7] the highest friction is developed at the slip level of about 12% on the dry road and at 5% on the icy road. On the contrary, in [12] the biggest friction appears at 20% slip on the dry road and at 30% on the icy road.…”

“…To exclude the above vagueness, in [7] the torque gradient control method is designed, where the derivative λ d dT of the application torque T with respect to slip λ is used as a control feedback instead of slip applied in conventional intelligent braking ABS, such as [12]. The main benefit of this method is that it does not require any tire model for implementation.…”