This paper deals with the control system development for a hybrid energy storage system, consisting of a battery and a supercapacitor, for a through-the-road-parallel hybrid electric vehicle. One of the main advantages deriving from the coupling of a battery and a supercapacitor is the possibility of reducing battery ageing, in addition to energy efficiency improvements when the system operates in critical climate conditions. At the moment, no specific controller has been proposed with the aim of directly reducing battery wear. This paper presents a novel model predictive controller and a dynamic programming algorithm including a simplified battery ageing model in their formulations. The simulation results of the model predictive controller and dynamic programming algorithm are compared with the results deriving from a rule-based strategy. The rule-based controller achieves a 67% reduction of the root mean square values of battery current along a selection of driving cycles in comparison with the same vehicle equipped with battery only. In the same conditions the battery peak current is reduced by 38%. The model predictive controller and the dynamic programming algorithm further reduce the root mean square value by 6% and 10% respectively, whilst the peak values are additionally decreased by 17% and 45%

The continuous and precise modulation of the driving and braking torques of each wheel is considered the ultimate goal for controlling the performance of a vehicle in steady-state and transient conditions. To do so, dedicated torque-vectoring (TV) controllers that allow optimal wheel torque distribution under all possible driving conditions have to be developed. Commonly, vehicle TV controllers are based on a hierarchical approach, consisting of a high-level supervisory controller that evaluates a corrective yaw moment and a low-level controller that defines the individual wheel torque reference values. The problem of the optimal individual wheel torque distribution for a particular driving condition can be solved through an optimization-based control-allocation (CA) algorithm, which must rely on the appropriate selection of the objective function. With a newly developed offline optimization procedure, this paper assesses the performance of alternative objective functions for the optimal wheel torque distribution of a four-wheel-drive (4WD) fully electric vehicle. Results show that objective functions based on the minimum tire slip criterion provide better control performance than functions based on energy efficiency.

Electric vehicles (EVs) with four individually controlled drivetrains are over-actuated systems, and therefore, the total wheel torque and yaw moment demands can be realized through an infinite number of feasible wheel torque combinations. Hence, an energy-efficient torque distribution among the four drivetrains is crucial for reducing the drivetrain power losses and extending driving range. In this paper, the optimal torque distribution is formulated as the solution of a parametric optimization problem, depending on the vehicle speed. An analytical solution is provided for the case of equal drivetrains, under the experimentally confirmed hypothesis that the drivetrain power losses are strictly monotonically increasing with the torque demand. The easily implementable and computationally fast wheel torque distribution algorithm is validated by simulations and experiments on an EV demonstrator, along driving cycles and cornering maneuvers. The results show considerable energy savings compared to alternative torque distribution strategies.