A non-convex control allocation strategy as energy-efficient torque distributors for on-road and off-road vehicles

A Vehicle with multiple drivetrains, like a hybrid electric one, is an over-actuated system that means there is an infinite number of combinations of torques that individual drivetrains can supply to provide a given total torque demand. Energy efficiency is considered as the secondary objective to determine the optimum solution among these feasible combinations. The resulting optimisation problem, which is nonlinear due to the multimodal operation of electric machines, must be solved quickly to comply with the stability requirements of the vehicle dynamics. A theorem is developed for the first time to formulate and parametrically solve the energyefficient torque distribution problem of a vehicle with multiple different drivetrains. The parametric solution is deployable on an ordinary electronic control unit (ECU) as a small-size lookup table that makes it significantly fast in operation. The fuel-economy of combustion engines, load transformations due to longitudinal and lateral accelerations, and traction efficiency of the off-road conditions are integrated into the developed theorem. Simulation results illustrate the effectiveness of the provided optimal strategy as torque distributors of on-road and off-road electrified vehicles with multiple different drivetrains

Machine learning-based prediction and optimisation system for laser shock peening

Laser shock peening (LSP) as a surface treatment technique can improve the fatigue life and corrosion resistance of metallic materials by introducing significant compressive residual stresses near the surface. However, LSP-induced residual stresses are known to be dependent on a multitude of factors, such as laser process variables (spot size, pulse width and energy), component geometry, material properties and the peening sequence. In this study, an intelligent system based on machine learning was developed that can predict the residual stress distribution induced by LSP. The system can also be applied to “reverse-optimise” the process parameters. The prediction system was developed using residual stress data derived from incremental hole drilling. We used artificial neural networks (ANNs) within a Bayesian framework to develop a robust prediction model validated using a comprehensive set of case studies. We also studied the relative importance of the LSP process parameters using Garson’s algorithm and parametric studies to understand the response of the residual stresses in laser peening systems as a function of different process variables. Furthermore, this study critically evaluates the developed machine learning models while demonstrating the potential benefits of implementing an intelligent system in prediction and optimisation strategies of the laser shock peening process

Short term traffic congestion forecasting using hybrid metaheuristics and rule based methods a comparative study

In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and 30 min. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application. Document type: Part of book or chapter of boo

Adaptive regenerative braking for electric vehicles with an electric motor at the front axle using the state dependent Riccati equation control technique

In this paper a novel adaptive regenerative braking control concept for electric vehicles with an electric motor at the front axle is presented. It is well known that the "phased" type regenerative braking systems of category B maximize the amount of regenerative energy during braking. However, there is an increased risk of maneuvering capability loss especially during cornering. An integrated braking controller which determines - in a single step - the desired yaw moment and allocates the braking demand between hydraulic brakes and electric motor during cornering is designed using the State Dependent Riccati Equation (SDRE) method. A unique method for deriving the State Dependent Coefficient (SDC) formulation of the system dynamics is proposed. Soft constraints are included in the state dynamics while an augmented penalty approach is followed to handle hard constraints. The performance of the controller has been evaluated for different combined cornering-braking scenarios using simulations in a Matlab/Simulink environment. For this an eight degrees of freedom (DOF) nonlinear vehicle model has been utilized. The numerical results show that the controller is able to optimize (locally) the amount of regenerative braking energy while respecting system's constraints such as tire force saturation, vehicle yaw rate and slip angle errors