Battery Aging-Aware Online Optimal Control: An Energy Management System for Hybrid Electric Vehicles Supported by a Bio-Inspired Velocity Prediction

In this manuscript, we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed of the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances

Fast Planning and Tracking of Complex Autonomous Parking Maneuvers With Optimal Control and Pseudo-Neural Networks

This paper presents a framework to plan and execute autonomous parking maneuvers in complex parking scenarios. We formulate a minimum-time optimal control problem for trajectory planning, using an indirect optimal control approach. A novel smooth penalty function is devised for collision avoidance with optimal control, and an effective technique is adopted to compute an initial solution guess. The trajectory planning tasks are solved with low computational times, and a dense mesh is used to discretize the domain of the optimal control problems, resulting in accurate collision-free solutions. The planned parking maneuvers are tracked with an original pseudo-neural feedforward-feedback steering controller, which outperforms other techniques from the literature, and a feedback longitudinal controller, to drive a realistic 14-degree-of-freedom vehicle simulator. We validate the planning and tracking algorithms in challenging narrow parking scenarios, including reverse, parallel and angle parking, and unstructured environments. The framework robustness is assessed by changing the vehicle mass, the road adherence conditions, and by introducing measurement noise with realistic sensor models. A video of the trajectory planning and tracking results is available as supplementary material