Real-time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

With the objective of reducing fuel consumption, this paper presents real-time predictive energy management of hybrid electric heavy vehicles. We propose an optimal control strategy that determines the power split between different vehicle power sources and brakes. Based on the model predictive control (MPC) and sequential programming, the optimal trajectories of the vehicle velocity and battery state of charge are found for upcoming horizons with a length of 5-20 km. Then, acceleration and brake pedal positions together with the battery usage are regulated to follow the requested speed and state of charge that is verified using a vehicle plant model. The main contribution of this paper is the development of a sequential linear program for predictive energy management that is faster and simpler than sequential quadratic programming in tested solvers and gives trajectories that are very close to the best trajectories found by nonlinear programming. The performance of the method is also compared to two different sequential quadratic programs

INCORPORATING DRIVER’S BEHAVIOR INTO PREDICTIVE POWERTRAIN ENERGY MANAGEMENT FOR A POWER-SPLIT HYBRID ELECTRIC VEHICLE

The goal of this series of research is to advance hybrid electric vehicle (HEV) energy management by incorporating driver’s driving behavior and driving cycle information. To reduce HEV fuel consumption, the objectives of this research are divided into the following three parts. The first part of the research investigates the impact of driver’s behavior on the overall fuel efficiency of a hybrid electric vehicle and the energy efficiency of individual powertrain components under various driving cycles. Between the sticker number fuel economy and actual fuel economy, it is well known that a noticeable difference occur when a driver drives aggressively. To simulate aggressive driving, the input driving cycles are scaled up from the baseline driving cycles to higher levels of acceleration/deceleration. The simulation study is conducted using Autonomie®, a powertrain simulation and analysis software. The performance of the major powertrain components is analyzed when the HEV is operated at different level of aggressiveness. In the second part of the study, the vehicle driving cycles affect the performance of a hybrid vehicle control strategy and the corresponding overall performance of the vehicle. By identifying the driving cycles of a vehicle, the HEV supervisor controller system will be dynamically adapt the control strategy to the changes of vehicle driving patterns. With pattern recognition method, a driving cycle is represented by feature vectors that are formed by a set of parameters to which the driving cycle is sensitive. To establish reference driving cycle database, the representative feature vectors of four federal driving cycles are generated using feature extraction method. The performance of the presented adaptive control strategy based on driving pattern recognition is evaluated using Autonomie. In the last part of the study, a predictive control method is developed and investigated for hybrid electric vehicle energy management in effort to improve HEV fuel economy. Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of a power-split HEV. The study compares the performance of MPC method and conventional rule-base control method. A parametric study is conducted to understand the influence of 3 weighting factors in MPC formulation on the performance of the vehicles

Fast Optimal Energy Management with Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles

In this paper we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy management problem considering both power split and engine on/off decisions. The solution of a convex relaxation of the problem is used to initialize the optimization, which is necessarily nonconvex, and whilst only local convergence can be guaranteed, it is demonstrated that the algorithm will terminate with the optimal power split for the given engine switching sequence. The algorithm is compared in simulation against a charge-depleting/charge-sustaining (CDCS) strategy and dynamic programming (DP) using real world driver behaviour data, and it is demonstrated that the algorithm achieves 90\% of the fuel savings obtained using DP with a 3000-fold reduction in computational time

Power Management of a Plug-in Hybrid Electric Vehicle Based on Cycle Energy Estimation

2012 Workshop on Engine and Powertrain Control,Simulation and ModelingThe International Federation of Automatic ControlRueil-Malmaison, France, October 23-25, 2012Plug-in Hybrid Electric Vehicles (PHEV) are being investigated in many research and development programs motivated by the urgent need for more fuel-efficient vehicles that produce fewer harmful emissions. There are many potential advantages of hybridization such as the improvement of transient power demand, the ability of regenerative braking and the opportunities for optimization of the vehicle efficiency. The coordination among the various power sources requires a high level of control in the vehicle. In order to solve the power management problem, the controller proposed in this work is divided into two levels: the upper one calculates the power that must be supplied by the engine at each moment taking into account the estimation of the energy that must be supplied by the powertrain until the end of the journey. The lower one manages the torque/speed set points for all the devices. Besides, the operation modes are changed according to some heuristic rules. Several simulation results are presented, showing that the proposed control strategy can provide good performance with low computational load