Energy Management in Plug-In Hybrid Electric Vehicles: Convex Optimization Algorithms for Model Predictive Control

Abstract

This paper details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric vehicles with nonlinear losses. A projected interior point method is proposed, where the size and complexity of the Newton step matrix inversion is reduced by applying inequality constraints on the control input as a projection, and its properties are demonstrated through simulation in comparison with an alternating direction method of multipliers (ADMM) algorithm, and general purpose convex optimization software CVX. It is found that the ADMM algorithm has favourable properties when a solution with modest accuracy is required, whereas the projected interior point method is favourable when high accuracy is required, and that both are significantly faster than CVX.Index Terms-alternating direction method of multipliers, energy management, interior point method, model predictive control, plug-in hybrid electric vehicles.

Optimal Power Allocation in Battery/Supercapacitor Electric Vehicles Using Convex Optimization

“…The work presented in this paper builds on the authors' previous work developing fast optimization algorithms for energy management in plug-in hybrid electric vehicles (PHEVs). A similar ADMM algorithm was proposed in [23] and subsequently developed in [24] and [25], but the structure of this problem is different and all of the work presented here is new.…”

“…A two-dimensional parameter search similar to that detailed in [24, §V-B] was used to optimize the parameters ρ 1 and ρ 3 , with ρ 2 = ρ 1 and ρ 4 = ρ 3 as these parameters correspond to constraints of similar magnitude. The chosen values, ρ 1 = 5×10 −5 and ρ 3 = 1×10 −8 , were used for all simulations (the results in [24] and [25] suggest that the ρ values are tuned to hardware characteristics, and fixed values perform well across a diversity of drive cycles).…”

Fuel efficiency optimization Techniques in Hybrid Vehicle

“…The rapid DP was then joined with PSO for testing the vehicle performance. In [36] a projected interior point method was proposed for solution of convex formulation of optimization problem coupled with nonlinear model predictive control of energy management in HEV. The theoretical framework for the proposed strategy was introduced and the global convergence was demonstrated by analyzing the backtracking line search method.…”

Co-Design of CVT-Based Electric Vehicles

“…An alternative is to use convex programming (CP) as a simultaneous approach, which is also a co-design optimization method. It finds optimal design parameters and control trajectories simultaneously without the need of checking optimality [14,15]. It ensures a unique optimum with a convex objective function and a convex feasible region.…”