Fast Dual-Loop Nonlinear Receding Horizon Control for Energy Management in Hybrid Electric Vehicles

Abstract

This paper proposes a receding horizon optimization strategy for the problem of energy management in plugin hybrid electric vehicles. The approach employs a dual loop Model Predictive Control (MPC) strategy. An inner feedback loop addresses the problem of optimally tracking a given reference trajectory for the battery state of energy over a short future horizon using knowledge of the predicted driving cycle. An outer feedback loop generates the battery state of energy reference trajectory by solving approximately the optimal energy management problem for the entire driving cycle. The receding horizon optimization problems associated with both inner and outer loops are solved using a specialized projected Newton method. The controller is compared with existing approaches based on Pontryagin's Minimum Principle and the effects of imprecise knowledge of the future driving cycle are discussed. The paper contains a detailed simulation study: first, this assesses the optimality of the associated uncertainty-free approach and its computational load. Secondly, the effects of imprecise knowledge of the future driving cycle are illustrated.

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

“…This paper extends our earlier work on the PHEV energy management problem. In [13] we demonstrated a dual-loop MPC framework with a projected-Newton method, but only considered a terminal state of charge constraint and ignored engine switching. In [14] we proved that a formulation including general state of charge limits is convex when stated as a function of the battery power, and proposed an ADMM algorithm for its solution, but still did not consider engine switching.…”

Parallel ADMM for robust quadratic optimal resource allocation problems

“…We consider a parallel hybrid architecture [1], [2] in which the propulsive power (y) demanded from the vehicle's powertrain is provided by the sum of power from the combustion engine (x (1) ) and power from the electric motor (x (2) ). Independent samples, {y (j) k } j∈Nq,k∈Nn , of the future demand sequence are assumed to be available, and the power balance…”

“…No constraint is applied to total fuel usage and no cost associated with electrical energy usage (i.e. g (1,j) k = 0 and f (2,j) k = 0), so the optimization problem (2) becomes…”

“…The bounds x (i) k are determined by constraints on power and torque output of powertrain components (see e.g. [2]); for example engine braking (x…”

“…An important class of control problems can be formulated as optimal resource allocation problems with separable convex cost functions, separable convex constraints and linear coupling constraints. Examples include energy management in hybrid electric vehicles [1], [2], optimal power flow and economic dispatch problems [3], portfolio selection [4], and diverse problems in manufacturing, production planning and finance [5]. The computation required to solve these problems grows with the length of the horizon over which performance is optimized, and the rate of growth becomes more pronounced for scenario programming formulations [6] that provide robustness to demand uncertainty.…”