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

Abstract

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 3000fold reduction in computational time.Index Terms-alternating direction method of multipliers, automotive control, energy management, optimization algorithms.

A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization

“…where is the driver torque demand; , and are the EM control set points, the clutch control set points and the engine control set points respectively; and are constant values of the EM torque time delay and the clutch friction torque time delay, respectively; is related to the engine speed and the number of cylinders as follows: (8) Thus, the driveline model can be expressed in the form of a continuous state-space with states, manipulated variables and disturbance variables shown in:…”

“…When driving a hybrid vehicle, the ICE may restart frequently. For example, when the state-of-charge is predicted to be insufficient [8] or the temperature of the catalyst component is about to decrease and lead to a reduction in catalytic capacity [9], the ICE needs to be restarted. If the vehicle decelerates in low gear when the ICE is required to be started, the MoI of the car can be used to start the engine instead of using the starter.…”

Power Allocation Strategy Based on Decentralized Convex Optimization in Modular Fuel Cell Systems for Vehicular Applications

“…Moreover, the additional degrees of freedom in the MESs make the centralized algorithms substantially complicated and time-consuming to be solved. In this respect, some papers have focused on the distributed optimization algorithms to solve the PAS optimization problems [30][31][32][33][34]. In [30], a projected interior point method is proposed under the framework of model predictive control (MPC) to solve the power allocation problem and concluded that this strategy is faster than CVX tool, which is a general-purpose convex optimization software.…”

“…It is concluded that this approach is much faster than interior point or active set methods. In [33], a PAS for a hybrid electric vehicle is proposed based on ADMM and concluded that this strategy can achieve up to 90% of fuel saving obtained by dynamic programming (DP) while it is 3000 times faster than DP. In [34], a distributed optimization approach is put forward to solve the PAS of a hybrid vehicle.…”

“…The comparison of this distributed algorithm with a centralized convex optimization problem shows that the proposed algorithm can result in the same fuel economy as the centralized method while its computational time is declined up to 1825 times. Although the discussed papers in [30][31][32][33][34] have improved the PAS formulation to a further step regarding the accuracy and computational time reduction, they are not still fully decentralized, and are sensitive to the occurrence of malfunction in their systems. In [35,36], a decentralized approach based on non-cooperative game theory is proposed to formulate the PAS in a multi-source hybrid vehicle.…”