Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm

Abstract

Abstract:The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs) owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

“…In [37], a hybrid algorithm combining GA with SA was applied to simultaneously optimize powertrain and control parameters, resulting in a better convergence speed and offering a global searching ability to obtain the best comprehensive performance for a plug-in hybrid electric bus (PHEB). In order to have good real-time performance, the direct algorithm has been used to optimize extracted key parameters globally due to its low computational burden and rapid convergence [38].…”

A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

“…Because the calculating quantity is large and complex, the optimization process cannot be carried out online. The representative strategies mainly include dynamic programming (DP) based strategy [5,6], Pontryagin's minimum principle (PMP) based strategy [7,8], Quadratic programming (QP) based strategy [9,10], divide rectangle (DIRECT) algorithm based strategy [11][12][13], and convex optimization based strategy [14]. The latter is aimed at achieving optimal energy management in instantaneous state, which is not restricted by the specific driving cycle.…”

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

“…A genetic algorithm with simulated annealing is proposed in [12] to balance between economy and dynamic performance. The DIRECT algorithm global optimization method has been used for calibrating the parameters of the vehicle EMS from the perspective of fuel economy [13]. Compared with the mentioned optimization algorithms, simulated annealing particle swarm optimization (SA-PSO) has the advantages of achieving a global optimal solution [14].…”