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Abstract
In this paper, an improved fuel consumption and emissions control strategy based on a mathematical and heuristic approach is presented to optimize Parallel Hybrid Electric Vehicles (HEVs). The well-known Sequential Quadratic Programming mathematical method (SQP-Hessian approach) presents some limitations to achieve fuel consumption and emissions control optimization, as it is not able to find the global minimum, and it generally shows efficient results in local exploitation searches. The usage of a combined Modified Artificial Bee Colony algorithm (MABC) with the SQP approach is proposed in this work to obtain better optimal solutions and overcome these limitations. The optimization is performed with boundary conditions, considering that the optimized vehicle performance has to satisfy Partnership for a New Generation of Vehicles (PNGV) constraints. The weighting factor of the vehicle’s performance parameters in the objective function is varied, and optimization is carried out for two different driving cycles, namely Federal Test Procedure (FTP) and Economic commission Europe—Extra Urban Driving Cycle (ECE-EUDC), using the MABC and MABC with SQP approaches. The MABC with SQP approach shows better performance in terms of fuel consumption and emissions than the pure heuristic approach for the considered vehicle with similar boundary conditions. Moreover, it does not present significant penalties for final battery charging and it offers an optimized size of the key vehicle’s components for different driving cycles.
Highlights
Nowadays, the modern ways are initialized to minimize the impact on natural resources and to increase the dependency over the non-renewable resources, due to the increasing fuel prices and environmental issues [1]
The results show that emissions andlocal still with an for improved fuel economy compared to the initial design, aswith shown in the optimal solution compared to the other individual heuristic approach
The design of a parallel Hybrid Electric Vehicles (HEVs) involves a number of variables that must be optimized for better fuel economy and vehicle performance
Summary
The modern ways are initialized to minimize the impact on natural resources and to increase the dependency over the non-renewable resources, due to the increasing fuel prices and environmental issues [1]. This paper studies the optimization of parameters of an electric assist control strategy for a parallel HEV. A Sequential Quadratic Programming (SQP) mathematical method for a parallel HEV has been developed with a Modified Artificial Bee Colony algorithm (MABC) to minimize the instantaneous fuel consumption of the vehicle. Results show that the MABC with SQP approach shows better performance in terms of fuel consumption and emission than the pure heuristic approach for the considered vehicle with similar boundary conditions. This research paper can be summarized as follows
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