Management Strategy Based on Genetic Algorithm Optimization for PHEV

Zhang, Yu; Meng, Dawei; Zhou, Ming; Lu, Dan

doi:10.14257/ijca.2014.7.11.37

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Compared with the unoptimized FLRCS, the braking energy recovery rate was improved, and the fuel consumption and emissions were reduced. In [52], an FLRCS was constructed based on GA optimization. The simulation experiments showed that every kind of gas emission was obviously reduced by 12-47% in an FLRCS based on GA optimization compared to strategy based on the RCS.…”

Section: Fuzzy Logic Rule-based Control Strategymentioning

confidence: 99%

A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles

et al. 2020

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The energy management strategy (EMS) and control algorithm of a hybrid electric vehicle (HEV) directly determine its energy efficiency, control effect, and system reliability. For a certain configuration of an HEV powertrain, the challenge is to develop an efficient EMS and an appropriate control algorithm to satisfy a variety of development objectives while not reducing vehicle performance. In this research, a comprehensive, multi-level classification for HEVs is introduced in detail from the aspects of the degree of hybridization (DoH), the position of the motor, the components and configurations of the powertrain, and whether or not the HEV is charged by external power. The principle and research status of EMSs for each type of HEV are summarized and reviewed. Additionally, the EMSs and control algorithms of HEVs are compared and analyzed from the perspectives of characteristics, applications, real-time abilities, and historical development. Finally, some discussions about potential directions and challenges for future research on the energy management systems of HEVs are presented. This review is expected to bring contribution to the development of efficient, intelligent, and advanced EMSs for future HEV energy management systems.

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Section: Fuzzy Logic Rule-based Control Strategymentioning

confidence: 99%

A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles

et al. 2020

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“…First, to achieve the real-time speed trajectory on-line, a speed prediction model is built using a recurrent neural network (RNN) [36][37][38]. Specifically, the hybrid optimization method of the genetic algorithm (GA) [39,40] and particle swarm optimization algorithm (PSOA) [41] are utilized to optimize the initial parameters of the prediction model to enhance the speed prediction precision of the model. Then, an ECMS-MPC is proposed.…”

Section: Introductionmentioning

confidence: 99%

An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks

et al. 2023

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This paper presents an equivalent consumption minimization strategy (ECMS) based on model predictive control for series hybrid electric mine trucks (SHE-MTs), the objective of which is to minimize fuel consumption. Two critical works are presented to achieve the goal. Firstly, to gain the real-time speed trajectory on-line, a speed prediction model is established by utilizing a recurrent neural network (RNN). Specifically, a hybrid optimization algorithm based on the genetic algorithm (GA) and the particle swarm optimization algorithm (PSOA) is used to enhance the prediction precision of the speed prediction model. Then, on this basis, an ECMS based on MPC (ECMS-MPC) is proposed. In this process, to improve the real-time and working condition adaptability of the ECMS-MPC, the power-optimal fuel consumption mapping model of the range extender is established, and the equivalent factor (EF) is real-time adjusted by means of the PSOA. Finally, taking a cement mining road as the research object, the proposed strategy is simulated with the collected actual vehicle data. The experimental results indicate that the prediction precision of the proposed speed prediction model is over 98%, realizing on-line speed prediction effectively. Furthermore, compared to the existing real-time EMSs, its fuel-saving rate had an increase of more than 13%. This indicates that the designed ECMS-MPC is able to offer a novel and effective method for the on-line energy management of the SHE-MTs.

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“…This method has slower convergence speed, lower the probability of the global value. Non-gradient based algorithm could calculate the global optimal solution without the gradient information of the objective function [7][8][9]. GA has the stronger ability of global searching quickly and parallel computing, so genetic algorithm is suitable for solving the optimization problem of HEV [10][11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Management for Series-Parallel Hybrid Electric City Bus Based on Improved Genetic Algorithm

Zhang

Zhao

Huang

et al. 2020

mech

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This paper aims at proposing an efficient energy management strategy of the series-parallel hybrid electric bus (SPHEB) by using improved genetic algorithm. Firstly, the energy management strategy based on the logical threshold value is developed. The simulation model considering the vehicle dynamic performance is established by the combination of Matlab and Cruise software. Then, an improved genetic algorithm based on adaptive crossover probability and mutation probability is proposed to solve local convergence and premature convergence. Eventually, Chinese typical city bus driving cycle and the composite driving cycle are considered to show the effectiveness of the proposed energy management strategy in terms of the fuel economy. The results indicate that the fuel consumption are improved by 5.85% and 5.01% respectively, and the parameters obtained by optimizing for the composite driving cycle are more adaptable to the driving conditions and have better economic performance in all driving scenarios.

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Management Strategy Based on Genetic Algorithm Optimization for PHEV

Abstract: Aiming at the refitted HAFEI hybrid electric vehicle (HEV)

Cited by 8 publications

References 10 publications

A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles

A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles

An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks

Optimal Energy Management for Series-Parallel Hybrid Electric City Bus Based on Improved Genetic Algorithm

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