Co-simulation of energy management strategy for hybrid electric vehicle in AVL InMotion

2020

Applied Sciences

Self Cite

Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.

Section: Introductionmentioning

confidence: 99%

A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition

2020

Applied Sciences

Self Cite

“…The parameter matching between the power components (including the engine, drive motor, and power battery, etc.) and the transmission system directly affects the overall performance of the vehicle's power, fuel consumption and emissions [5,[7][8][9][10]. Therefore, it is very important to study the parameter matching optimization of the powertrain system of HEVs.…”

Section: Introductionmentioning

confidence: 99%

Parameter Matching Optimization of a Powertrain System of Hybrid Electric Vehicles Based on Multi-Objective Optimization

Tang

et al. 2019

Electronics

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Aiming at problems of large computational complexity and poor reliability, a parameter matching optimization method of a powertrain system of hybrid electric vehicles based on multi-objective optimization is proposed in this paper. First, according to the vehicle basic parameters and performance indicators, the parameter ranges of different components were analyzed and calculated; then, with the weight coefficient method, the multi-objective optimization (MOO) problem of fuel consumption and emissions was transformed into a single-objective optimization problem; finally, the co-simulation of AVL Cruise and Matlab/Simulink was achieved to evaluate the effects of parameter matching through the objective function. The research results show that the proposed parameter matching optimization method for hybrid electric vehicles based on multi-objective optimization can significantly reduce fuel consumption and emissions of a vehicle simultaneously and thus provides an optimized vehicle configuration for energy management strategy research. The method proposed in this paper has a high application value in the optimization design of electric vehicles.

“…Hybrid electric vehicles (HEVs) combine the advantages of both traditional fuel vehicles and battery electric vehicles (BEVs), which include two or more energy sources and can work in coordination [1][2][3]. Under the current technical level and application conditions, HEVs are undoubtedly the most industrialized and market-oriented vehicle in the electric vehicle (EV) category, as a transitional product from traditional fuel vehicles to BEVs.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the complex structure of HEVs, the optimal energy distribution and control between the two or more energy sources are extremely challenging [1][2][3]. How to design an efficient energy management strategy (EMS) is one of the research hotspots in the field of vehicle control for HEVs.…”

Section: Introductionmentioning

confidence: 99%

Torque Coordination Control of Hybrid Electric Vehicles Based on Hybrid Dynamical System Theory

Wang

et al. 2019

Electronics

Self Cite

In order to reduce the vibration caused by mode switching of hybrid electric vehicles (HEVs) and achieve smooth mode switching, the hybrid input and output automation (HIOA) model of power control system of a parallel HEV is established based on the theory of hybrid dynamical system (HDS). Taking the switching from electric drive mode to hybrid drive mode for example, the torque coordination control is considered, and the performance is compared with the method without the torque coordination by using a rule-based control strategy. The simulation results in AVL Cruise show that, on the premise of ensuring the fuel economy and the emission, the mode switching process becomes smoother with smaller torque fluctuation and better driving comfort by considering the torque coordination.