Fuzzy Energy Management Optimization for a Parallel Hybrid Electric Vehicle using Chaotic Non-dominated sorting Genetic Algorithm

Liang, Junyi; Zhang, Jianlong; Zhang, Hu; Yin, Chengliang

doi:10.7305/automatika.2015.07.714

Cited by 13 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where r is the weight coefficient, and p = 8 and q = 40, 000 are the normalizing coefficients between the two indicators. The multi-objective genetic algorithm has been used to optimize a nonlinear problem in the vehicle field [25,26]. Especially, non-dominated sorting genetic algorithm II (NSGA-II) is claimed to have better performance compared with the other optimization methods.…”

Section: Multi-objective Optimization Of Dtsmentioning

confidence: 99%

Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

et al. 2020

View full text Add to dashboard Cite

In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time.

show abstract

Section: Multi-objective Optimization Of Dtsmentioning

confidence: 99%

Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Literature shows that engine and equivalent fuel consumption can be reduced by optimising the EMS. There are different types of energy management strategies used in HEVs, such as rule-based [13,14], instantaneous optimisation-based [15][16][17][18][19], learning-based EMS [20] and predictive EMS [21,22]. A gap in these strategies is that they do not incorporate mode selection because it leads to a challenging mixed-integer nonlinear optimisation problem.…”

Section: Introductionmentioning

confidence: 99%

Electrified Powertrain with Multiple Planetary Gears and Corresponding Energy Management Strategy

et al. 2021

View full text Add to dashboard Cite

Modern hybrid electric vehicles (HEVs) like the fourth generation of Toyota Prius incorporate multiple planetary gears (PG) to interconnect various power components. Previous studies reported that increasing the number of planetary gears from one to two reduces energy consumption. However, these studies did not compare one PG and two PGs topologies at their optimal operation. Moreover, the size of the powertrain components are not the same and hence the source of reduction in energy consumption is not clear. This paper investigates the effect of the number of planetary gears on energy consumption under optimal operation of the powertrain components. The powertrains with one and two PGs are considered and an optimal simultaneous torque distribution and mode selection strategy is proposed. The proposed energy management strategy (EMS) optimally distributes torque demands amongst the power components whilst also controlling clutches (i.e., mode selection). Results show that increasing from one to two PGs reduces energy consumption by 4%.

show abstract

“…In [27], the DP algorithm was used to optimize the performance of ISG hybrid electric vehicle, and combined with the optimization results, a more practical fuzzy controller was proposed. In [28][29][30], some intelligent algorithms, such as genetic algorithm and particle swarm optimization, were used to optimize the membership function or control rules of the fuzzy controller. In [31][32][33], the mixed-fuzzy control strategy was designed by using neural network, working condition identification, machine learning, and other intelligent techniques.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

Pei

Yan

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.

show abstract

Fuzzy Energy Management Optimization for a Parallel Hybrid Electric Vehicle using Chaotic Non-dominated sorting Genetic Algorithm

Cited by 13 publications

References 31 publications

Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

Electrified Powertrain with Multiple Planetary Gears and Corresponding Energy Management Strategy

A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

Contact Info

Product

Resources

About