Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles

Jiang, Xingyue; Hu, Jianjun; Jia, Meixia; Zheng, Yong

doi:10.3390/en11081933

Cited by 17 publications

(10 citation statements)

References 31 publications

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“…Therefore, the statistical conclusion shows that bond-breaking and atomic size effects are independent and substantial contributors to GB cohesion. To quantitatively explore the relative significance of each input variable for improving the prediction performance, the mean impact value (MIV) analysis was conducted using the similar method previously by Jiang et al [50] and Liu et al [51]. The MIV values for each input variable on each output variable are calculated and shown in Figure 5.…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

Wang

et al. 2020

Materials

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The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to uncover the physical mechanisms and design novel materials with enhanced properties. In this work, to accurately predict and uncover the key features in determining the strengthening energies, three machine learning methods were used to model and predict strengthening energies of solutes in different metallic GBs. In addition, 142 strengthening energies from previous density functional theory calculations served as our dataset to train three machine learning models: support vector machine (SVM) with linear kernel, SVM with radial basis function (RBF) kernel, and artificial neural network (ANN). Considering both the bond-breaking effect and atomic size effect, the nonlinear kernel based SVR model was found to perform the best with a correlation of r2 ~ 0.889. The size effect feature shows a significant improvement to prediction performance with respect to using bond-breaking effect only. Moreover, the mean impact value analysis was conducted to quantitatively explore the relative significance of each input feature for improving the effective prediction.

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Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

Wang

et al. 2020

Materials

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“…The performance requirement-indicator correlation matrix R is obtained by equation 12- (13). And the final weight coefficient of vehicle performance indicator v f is obtained by equation (14). After calculation and analysis, the final obtained HOQM of R-EEV based on market requirements is shown in Figure 4.…”

Section: Determination Of the Weight Coefficient Of Vehicle Technicalmentioning

confidence: 99%

“…The other focus is the optimization and analysis of vehicle performance indicators under a multi-objective. XY Jiang et al 14 carried out a multi-objective optimization (MOO) research on semi-active battery/supercapacitor energy storage system of electric vehicle. Q Zhu et al 15 used Advisor and Matlab simulation to simulate and analyze the powertrain of R-EEV.…”

Section: Introductionmentioning

confidence: 99%

Parameter matching and optimization for power system of range-extended electric vehicle based on requirements

Liu

Lei

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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For the research on crucial technologies of range-extended electric vehicle, the first problem to be solved is parameter matching and efficiency optimization for range-extended electric vehicle power and transmission system. Parameter matching and optimization of range-extended electric vehicle power and transmission system are multi-objective optimization problem. Evaluation and analysis of multi-objective optimization problem should be mutually independent and balanced. With the aim of guaranteeing vehicle’s comprehensive performance, a parameter matching and optimization method for range-extended electric vehicle power and transmission system is proposed in this paper. First, the house of quality model of range-extended electric vehicle is established to determine weight coefficient of vehicle performance indicator based on market requirements instead of experience. Based on co-simulation control model which is established in Matlab-Simulink and AVL-Cruise, 40 groups of orthogonal tests are performed, and the sensitivity of characteristic parameters is analyzed to explore the coupling law among vehicle performance indicators, so as to clarify the entry point for parameter matching and optimization. The simulation results show that the characteristic parameters not only have a significant influence but also have a coupling effect on the vehicle performance indicators. The analysis of variance shows that there is a limitation in optimal level combination of various factors only by range. Then, particle swarm optimization algorithm is selected to optimize the parameters of range-extended electric vehicle power and transmission system based on sensitivity analysis results obtained above. The study reveals that it is more efficient and reasonable to match the range-extended electric vehicle power and transmission system with a smaller battery capacity and a “medium-sized” auxiliary power unit which can achieve adequate dynamic performance, lower purchase cost, longer driving range and less energy consumption. Finally, a comparative simulation between the range-based analysis and particle swarm optimization-based analysis is conducted, the simulation results indicate that the optimized design parameters solution can significantly improve the technical indicators of the vehicle.

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“…The results indicated that the optimization results can be applied to multiple operating conditions and improve the vehicle fuel economy. Jiang et al [23] proposed an optimal methodology integrating system parameters and control strategies for pure electric vehicles (PEVs) with a hybrid energy storage system. This approach significantly improved the vehicle economy in urban and suburban conditions.…”

Section: Introductionmentioning

confidence: 99%

Parameter closed-loop optimization for pure electric vehicles: unified design of power system and control parameters

Zhang

Yang

Zhou³

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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To solve the problem of unreasonable vehicle parameters caused by unknown curb mass and open-loop power system and control strategy optimization in the development of pure electric vehicles, this paper presents a parameter closed-loop optimization algorithm (COA) via unified design of system and control parameters. First, a mass closed-loop algorithm (MCA) was adopted to optimize the parameters of power systems under an unknown curb mass and its convergence was proven. Next, with energy consumption being the index function, the torque distributions of the front and rear motors were optimized by dynamic programming (DP). Additionally, vehicle power system and control strategy parameter optimization were realized by combining the MCA, DP, and genetic algorithm. Finally, two comparative optimization algorithms which are the assumed curb mass optimization algorithm (AOA) and the torque equal ratio distribution optimization algorithm (TOA) were implemented to validate the proposed algorithm. The simulation results under China light-duty vehicle test cycle-passenger car (CLTC-P) and New European Driving Cycle (NEDC) operating conditions indicate that the proposed algorithm achieves minimum energy consumption compared with the competing optimization algorithms. The energy consumption resulting from the COA is reduced by 18.98% and 6.36%, compared with those of the TOA and AOA, respectively, under CLTC-P conditions. The energy consumption of the COA is 16.57% and 6.91% lower than those of the TOA and AOA, respectively, under NEDC conditions. This algorithm can optimize the curb mass, peak powers of the motors, mass of the motors, battery energy, battery mass, and torque distribution coefficient simultaneously, and can be applied to different operating conditions.

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Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles

Cited by 17 publications

References 31 publications

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

Parameter matching and optimization for power system of range-extended electric vehicle based on requirements

Parameter closed-loop optimization for pure electric vehicles: unified design of power system and control parameters

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