Implementation of an Energy Management Strategy with Drivability Constraints for a Dual-Motor Electric Vehicle

Wang, Haiqing; Wu, Hanfei

doi:10.3390/wevj10020028

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…In this paper, the working modes of dual-motor powertrain are divided into three types according to the driving cycle of the vehicle: motor 1 alone, motor 2 alone, and dual-motor co-driving. The control strategy mainly consists of three parts: demand torque calculation, working modes recognition, and demand torque distribution [18]. As shown in Figure 2a, the high-efficiency zone of motor 1 is concentrated in the middle and high-speed region of 1300~3100 r/min, the corresponding velocity is 10~27 km/h when the transmission is on the first gear, and 28~68 km/h when the transmission is on the second gear.…”

Section: Research On Driving Control Strategymentioning

confidence: 99%

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

Song,

Xi,

Gao

2024

WEVJ

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In order to reduce energy consumption, improve driving mileage, and make vehicles adopt driver styles, research on automatic optimization of control strategy based on driver style is conducted in this paper. According to the structure of the powertrain, the vehicle control strategy is designed and a driver-style recognition model based on fuzzy recognition is added to the rule-based control strategy to improve the driver adaptation of the vehicle. In order to further improve the energy-saving effect of the strategy, the control strategy based on driver style is automatically optimized by the Isight optimization platform to make the strategy reach optimum. The test results show that the strategy based on driver style is able to adapt to different styles of drivers and the economy of the vehicle is improved by 2.06% compared with pre-optimization, which validates the effectiveness of the strategy.

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Section: Research On Driving Control Strategymentioning

confidence: 99%

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

Song,

Xi,

Gao

2024

WEVJ

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“…An interesting manuscript "The effect of perceived risk on the purchase intention of electric vehicles: an extension to the technology acceptance model" (Thilina, 2019) seeks to analyze significant market penetration for the sale of electric vehicles accompanied by the analysis (Mo, 2018) of life-cycle cost of ownership including congestion and environmental impacts (Tu, 2019), (Rajeev, et al, 2019), (Hao, 2017), (Philipsen, et al, 2019), (Lopez-Arboleda, et al, 2019), (Almeida, et al, 2019). Considerable emphasis have been placed on improvements in vehicle charging (Wolbertus, et al, 2019) amongst many other underlying technological areas seeking to improve the value proposition to potential buyers (Jager, et al, 2019), , , (Minnerup, et al, 2019), (Muller, 2019) and also make recommendations in both technology (Zha, et al, 2019), , (Zha, et al, 2019), , (Jiyan, et al, 2019), (Pier, et al, 2019), (Agaton, et al, 2019), , (Watanabe, et al, 2019), (Kusaka, et al, 2019), (Zhang, W., et al, 2019), , (Mayer, et al, 2019), (Ricciardi, et al, 2019), , (Yu, Z., et al, 2019), (Senda, et al, 2019), (Marquez-Fernandez, et al, 2019), (Wu, D., et al, 2019), (Wang, H., et al, 2019), (Obayashi, et al, 2019), (Gong, et al, 2019), (Vermeulen, et al, 2019), (Jia, J., et al, 2019), (Li, Q., et al, 2019) and policy incentives (Zhang, X., et al, 2019), (Ortar, et al, 2019), culminating in charging strategies to influence the obvious trade-off between gasoline prices and charging availability , (Wolbertus, et al, 2019),…”

Section: Materials (Literature Review)mentioning

confidence: 99%

Electric Vehicle Sales Catastrophe Averted (?)

Sands¹

2020

MAS

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The literature indicates catastrophic potential for electric vehicles around the year 2019. Amidst the predicted time, this research revisits the prequel analysis with state-of-the-art deterministic artificial intelligence methodologies to posit the potential for catastrophe in the upcoming year. The methodology has proven effective with motion mechanics, electrodynamics, and even financial analysis of sales date in the prequels, since the model commences with simple regression for mathematical model formulation asserting the certainty equivalence principle, followed by derivative modeling and eventually catastrophe analysis of the derivative models. The prequel analysis paradigms are retained in this sequel utilizing both monthly and cumulative sales data in simple least squares algorithms for predictive curve fitting to establish context and help correctly model the mathematical degree of the data. Extrapolation by forward time-propagation established predictions for models of various mathematical degrees (again merely for context). Next, catastrophe analysis (of the derivative form) revealed stable and unstable equilibrium points and then parametric variation was induced to evaluate the resulting behavior of the derivative models, highlighting the importance of the coefficient of the second order term (the acceleration or change of rate of sales as a forcing function). While the forcing function typically embodies both gasoline prices and vehicle charging proliferation, the relative stability of gas prices together with factors such as vehicle-to-grid elevate charging-station proliferation as the primary forcing function of slow-dynamics in catastrophe analysis. This brief manuscript revisits the prequel research to test the validity of those conclusions and with the benefit of the passage of time, reveal how well the mathematical modeling predicted real behavior. The main finding is the predicted potential catastrophe is less likely to occur and recommendations are made to insure catastrophe is averted.

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“…By combining the Udwadia-Kalaba method with PID, trajectory tracking control for different shifting targets was materialized [15,16]. For the sake of solving the problem of frequent shifting, the multiobjective optimization particle swarm optimization method is adopted to eliminate unnecessary gear shifting [17]. Liu Tong et al [18] exploited the segmented control concept based on model predictive control for the shifting process, which efficaciously improved the smoothness of shifting and reduced shift jerk.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Shift Control Strategy for Pure Electric Commercial Vehicles Based on Driving Intention

Xi,

Si,

Gao

2024

WEVJ

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In order to improve the shifting quality of pure electric commercial vehicles, a torque control strategy based on the driving intention during the shifting process is presented in this paper. Firstly, dynamic analysis is conducted on the lifting and twisting stage in the two-speed Automated Mechanical Transmission (AMT) shift process without a synchronizer. Secondly, fuzzy identification is performed on the driver’s expected acceleration, incorporating the driver’s acceleration intention into the lifting and twisting process, and, further, the output time correction factor k is deblurred. Finally, the control time of the lifting and reducing torque is corrected to achieve dynamic adjustment of the control parameters during the shift process. The actual vehicle test results indicate that the proposed control strategy can enhance the shifting quality and adapt the performance of a vehicle to the driver’s expectations and requirements.

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Implementation of an Energy Management Strategy with Drivability Constraints for a Dual-Motor Electric Vehicle

Cited by 4 publications

References 30 publications

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

Research on Automatic Optimization of a Vehicle Control Strategy for Electric Vehicles Based on Driver Style

Electric Vehicle Sales Catastrophe Averted (?)

Optimization of a Shift Control Strategy for Pure Electric Commercial Vehicles Based on Driving Intention

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