Robust shifting control of a motor‐transmission integrated system considering anti‐jerking and speed regulation for electric vehicles

Chai, Benben; Zhang, Jianwu; Wu, S. M.

doi:10.1049/iet-its.2018.5057

Cited by 12 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation 1needs to be transformed equivalently before it can be solved. Adopted the robust discrete transformation principle [26][27][28], the formula (1) is transformed into the following formula: (see (9)) . So, Z 1 = min (I, J) ∈ W H IJ…”

Section: Multi-objective Robust Scheduling Model and The Associated Amentioning

confidence: 99%

“…Equation (1) needs to be transformed equivalently before it can be solved. Adopted the robust discrete transformation principle [26–28], the formula (1) is transformed into the following formula:

\begin{matrix} right leftthickmathspace.5em \\ H^{U} & = \min \{\underset{p^{normal′} \in P_{0}}{false\sum} \sum_{k \in (}{, V_{p^{'}} | p^{'} \in P_{0})} {false\sum}_{\{i = 1 : (i, j \in W)\}}^{\{I = 1 : (I, j \in W)\}} \sum_{(}{, j = 1 : false(i, j \in W false))}^{(}{, J = 1 : false(i, J \in W false))} δ_{i j} x_{i j k}^{p^{normal′}} I R_{i j k}^{p^{normal′}} \\ + \underset{p^{normal′} \in P_{0}}{false\sum} \sum_{k \in (}{, V_{p^{'}} | p^{'} \in P_{0})} {false\sum}_{\{i = 1 : (i, j \in W)\}}^{\{I = 1 : (I, j \in W)\}} \sum_{(}{, j = 1 : false(i, j \in W false))}^{(}{, J = 1 : false(i, J \in W false))} (δ_{i j} - δ_{IJ}) x_{i j k}^{p^{normal′}}\} \end{matrix}

…”

Section: Robust Scheduling Modelmentioning

confidence: 99%

See 1 more Smart Citation

Vehicles robust scheduling of hazardous materials based on hybrid particle swarm optimisation and genetic algorithm

Liu

2020

IET intell. transp. syst

View full text Add to dashboard Cite

This paper takes the vehicles scheduling of hazardous materials as the research object. First, considered the four objectives of risk minimization, cost minimization, risk equilibrium value minimization, and duration minimization, the vehicles robust scheduling of hazardous materials in multiple distribution centers with risk balance is constructed. Then, uncertain models are transformed into peer-to-peer models through the idea of robust discrete optimization. By introducing the crossover and mutation operators of genetic algorithm into particle swarm algorithm, a hybrid particle swarm algorithm is constructed, and three-stage coding rules are adopted. Finally, case study is exemplified to prove the feasibility of the model and algorithm. The results show that the obtained scheduling scheme for the vehicles of hazardous materials minimizes the risk value and makes the risk distribution more balanced for the government regulatory department, and the vehicle can avoid certain road sections with relatively large risk values. For transportation enterprises, the vehicle scheduling scheme for transporting hazardous materials minimizes the cost and shortens the task duration at the same time. In the case of risk uncertainty, different robust control parameters are introduced to make the vehicles robust scheduling of hazardous materials have different degrees of stability.

show abstract

Section: Multi-objective Robust Scheduling Model and The Associated Amentioning

confidence: 99%

\begin{matrix} right leftthickmathspace.5em \\ H^{U} & = \min \{\underset{p^{normal′} \in P_{0}}{false\sum} \sum_{k \in (}{, V_{p^{'}} | p^{'} \in P_{0})} {false\sum}_{\{i = 1 : (i, j \in W)\}}^{\{I = 1 : (I, j \in W)\}} \sum_{(}{, j = 1 : false(i, j \in W false))}^{(}{, J = 1 : false(i, J \in W false))} δ_{i j} x_{i j k}^{p^{normal′}} I R_{i j k}^{p^{normal′}} \\ + \underset{p^{normal′} \in P_{0}}{false\sum} \sum_{k \in (}{, V_{p^{'}} | p^{'} \in P_{0})} {false\sum}_{\{i = 1 : (i, j \in W)\}}^{\{I = 1 : (I, j \in W)\}} \sum_{(}{, j = 1 : false(i, j \in W false))}^{(}{, J = 1 : false(i, J \in W false))} (δ_{i j} - δ_{IJ}) x_{i j k}^{p^{normal′}}\} \end{matrix}

…”

Section: Robust Scheduling Modelmentioning

confidence: 99%

Vehicles robust scheduling of hazardous materials based on hybrid particle swarm optimisation and genetic algorithm

Liu

2020

IET intell. transp. syst

View full text Add to dashboard Cite

show abstract

“…Thus, to improve the control robustness of motor speed regulation, a robust controller can be a more suitable choice. For instance, the H ∞ control scheme is adopted 20–22 to reduce the influence of disturbance on the controlled output and linear quadratic cost function, pole assignment technique is used to ensure the balance between transient response and maximum control effort. However, the setting process for these control parameters is time consuming and complicated.…”

Section: Introductionmentioning

confidence: 99%

Novel shift process control strategy for two-speed clutchless automatic mechanical transmission based on segmented speed regulation

Wang

Yao

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

Battery electric vehicles (BEV) equipped with two-speed automatic mechanical transmission (AMT) have gained increasing popularity owing to advantages of a widened high-efficiency operation zone and system costs. In this study, a combined gear shift control strategy including a segmented speed regulation control and torque recovery control was developed for a two-speed AMT in BEV. The switching point of proportional integral control and sliding mode control during speed regulation process is adaptively optimized to ensure minimization of the shift time, while the maximum vehicle jerk is controlled within a defined range. The effectiveness of proposed control strategy is verified by a test bench. Results show that proposed control strategy can not only ensure the impact degree fluctuates within a predetermined range, but also achieve fast and accurate speed regulation which can reduce the speed regulation time by 17%–33%.

show abstract

“…Also, a comparative research work was conducted by Huang et al (2019), in which proportion-integral (PI) control, disturbance observer-based proportional control, integral sliding mode-based proportional control, and disturbance observer-based act-and-wait control were all investigated in the speed synchronization control of IMT system. Chai et al (2019) proposed a robust speed synchronization controller design for IMT system, in which reference speed trajectory is optimized by using cubic polynomial fit method. It can be seen that controller design for speed synchronization in AMT system has been fully investigated in both theory and experimental aspects, which also shows good effects in performance improvement.…”

Section: Introductionmentioning

confidence: 99%

Coordinated speed and position control of integrated motor-transmission system

Chen

Zhu

2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, a coordinated driving motor speed and shifting motor displacement control strategy is proposed for the integrated motor-transmission (IMT) system during the gearshift process. For active speed synchronization of IMT system, speed reference to driving motor is redesigned by using a polynomial speed trajectory. Compared with conventional step speed change reference, it can help improve the ride performance of IMT system. While in the gear release as well as engagement phase, a robust optimal preview controller is developed for the shifting motor to realize rapid and reliable position tracking of the sleeve in spite of load disturbance. Based on real time value of the driving motor speed and also sleeve axial position, proposed speed and position controllers are coordinated in plan during the whole gearshift process. Co-simulations with Matlab/Simulink and AMEsim are conducted to demonstrate dynamical characteristics of the IMT system during the whole gear shifting process, in which a two-layer switching logic is built by using Matlab/Stateflow. Comparative simulation tests are carried out to show the effectiveness as well as performance of proposed control strategies.

show abstract

Robust shifting control of a motor‐transmission integrated system considering anti‐jerking and speed regulation for electric vehicles

Cited by 12 publications

References 26 publications

Vehicles robust scheduling of hazardous materials based on hybrid particle swarm optimisation and genetic algorithm

Vehicles robust scheduling of hazardous materials based on hybrid particle swarm optimisation and genetic algorithm

Novel shift process control strategy for two-speed clutchless automatic mechanical transmission based on segmented speed regulation

Coordinated speed and position control of integrated motor-transmission system

Contact Info

Product

Resources

About