Road slope estimation based on acceleration adaptive interactive multiple model algorithm for commercial vehicles

Liu, Yicai; Wei, Lingtao; Zhi-xian, Fan; Wang, Xiangyu; Li, Liang

doi:10.1016/j.ymssp.2022.109733

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…The estimated values of heavy-duty vehicle mass m [40] and road slope θ [31] are calculated as shown in Equations ( 20) and (21).…”

Section: Launching Equivalent Resistance Recognitionmentioning

confidence: 99%

“…Firstly, heavy-duty vehicles carry different masses of cargo each time they are transported, which means that when the vehicle is launching, the clutch torque needs to overcome different vehicle inertia and rolling resistance [29,30]. Secondly, heavy-duty vehicles usually travel on roads with different gradients, such as mountainous and hilly areas, and launching on different road gradients requires clutch torque to overcome different ramp resistance [31]. Therefore, it is not negligible that the variation of vehicle load and road gradient has a great influence on the launching resistance [32].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Torque Control of the Launching Process with AMT Clutch for Heavy-Duty Vehicles

Geng,

Liu,

Liu

et al. 2024

Machines

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When launching a heavy-duty vehicle, torque and position control during automatic clutch engagement is critical, and the driver’s intention to launch and changes in the vehicle’s launching resistance make clutch control more complex. This paper analyses the automatic engagement process of automated mechanical transmission (AMT) clutches and proposes an optimal control of the clutch torque for launching heavy-duty vehicles. Firstly, a fuzzy neural network (FNN)-based vehicle launching states recognition (LSR) system is designed for distinguishing the driver’s launching intention and the vehicle’s launching equivalent moment of resistance. Secondly, jerk, friction work, and launching reserve power are taken as the performance indexes for clutch torque optimization, the weight coefficients of each performance index are adjusted according to the LSR results, and the optimal clutch torque is solved by using the minimum value principle based on the shooting method. Finally, simulations and tests are conducted to validate the strategy of optimizing clutch torque, and the impact of torque optimization on the position change during the engagement process is analyzed. The results indicate that under different driver’s intentions, vehicle masses, and road gradient conditions, the jerk, friction work, and slipping time of heavy vehicles during the launching process are improved by applying the optimization strategy.

show abstract

“…The estimated values of heavy-duty vehicle mass m [40] and road slope θ [31] are calculated as shown in Equations ( 20) and (21).…”

Section: Launching Equivalent Resistance Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Torque Control of the Launching Process with AMT Clutch for Heavy-Duty Vehicles

Geng,

Liu,

Liu

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…Many countries are developing electric vehicles because of their low energy consumption and zero pollution [2][3]. Road slope is an important parameter of the vehicle control system [4]. Accurate road slope information has a significant impact on enhancing the comfort, safety, and economy of electric vehicles [5][6].…”

Section: Introductionmentioning

confidence: 99%

The Prediction Model for Road Slope of Electric Vehicles Based on Stacking Framework of Deep Learning

Zhu

et al. 2023

IEEE Access

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Accurate real-time information on road slopes and the capacity to forecast future moment gradient values are critical for the vehicle control, stability, and driving comfort. Thus, this study proposes a stacking model method for road slope estimation of electric vehicles. Gated Circulation unit (GRU), Convolutional Neural Network (CNN), and CNN-GRU are used as the base classifiers, and Multilayer Perceptron (MLP) is used as a meta-classifier. The vehicle dynamics equations are examined to select the appropriate parameters to feed into the base classifier for training. The meta-classifier is trained using the estimated results from the basic classifier. The current slope values are estimated by slicing the training set by data sampling time and windowing the training data set to predict the future slope values in 2s, 3s, and 4s. Road experiments are conducted, and error indicators are selected for evaluation. The stacking model is compared with each base classifier, Adaptive Kalman filter, Recursive Least Squares with Forgetting Factor and Back Propagation Neural Network for estimating the current moment slope, and it is verified that the stacking model can better estimate the current slope value and outperform the conventional algorithm. Comparing the stacking model with the predicted results of each base classifier for future time slope prediction shows that the stacking model is more accurate at predicting the slope values in the short future time.

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Vehicle Road Grade Prediction Based on CNN-LSTM

Qin,

Yao,

Fan

et al. 2023

IFAC-PapersOnLine

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Road slope estimation based on acceleration adaptive interactive multiple model algorithm for commercial vehicles

Cited by 12 publications

References 34 publications

Optimal Torque Control of the Launching Process with AMT Clutch for Heavy-Duty Vehicles

Optimal Torque Control of the Launching Process with AMT Clutch for Heavy-Duty Vehicles

The Prediction Model for Road Slope of Electric Vehicles Based on Stacking Framework of Deep Learning

Vehicle Road Grade Prediction Based on CNN-LSTM

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