State estimation of four-wheel independent drive electric vehicle based on adaptive unscented Kalman filter

Huang, Bin; Wu, Sen; Fu, Xiang; Luo, Jie

doi:10.1504/ijehv.2017.085348

Cited by 5 publications

(3 citation statements)

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“…Equation (2) is used to determine the stability boundary of side slip angle. After that, we obtain the estimated values of side slip angle and its derivative through the state estimation procedure which has been developed and validated by the authors in [18], thus deriving actual state point of the vehicle. The driving state of vehicle can be determined and expressed as (3) according to the distance between actual state point and stable boundary.…”

Section: Yaw Stability Control Analysismentioning

confidence: 99%

Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicles Based on Model Predictive Control

Huang

et al. 2018

Mathematical Problems in Engineering

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Four-wheel independent drive electric vehicle was used as the research object to discuss the lateral stability control algorithm, thus improving vehicle stability under limit conditions. After establishing hierarchical integrated control structure, we designed the yaw moment decision controller based on model predictive control (MPC) theory. Meanwhile, the wheel torque was assigned by minimizing the sum of consumption rates of adhesion coefficients of four tires according to the tire friction ellipse theory. The integrated simulation platform of Carsim and Simulink was established for simulation verification of yaw/rollover stability control algorithm. Then, we finished road experiment verification of real vehicle by integrated control algorithm. The result showed that this control method can achieve the expectation of effective vehicle tracking, significantly improving the lateral stability of vehicle.

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Section: Yaw Stability Control Analysismentioning

confidence: 99%

Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicles Based on Model Predictive Control

Huang

et al. 2018

Mathematical Problems in Engineering

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“…To solve the undetermined measurement noise and system noise, a classical Sage-Husa adaptive filter was involved. Combining the traditional UKF and traditional Sage-Husa filter, Bin Huang et al [18] proposed a method based on a four-degrees-of-freedom nonlinear vehicle dynamic model and a simplified Magic Formula tire model. Their method could effectively detect the change in vehicle state errors.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-Dimensional Vehicle Driving State Observer Based on Modified Sage–Husa UKF Algorithm

Luo

2020

Sensors

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An accurate vehicle driving state observer is a necessary condition for a safe automotive electronic control system. Vehicle driving state observer is challenged by unknown measurement noise and transient disturbances caused by complex working conditions and sensor failure. For the classical adaptive unscented Kalman filter (AUKF) algorithm, transient disturbances will cause the failure of state estimation and affect the subsequent process. This paper proposes an AUKF based on a modified Sage–Husa filter and divergence calculation technique for multi-dimensional vehicle driving state observation. Based on the seven-degrees-of-freedom vehicle model and the Dugoff tire model, the proposed algorithm corrects the measurement noise by using modified Sage–Husa maximum posteriori. To reduce the influence of transient disturbance on the subsequent process, covariance matrix is updated after divergence is detected. The effectiveness of the algorithm is tested on the double lane change and Sine Wave road conditions. The robustness of the algorithm is tested under severe transient disturbance. The results demonstrate that the modified Sage–Husa UKF algorithm can accurately detect transient disturbance and effectively reduce the resulted accumulated error. Compared to classical AUKF, our algorithm significantly improves the accuracy and robustness of vehicle driving state estimation. The research in this paper provides a reference for multi-dimensional data processing under changeable vehicle driving states.

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“…Various solutions employing both in-body and in-wheel motors have been studied (De Novellis et al, 2013;Othaganont et al, 2016). Although the latter solution optimises efficiency and energy consumption, it should be noted that in-wheel motor configurations are more difficult and expensive to engineer for passenger cars than the in-body solutions (Chen et al, 2016;Huang et al, 2017). Increment of the unsprung mass makes worse the driving comfort and precision.…”

Section: Introductionmentioning

confidence: 99%

Comparing battery electric vehicle powertrains through rapid component sizing

Anselma

Belingardi

2019

IJEHV

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The current transition from internal combustion engine vehicles to battery electric vehicles (BEVs) requires the development and implementation of novel design methodologies. This paper introduces an original design procedure for battery electric light vehicle powertrains based on rapid brute force optimisation. At first, a large design space is generated by sweeping the relevant design parameters, specifically the motor size, the number of gears and the transmission ratios. Analytical modelling for electric vehicle powertrains is then presented and combined with lookup tables for the actual system components. The feasible candidate designs are identified by introducing evaluation criteria for the transmission ratios according to the vehicle and electric machine data considered. Subsequently, these candidate designs are evaluated in terms of both launching performance and energy consumption by adopting an efficiency based gear-shifting strategy. Optimal designs can finally be graphically identified through a Pareto frontier analysis as effectively demonstrated in the case of four actual vehicles of different classes. The methodology introduced efficiently addresses the non-linear multidimensional optimisation problem of electric vehicle powertrain design.

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State estimation of four-wheel independent drive electric vehicle based on adaptive unscented Kalman filter

Cited by 5 publications

References 0 publications

Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicles Based on Model Predictive Control

Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicles Based on Model Predictive Control

An Adaptive Multi-Dimensional Vehicle Driving State Observer Based on Modified Sage–Husa UKF Algorithm

Comparing battery electric vehicle powertrains through rapid component sizing

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