Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system

Xu, Wei; Chen, Hong; Zhao, Haiyan; Ren, Bingtao

doi:10.1016/j.mechatronics.2018.11.006

Cited by 129 publications

(60 citation statements)

References 19 publications

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“…To meet the requirements of braking safety and braking stability, the braking force distribution between the front axle and rear axle should comply with the ECE R13 regulation. [29] For heavy-duty trucks, the adhesion utilization coefficient of the front wheels should exceed the adhesion utilization coefficient of the rear wheels under various loading conditions. When the friction coefficient φ is between 0.2 and 0.8, the requirements of the braking intensity should satisfy z ≥ 0.1 þ 0.85 (φ-0.2).…”

Section: Braking Force Predistribution Strategy Based On Braking Intementioning

confidence: 99%

Control Strategy of Braking Energy Recovery for Range‐Extended Electric Commercial Vehicles by Considering Braking Intention Recognition and Electropneumatic Braking Compensation

Zhao

Yang

et al. 2020

Energy Tech

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Braking energy recovery is a key technology for improving energy efficiency and extending the driving range of electric vehicles. However, there are challenges for maximizing energy recovery and improving braking sense. To solve these problems, a hierarchical control strategy of braking energy recovery that considers braking intention recognition and electropneumatic braking compensation is proposed herein. For the upper controller, Layer Hidden Markov Model‐Dynamic Compensatory Fuzzy Neural Network is applied to recognize the driver's braking intention. For the medium controller, a braking force predistribution strategy based on braking intention recognition results is designed. For the lower controller, an electropneumatic braking compensation method is used to reduce the adverse effects due to the composite braking system. Subsequently, vehicle tests in single‐braking conditions and hardware in loop (HIL) simulation in the World Transient Vehicle Cycle (C‐WTVC) are conducted to verify the performance of the control strategy. The results indicate that the proposed control strategy optimizes the braking force in all the tests and simulations, and the energy recovery rate reaches 12.75% in C‐WTVC, thereby surpassing that of the other three control strategies. Therefore, the results of the study indicate an improvement in the economic performance and ride comfort of electric vehicles.

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Section: Braking Force Predistribution Strategy Based On Braking Intementioning

confidence: 99%

Control Strategy of Braking Energy Recovery for Range‐Extended Electric Commercial Vehicles by Considering Braking Intention Recognition and Electropneumatic Braking Compensation

Zhao

Yang

et al. 2020

Energy Tech

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show abstract

“…According to the description of the vehicle steering in vehicle theory, the effect of each wheel generating a braking force on the yaw moment of the vehicle is different. e main contribution to the internal yaw moment of the vehicle comes from the rear inner wheel, whereas the lateral yaw moment produced by the front inner wheel is the most effective [22]. First, we determine the value and direction of the yaw moment of the required motion.…”

Section: Rule-based Braking Torque Distributionmentioning

confidence: 99%

“…However, this method is highly dependent on engineering and a lack of control accuracy [21]. A new control method is proposed based on model predictive control (MPC) theory to address the issues of multiple objectives with constraints, which can maximize the regeneration efficiency while maintaining the vehicle dynamics [22]. ese methods focus on the vehicle torque allocation but do not optimize the vehicle handling stability for vehicle motion control.…”

Section: Introductionmentioning

confidence: 99%

Electronic Stability Control for Improving Stability for an Eight In‐Wheel Motor‐Independent Drive Electric Vehicle

Zhao

Zhang

2019

Shock and Vibration

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An electronic stability control (ESC) based on torque distribution is proposed for an eight in-wheel motor-independent drive electric vehicle (8WIDEV). The proposed ESC is extremely suitable for the independent driving vehicle to enhance its handling stability performance. The vehicle model is established based on a prototype 8WIDEV. A hierarchical control strategy, which includes a reference state generation controller, an upper-level vehicle controller, and a lower-level optimal control allocation controller, is utilized in the ESC. The reference state generation controller is used to obtain the ideal reference vehicle state. The upper-level vehicle controller is structured based on sliding mode control, which obtains the generalized objective force during 8WIDEV movement, therein considering the side slip angle and yaw rate. The lower-level optimal control allocation controller attempts to allocate the vehicle’s objective force in each motor optimally and reasonably. The model is validated by field measurement results under the step input condition and snake input condition. Simulation results from a hardware-in-the-loop (HIL) simulation platform indicate that the ESC based on the optimized allocation proposed for 8WIDEV achieves better stability performance compared with direct yaw moment control (DYC).

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“…The amount of energy generated by regenerative braking can be linked to the variations of the brake pedal depression [12]. The regenerative braking efficiency can be improved further by applying the predictive control theory by means of determining hydraulic braking torque and motor braking torque [13].…”

Section: Introductionmentioning

confidence: 99%

The influence of the regenerative braking on the overall energy consumption of a converted electric vehicle

et al. 2020

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Electrical energy consumption of a converted electric vehicle in the real-world environment has been examined. The objective of such test is to compare the overall energy consumption of a vehicle with and without regenerative braking features. For each test, sets of data such as the total testing time, the batteries charge and/or discharge power, and the vehicle mileage were collected and analyzed to determine the energy consumption. The reliability of the data was tested using confidence intervals, which could be used as an indication of the level of confidence that the average value taken could represent the average population. The average value of energy consumption in the vehicles with and without regenerative braking is 145.26 Wh/km and 154.79 Wh/km, respectively. The difference between the average energy consumption from those two conditions, which is 9.53 Wh/km, has 95% probability to be accurate, as indicated by the confidence intervals test. This shows a decrease in electrical energy consumption by about 6.16%, which indicates an enhancement in mileage or vehicles efficiency.

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Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system

Cited by 129 publications

References 19 publications

Control Strategy of Braking Energy Recovery for Range‐Extended Electric Commercial Vehicles by Considering Braking Intention Recognition and Electropneumatic Braking Compensation

Control Strategy of Braking Energy Recovery for Range‐Extended Electric Commercial Vehicles by Considering Braking Intention Recognition and Electropneumatic Braking Compensation

Electronic Stability Control for Improving Stability for an Eight In‐Wheel Motor‐Independent Drive Electric Vehicle

The influence of the regenerative braking on the overall energy consumption of a converted electric vehicle

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