Review on Torque Distribution Scheme of Four-Wheel In-Wheel Motor Electric Vehicle

He, Shuwen; Fan, Xiaobin; Wang, Quanwei; Chen, Xinbo; Zhu, Shuaiwei

doi:10.3390/machines10080619

Cited by 18 publications

(11 citation statements)

References 57 publications

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“…Furthermore, the electromagnetic excitation generated by the hub motors during driving affects the vertical vibration of the vehicle, and the secondary excitation caused by the vehicle vibration acting on the road surface further affects the ride comfort of the vehicle. Therefore, it is necessary to study the vehicle–road coupling of the suspension system for hub-motor EVs with a focus on their vertical dynamic characteristics [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Li,

Xu,

Wang

et al. 2024

Sensors

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Electric vehicles with hub motors have integrated the motor into the wheel, which increase the unsprung mass of the vehicle, and intensifies the vibration of the underspring components. The motor excitation during driving also intensifies the wheel vibration. The coupling effect between the two makes the performance of electric vehicles deteriorate. The article employed a disc-type permanent-magnet motor as the hub motor, taking into consideration the increase in sprung mass caused by the hub motor and the adverse effects of vertical vibration from motor excitation. Based on random road-surface excitation, and considering the secondary excitation caused by wheel motor drive and vehicle-road coupling, a coupled-dynamics model of a semi-active-suspension vehicle-road system for vertical vehicle motion is investigated under multiple excitations. Using body acceleration, suspension deflection, and dynamic tire load as evaluation indicators, a BP neural network PID controller based on the sparrow search algorithm optimization is proposed for the semi-active-suspension system. Compared with PID control and particle swarm optimization (PSO-BPNN-PID), the research findings indicate that the optimized semi-active suspension significantly improves the ride comfort of hub-motor electric vehicles, and meets the requirements for control performance under different vehicle driving conditions.

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Section: Introductionmentioning

confidence: 99%

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Li,

Xu,

Wang

et al. 2024

Sensors

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“…(1) The Overall Control Strategy of the Motor This strategy mainly controls the driving motor, how much torque it outputs during operation and how much negative torque it outputs during power generation [50], and how much mechanical pressure the brake pad needs to output when the automatic braking cannot be met. The overall control strategy and input and output signals are shown in Figure 9.…”

Section: Design Of Control Strategy For the Drive Systemmentioning

confidence: 99%

Study of a Hybrid Vehicle Powertrain Parameter Matching Design Based on the Combination of Orthogonal Test and Cruise Software

Wang

Liu

et al. 2023

Sustainability

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In order to further improve the power and fuel economy of hybrid vehicles, this paper proposes a method of hybrid vehicle powertrain matching by combining orthogonal tests with Cruise software, supplemented by the control strategy formulation of critical components of the whole vehicle on the MATLAB/Simulink platform. Considering the influence of vehicle engine, electric motor, battery and overall mass on the powertrain design, the L9(34)-type orthogonal table is selected for the orthogonal test design. After verifying the feasibility and accuracy of each design solution of the powertrain, the different design solutions are simulated for power and economic performance. Finally, the best performance indicators of the vehicle are as follows: the maximum speed is 183.35 km/h, the 0–100 km/h acceleration time is 6.87 s, and the maximum degree of climbing is 39.65 percent. The fuel consumption of 100 km is 3.47 L. The optimal solution was compared with the third-generation Harvard H6 and AITO M5 in terms of fuel saving and emission reduction, and it was found that for every 15,000 km driven, it is expected to save 469.5 L of fuel and 109.5 L of CO2, respectively, which can reduce fuel use and emission by about 1051.21 kg and 245.17 kg CO2, respectively. This simulation experiment can reduce the workload of traditional power system matching. It can provide ideas for power system matching and optimization for Corun CHS Technology Co., Ltd. (Foshan City, Guangdong Province, China) and offer a certain degree of reference for hybrid vehicle power system design and simulation.

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“…Dizqah [12] developed a multi-dimensional torque distribution ratio lookup table using the total power loss curve and a multi-parameter quadratic programming method to address the optimal energy-saving torque allocation issue in vehicles. Torinsson [13] and He [14], respectively, utilized quadratic programming and model predictive control for torque allocation through offline optimization. However, these approaches could not adequately take into consideration variations in motor parameters during real-time operation.…”

Section: Introductionmentioning

confidence: 99%

An Online Energy-Saving Control Allocation Strategy Based on Self-Updating Loss Estimation for Multi-Motor Drive Systems

Chen,

Peng,

et al. 2024

Processes

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In this paper, an online energy-saving control allocation strategy based on self-updating loss estimation for multi-motor drive systems is proposed, where the impact of variations in motor parameters and distribution coefficients is considered. Firstly, a drive system model for multi-motor drive systems incorporating iron loss in permanent magnet synchronous motor (PMSM) is established. Then, a self-updating PMSM loss estimation method based on dynamic torque–current mapping is proposed. The torque–current mapping is initially identified based on the conv-fusion curve, and iteratively updated by optimal estimation. Subsequently, an online control allocation method based on line search is proposed, which mitigates the adverse effects caused by variations in distribution coefficients and reduces the total motor loss. Finally, the effectiveness of the proposed strategy is verified on the hardware-in-the-loop (HIL)-based platform. The results demonstrate that the strategy effectively enhances energy efficiency while maintaining the original control performance of the system.

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Review on Torque Distribution Scheme of Four-Wheel In-Wheel Motor Electric Vehicle

Cited by 18 publications

References 57 publications

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Study of a Hybrid Vehicle Powertrain Parameter Matching Design Based on the Combination of Orthogonal Test and Cruise Software

An Online Energy-Saving Control Allocation Strategy Based on Self-Updating Loss Estimation for Multi-Motor Drive Systems

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