Active Disturbance Rejection and Low-Speed Performance Enhancement in EV Drives Using ASISMC Under Dynamic Load Conditions

Electric vehicle (EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance leads to significant torsional vibrations and speed fluctuations, which not only compromise passenger comfort but also exert additional mechanical stress on the EVs. Conventional sensorless methods offer speed estimation and control; however, they provide suboptimal performance with sudden load torque disturbances and operational uncertainties, especially at low speeds and across diverse real-world driving cycles. To address these challenges and improve system robustness, this work proposes an advanced sensorless integral sliding mode control (ASISMC) that enhances performance under diverse operating conditions. The proposed ASISMC methodology shows robust performance across a wide speed range, effectively mitigating abrupt load torque disturbances while minimizing the effect of uncertainties within the system dynamics. The approach is experimentally validated for a wide range of speeds and periodic/non-periodic load torque disturbances. Additional validation through the new European driving cycle (NEDC) and urban dynamometer driving schedule (UDDS) demonstrates the method's effectiveness and reliability in real-world driving conditions.

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Publication Details

Journal
CES Transactions on Electrical Machines and Systems
Published
2025-12-01
DOI
https://doi.org/10.30941/cestems.2025.00033
Primary Topic
Electric and Hybrid Vehicle Technologies
Type
article
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article

Active Disturbance Rejection and Low-Speed Performance Enhancement in EV Drives Using ASISMC Under Dynamic Load Conditions

Mohit Kachhwaha, Deepak Fulwani, Rahul Singh
CES Transactions on Electrical Machines and Systems
Electric and Hybrid Vehicle Technologies
article

Active Disturbance Rejection and Low-Speed Performance Enhancement in EV Drives Using ASISMC Under Dynamic Load Conditions

Mohit Kachhwaha, Deepak Fulwani, Rahul Singh
article en

Abstract

Electric vehicle (EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance leads to significant torsional vibrations and speed fluctuations, which not only compromise passenger comfort but also exert additional mechanical stress on the EVs. Conventional sensorless methods offer speed estimation and control; however, they provide suboptimal performance with sudden load torque disturbances and operational uncertainties, especially at low speeds and across diverse real-world driving cycles. To address these challenges and improve system robustness, this work proposes an advanced sensorless integral sliding mode control (ASISMC) that enhances performance under diverse operating conditions. The proposed ASISMC methodology shows robust performance across a wide speed range, effectively mitigating abrupt load torque disturbances while minimizing the effect of uncertainties within the system dynamics. The approach is experimentally validated for a wide range of speeds and periodic/non-periodic load torque disturbances. Additional validation through the new European driving cycle (NEDC) and urban dynamometer driving schedule (UDDS) demonstrates the method's effectiveness and reliability in real-world driving conditions.

CES Transactions on Electrical Machines and SystemsVol. 9(4)
Indian Institute of Technology Jodhpur (IN)
Sustainable cities and communities
Openalex Percentile: Top 43%
Electric and Hybrid Vehicle Technologies
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Active Disturbance Rejection and Low-Speed Performance Enhancement in EV Drives Using ASISMC Under Dynamic Load Conditions — Mohit Kachhwaha, Deepak Fulwani, et al. · CES Transactions on Electrical Machines and Systems (2025) | TGRS Research Map | TGRS