Gear downshift control of inverse-automatic mechanical transmission of electric vehicle

Hong, Jinlong; Zhang, Chao; Chu, Hankun; Gao, Bingzhao; Wu, Hao; Wei, Guangjie; Liu, Hua; Xu, Fuguo

doi:10.1016/j.geits.2022.100005

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Electric vehicles (EVs) and autonomous vehicles (AVs) have recently become a major direction for development due to climate change and the resulting emission restrictions in nations around the world [1][2][3]. These vehicles are expected to make up thirty-one percent of the global vehicle fleet by 2050 [4].…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Bala,

Adeshina,

Aibinu

2023

WEVJ

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The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains.

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Section: Introductionmentioning

confidence: 99%

“…Contrast adjustment: In this case, the contrast of the image was enhanced by altering the hue, saturation, and brightness values. This is achieved using Equation (2).…”

mentioning

confidence: 99%

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Bala,

Adeshina,

Aibinu

2023

WEVJ

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“…A fully electrified automatic transmission system has several advantages, including new functionalities, design adaptability, enhanced user experience, alignment with prevailing trends, and cost-effectiveness for both manufacturers and consumers. However, several considerations should be addressed [5,6], most importantly the nominal time delay between the driver manipulating buttons or controls and the execution of the corresponding gear selection within the vehicle. This temporal lag emanates from the time required for the VCU to interpret, process, and transmit the signal of the gear shift selector to the actuator inside the automatic transmission.…”

Section: Introductionmentioning

confidence: 99%

Robust Position Control for an Electrical Automatic Transmission under Gear-Shifting Link Friction

Kim,

Choi

2023

Electronics

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The automotive industry is evolving, with software becoming a vital part of vehicles. Conventional automakers are shifting to software-centric entities, embracing over-the-air (OTA) updates and service-centric models. To move software-driven vehicles, the vehicle must also be electrified. Several automobile manufacturers are electrifying vehicle parts, and recently, a gear shift selector for automatic transmissions was adapted from mechanical to electronic. However, as conventional mechanical systems are modified to electrical systems, problems such as shift delay and accuracy emerge. This study addresses these problems that emerge in the electronic system type of automatic transmission, including a gear shift selector developed to electrify automobiles. Accordingly, we first analyze the structure of automatic transmission systems, then define the operation sequence. Next, a novel position control algorithm based on a disturbance observer is proposed to reduce shift delay and increase accuracy. The proposed algorithm operates harmoniously with the vehicle control unit (VCU). To verify the proposed algorithm, a hardware-in-the-loop simulation (HILS) was developed to experiment with vehicle shifting using a commercial electronic gear shift selector. Moreover, the proposed control algorithm for gear shifting in an automatic transmission was analyzed using experimental results obtained by assuming a specific driving situation in the HILS.

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Neural network-based self-tuning control for hybrid electric vehicle engines

Urooj,

Nasir

2024

Engineering Applications of Artificial Intelligence

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Gear downshift control of inverse-automatic mechanical transmission of electric vehicle

Cited by 7 publications

References 19 publications

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Robust Position Control for an Electrical Automatic Transmission under Gear-Shifting Link Friction

Neural network-based self-tuning control for hybrid electric vehicle engines

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