Vision based solution for auto- maneuvering of vehicle for emerging market: Author/Co-Author: Singh Ashwani, SDV Ram Kumar, Bose Souvik, Lalwani Chandraprakash General Motors Technical Centre India Key words: Image Processing, Gap finding, virtual/Imaginary lines, Advance Driver Assist System (ADAS), Vehicle to vehicle(V2V)/Vehicle to Infrastructure(V2I/V2X) Research & Engineering Objective: For the various levels of autonomous, the current perception algorithms involve considerable number of sensor inputs like cameras, radars and Lidars and their fusion logics. The planning route for the vehicle navigation is done through map information which is highly volatile and keep changing many at times. Existing steering assist feature during a curve is available by combining additional driver monitoring camera & 360 degree camera. The complexity is very high in the implementation and computation of these algorithm. These solutions are not cost-effective for emerging markets.
Shared mobility and Autonomous shared mobility take major share in Mobility 4.0. Personalization in a shared mobility will play a significant role in customer engagement in Autonomous world. In case of personal vehicle each customer will have their own personal settings in their own vehicle; in case of Autonomous shared mobility or shared mobility, we can satisfy individual customer need only by personalizing the vehicle for each individual user needs. This will give a cognitive feel of personal vehicle in a shared environment. We need technologies in improving vehicle interior and exterior systems and design to address personalization. We will be discussing on feasible opportunities of personalization and with illustrations in Vehicle Interior Cabin Space, Seat comfort, Compartments, Vehicle interior & Exterior Access / Controls.
Shared Mobility is changing the trends in Automotive Industry and its one of the Disruptions. The current vehicle customer usage and life of components are designed majorly for personal vehicle and with factors that comprehend usage of shared vehicles. The usage pattern for customer differ between personal vehicle, shared vehicle & Taxi. In the era of Autonomous and Shared mobility systems, the customer usage and expectation is high. The vehicle needs systems that will control customer interactions (Self-Expressive) & fix the issues on their own (Self-Healing). These two systems / methods will help in increasing customer satisfaction and life of the vehicle. We will be focusing on vehicle Closure hardware & mechanisms and look for opportunities to improve product life and customer experience in ride share and shared mobility vehicles by enabling integrated designs, which will Self-Express & Self-Heal.
Ride Comfort forms a central design aspect for suspension and is to be considered as primary requirement for vehicle performance in terms of drivability and uptime of passenger. Maintaining a balance between ride comfort and handling poses a major challenge to finalize the suspension specifications. The objective of this project it to perform ride- comfort analysis for heavy commercial truck using MATLAB Simulink. First, bench-marking was carried out on a 4x2 heavy commercial truck and the physical parameters were obtained. Further, a mathematical model is developed using MATLAB Simulink R2015a and acceleration- time data is collected. An experimentation was carried out on the truck at speeds of 20 kmph, 30 kmph, 40 kmph and 50 kmph over a single hump to obtain actual acceleration time domain data. This is followed by running the vehicle on Class A, B & C road profiles-irrespective of vehicle speed- to account for random vibrations.
This paper investigates and proposes the possibilities of standardizing the software/firmware package format and flash jobs in order to provide the possibility of productizing the update-over-the-air solution regarding on-board vehicle components and make use of it in all OEMs with minimum configuration changes and customization. The update-over-the-air solution in the automotive sector is provided by various suppliers and needs to be customized to meet various OEMs requirements. Possible Variants of OEM requirements are: • Variant 1 o Customer Portal + Backend + vehicle on-board components solution from supplier • Variant 2 o Customer Portal + Backend solution from OEM o Vehicle on-board components from supplier • Variant 3 o Backend from OEM o Customer Portal + vehicle on-board components from supplier ODX, VBF, and many other formats from OEMs include software/firmware packages.
DEVELOPMENT OF A FLEET MANAGEMENT SYSTEM FOR AN OFF-HIGHWAY VEHICLE V.Jagannathan 1.a* , B.Jaiganesh 2.b & S.Sudarsanam 3.c Mahindra & Mahindra Limited, Mahindra Research Valley, Mahindra World City, Anjur PO, TN, India Corresponding author Email- V.JAGANNATHAN@mahindra.com Managing an off-highway vehicle fleet during validation is a challenging task. Complexity is acquainted when more than 100 vehicles with different horse power (hp) & with different product configuration working across India and other parts of countries. Traditionally, a tractor validation involves data collection such as usage hours (Hour meter reading on cluster), locations etc. which are recorded in spread sheet and updated to the respective project owners on daily basis through mail communications. A manual recording and consolidation of tractors validation status is prone to error, reiterative work, consumes more resource and effort.
Digital Twins for Prognostic Profiling Authors: Sreeram Mohan*, Painuri Thukaram**, Panduranga Rao*** Objective / Question: Ability to have least failures in products on the field with minimum effort from the manufacturers is a major area of focus driven by Industry 4.0 initiatives. Amidst traditional methods of performing system / subsystem level tests often does not enable the complete coverage of a machine health performance predictions. This paper highlights a workable workflow that could be used as a template while considering system design especially employing Digital Twins that help in mimicking real-life scenarios early in the design cycle to increase product’s reliability as well as tend to near zero defects. Methodology: With currently available disruptive technologies , systems are integrated multi-domain 'mechatronics' systems operating in closed-loop/close-interaction.
The SRM is gaining much interest for EVs due to its rare-earth-free characteristic and excellent performance. SRM possess several advantages such as low cost, high efficiency, high power density, fault-tolerant and it can produce extended constant power region, and this makes SRM as viable alternative over conventional PM drives. Objective: The objective of this paper is to establish proof of theoretical concepts related to SRM. The key to achieve an effective SRM modeling is to use a methodology that allow the nonlinearity of its magnetic characteristics to be represented while maximizing the simulation speed. This paper represents how magnetization data obtained from FEA in the form of look up tables is most appropriate way to represent SRM model. In this paper, performance analysis of SRM is done with the help of Open loop and Closed loop MATLAB simulations. These dynamic simulations of SRM will assist in understanding behavior of SRM in various loading and speed conditions.
Objective / Question: Is it possible to extend the envelope of simulation driven design and its advantages to development of complex dynamic systems viz. traction motor drives? The objective that then follows is how to enable OEM/Tier-1s to reduce wastes in the process of traction motor controller design, development, optimization and implementation. Motor control design to validation process is time consuming and tricky! Additionally, the requirement of software knowledge to write code to implement drive engineer's control ideas. The challenges here are - to name a few - algorithm for real time, addressing memory constraints, debugging, comprehending mathematical overflows, portability & BOM cost. These introduces wastes in parameters like time, cost, performance, efficiency and reliability. Methodology: Developing a new traction motor controller for E Mobility takes 18 - 24 months typically. 2 distinct activities take place in a loop.
Battery operated vehicle need accurate management system because of its quick changes in State of charge (SOC) due to aggressive acceleration profiles and regenerative braking. Li-ion battery needs control over its operating area for its safe working. So, the main objective of the proposed system is to develop a BMS having algorithms to estimate accurate SOC, predict degradation parameters, balance individual cells, manage cell temperature, and provide safe area of operation defined by voltage and temperature. Proposed methodology uses Model-based Design approach wherein nonlinear behavior of battery is modeled as Equivalent Circuit Model to compute the SOC and degradation effect on battery to decide the end of life of battery, also performing inductive Active balancing on cells to equalize the charge. proposed algorithms communicate with the vehicle ECU through CAN to assist the driver for runtime estimation, time for battery swapping, Alerts.