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.
Objective This paper explores the usage of Altair simulation driven concept process, C123 for developing the chassis frame of the SUV along with Multidisciplinary optimisation tool. C123 process is useful for strategic & systematic usage of optimisation to generate design alternatives, trade-off information, best balanced designs, design sensitivities, to actively support the concept development process on daily basis. Methodology C123 is used for developing initial concept design of the chassis frame of the SUV. C123 process is independent of vehicle architectures, manufacture process (e.g. extrusions, sheet metal) & material selection (e.g. metals, composites, mixed etc.) and platform sharing strategy. C1 process is used for identification of optimum Structural Layout, C2 is for rapid optimum Sizing of idealized Sections, C3 is used for detailed optimum Sizing of Manufacturable Sections. Automatic process is used for handling pre and post processing process very efficiently.
Objective This paper explores the usage of Altair simulation driven optimisation process, Front Suspension hard points of a sedan Car model are optimised for specific target toe curves using MotionView, MotionSolve and HyperStudy This process gives the optimal hard point values to match the target curves without much iterations. Methodology Parametric Multibody model of the front end of sedan is built in MotionView. To Carry out optimisation HyperStudy is used where few of the suspension hard points which affect the toe curves are chosen as design variable. For the chosen Design variables upper and lower bound limits are specified. Ride, Roll and lateral force tests are performed. Optimisation is performed using HyperStudy where it iterates the suspension hard points to match the target toe curves. Each iteration response can be visualized in HyperStudy and can be compared with the target toe curve.
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.
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.