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Recently, Hybrid electric vehicles have become significant. Electric vehicle is still in its infancy while grappling with multiple solutions to its problem of range anxiety and heavy weight. It makes HEV the viable and intermediate solution which can facilitate the transition. The battery behaviour is grossly defined by its dependence on variation due to temperature change. Hence, this present work focuses on understanding thermal characterization & pure behaviour of the Li-Ion Phosphate (LiFePO4) P1-HEV battery using the HPPC test. This becomes imperative because of the varying driver demands and ambient temperatures over the use during the day. Thus, the current drawn from battery varies (different C rate) leading to heat generation (I2R heating) within the pack/individual cell. Cyclically, impacting the cell performance and battery cycle life.

To enhance the crashworthiness of electric vehicles, designing the optimized and safer battery pack is very essential. The deformed battery cell can result in catastrophic events like thermal runaway and thus it becomes crucial to study the mechanical response of battery cell. The goal of the research is to experimentally investigate the effect of mechanical deformation on Lithium-ion battery cell. The paper thoroughly studies the phenomenon of short circuiting at the time of failure. Various experiments are carried on 18650 cylindrical cells (NCA chemistry) under custom designed fume hood. The setup captures the failure modes of battery cell. The loading conditions have been designed considering the very possible physical conditions during crash event. The study has been done for radial compression, semicircular indentation, hemispherical indentation, flat circular indentation and case of three-point bending.

The Electric Vehicles (EV) or Hybrid Electric Vehicle (HEV) has a bunch of electrical/electronic components and its operation give rise to complicated EMI/EMC issues. The Power Electronics Module (PEM), comprising of DC-DC convertor/invertor and Battery Management Unit (BMU), is driving the motor to propel the vehicle. “Battery Pack Module” powers these units through cables. The fast switching of these circuit elements present in the system leads to noise propagation through the cables. These noise signals give rise to various Electromagnetic (EM) related issues in the cable harness of vehicle. It is essential that these cables should not interfere with other electronic components and also does not get effected by external EM disturbances.

Design of real time active controls for structural dynamics problems requires a very precise mathematical model, to closely determine the system dynamic behavior, under virtual simulation. The finite element models can somehow be used as a mathematical model but due to complex shape/structure of the component, the size of discrete models resulting from finite element analysis is usually very large, causing the virtual simulation to be extremely computationally intensive and time consuming, also the boundary conditions applied are not very scalable, making the system deviate from its real dynamic behavior. Thus, this paper deals with the design of a Model Order Reduction technique, using orthogonal decomposition of system matrices, which can be used for creating accurate low-order dynamic model with scalable boundary conditions.

Electric Vehicles (EVs), with its inherent advantage of zero tailpipe emissions, are gaining importance because of aggressive push from government not only to reduce air pollution but also to reduce dependency of fossil fuel. EVs and necessary charging infrastructure along with ‘connected’ technology is redefining mobility. Considering the fast growing EV market, it becomes important for an EV Powertrain Architect to design and develop a powertrain solution having low engineering efforts and satisfying business, market and regulatory requirements at a competitive price. This paper presents a compact, flexible, convenient and smart featured simulation tool for an EV Powertrain Architect for estimating the specifications of key powertrain components such as traction battery and electric motor. The proposed tool takes into consideration the end-user as well as the regulatory requirements of range, maximum speed, acceleration and gradeability.

The use of Hybrid Electric Vehicles (HEV) will become imperative to meet the emission challenges. HEV have two power sources-fossil fuels driven I.C. Engine and the battery based drive. Battery technologies have seen a tremendous development, and therefore HEV’s have been benefited. Even as the battery capacities have improved, maintaining and monitoring their health has been a challenge. This research paper uses open-source platform to build a BMS. The flexibility in the implementation of the system has helped in the rapid prototyping of the system. The BMS system was evaluated on a scaled-down electric toy car for its performance and sustainability. The BMS was evaluated for reverse polarity, protection against overcharge, short-circuit, deep discharge and overload on lead acid battery. It also includes temperature monitoring of the batteries. This proposed system is evaluated on the in-house HEV two-wheeler. The initial results are promising.

The technology development in automobiles is progressing towards providing smarter vehicles with increased efficiency and reduced emission. To cater this need, Electric Vehicles (EV) and Hybrid Electric Vehicles (HEV) are slowly thriving in Indian roads. Conversion of existing IC engine powered vehicle to HEV reduces complication in new vehicle development and also results in vehicles with increased efficiency and reduced emission. In order to convert the Conventional Vehicle to Hybrid Electric Vehicle, drive from electric motor was coupled with existing driveline by modifying mechanical systems suitably. Hybrid vehicle includes systems such as electric motors, inverters, high-voltage batteries and electronic control units, which are mounted in chassis members. Being a major load carrying member, any modifications in chassis system may affect the performance of vehicle, therefore it is necessary to evaluate the modified design of chassis members.

Role of wheel and underbody aerodynamics of vehicle in the formation of drag forces is detrimental to the fuel (energy) consumption during the course of operation at high velocities. This paper deals with the CFD simulation of the flow around the wheels of a bus with different wheel housing arrangements. Based on benchmarking, a model of a bus is selected and analysis is performed. The aerodynamic drag coefficient is obtained and turbulence around wheels is observed using ANSYS Fluent CFD simulation for different combinations of wheel-housing- at the front wheels, at the rear wheels and both in the front and rear wheels. The drag force is recorded and corresponding influence on energy consumption of a bus is evaluated mathematically. A comparison is drawn between energy consumption of bus body without wheel housing and bus body with wheel housing. The result shows a significant reduction in drag coefficient and fuel consumption.

Active control mainly comprises of three parts; sensor-detects the input disturbance, actuator -provide counter measures and control logic -processing of input disturbances and converting it into logical output. Lot of methods for active vibration control are available but this paper deals with active control of steering wheel vibrations of an LCV. A steering wheel is, one such component that directly transfers vibration to the driver. Active technique described here is implemented using accelerometer sensor, IMA (Inertial Mass Actuator) and feed forward Fx-LMS (Filtered reference Least Mean Square) control algorithm. IMA is a single-degree-of-freedom oscillator. To enable a control, IMA needs to be coupled to the structure at a single point, acting as an add-on to the passive system. Fx-LMS is a type of adaptive algorithm which is computationally simple and it also includes compensation for secondary path effects by using an estimate of the secondary path.

The Indian market for battery-powered electric vehicles (xEV) is growing exponentially in the coming years, fueled by tumbling lithium-ion battery prices and favorable government policies. Lithium-ion battery is leading in clean mobility ecosystem for electric vehicles. LiBs efficient and safe performance for tropical climatic conditions is one of the primary requirements for xEV to succeed in India. The performance of LiBs, however, is impacted due to ambient temperature as well as the heat generated within cell due to the load cycle electrochemical reaction. The acceptable operating temperature region for LiBs normally is between 20 °C to 45 °C and anything outside of this region will lead to degradation of performance and irreversible damages. Therefore, understanding the thermal behavior is very crucial for an efficient battery thermal management.

Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology. ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision. There are number of open source annotated datasets available viz. COCO, KITTI etc.

The Light commercial vehicle (LCV) is primarily used for the last mile delivery and it hold the volume share of around 61% in the commercial vehicle segment. The last mile delivery services have seen a massive surge after the CoVID 19 pandemic resulting is the increase sale of LCV in last few years and is expected to grow further by 8-11% in the coming years. However, city logistic is also responsible for most pollution and noise in the city. Hence, policymakers are aiming to reduce carbon footprint by promoting the use of Electric vehicle by providing incentive to automakers though schemes like FAME I and FAME II. In order to effectively reduce the carbon footprint within city it is important to increase the use of new electric vehicle and convert the old polluting vehicles to electric. Hence, a retro fitment solution for converting used LCV to electric can help in reducing emission as well as noise pollution. Later the same solution can be offered as OE fitment solution.

Researchers are under pressure to investigate and discover ways to improve the efficacy and reduce emissions from ICE due to the depletion of energy resources and the growing concern over global warming. Hydrogen is viewed as a promising fuel and has been investigated as a potential fuel in combustion because to several desirable qualities like carbon-less content and strong flammability limitations. When equated to other alternative fuels like LPG, CNG, LNG, etc., hydrogen has inimitable qualities because it lacks carbon, making it one of the promising alternatives fuels. In order to achieve zero CO2 emissions for traffic applications in the near future, hydrogen being an automotive fuel in ICE is a solution. The ICE powered by hydrogen is prepared for that. The actual drawbacks of using hydrogen in ICE generally are manufacturing, storage, and development of the requisite infrastructure. Hydrogen can be produced in its many forms.

Advanced Driver Assistance Systems (ADAS) play a crucial role in enhancing road safety by providing intelligent assistance to drivers. To ensure the reliability and effectiveness of ADAS functions, rigorous verification and validation processes are necessary. One critical aspect of this process is scenario generation, which involves creating diverse and representative driving scenarios for testing and evaluating ADAS functions. This paper proposes a novel approach for synthetic scenario generation specifically tailored for Indian road conditions. The approach leverages real-time road data collected from various sources, including camera sensors, Lidar sensor, GPS devices, and traffic monitoring systems. The collected data is processed and analyzed to extract relevant information, such as road geometries, traffic patterns, and environmental conditions.

One of the primary reasons for road accidents is driving while distracted or drowsy. Often, long and monotonous road journeys lead to distracted or drowsy driving. Therefore, there is a need for a system which alerts a distracted or drowsy driver. Moreover, as the levels of autonomy move beyond SAE Level 2, the system assumes a larger share of the dynamic driving task. Under challenging circumstances, the system might ask the driver to take back vehicle control. To guarantee safety, it’s crucial to monitor the driver’s condition in order to assess their readiness to regain control of the vehicle. An advanced safety feature known as a driver monitoring system (DMS), sometimes referred to as a driver state sensing (DSS) system, is designed to monitor a driver’s attentiveness and alertness, providing warnings or alerts to refocus their attention on driving when drowsiness or distraction is detected.

Battery is one of the safety critical systems in EV. As the number of EVs increases, battery safety becomes an important task to avoid any mishap during its use, as even small accidents may slow down the adaptation of EVs. Automotive environment being one of the harshest operating environments, it is important to ensure both mechanical and electrical safety of the battery pack. Li-Ion batteries are most popular among traction batteries, due to their high energy density, long life, and fast charging capabilities. But mechanical damage, over temperature, short-circuit, etc. may lead to battery thermal runaway, causing a major accident. Mechanical abuse of battery can be one of the reasons that may lead to the damages mentioned above, eventually causing thermal runaway in batteries. That’s why all major battery safety standards have requirements for vibration and mechanical shock tests.

Computer-aided engineering (CAE) is a routinely used technology for the design and testing of road vehicles, including the simulation of their response to an impact. To increase automotive industry competitiveness by reducing physical test-based type approval and to improve road safety, recent initiatives have been taken by both industry and public authorities to promote the use of virtual testing through numerical simulation as an alternative way to check regulatory compliance. [1] To ensure acceptance of this alternative method, the accuracy of the simulation models and procedures needs to be assured and rated independently of the modelling process, software tools, and computing platform. Similarly, it is also imperative to understand the uncertainties emerging out of different component design parameters and analyze their sensitivity towards producing deviations in the reported results as per the requirements of the regulatory standard.

The term Industry 4.0 is well known in contemporary automotive landscape. It encompasses a smart integrated framework of IIoT (Internet of Things) and industrial automation with machine learning, artificial intelligence and big data analytics to arrive at optimal solutions to running the processes in a streamlined, efficient and effective manner. Industry 4.0 has assumed critical significance in the contemporary era of people working from remote locations to operate processes in order to build products, thereby ensuring business continuity. Consequently, it follows that if industry 4.0 is applied to automotive homologation activity, it will lead to a standardized evaluation, consistent fidelity of testing, accurate judgement of the product under test with regards to its certification, and most importantly, timed delivery to release in the market. The author hereby elucidates a unified Industry 4.0 Framework for Automotive homologation in India which is the need of the hour.

With the recent development in virtual modelling and vehicle simulation technology, many OEM’s worldwide are using digital road profiles in virtual environment for vehicle durability load prediction and virtual design evaluation. For precise simulation results, it is important to have the tire digital twin which is the realistic representation of tire in the virtual environment. The study comprises of discussion about different types of tire models such as empirical, solid model, rigid ring model and flexural ring models such as Pacejka, MF Swift, CD tire, F tire etc. and also the complexity involved in development of these tire models. Generation of virtual tire model requires highly sophisticated test rigs as well as vehicle level testing with Wheel Force transducers and other vehicle dynamics sensors. The large number of data points generated with testing are converted in standard TYDEX format to be further processed in various software tool for virtual model generation.