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With the current state of ever rising fuel prices and unavailability of affordable alternate technologies, significant research and development efforts have been invested in recent times towards improving fuel efficiency of vehicles powered with conventional internal combustion engines. To achieve this, a varied approach has been adopted by researchers to cover the entire energy chain including fuel quality, combustion quality, power generation efficiency, down-sizing, power consumption efficiency, etc. Apart from energy generation, distribution and consumption, another domain that has been subjected to significant scrutiny is energy recuperation or recovery. A moving vehicle and a running engine provide a number of opportunities for useful back-recovery and storage of energy. The most significant sources for recuperation are the kinetic energy of the moving vehicle or running engine and to a lesser extent the thermal energy from medium such as exhaust gas.

The use of Wheel Force Transducers (WFTs) to acquire data for laboratory simulation is becoming standard industry practice. However, in test rigs where we have only the suspension module and not the complete vehicle, does the reproduction of the orthogonal forces and moments at the wheel centre guarantee an accurate replication of the fatigue damage in the suspension components? The objective of this paper is to review the simulation methodology for a highly non-linear suspension in a 3 DOF (degree-of-freedom) suspension test rig in which the simulation was carried out using only the three orthogonal loads and vertical displacement. The damage at critical locations in the suspension is compared with that on the road and an assessment of the simulation using the WFT is made based on a comparison of the damage on the road vs. the rig.

On Board Diagnostics norms enforced by regulatory authorities of many countries require logging of distance traveled by the vehicle with MIL (malfunction indicator lamp) illuminated. This log needs to be maintained in non-volatile ECU memory. Conventional techniques maintain the log in a volatile memory during vehicle run-time and transfer the same to non-volatile memory when ignition is turned off. This requires use of a “power-hold” relay to keep an ECU power alive while the logged data in volatile memory is being transferred to non-volatile memory when ignition is switched-off. A novel strategy described in this paper avoids interface with power-hold relay, thereby reducing the system complexity. The design philosophy described makes use of an EEPROM to maintain the distance log. An innovative algorithm is employed to ensure that endurance specifications are not violated during the vehicle life-time.

Automotive embedded control systems need to implement real-time closed-loop control strategies for controlling valves, motors, etc. The implementation needs to focus on use of low cost hardware and efficient software with minimal foot-print so as to adequately meet the application requirement. This paper highlights the low cost hardware and software design concepts by way of a case study related to control of progressive EGR valve. The control strategy is based on "map-driven set-points" where percentage opening of the valve is stored in the form of 16x16 matrices. The set-points are accessed based on instantaneous throttle and engine rpm values which form the row and column indices of the map. The closed loop control algorithm eliminates the need for multiplication by implementing "feed-forward with integral control algorithm." A feed-forward map specifies the most likely PWM duty cycle to be applied to the valve for a given set-point.

Micro Hybrid Systems are a premier approach for improving fuel efficiency and reducing emissions, by improving the efficiency of electrical energy generation, storage, distribution and consumption, yet with lower costs associated with development and implementation. However, significant efforts are required while implementing micro hybrid systems, arising out of components like Intelligent Battery Sensor (IBS). IBS provides battery measurements and battery status, and in addition mission critical diagnostic data on a communication line to micro hybrid controller. However, this set of data from IBS is not available instantly after its initialization, as it enters into a lengthy learning phase, where it learns the battery parameters, before it gives the required data on the communication line. This learning period spans from 3 to 8 hours, until the IBS is fully functional and is capable of supporting the system functionalities.

Engine Stop/Start System (ESS) promises to reduce greenhouse emissions and improve fuel economy of vehicles. Previous work of the Authors was concentrated on bridging the gap of improvement in fuel economy promised by ESS under standard laboratory conditions and actual driving conditions. Findings from the practical studies lead to a conclusion that ESS is not so popular among the customers, due to the complexities of the system operation and poor integration of the system design with the driver behavior. In addition, due to various functional safety requirements, and traffic conditions, actual benefits of ESS are reduced. A modified control algorithm was proposed and proven for the local driving conditions in India. The ways in which a given driver behaves on the controls of the vehicles like Clutch and Brake Pedals, Gear Shift Lever were not uniform across the demography of study and varied significantly.

During every clutch engagement energy is dissipated in clutch assembly because of relative slippage of clutch disc w.r.t. flywheel and pressure plate. Energy dissipated in clutch is governed by many design parameters like driveline configuration of the vehicle vis-a-vis vehicle mass, and operational parameters like road conditions, traffic conditions. Clutch burning failure, which is the major failure mode of clutch assembly, is governed by energy dissipation phenomenon during clutch engagement. Clutch undergoes different duty cycles during usage in city traffic, highways or hilly regions during its lifetime. A test schedule was derived using energy dissipated during every clutch engagement event as a base and using road load data collected on the vehicle. Road load data was collected in different road mix conditions comprised of city traffic, highway, hilly region, rough road for few hundred kilometers.

Spot welding is the primary joining method used in automobiles. Spot-weld plays a major role to maintain vehicle structural integrity during impact tests. Robust spot weld failure definitions is critical for accurate predictions of structural performance in safety simulations. Spot welds have a complex metallurgical structure, mainly consisting of fusion and heat affected zones. For accurate material property definitions in simulation models, huge number of inputs from test data is required. Multiple tests, using different spot weld joinery configurations, have to be conducted. In order to accurately represent the spot-weld behavior in CAE, detailed modeling is required using fine mesh. The current challenge in spot-weld failure assessment is developing a methodology having a better trade-off between prediction accuracy, testing efforts and computation time. In view of the above, cohesive zone models have been found to be very effective and accurate.

The automotive industry is heading in the direction of signing off the exhaust system durability based on computer simulation rather than rig simulation and physical vehicle testing. This is due to the cost, time and availability of prototype vehicles and test track. Use of Finite Element Method (FEM) enables to assure the structural integrity of the exhaust system and also contribute to better understanding of the system behavior in the various operating conditions and evaluation of structural strength. This paper deals with dynamic analysis of a modular automotive exhaust system where it is directly mounted on power train pack. Selection of dynamic loads, processing of the test data, and effect of assembly loads along with material property variation due to temperature are explained. It also includes validation of the CAE model, prediction of probable failure locations and improving the design based on analysis outcome.

Authors were challenged with a task of developing a full vehicle simulation model, with a target to simulate the electrical system performance and perform digital tests like Battery Charge Balance, in addition to the fuel efficiency estimation. A vehicle is a complicated problem or domain to model, due to the complexities of subsystems. Even more difficult task is to have a control algorithm which controls the vehicle model with the required control signals to follow the test specification. Particularly, simulating the control of a vehicle with a manual transmission is complicated due to many associated control signals (Throttle, Brake and Clutch) and interruptions like gear changes. In this paper, the development of a full vehicle model aimed at the assessment of electrical system performance of the vehicle is discussed in brief.

Steering pull during high speed braking of heavy commercial vehicles possesses a potential danger to the occupants. Even with negligible wheel-to-wheel brake torque variation, steering pull during the high speed braking has been observed. If the steering pull (i.e. steering rotation) is forcibly held at zero degree during high speed braking, the phenomena called axle twist, wheel turn and shock absorber deflection arise. In this work the data have been collected on the mentioned measures with an intention to develop a mathematical model which uses real time data, coming from feedback mechanism to predict the values of the measures in coming moments in order to aid steering system to ‘auto-correct’. Driven by the intention, ‘Time Series Analysis’, a well-known statistical methodology, has been explored to see how suitable it is in building the kind of model.

Shock tube is used to simulate blast loading conditions on materials for studying the failure behavior of different materials under blast pressures on smaller scale. This paper describes CAE method developed for simulating shock tube experiment in LS-DYNA3D environment. The objective of shock tube simulation is to characterize material failure parameters so as to predict risk of material failure in full vehicle blast simulations while developing vehicle for blast protection applications. The paper describes modeling of shock wave and its interaction with test specimen in shock tube environment. Arbitrary Lagrangian-Eulerian (ALE) techniques are applied to simulate shock tube experiment in LS-DYNA3D and simulation predictions are compared with experimental test data. CAE correlation studies were carried out with respect to incident and reflected pressures in shock tube, deformation and plastic strains on test specimen, shock wave velocity etc.

Tire inflation pressure has a significant impact over vehicle driving dynamics, fuel consumption as well as tire life. Therefore, continuous monitoring of tire pressure becomes imperative for ride comfort, safety and optimum vehicle handling performance. Two types of tire pressure monitoring systems (TPMS) used by vehicles are - direct and indirect TPMS. Direct systems deploy pressure sensors at each wheel and directly send pressure value to the vehicle Controller Area Network (CAN). Indirect sensors on the other hand use the information from already existing sensors and some physics-based equations to predict the value of tire pressure. Direct TPMS tend to be more accurate but have higher cost of installation while indirect TPMS comes with a minimum cost but compromised accuracy. A digital proof-of-concept study for indirect TPMS development of a non-ESP vehicle based on machine learning (ML) technique is elaborated in this paper.

With the revolutionary advancements in modern transportation, offering advanced connectivity, automation, and data-driven decision-making has put the intelligent transportation systems (ITS) to a high risk from being exposed to cyber threats. Development of modern transportation infrastructure, connected vehicle technology and its dependency over the cloud with an aim to enhance safety, efficiency, reliability and sustainability of ITS comes with a lot more opportunities to protect the system from black hats. This paper explores the landscape of cyber threats targeting ITS, focusing on their potential impacts, vulnerabilities, and mitigation strategies. The cyber-attacks in ITS are not just limited to Unauthorized Access, Malware and Ransomware Attacks, Data Breaches, Denial of Service but also to Physical Infrastructure Attacks.

The auto industry is one of the major contributors for noise pollution in urban areas. Specifically, highly populated heavy commercial diesel vehicle such as buses, trucks are dominant because of its usage pattern, and capacity. This noise is contributed by various vehicle systems like engine, transmission, exhaust intake, tires etc. When the pass by noise levels exceeds regulatory limit, as per IS 3028, it is important for NVH automotive engineer to identify the sources & their ranking for contribution in pass by noise. The traditional methods of source identification such as windowing technique, sequential swapping of systems and subsystems which are time consuming.Also advanced method in which data acquisition with a synchronizing technology like telemetry or Wi-Fi for source ranking are effective for correctness.However they are time and resource consuming, which can adversely impact product development timeline.

One of the very first customer touchpoint in a vehicle is quality of gear shifting. Gearshift quality is perceived as a symbol of refinement of a vehicle. Globally, lot of efforts are taken to refine the gearshift quality. Design improvements in internal components of transmission, cable and shifter assembly, knob design iterations are carried out to arrive at optimum gearshift quality at the vehicle level. Current practice for this activity includes processes such as design modification, manufacturing of proto components, assembly of components and fitment in the vehicle. This vehicle is then instrumented with sensors and data acquisition units to capture the parameters which determine the gearshift quality. This is an iterative process which goes on until necessary refinement/improvement is achieved. This process requires investment of lot of time, efforts and the budget. This paper describes a virtual approach to arrive at optimum design of components.

This paper focuses on developing an application to extract insights from Android app reviews of Connected Car Applications and Twitter conversations related to OEM’ PV & EV Vehicles and features. Analyzing user sentiments and preferences in real-time can drive innovation and elevate OEMs' customer satisfaction. These insights have the potential to enhance vehicle performance and the manufacturing process. The application employs data collection and Natural Language Processing (NLP) techniques, including User-Driven Sentiment Classification and topic modeling, to analyze user sentiments and identify key discussion topics visually.

The automotive world is rapidly moving towards achieving shorter lead time using high-end technological solutions by keeping up with day-to-day advancements in virtual testing domain. With increasing fidelity requirements in test cases and shorter project lead time, the virtual testing is an inevitable solution. This paper illustrates method adopted to achieve best approximation to emulate driver behavior with 1-D (one dimensional) simulation based modeling approach. On one hand, the physical testing needs huge data collection of various parameters using sensors mounted on the vehicle. The vehicle running on road provides the real time data to derive durability test specifications. One such example includes developing duty cycle for powertrain durability testing using Road Torque Data Collection (RTDC) technique. This involves intense physical efforts, higher set-up cost, frequent iterations, vulnerability to manual errors and causing longer test lead-time.

This paper introduces a novel approach to automate PID calibration for closed-loop control systems and the creep control function in an electric vehicle. Through a comprehensive literature survey, it is found that this method is the first of its kind to be applied in the field of automated electric vehicle calibration for Creep function. The proposed approach utilizes a systematic methodology that automatically tunes the PID parameters based on predefined performance criteria, including energy consumption and jerk. To implement this methodology, the ETAS INCA FLOW software, which provides guided calibration methods for in-vehicle testing & calibration, is employed. The calibration process is performed on a real-time electric vehicle platform to validate the effectiveness of the proposed approach. The results of this study showcases the advantages of automated PID calibration for closed-loop control systems and creep control function in small commercial electric vehicle.