Search Results

The public transport in India is gradually shifting towards electric mobility. Long range in electric mobility can be served with High Voltage Battery (HVB), but HVB can sustain for its designed life if it’s maintained within a specific operating temperature range. Appropriate battery thermal management through Battery Cooling System (BCS) is critical for vehicle range and battery durability This work focus on two aspects, BCS sizing and its coolant flow optimization in Electric bus. BCS modelling was done in 1D CAE software. The objective is to develop a model of BCS in virtual environment to replicate the physical testing. Electric bus contain numerous battery packs and a complex piping in its cooling system. BCS sizing simulation was performed to keep the battery packs in operating temperature range.

Thermal Management System (TMS) is equally or more important part of Battery Electric (BEV)/Hybrid Electric vehicle (HEV) than an internal combustion engine (ICE) vehicle. In an ICE vehicle, TMS ensures performance of power train/engine, after treatment/exhaust system and HVAC (Climate control) whereas it connected with safety and Range anxiety elimination additionally for the case of Electric Vehicle. Electric powertrain is not a new technology to the world but the technology is evolving in last few decades, to overcome the cost and make it commercially viable, charging infrastructural development and elimination of Range Anxiety. In last few years, Indian automotive industry has taken some major steps towards electrification journey for both passenger car and commercial vehicle. In BEVs, Battery Cooling or Battery thermal management System (BTMS or BCS) and Traction cooling system (TCS) are couple with nearly conventional HVAC circuit used in any ICE vehicle.

The automotive sector is going through a phase of stiff competition among various Original Equipment Manufacturers for increasing their profitability while ensuring highest levels of customer satisfaction. The biggest challenge for such companies lies in minimizing their overall cost involving investments in Research and Development, manufacturing, after sales service and warranty costs. Higher warranty costs not only affect the net profit but in turn it also affects the brand image of the company to a large extent in the long run. An effort is made here to target such warranty costs due to frequent tail pinion and hub seal leakages on single reduction/hub reduction axles of Heavy Commercial Vehicles in the field. A preliminary study involving the severity analysis of such failures is followed by a step by step investigation of these failures.

Globalization has intensively driven focus of car manufacturers on comfort and ergonomics. Luxuries are becoming essential features of product mix. Customer’s expectations and desires are changing because of cut throat competition and increasing variety of options. In order to sustain in marketplace, OEM has to be competitive while providing features and options with appropriate quality. Vigorously changing dimensions and definitions of comfort level, luxury and aesthetics has driven the intense focus of OEM’s on customer touch points, customer touch points are those components of vehicle which customer accesses while driving the vehicle and they play vital role in generating drive feel of vehicle. Customer’s drive feel about the vehicle is most complex and critical factor and is of subjective nature. Now days drive feel is an important aspect of product differentiation. Gear shift feel is very crucial touch point in overall drive feel of vehicle.

The micro hybrid system, also known as the engine stop start system, has recently gained prominence world over due to its considerable fuel saving potential and relatively low costs. In spite of being a relatively non-complex function, the stop start system works hand-in-hand with a wide range of vehicle systems and components, specially the starting system and the battery. Frequent idle stop periods during city driving conditions can result in excessive battery discharge and gradually lead to loss of engine restartability. Increased number of charging and discharging cycles tend to reduce the life of the battery significantly. Hence it is very essential that the micro hybrid vehicles have a system in place that monitors and maintains the battery status within its operating limits.

This paper is related to a vehicle with rear engine which is turbo charged and inter cooled. Due to packaging constraints the intercooler was placed in front of turbocharger and was exposed to hot air radiated out from the turbo charger. This was in turn reducing the efficiency of the intercooler. In such scenario, it is essential to shield the turbo charger from the intercooler for proper hot air management. Also rear engine vehicles don't have the benefit of ram air affect. This necessitates increasing the air entering in to the core of the intercooler. Both the above mentioned issues associated with such a vehicle was resolved by ensuring that the hot air from turbo-charge is guided away from the intercooler as well as the air flow to Intercooler is increased. Guiding or throwing out the hot air away from Intercooler was done by introducing a heat shield or a baffle between the two.

Lithium-Ion batteries are popular for use in Electric vehicle (EV) applications. To improve and understand the use of Lithium-Ion batteries (LIBs) in EV application, present study focused and utilized equivalent circuit models (ECMs). Model parameters are identified using pulse charge and discharge test carried on 20Ah Lithium Iron Phosphate cell. Curve-fitting technique is utilized and detailed procedure to extract model parameters is presented. Models are validated with experimental data of pulse discharge test. Accuracy obtained using 1-RC, 2-RC, 3-RC circuit models is verified and high accuracy of 3RC circuit model can make it act as a battery emulator. Extended Kalman Filter (EKF) is utilized for estimation of State of Charge (SOC) of Lithium Iron Phosphate cell. As per our observation, a good accuracy with low computational burden can be achieved with 1RC model parameters.

Reckoning today's environmental rules, legislative regulation and market requirements- the automotive industry of late has witnessed an increased vigor and enthusiasm by auto makers towards electrification of vehicles across all platforms in a bid to improve fuel economy and performance. Hybridization of a vehicle often involves the use of expensive high performance motors and large battery packs. However due to the challenges associated with the packaging of bulky battery and motor systems in existing drive train, mild hybrid systems have been preferred over strong or full hybrids especially in current production models as they don't entail any major change in architecture and the reduced battery size, both of which provide for easier packaging of components.

The OEM's aim is to reduce development time and testing cost, hence the objective behind this work is to achieve a flexible stateflow model so that changes in the application during supply chain or development, on adding/deleting any switches, varying timer cycle, changing the logic for future advancements or else using the logic in different application, would end in minimal changes in the chart or in its states which would reflect least changes in the code. This research is about designing state machine architecture for chime/buzzer warning system and wiper/washer motor control system. The chime/buzzer stateflow chart includes various input switches like ignition, parking, seat belt buckle, driver door and speed accompanied with warning in the form of LED, lamp and buzzer. The logic is differentiated according to gentle and strong warning. Various conditions and scenarios of the vehicle and driver are considered for driver door and seat belt which is resolved in the chart.

In the automotive industry, multiple prototypes are used for vehicle development purposes. These prototypes are typically put through rigorous testing, both under accelerated and real world conditions, to ensure that all the problems related to design, manufacturing, process etc. are identified and solved before it reaches the hands of the customer. One of the challenges faced in testing, is the low repeatability of the real world tests. This may be predominantly due to changes in the test conditions over a period of time like road, traffic, climate etc. Estimating the repeatability of a real world test has been difficult due to the complex and multiple parameters that are usually involved in a vehicle level test and the time correlation between different runs of a real world test does not exist. In such a scenario, the popular and the well-known univariate correlation methods do not yield the best results.

Indian automotive market has grown extremely competitive in the recent past. In order to meet the ever growing expectations of the customers, automobile manufacturers are compelled to offer their products under superior quality with supreme comfort. Customers wish of high levels of tactile comfort in the cabin compartment and effortless operation of peripherals will result in negligible fatigue and a pleasant drive, needs to be duly fulfilled. One has to focus more on Gear shift lever and Steering wheel, which are being the most sensitive tactile points in an automobile. The gear shift lever knob is frequently used and significantly influences the perception of the shift comfort for a driver during actual vehicle application.

Various vehicle manufacturers are launching electric vehicles, which are more sustainable and environmentally friendly. The major component in electric vehicles is the battery, and its performance plays a vital role. Usually, the end of life of a battery in the automobile sector is when the battery capacity reaches 80% of its maximum rated capacity. The capacity of a lithium-ion cell declines with the number of cycles. So, a semi-empirical model is developed for estimating the maximum stored capacity at the end of each cycle. The parameters considered in the model explain the changes in battery internal structure, like capacity losses at different conditions. The capacity estimated using the semi-empirical model is further taken as the inputs for estimating capacity using the Artificial Neural Network (ANN) and Machine Learning (ML) techniques i.e., Linear Regression (LR), Gaussian Process Regression (GPR), Support Vector Machine methods (SVM).