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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.

State of Charge (SoC) estimation of battery plays a key role in strategizing the power distribution across the vehicle in Battery Management System. In this paper, a model for SoC estimation using Extended Kalman Filter (EKF) is developed in Simulink. This model uses a 2nd order Resistance-Capacitance (2RC) Equivalent Circuit Model (ECM) of Lithium Ferrous Phosphate (LFP) cell to simulate the cell behaviour. This cell model was developed using the Simscape library in Simulink. The parameter identification experiments were performed on a new and a used LFP cell respectively, to identify two sets of parameters of ECM. The cell model parameters were identified for the range of 0% to 100% SoC at a constant temperature and it was observed that they vary as a function of SoC. Hence, variable resistance and capacitance blocks are used in the cell model so that the cell parameters can vary as a function of SoC.

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 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.

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.

Hybridization with engine downsizing is a regular trend to achieve fuel economy benefits. However this leads to a development of new downsized engine which is very costly and time consuming process, also engine downsizing demands for expensive higher power electric system to meet performance targets. Various techniques like gear ratio optimization, reducing number of gears, battery size and control functionalities optimization have been evaluated for maximum fuel economy keeping system cost very low and improving vehicle performance. With optimized gear ratios and reduced number of gears for parallel hybrid, it is possible to operate the engine in the best efficiency zones without downsizing. Motor is selected based on power to weight ratio, gradient requirements, improved acceleration performance and top speed requirement of vehicle in EV mode.

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.

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).