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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.

In previous work, AC Compressor Cycling (ACC) was modeled by incorporating evaporator thermal inertia in Mobile Air Conditioning (MAC) performance simulation. Prediction accuracy of >95% in average cabin air temperature has been achieved at moderate ambient condition, however the number of ACC events in 1D CAE simulation were higher as compared to physical test [1]. This paper documents the systematic approach followed to address the challenges in simulation model in order to bridge the gap between physical and digital. In physical phenomenon, during cabin cooldown, after meeting the set/ target cooling of a cabin, the ACC takes place. During ACC, gradual heat transfer takes place between cold evaporator surface and air flowing over it because of evaporator thermal inertia.

Mobile Air Conditioning (MAC) system provides year round thermal comfort to the occupants inside vehicle cabin. In present scenario, 1D CAE simulation tools are widely used for MAC system design, component sizing, component selection and cool down performance prediction. The MAC component sizing and selection mainly depends on cooling load which varies with ambient conditions, occupancy, cabin size, geometry and material properties. Therefore, detailed modeling of vehicle cabin is essential during MAC system digital validation as it helps to predict performance across wide number of contributing factors. There are two different methods available in 1D Simulation for vehicle cabin modeling, viz. ‘simple cabin’ and ‘advance cabin’. With the simple cabin modeling approach, vehicle cabin is modelled as a group of lumped masses, which only enables prediction of average vent and average cabin temperatures. In advance cabin modeling approach, vehicle cabin is modelled more comprehensively.

Vehicle thermal management system (VTMS) is a means of monitoring and controlling temperatures of vehicular components and aggregates to within optimum limits, thereby ensuring the proper functioning of the component or aggregate in an automobile. An integrated approach is required for developing VTMS, to satisfy the complex requirements of performance, reliability, fuel economy and human thermal comfort in modern vehicles. Fan motors and blowers play a crucial role in vehicle thermal management. These fan motors/ blower systems need to be designed in a manner such that there is minimum parasitic load on the prime mover. This work comprises performing Transient Powertrain Cooling (T-PTC) and Transient Air-conditioning (T-AC) simulation on a vehicle for prediction of parameters affecting fan operation of Condenser Radiator Fan Module (CRFM) during simulated city drive cycles.

Electric vehicles (EVs) carries two main anxieties in users which are its range and battery life, hence these are important parameters to be taken care of during electric vehicle development. Range of EV depends on many parameters such as vehicle weight, heating ventilation and air conditioning (HVAC) system, battery cooling system (BCS), traction cooling system (TCS) and other electrical loads, which consumes power from a High Voltage (HV) battery. Severe hot ambient in India requires a big size HVAC system, on the other hand, the battery pack needs refrigerated cooling system to keep its temperature in control. Hence, the major parasitic consumers in an EV are HVAC and BCS. In order to enhance the overall efficiency, a trade-off between these two systems is crucial, as both the systems are served with common compressor and condenser in dual loop refrigerant circuit.

High temperatures in the surface mounted permanent magnet (SMPM) synchronous motor adversely affect the power output at the motor shaft. Temperature rise may lead to winding insulation failure, permanent demagnetization of magnets and encoder electronics failure. Prediction and management of temperatures at different locations in the motor should be done right at the design stage to avoid such failures in the motor. The present work is focused on the creation of Lumped Parameter Thermal Network (LPTN) and CFD models of SMPM synchronous motor to predict the temperature distribution in the motor parts. LPTN models were created in Motor-CAD and Simulink which are suitable for parameter sensitivity analysis and getting quick results. Air is assumed to be a cooling medium to extract heat from the outer surface of motor. CFD models were useful in providing elaborate temperature distribution and also locating the hot-spots. Correlation models by both the methods, viz.

EPA 2017-2025 regulations have increased the corporate average fuel economy (CAFE) requirement by 33% for 2025 model year vehicles. Similarly, the EU has set a target of reducing CO2 emission by 27% (with respect to 2015 targets) for vehicles launching after 2021. These constraints have diverted the attention of most of the OEMs towards hybrid electric vehicles, which give out lower emissions and have higher fuel economy as compared to the vehicles propelled by an IC engine. Hence, many automakers have started working on plug-in hybrid electric (PHEV) variants of the existing vehicle models. Vehicle architecture of a PHEV consists of additional electrical components apart from the conventional coolant consumers. The coolant flow requirements to all these additional components becomes challenging for its efficient operation and coolant flow balancing.

In developing nations, most passenger vehicles are equipped with mobile air conditioning (MAC) systems that work on Hydro Fluoro Carbons (HFC) based refrigerants. These refrigerants have a high global warming potential (GWP) and hence adversely affect the environment. According to the Kigali amendment to Montreal Protocol, Article-5 Group-2 countries including India must start phasing down HFCs from 2028 and replace them with low Global Warming Potential (GWP) refrigerants. One such class of low GWP refrigerant is Hydro Fluoro Olefins (HFO) In order to replace HFCs with HFOs in existing MAC systems, the various system performance parameters with the new refrigerant are required to be evaluated. Performance evaluation of MAC system is rendered quicker and cost-effective by deploying a digital simulation tool. There is good correlation and confidence established for MAC performance prediction with HFCs through 1D CAE.