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As battery temperature greatly affects performance, safety, and life of Li-ion batteries in plug-in and electric vehicles under various driving conditions, automakers and battery suppliers are paying increased attention to thermal management for Li-ion batteries in order to reduce the high temperature excursions that could decrease the life and reduce safety of Li-Ion batteries. Currently, the lack of fundamental understanding of the heat generation mechanism due to complex electrochemical phenomena prohibits accurate estimation of the heat generation within Li-ion cells under various operating conditions. Heat from Li-ion batteries can be generated from resistive dissipation, the entropy of the cell reaction, heat of mixing, and other side chemical reactions. Each of these can be a significant source of heat under a range of circumstances.

An ultrasonic air temperature sensor, intended to help improve automatic climate control (ACC), has been demonstrated in a vehicle. Ideally, ACC should be based on inputs correlated with thermal comfort. Current ACC systems do not measure the air temperature best correlated to thermal comfort - at breath level in front of an occupant. This limits the thermal comfort that ACC can provide under transient conditions. An ultrasonic sensor measures the bulk air temperature, is transparent to the driver, and can use commercially available components. In a proof-of-concept test, we monitored the thermal transients in a vehicle during cool-down after a hot soak and also during warm-up after a cold soak. The ultrasonic path was along the roof console. The ultrasonic temperature always agreed to ±1 °C with the air temperature measured by a thermocouple at the midpoint of the ultrasonic path.

Automatic climate control could be improved by measuring air temperature ultrasonically. Thermal comfort correlates better with bulk air temperature than with the temperature measured by the in-car sensor. The time of flight of an ultrasonic pulse through the air gives the bulk air temperature. In a proof-of-concept experiment, it is accurate to ± 0.5 °C from -40 to+60 °C. Two operational modes are demonstrated: pulse-echo in which a single transducer creates a pulse and detects its return from a reflector, and single-pass in which a source transducer creates a pulse that travels directly to a separate transducer.

As one of many pack-level battery simulation approaches developed within the General Motors-led Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project, the system approach treats the entire battery pack as a dynamic system which includes multiple engineering disciplines for simulation. It is the most efficient approach of all the CAEBAT battery pack-level approaches in terms of computational time and resources. This paper reports the application of the system approach for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The results using the system approach are found to have a very good agreement with the measurements.

The Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project is a U.S. Department of Energy-funded, multi-year project which is aimed at developing a complete CAE tool set for the automotive battery pack design. This paper reports the application of the full field approach of the CAEBAT which is developed by the General Motors-led industry team, for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The simulation results using the Full Field Approach are found to have a very good agreement with the measurement data.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.