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Government regulations and growing concerns regarding global warming has lead to an increasing number of passenger vehicles on the roads today that are not powered by the conventional internal combustion (IC) engine. Automotive manufacturers have introduced electric powertrains over the last 10 years which have introduced new challenges regarding powering accessory loads historically reliant on the mechanical energy of the IC engine. High density batteries are used to store the electrical energy required by an electric powertrain and due to their relatively narrow acceptable temperature range, require liquid cooling. The cooling system in place currently utilizes the A/C compressor for cooling and a separate electric element for heating which is energy expensive when the source of energy is electricity. The proposed solution is a thermoelectric heat pump for both heating and cooling. A model predictive controller (MPC) is designed, implemented and tested to optimize the operation of the thermoelectric heat pump. The model predictive controller is chosen due to its ability to accept multiple constrained inputs and outputs as well as optimize the system according to a cost function which may consist of any parameters the designer chooses. The system is highly non-linear and complex therefore both physical modelling and system identi cation were used to derive an accurate model of the system. A steepest descent algorithm was used for optimization of the cost function. The controller was tested in a test bench environment. The results show the thermoelectric heat pump does hold the battery at the speci ed set point however more optimization was expected from the controller. The controller fell short of expectation due to operational restriction enforced during design meant to simplify the problem. The MPC controller is capable of much better performance through adding more detail to the model, an improved optimization algorithm and allowing more flexibility in set point selection.

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