Method and System for Efficient Thermal Management in Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">Electric Trucks offer one of the most promising alternatives to vehicles in the field of transport of goods. In battery electric trucks, heat is generated by components present in the electric truck such as battery of the electric vehicle, electric drive system, Endurance Brake System etc. which require cooling and Thermal management system to control and monitor the cooling system.</div><div class="htmlview paragraph">The thermal management system considered here includes two coolant tanks. The first coolant tank performs thermal management for the battery and Electric-Drive(e-Drive) components which can heat up to 60<sup>0</sup>C and the second coolant tank performs thermal management for HPR circuit, and it is used to break the charging circuit to protect the battery getting charged beyond 100% using regenerative braking concept. HPR (High performance resistor) is the component which can heat up to ~95<sup>0</sup>C and make sure the battery is not getting charged beyond the safe limits. Since HPR is a critical component and operates at high temperatures, it has a separate coolant tank.</div><div class="htmlview paragraph">However, in the conventional thermal management systems, the second coolant tank is used less than 5% in entire lifetime of the vehicle. It is underutilized because of less frequent operation of the EB circuit and the first coolant circuit experiences more frequent operation due to more frequent operation of the battery. This disparity in utilization results in an inefficient use of the coolant in the thermal management system. The inefficient use of the coolant provides inefficient cooling and reduce electric vehicle performance. The proposed solution is to predict Cooling and heating requests for system level components so that coolant from Battery Circuit/e-Drive Circuit and HPR Circuit can be dynamically transferred between the circuits depending on the enable requests and climatic conditions. This will improve the overall cooling efficiency and reduce the time taken for components to cool. In the MATLAB model, Percentage decrease in time taken to cool is measured around 30%</div></div>