Predictive battery thermal management strategy for heavy-duty fuel cell electric trucks

Abstract

Current and future regulations are increasingly pushing to replace fossil fuels with alternative fuels. There are several other methods of energy sources available besides environmentally harmful sources, such as fossil fuels. One possible solution is using fuel cells in combination with a battery pack. This thesis aims to get an overview of how predictive control can be used to keep the temperature of a battery within a specific temperature range. To this end, a model predictive control system is being implemented in a hydrogen fuel cell truck. Typically, fuel cell trucks gain the driving power from two energy sources: fuel cell and battery. The fuel cell is an electrochemical process using hydrogen and oxygen. This outputs the desired electrical power for the drive and, as a by-product, pure water. Thus, this fuel cell truck can be considered emission-free for operation. The battery energy is used as a backup source when the power demand exceeds the maximum fuel cell power. This happens especially on steeper routes. Another function of the battery includes the recuperated energy during braking to be considered a buffer. An electric powertrain is installed in the truck to control the power distribution in these two systems. This powertrain regulates the energy management in the system and decides automatically when the fuel cell power is not sufficient anymore and transmits the battery energy to the drive. For this system, there exist some restrictions, especially in the size of the cooling system, because of the limited space in the truck. In order to ensure sufficient cooling for the truck’s battery, a model predictive controller is investigated in this thesis. This uses a linear model and constraint for the calculation. A reference target for the battery temperature and soft constraint is implemented for better results to keep the battery temperature close to the desired reference temperature. Higher temperatures lead to accelerated battery degradation, resulting in a shorter lifespan and decreased efficiency. Therefore, cooling must be applied in advance to keep the battery’s temperature within a predefined range. A forecast on the route is used, which contains route information, including elevation and speed limits. With this data, it is possible to forecast the electrical load on the system. In addition, predictive energy management can be used to estimate the ohmic losses. All this information is needed for the MPC, which predicts the desired cooling power of the battery. The result of this study shows a significant improvement in maintaining the battery temperature close to the desired reference temperature under different load requirements. The use of a predictive cooling strategy can, therefore, increase the service life of the battery.