Energy productivity improvement using an operation-aware optimal predictive cruise controller for heavy-duty electric vehicles

The goal of this research is to improve the energy productivity of heavy-duty electric vehicles through the development of a predictive cruise controller (PCC), which is optimized for various payload conditions (operation-aware). Energy productivity is defined as the amount of freight transported over a given distance per unit of average energy consumed within a specified travel time. In the trucking industry, both energy consumption and travel time are critical performance metrics. While reducing energy consumption is essential for extending driving range, it must not lead to excessive increases in travel time, which can cause delayed deliveries and higher operational costs. Accordingly, the proposed PCC is designed to be practically feasible for real-world truck operations. The predictive cruise controller is formulated as an optimal control problem (OCP) that simultaneously minimizes energy consumption and reference speed tracking error, with their relative importance governed by a penalty factor. This study demonstrates that the optimal selection of this penalty factor must depend on truck operating conditions, particularly payload variation and travel time constraints, in order to achieve improved energy productivity. The formulated OCP is shown to be convex, which makes it well suited for solution using gradient-based optimization methods. Sequential Quadratic Programming (SQP) is employed to solve the OCP in both simulation and hardware-in-the-loop environments. The results demonstrate that the proposed PCC, combined with an optimal and operation-aware selection of penalty factors, achieves an energy productivity improvement of up to 3.1% while effectively balancing energy consumption and travel time increments. This balanced performance is essential for enabling efficient, reliable, and cost-effective heavy-duty truck operations.