Optimal torque management in multi E-axle heavy-duty electric vehicles for range extension

• Proposed a two-step optimal torque management framework for multi e-axle heavy-duty EVs. • Convex problem formulations in both stages guarantee unique optimal solutions and maximum performance. • Optimal torque allocation minimizes energy loss and reduces stress on e-axle components. • Achieved up to 16.9% ± 5.0% range extension under no-payload and 4.9% ± 3.1% under full payload. • Hardware-in-Loop (HiL) experiments validate the real-time feasibility using a digital twin of an actual electric truck. We address range anxiety in multi e-axle heavy-duty electric vehicles through optimal torque management. A primary cause of reduced driving range and increased energy consumption in heavy-duty electric vehicles is the frequent demand for peak driving torques from the e-axles due to significant payload variations, which can increase the vehicle weight by up to four times its curb weight. An additional challenge lies in the delivery of the requested driving torque from multiple e-axles. Conventional powertrain controllers for multi e-axles evenly distribute the requested driving torque, which often proves suboptimal. It is critical to distribute requested driving torque optimally for low power requests when the e-axles operate in low-efficiency regions. This research work presents a two-step optimal torque management strategy to increase the range. Step 1 utilizes look-ahead (road grade and speed limit) and truck operation (payload) information to generate an optimal driving torque profile for e-axles, particularly suited for highway driving conditions. Step 2 optimally distributes torque across all e-axles to minimize powertrain losses while ensuring smooth operational transitions. The formulated optimal control problems for step 1 and step 2 are solved using Sequential Quadratic Programming (SQP) in both simulation and hardware-in-the-loop (HiL) environments to quantify the performance improvements relative to the baseline, defined as a conventional cruise controller with even torque distribution. The HiL setup employs a Speedgoat Baseline Real-Time Target as the optimal controller and a dSPACE Scalexio unit as the validated digital twin of an actual truck. Dynamic Programming (DP) and a brute-force method establish benchmark performance. Both simulation and HiL results are comparable and show significant gains in both range and freight efficiency. The proposed strategy yields a range improvement of 16.9% ± 5.0% under no-payload conditions and 4.9% ± 3.1% under full-payload conditions, while enhancing freight efficiency by 10.5% ± 1.8%. The results demonstrate significant improvements for heavy-duty electric truck operations.