The Need

Fleet operators face increasing pressure to reduce operating costs and emissions while maintaining performance and reliability. Choosing the “right” truck (diesel, alternative fuel, or battery electric) for a specific duty cycle remains largely heuristic, conservative, and error‑prone. As a result, fleets often over‑spec engines, underutilize electrification opportunities, or deploy electric trucks on unsuitable routes, leading to higher total cost of ownership, range anxiety, and slower decarbonization.

The Technology

OSU engineers have developed a data‑driven vehicle and powertrain recommendation system (PRS) that predicts energy consumption and performance for heavy‑duty trucks under real‑world operating conditions. Using machine‑learning models trained on vehicle specifications and route/drive‑cycle data, the system rapidly evaluates millions of feasible configurations. It then applies multi‑objective optimization to recommend Pareto‑optimal powertrain and vehicle configurations tailored to specific routes, payloads, and operational constraints.

Commercial Applications

• Fleet vehicle procurement and specification decision‑support tools
• Fleet management and route‑to‑vehicle assignment software
• Electric truck deployment and electrification planning platforms

Benefits/Advantages

• Route‑specific optimization: Matches powertrain and vehicle specs to actual duty cycles rather than generic assumptions
• Technology‑agnostic: Enables objective comparison of diesel, alternative fuel, and electric powertrains
• Scalable and fast: Evaluates large design spaces without time‑intensive physics‑based simulation
• Reduced risk: Improves confidence in electrification and right‑sizing decisions, lowering total cost of ownership