Unlocking the potential of electric and hybrid tractors via sensitivity and techno-economic analysis

The majority of agricultural vehicles in use today still rely on diesel-based propulsion, a major source of air pollution. Electrification is seen as a potential solution for decarbonizing these off-road vehicles but is hampered by higher upfront costs in energy storage and charging infrastructure. To better quantify these barriers, this paper proposes a techno-economic tool that can assist farmers in evaluating the total costs of ownership of electric and hybrid tractors. A pragmatic simulation model of the tractor is developed to predict the annual energy/fuel consumption and NOx emissions, while an economic model estimates the total acquisition, operation, and maintenance costs. For managing the energy in hybrid tractors, a new power split strategy based on model predictive control is developed, allowing the designer to balance energy efficiency and NOx emissions, while taking into account operational constraints of the electric powertrain. Additionally, we propose novel decision maps that allow farmers to quickly identify operating regions (in term of average load and yearly working time) where the deployment of electric/hybrid tractors is economically viable. To account for variability in the tractor’s operational conditions, we conduct both a deterministic sensitivity analysis with multi-parameter variation and a stochastic analysis of the total cost of ownership. The tool is validated with different mission profiles based on data from a California farm. The results show that, compared to diesel powertrains, electric tractors are more cost-effective for light-duty farming activities (engine loads less than 20%). On the other hand, hybrid powertrains are more economical for medium-duty tasks, where engine loads range from 20% to 60%.