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Optimal Eco-Driving of a Heavy-Duty Vehicle Behind a Leading Heavy-Duty Vehicle

Nalin Kumar Sharma, Ahad Hamednia, Nikolce Murgovski, Esteban R. Gelso, Jonas Sjöberg

2020IEEE Transactions on Intelligent Transportation Systems37 citationsDOI

Abstract

We propose an eco-driving technique for a heavy-duty ego vehicle that drives behind a leading heavy-duty vehicle. By observing a decrease in speed of the leading vehicle when driving uphill, its power capability is estimated and its future speed is predicted within a look-ahead horizon. The predicted speed is utilised in a model predictive controller (MPC) to plan the optimal speed of the ego vehicle such that its fuel consumption is minimised, while keeping a safe distance to the leading vehicle and reducing the need for braking. The effectiveness of the proposed technique is analysed in two case studies on real road topographies. By using the leading vehicle observer, fuel savings are achieved up to 8% compared to the case where the preceding vehicle is assumed to have a constant speed within the look-ahead horizon.

Topics & Concepts

Heavy dutyAutomotive engineeringFuel efficiencyEngineeringObserver (physics)Controller (irrigation)Control theory (sociology)Computer scienceControl (management)BiologyQuantum mechanicsArtificial intelligenceAgronomyPhysicsVehicle emissions and performanceElectric and Hybrid Vehicle TechnologiesAdvanced Combustion Engine Technologies
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