Mission Optimized Speed Control

Abstract: Transportation underlines the vehicle industry's critical role in a country's economic future.The amount of goods moved, specically by trucks, is only expected to increase inthe near future. This work attempts to tackle the problem of optimizing fuel consumptionin Volvo trucks, when there are hard constraints on the delivery time and speed limits.Knowledge of the truck such as position, state, conguration etc., along with the completeroute information of the transport mission is used for fuel optimization.Advancements in computation, storage, and communication on cloud based systems, hasmade it possible to easily incorporate such systems in assisting modern eet. In this work,an algorithm is developed in a cloud based system to compute a speed plan for the completemission for achieving fuel minimization. This computation is decoupled from thelocal control operations on the truck such as prediction control, safety, cruise control, etc.;and serves as a guide to the truck driver to reach the destination on time by consumingminimum fuel.To achieve fuel minimization under hard constraints on delivery (or arrival) time andspeed limits, a non-linear optimization problem is formulated for the high delity modelestimated from real-time drive cycles. This optimization problem is solved using a Nonlinearprogramming solver in Matlab.The optimal policy was tested on two drive cycles provided by Volvo. The policy wascompared with two dierent scenarios, where the mission demands hard constraints ontravel time and the speed limits in addition to no trac uncertainties (deterministic). with a cruise controller running at a constant set speed throughout the mission. Itis observed that there is no signicant fuel savings. with maximum possible fuel consumption; achieved without the help of optimalspeed plan (worst case). It is seen that there is a notable improvement in fuelsaving.In a real world scenario, a transport mission is interrupted by uncertainties such as trac ow, road blocks, re-routing, etc. To this end, a stochastic optimization algorithm is proposedto deal with the uncertainties modeled using historical trac ow data. Possiblesolution methodologies are suggested to tackle this stochastic optimization problem.