IEEE Access · 2020 · 30 citations · 28 references
EngineeringAutomotive EngineeringElectrical EngineeringElectric Vehicle EngineeringOptimizationPowertrain EngineeringElectric Motor Drive SystemsElectric PowertrainsElectric Traction MotorsVehicle Control SystemsVehicle Propulsion SystemsVehicle SchedulingEco-driving StrategyEnergy ConsumptionEnergy-efficient DrivingOptimality ConditionsOptimization AlgorithmsSequential Quadratic ProgrammingSpeed Profile OptimizationVehicle Speed Profile Optimization
The main question in eco-driving is - what speed or torque profile should the vehicle follow to minimize its energy consumption over a certain distance within a desired trip time? Various techniques to obtain globally optimal energy-efficient driving profiles have been proposed in the literature, involving optimization algorithms such as dynamic programming (DP) or sequential quadratic programming. However, these methods are difficult to implement on real vehicles due to their significant computational requirements and the need for precise a-priori knowledge of the scenario ahead. Although many predictions state that electric vehicles (EVs) represent the future of mobility, the literature lacks a realistic analysis of optimal driving profiles for EVs. This paper attempts to address the gap by providing optimal solutions obtained from DP for a variety of trip times, which are compared with simple intuitive speed profiles. For a case study EV, the results show that the DP solutions involve forms of Pulse-and-Glide (PnG) at high frequency. Hence, detailed investigations are performed to: i) prove the optimality conditions of PnG for EVs; ii) show its practical use, based on realistic electric powertrain efficiency maps; iii) propose rules for lower frequency PnG operation; and iv) use PnG to track generic speed profiles.
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Guido Wäger, Jonathan Whale, Thomas Bräunl · Renewable and Sustainable Energy Reviews · 2016 · 176 citations
Engineering, Automotive Engineering, Electrical Engineering +17
Jihun Han, Antonio Sciarretta, Luis Leon Ojeda et al. · IEEE Transactions on Intelligent Vehicles · 2018 · 148 citations · Full text