An Onboard Real-Time-Implementable Framework for Calculating the Optimal Cruising Speed of Electric Autonomous Vehicles

Meshginqalam, Ata; Bauman, Jennifer

doi:10.1109/itec.2019.8790538

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Equation (12) shows the rolling resistance where m is vehicle mass, g is the gravitational constant, θ is the road grade in radians, and µ 1 and µ 2 are rolling coefficients. The resisting force of grade is shown in (13).…”

Section: B Losses Formulationmentioning

confidence: 99%

“…in [12]. These losses are formatted in Joules per meter so that they become suitable to use in a distance domain formulation.…”

Section: B Losses Formulationmentioning

confidence: 99%

“…Similarly, [10] and [11] do not consider inverter loss, motor mechanical loss, battery loss, or accessory loss, and seek to find the local optimum speed rather than the global optimum for a trip segment. Reference [12] has developed a simple and fast framework for determining the energy-optimal cruising speed while considering all vehicle internal and external losses, but as this algorithm is focused on cruising, it is not suitable for urban stop-and-go driving patterns. However, [12] does uniquely show the importance of considering vehicle internal losses, especially accessory losses, in the determination of energy-optimal speed.…”

Section: Introductionmentioning

confidence: 99%

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Two-Level MPC Speed Profile Optimization of Autonomous Electric Vehicles Considering Detailed Internal and External Losses

Meshginqalam

Bauman

2020

IEEE Access

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This paper proposes a novel two-level model predictive control (MPC) speed control algorithm for autonomous vehicles as a successive convex optimization problem focused on both energy use and arrival time. Internal losses such as detailed motor/inverter efficiency and battery loss, as well as external losses, such as wind and grade, are considered. The effect of the higher accessory energy usage of autonomous vehicles on the energy-optimal speed profile is considered in the algorithm and investigated in the paper. The proposed successive convex approach produces a highly accurate optimal speed profile while also being solvable in real-time with the vehicle on-board computing resources. An electric vehicle model is created in MATLAB/Simulink and validated to real-world logged driving data. This vehicle model is used to perform a variety of simulated test cases, which show an energy savings potential of about 1% to 20% for different driving conditions, compared to a non-energy-optimal driving profile.INDEX TERMS Electric vehicles, optimization, autonomous agents, modeling, model predictive control.

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Section: B Losses Formulationmentioning

confidence: 99%

“…in [12]. These losses are formatted in Joules per meter so that they become suitable to use in a distance domain formulation.…”

Section: B Losses Formulationmentioning

confidence: 99%