Minimum-Fuel Energy Management of a Hybrid Electric Vehicle via Iterative Linear Programming

This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shi...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2020-12, Vol.69 (12), p.14575-14587
Main Authors: Robuschi, Nicolo, Salazar, Mauro, Viscera, Nicola, Braghin, Francesco, Onder, Christopher H.
Format: Article
Language:English
Subjects:
Algorithms
Batteries
Computer architecture
convex optimization
Electric motors
Electric vehicles
Energy management
Engines
Fuel consumption
Fuels
Hybrid electric vehicles
Ice
Iterative algorithms
Iterative methods
Linear programming
Mathematical model
Mechanical power transmission
Mixed integer
mixed-integer linear programming
Optimal control
Optimization
Powertrain
Simulator fidelity
supervisory control
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Summary:This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shift commands. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies including the optimal gear-shift trajectory. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear program and with the one resulting from the implementation of the optimal strategies in a high-fidelity nonlinear simulator. We showcase the effectiveness of the presented algorithm by assessing the impact of different powertrain configurations and electric motor size on the achievable fuel consumption. Our numerical results show that the proposed algorithm can assess fuel-optimal control strategies with low computational burden, and that powertrain design choices significantly affect the achievable fuel consumption of the vehicle.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.3030088