Predictive Energy Management of Hybrid Electric Vehicles via Multi-Layer Control

This paper presents predictive energy management of hybrid electric vehicles (HEVs) via computationally efficient multi-layer control. First, we formulate an optimization problem by considering driveability and a penalty for using service brakes in the objective function to optimize gear, engine on/...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 70; no. 7; pp. 6485 - 6499
Main Authors: Razi, Maryam, Murgovski, Nikolce Murgovski, McKelvey, Tomas, Wik, Torsten
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Batteries
Clutches
Driveability
Dynamic programming
Electric vehicles
Energy management
Engines
Gears
Hybrid electric vehicle (HEV)
Hybrid electric vehicles
Ice
Iterative methods
model predictive control (MPC)
multi-layer control
Multilayers
Optimization
Powertrain
Predictive control
Real time
real-time iteration Secant method
State of charge
Supervisory control
ISSN:0018-9545, 1939-9359, 1939-9359
Online Access:Get full text
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Summary:This paper presents predictive energy management of hybrid electric vehicles (HEVs) via computationally efficient multi-layer control. First, we formulate an optimization problem by considering driveability and a penalty for using service brakes in the objective function to optimize gear, engine on/off, engine clutch state, and power-split decisions subject to constraints on the battery state of charge (SOC) and charge sustenance. Then, we split it into two control layers, including a supervisory control in a higher layer and a local power-split control in a lower layer. In the supervisory layer, a gear and powertrain mode manager (PM) is designed, and optimal gear, engine on/off and clutch states are obtained by using a combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP). Moreover, a real-time iteration Secant method is proposed to calculate optimal battery costate such that the constraint on charge sustenance is satisfied. In the local controller layer, a linear quadratic tracking method (LQT) is used to optimally split power between the engine and the electric machine and keep battery SOC within its bounds.
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ISSN:0018-9545
1939-9359
1939-9359
DOI:10.1109/TVT.2021.3081346