A Battery Management System using Nonlinear Model Predictive Control for a Hybrid Electric Vehicle

In this paper, the nonlinear model predictive control (NMPC) for the energy management of a power-split hybrid electric vehicle (HEV) has been studied to improve battery aging while maintaining the fuel economy at a reasonable level. A first principle battery model is built with simulation capacity of the battery aging features. The built battery model is integrated with an HEV model from autonomie software to investigate the vehicle and battery performance under control strategies. The NMPC has simplified battery models to predict the state of charge (SOC) change, the fuel consumption of the engine, and the battery aging index over the predicted horizon. The purpose of the NMPC is to find an optimized control sequence over the prediction horizon, which minimizes the designed cost function. The proposed control strategy is compared with that of an NMPC, which does not consider the battery aging. It is found that, with the optimized weighting factor selection, the NMPC with the consideration of battery aging has better battery aging performance and similar fuel economy performance comparing with the NMPC without the consideration of battery aging.

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Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS)

Journal of The Electrochemical Society ◽

10.1149/1945-7111/ab7bd7 ◽

2020 ◽

Vol 167 (6) ◽

pp. 063505 ◽

Cited By ~ 4

Author(s):

Suryanarayana Kolluri ◽

Sai Varun Aduru ◽

Manan Pathak ◽

Richard D. Braatz ◽

Venkat R. Subramanian

Keyword(s):

Model Predictive Control ◽

Lithium Ion Battery ◽

Real Time ◽

Predictive Control ◽

Nonlinear Model ◽

Management System ◽

Lithium Ion ◽

Nonlinear Model Predictive Control ◽

Battery Management System ◽

Battery Management

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Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle: Influence of Inclusion of Powertrain Dynamics

ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 2 ◽

10.1115/dscc2011-6114 ◽

2011 ◽

Cited By ~ 1

Author(s):

Hoseinali Borhan ◽

Ardalan Vahidi ◽

Wei Liang ◽

Anthony Phillips ◽

Stefano Di Cairano ◽

...

Keyword(s):

Model Predictive Control ◽

Electric Vehicle ◽

Predictive Control ◽

Nonlinear Model ◽

Hybrid Electric Vehicle ◽

Closed Loop ◽

Nonlinear Model Predictive Control ◽

Power Split ◽

Hybrid Electric ◽

Engine Start

This paper builds on our previous published works in which we had employed nonlinear model predictive control for the (sub)optimal power management of a power-split hybrid electric vehicle (HEV). In addition to the battery’s state of charge, in this work we include the effect of inertial powertrain dynamics in the control-oriented model that are usually ignored because of their fast dynamics. We show how inclusion of the new state removes the need for a separate rule-based strategy for engine start/stop and can result in considerable improvement in the fuel economy as shown by closed-loop simulations over a high-fidelity power-split HEV model.

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