Article: Energy management of hybrid electric vehicle based on linear time-varying model predictive control Journal: International Journal of Powertrains (IJPT) 2024 Vol.13 No.1 pp.95 - 111 Abstract: Energy management of hybrid electric vehicle (HEV) is crucial for improving fuel economy and reducing emissions. Due to the challenges in both development and implementation, simplified algorithms using rule-based strategies or equivalent consumption minimisation strategy (ECMS), still prevail in real vehicle applications. Taking an HEV with P2 hybrid powertrain for example, a bi-level hybrid model predictive control (bi-HMPC) algorithm is proposed. The upper level calculates the optimal engine/motor torque distribution based on linear time-varying model predictive control (LTV-MPC), while the lower level optimises the gear ratio via hybrid model predictive control (HMPC). The algorithm is preliminarily validated via simulations, which demonstrate that it has better fuel-saving performances than ECMS. Then the LTV-MPC is implemented in real vehicle and validated via dynamometer tests. Results show that it can run real-time and reduce the fuel consumption from 7.05 L/100 km to 6.2 L/100 km, together with noticeable improvements in pollutant emissions. Inderscience Publishers - linking academia, business and industry through research

Title: Energy management of hybrid electric vehicle based on linear time-varying model predictive control

Authors: Daofei Li; Jiajie Zhang; Dongdong Jiang

Addresses: Institute of Power Machinery and Vehicular Engineering, College of Energy Engineering, Zhejiang University, China ' Institute of Power Machinery and Vehicular Engineering, College of Energy Engineering, Zhejiang University, China ' Institute of Power Machinery and Vehicular Engineering, College of Energy Engineering, Zhejiang University, China

Abstract: Energy management of hybrid electric vehicle (HEV) is crucial for improving fuel economy and reducing emissions. Due to the challenges in both development and implementation, simplified algorithms using rule-based strategies or equivalent consumption minimisation strategy (ECMS), still prevail in real vehicle applications. Taking an HEV with P2 hybrid powertrain for example, a bi-level hybrid model predictive control (bi-HMPC) algorithm is proposed. The upper level calculates the optimal engine/motor torque distribution based on linear time-varying model predictive control (LTV-MPC), while the lower level optimises the gear ratio via hybrid model predictive control (HMPC). The algorithm is preliminarily validated via simulations, which demonstrate that it has better fuel-saving performances than ECMS. Then the LTV-MPC is implemented in real vehicle and validated via dynamometer tests. Results show that it can run real-time and reduce the fuel consumption from 7.05 L/100 km to 6.2 L/100 km, together with noticeable improvements in pollutant emissions.

Keywords: hybrid electric vehicle; HEV; energy management; linear time varying model predictive control; fuel economy; real vehicle tests.

DOI: 10.1504/IJPT.2024.137999

International Journal of Powertrains, 2024 Vol.13 No.1, pp.95 - 111

Received: 11 Jan 2022
Received in revised form: 30 Mar 2023
Accepted: 18 May 2023
Published online: 16 Apr 2024 *

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Title: Energy management of hybrid electric vehicle based on linear time-varying model predictive control

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