Abstract
This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller.
Highlights
With the growing energy shortages and environmental pollution, the development of new energy vehicles is getting more and more attention in the world
We present an energy management strategy for the hybrid electric vehicle based on model predictive control (MPC) with equivalent fuel consumption minimization strategy (ECMS)
To validate the optimal performance of the ECMS-MPC energy management strategy, the simulation is implemented with standard cycles and the speed sequence generated in MATLAB.The proposed ECMS-MPC control strategy, the control strategy based on MPC and the control strategy based on MPC-dynamic programming algorithm (DP) are simulated and optimized respectively
Summary
With the growing energy shortages and environmental pollution, the development of new energy vehicles is getting more and more attention in the world. An integrated model predictive control method is proposed in [8] that combines power management and adaptive velocity control during vehicle-following scenarios. Model predictive control is suitable for the energy efficiency optimization of hybrid electric vehicles in car-following scenarios. The research on hybrid electric vehicle energy management strategies focuses on the instantaneous optimization of energy management control strategies, mainly including model predictive control and equivalent fuel consumption minimization strategy (ECMS) [19]. We propose a control strategy for optimizing the battery energy of a hybrid electric vehicle traveling in the car-following condition. The car-following layer adopts sequential quadratic programming (SQP) algorithm to solve the MPC and to generate the predicted speed profiles of hybrid electric vehicles. The drive system model consisting of the engine, battery, clutch, motor and automatic mechanical transmission (AMT) is shown in the Figure 2
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