Real-Time Energy Management Strategy Based on Driver-Action-Impact MPC for Series Hybrid Electric Vehicles

Abstract

Precise prediction of future vehicle information can improve the control efficiency of hybrid electric vehicles. Nowadays, most prediction models use previous information of vehicles to predict future driving velocity, which cannot reflect the impact of the driver and the environment. In this paper, a real-time energy management strategy (EMS) based on driver-action-impact MPC is proposed for series hybrid electric vehicles. The proposed EMS consists of two modules: the velocity prediction module and the real-time MPC module. In the velocity prediction module, a long short-term memory (LSTM) neural network model, which is trained by the traffic data derived from a VR-based driving simulator, is adopted to predict the future driving information by using driver action information and current vehicle’s velocity. The obtained future driving velocity is treated as the inputs of the real-time MPC module, which outputs the control variables to act on the underlying controllers of power components by solving a standard quadratic programming (QP) problem. Compared with the rule-based strategy, a 5.6% average reduction of fuel consumption is obtained. The effectiveness of real-time computation of the EMS is validated and verified through a hardware-in-the-loop test platform.

Low-Cost Hardware in the Loop for Intelligent Neural Predictive Control of Hybrid Electric Vehicle

“…The mathematical description of HEVs is categorized into two main parts [10][11][12][13][14][15][16][17][18][19]. The first is related to mechanical components in the vehicle such as the engine and the dynamics of the vehicle body.…”

“…Furthermore, the simulation results revealed savings of up to 39% when compared to commercial solutions [16]. A study of the series type of HEVs is discussed based on the development of energy management by an MPC in [17]. The article [18] introduced different control techniques to maintain the required motor speed using an electronic throttle control system [18].…”

“…In this paper, the suggested type of HEV is a series HEV. A series drivetrain is used to achieve hybrids, in which only the electric motor gives mechanical power [2,3,6,[9][10][11]17]. The motor is operated by either an engine-powered generator or a battery pack.…”

“…In addition to the engine or generator, regenerative braking can be used to recharge the battery. The series type operates with good performance during go-and-stop traffic, where engines are considered inefficient [2,3,6,9,17]. The electronic card can control the operation of the motor by using the battery feed only, which will allow the engine to work in the operations in which it is better and more efficient.…”

A Review of Machine Translation: Implications to Human Translators and Translation Teaching

“…LSTM networks can be divided into two broad categories: LSTM-dominated networks and integrated LSTM networks (Ruan & Ma, 2020;Yu et al, 2019).The former focus on optimizing the connections between inner LSTM cells, while the latter mainly pay attention to integrating the advantageous features of different components (Yu et al, 2019). After comparing the vanilla LSTM in their term, with eight different variants with experimental study, Greff et al (2015) conclude that the former performs reasonably well on various datasets and using any of eight possible modifications does not significantly improve the LSTM performance.…”