An intelligent predictive controller for power and battery management in plug-in hybrid electric vehicles

2021 ◽  
pp. 1-18
Author(s):  
Mojtaba Hassanzadeh ◽  
Zahra Rahmani
Keyword(s):  
Real Time ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Fuzzy Inference ◽  
Battery Management ◽  
State Variables ◽  
Energy Management Strategy ◽  
Inference System ◽  
The Real ◽  
Hybrid Electric

Abstract This paper presents a novel real-time energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs), which combines the adaptive neuro-fuzzy inference system (ANFIS) and the model predictive control (MPC). A two-objective EMS with two state variables is defined by integrating the battery aging and fuel economy in the objective function. First, the dynamic programming (DP) approach is applied offline to obtain the globally optimal solutions. Then a real-time predictive EMS is proposed, in which DP carries out a moving-horizon optimization. Contrary to the charge-sustaining HEVs, the optimal trajectory of the battery state-of-charge (SOC) in PHEVs does not fluctuate around a constant level. Thus, determining the desired value of SOC for the real-time moving-horizon optimization is a challenging issue. Unlike the EMSs with a pre-determined reference for SOC, a trained ANFIS model constructs the real-time sub-optimal SOC trajectory in advance. Finally, the effectiveness of the proposed approach is shown through simulation. The proposed EMS is examined over multiple real-time driving cycles, and the results indicate that the total cost is increased compared to those unaware of battery aging. The real-time EMS is then compared to different approaches. While suboptimal, the proposed EMS is real-time implementable, and the results are found to be close enough to those of optimal controller, compared to the two other tested approaches.

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

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Shumin Ruan ◽  
Yue Ma
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Energy Management Strategy ◽  
The Real ◽  
Prediction Module ◽  
Velocity Prediction ◽  
Hybrid Electric

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.

scholarly journals A Real Time Energy Management Strategy for Plug-in Hybrid Electric Vehicles based on Optimal Control Theory

2014 ◽  
Vol 45 ◽  
pp. 949-958 ◽  
Author(s):  
Laura Tribioli ◽  
Michele Barbieri ◽  
Roberto Capata ◽  
Enrico Sciubba ◽  
Elio Jannelli ◽  
...  
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Optimal Control Theory ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Energy Management Strategy ◽  
Hybrid Electric

Robust Predictive Energy Management of Connected Power-split Hybrid Electric Vehicles Using Dynamic Traffic Data

2021 ◽  
Author(s):  
Seyedeh Mahsa Sotoudeh ◽  
Baisravan HomChaudhuri
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Traffic Data ◽  
Energy Management Strategy ◽  
Short Term ◽  
Low Level ◽  
Hybrid Electric

Abstract This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEV) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudospectral optimal controller with discounted cost is utilized at the high-level to find an approximate optimal solution for the entire driving cycle. At the low-level, a Long Short-Term Memory neural network is developed for higher quality driving cycle (velocity) predictions over the low-level's short horizons. Tube-based model predictive controller is then used at the low-level to ensure constraint satisfaction in the presence of driving cycle prediction errors. Simulation results over real-world driving cycles show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

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