scholarly journals Real-Time HEV Energy Management Strategy considering Road Congestion Based on Deep Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5270
Author(s):  
Shota Inuzuka ◽  
Bo Zhang ◽  
Tielong Shen
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Energy Management ◽  
Power Distribution ◽  
Energy Cost ◽  
Virtual Space ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Road Congestion ◽  
On The Road

This paper deals with the HEV real-time energy management problem using deep reinforcement learning with connected technologies such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I). In the HEV energy management problem, it is important to run the engine efficiently in order to minimize its total energy cost. This research proposes a policy model that takes into account road congestion and aims to learn the optimal system mode selection and power distribution when considering the far future by policy-based reinforcement learning. In the simulation, a traffic environment is generated in a virtual space by IPG CarMaker and a HEV model is prepared in MATLAB/Simulink to calculate the energy cost while driving on the road environment. The simulation validation shows the versatility of the proposed method for the test data, and in addition, it shows that considering road congestion reduces the total cost and improves the learning speed. Furthermore, we compare the proposed method with model predictive control (MPC) under the same conditions and show that the proposed method obtains more global optimal solutions.

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

2021 ◽  
pp. 095440702110297
Author(s):  
Hui Liu ◽  
Rui Liu ◽  
Riming Xu ◽  
Lijin Han ◽  
Shumin Ruan
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Hierarchical Control ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Hybrid Electric

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

scholarly journals Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 498
Author(s):  
Hao Wang ◽  
Hongwen He ◽  
Jianwei Li ◽  
Yunfei Bai ◽  
Yuhua Chang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Power Distribution ◽  
Management Strategy ◽  
Electricity Consumption ◽  
Air Pollution Control ◽  
Energy Management Strategy ◽  
Power Requirement ◽  
The Road ◽  
On The Road

Electric sanitation vehicles have increasingly been applied to cleaning work due to the requirement of air pollution control. The power distribution and energy management strategy (EMS) influence the vehicle’s performance a lot both in the aspects of cleaning effect and electricity consumption. Aiming to improve energy economy and ensure clean tasks, first, the electricity consumption percentages of the vehicle onboard devices are analyzed and the main contributors are clarified, and the power requirement model of the working motor is built based on experimental data. Second, a universal modeling method of garbage distribution on the road surface is proposed, which implements a nonlinear autoregressive neural network as the predictor. Third, an adaptive model predictive control (AMPC)-based EMS is proposed and verified. The results show the AMPC method can accurately predict the garbage density and the proposed EMS can approximate the energy consumption of the DP-based EMS with little deviation. Compared to conventional rule-based EMS, the AMPC-based EMS achieved a 15.5% decrease in energy consumption as well as a 14.6% decrease in working time.

scholarly journals Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach

2020 ◽  
Author(s):  
Yujian Ye ◽  
Dawei Qiu ◽  
Jonathan Ward ◽  
Marcin Abram
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Energy Management ◽  
Energy System ◽  
Management Problem ◽  
Energy Carriers ◽  
Energy Devices ◽  
Continuous State ◽  
Carrier Energy ◽  
Time Autonomous

The problem of real-time autonomous energy management is an application area that is receiving unprecedented attention from consumers, governments, academia, and industry. This paper showcases the first application of deep reinforcement learning (DRL) to real-time autonomous energy management for a multi-carrier energy system. The proposed approach is tailored to align with the nature of the energy management problem by posing it in multi-dimensional continuous state and action spaces, in order to coordinate power flows between different energy devices, and to adequately capture the synergistic effect of couplings between different energy carriers. This fundamental contribution is a significant step forward from earlier approaches that only sought to control the power output of a single device and neglected the demand-supply coupling of different energy carriers. Case studies on a real-world scenario demonstrate that the proposed method significantly outperforms existing DRL methods as well as model-based control approaches in achieving the lowest energy cost and yielding a representation of energy management policies that adapt to system uncertainties.

scholarly journals Energy management strategy based on dynamic programming with durability extension for fuel cell hybrid tramway

2021 ◽  
Vol 29 (3) ◽  
pp. 299-313
Author(s):  
Shiyong Tao ◽  
Weirong Chen ◽  
Rui Gan ◽  
Luoyi Li ◽  
Guorui Zhang ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Fuel Cell ◽  
Real Time ◽  
Energy Management ◽  
Power Distribution ◽  
Management Strategy ◽  
High Efficiency ◽  
State Machine ◽  
Energy Management Strategy ◽  
Hydrogen Consumption

AbstractThis paper proposes an energy management strategy for a fuel cell (FC) hybrid power system based on dynamic programming and state machine strategy, which takes into account the durability of the FC and the hydrogen consumption of the system. The strategy first uses the principle of dynamic programming to solve the optimal power distribution between the FC and supercapacitor (SC), and then uses the optimization results of dynamic programming to update the threshold values in each state of the finite state machine to realize real-time management of the output power of the FC and SC. An FC/SC hybrid tramway simulation platform is established based on RT-LAB real-time simulator. The compared results verify that the proposed EMS can improve the durability of the FC, increase its working time in the high-efficiency range, effectively reduce the hydrogen consumption, and keep the state of charge in an ideal range.

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