scholarly journals Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

2019 ◽  
Vol 9 (10) ◽  
pp. 2026 ◽  
Author(s):  
Nan Xu ◽  
Yan Kong ◽  
Liang Chu ◽  
Hao Ju ◽  
Zhihua Yang ◽  
...  
Keyword(s):  
Energy Management ◽  
Control Strategies ◽  
Large Data ◽  
Management Control ◽  
Mode Switching ◽  
Rule Based ◽  
Comprehensive Review ◽  
Main Research ◽  
Deterministic Dynamic ◽  
Management Optimization

This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

scholarly journals Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming

2021 ◽  
Vol 184 (1) ◽  
pp. 3-10
Author(s):  
Di Zhu ◽  
Ewan Pritchard ◽  
Sumanth Dadam ◽  
Vivek Kumar ◽  
Yang Xu
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Measurement System ◽  
Hybrid Electric Vehicle ◽  
Management Strategies ◽  
Optimization Approach ◽  
Rule Based ◽  
Management Optimization ◽  
Vehicle Test ◽  
Vehicle Dynamic Model

Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.

Real Time Energy Management Control Strategies for an Electrically Supercharged Gasoline Hybrid Vehicle

2020 ◽  
Author(s):  
Francesco Accurso ◽  
Alessandro Zanelli ◽  
Luciano Rolando ◽  
Federico Millo
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Control Strategies ◽  
Hybrid Vehicle ◽  
Management Control

scholarly journals Optimization and Application for Hydraulic Electric Hybrid Vehicle

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 322 ◽  
Author(s):  
Hsiu-Ying Hwang ◽  
Tian-Syung Lan ◽  
Jia-Shiun Chen
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Energy Management ◽  
Economic Performance ◽  
Control Strategy ◽  
Management Strategy ◽  
Hybrid Vehicle ◽  
Management Control ◽  
Energy Management Strategy ◽  
Rule Based

Targeting the application of medium and heavy vehicles, a hydraulic electric hybrid vehicle (HEHV) was designed, and its energy management control strategy is discussed in this paper. Matlab/Simulink was applied to establish the pure electric vehicle and HEHV models, and backward simulation was adopted for the simulation, to get the variation of torque and battery state of charge (SOC) through New York City Cycle of the US Environmental Protection Agency (EPA NYCC). Based on the simulation, the energy management strategy was designed. In this research, the rule-based control strategy was implemented as the energy distribution management strategy first, and then the genetic algorithm was utilized to conduct global optimization strategy analysis. The results from the genetic algorithm were employed to modify the rule-based control strategy to improve the electricity economic performance of the vehicle. The simulation results show that the electricity economic performance of the designed hydraulic hybrid vehicle was improved by 36.51% compared to that of a pure electric vehicle. The performance of energy consumption after genetic algorithm optimization was improved by 43.65%.

scholarly journals Neural Network- and Fuzzy Control-Based Energy Optimization for the Switching in Parallel Hybrid Two-Wheeler

2021 ◽  
Vol 12 (1) ◽  
pp. 35
Author(s):  
Supriya Kalyankar-Narwade ◽  
Ramesh Kumar Chidambaram ◽  
Sanjay Patil
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Energy Optimization ◽  
Switching Control ◽  
Management Control ◽  
Energy Utilization ◽  
Power Sources ◽  
Two Wheeler

Optimization of a two-wheeler hybrid electric vehicle (HEV) is a typical challenge compared to that for four-wheeler HEVs. Some of the challenges which are particular to two-wheeler HEVs are throttle integration, smooth switching between power sources, add-on weight compensation, efficiency improvisation in traffic, and energy optimization. Two power sources need to be synchronized skillfully for optimum energy utilization. A prominent variant of HEV is that it easily converts conventional scooters into parallel hybrids by “Through-the-Road (TTR)” architecture. This paper focuses on three switching control strategies of HEVs based on the state of charge, fuzzy logic, and neural network. Further, to optimize energy usage, all these control strategies are compared. Energy management control for the TTR model is developed with vehicle parameters in the Simulink environment and simulated using the “World Harmonized Motorcycle Test Cycle” (WMTC) drive cycle. The multivariable input model is presented with a fuzzy rule-based hybrid switching control. A similar system is also modeled with a neural network-based decision control and the observations are tabulated for the fuel economy and energy management. Simulation results show that the neural network-based optimization results in minimal energy consumption among all three hybrid operations.

scholarly journals On Energy Management Control of a PV-Diesel-ESS Based Microgrid in a Stand-Alone Context

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2164 ◽  
Author(s):  
Ahmed Belila ◽  
Mohamed Benbouzid ◽  
El-Madjid Berkouk ◽  
Yassine Amirat
Keyword(s):  
Energy Management ◽  
Power Flow ◽  
Control Strategies ◽  
Real Data ◽  
Management Control ◽  
Diesel Generator ◽  
Mode Control ◽  
Parallel Mode ◽  
Scale Test ◽  
Reduced Scale

This paper deals with the energy management control of a PV-Diesel-ESS-based microgrid in a stand-alone context. In terms of control, an Isolated Mode Control (IMC) strategy based on a resonant regulator is proposed. In Parallel Mode Control (PMC) conditions, the diesel generator (DG) is controlled to operate at its nominal power. In this context, a supervisory algorithm optimizing the power flow between the microgrid’s various components ensures switching between the two modes for different possible scenarios. To prove the effectiveness of the proposed control strategies, the energy management control (EMC) is tested first using a standard state of charge (SOC) profile emulating the microgrid different states. Then real data are used to simulate the load and solar radiations. An experimental validation on a reduced scale test bench is carried out to prove the feasibility and the effectiveness of the proposed energy management control strategies.

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