The Importance of HEV Fuel Economy and Two Research Gaps Preventing Real World Implementation of Optimal Energy Management

2017 ◽  
Author(s):  
Zachary D. Asher ◽  
Van Wifvat ◽  
Anthony Navarro ◽  
Scott Samuelsen ◽  
Thomas Bradley
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Real World ◽  
Research Gaps ◽  
Optimal Energy ◽  
Optimal Energy Management

Optimal energy management strategy for fuel cell/battery/supercapacitor vehicles using wavelet transform and equivalent consumption minimization strategy

2021 ◽  
pp. 095440702110697
Author(s):  
Pengfei Zou ◽  
Fazhan Tao ◽  
Zhumu Fu ◽  
Pengju Si ◽  
Chao Ma
Keyword(s):  
Wavelet Transform ◽  
Fuel Cell ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Low Frequency ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Optimal Energy ◽  
Optimal Energy Management

In this paper, the hybrid electric vehicle is equipped with fuel cell/battery/supercapacitor as the research object, the optimal energy management strategy (EMS) is proposed by combining wavelet transform (WT) method and equivalent consumption minimization strategy (ECMS) for reducing hydrogen consumption and prolonging the lifespan of power sources. Firstly, the WT method is employed to separate power demand of vehicles into high-frequency part supplied by supercapacitor and low-frequency part allocated to fuel cell and battery, which can effectively reduce the fluctuation of fuel cell and battery to prolong their lifespan. Then, considering the low-frequency power, the optimal SOC of battery is used to design the equivalent factor of the ECMS method to improve the fuel economy. The proposed hierarchical EMS can realize a trade-off between the lifespan of power sources and fuel economy of vehicles. Finally, the effectiveness of the proposed EMS is verified by ADVISOR, and comparison results are given compared with the traditional ECMS method and ECMS combining the filter.

Analysis of Effects of Fuel Cell System Dynamics on Optimal Energy Management

2017 ◽  
Author(s):  
Kai Wu ◽  
Ming Kuang ◽  
Milos Milacic ◽  
Xiaowu Zhang ◽  
Jing Sun
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Fuel Economy ◽  
Cell System ◽  
Fuel Cell Vehicle ◽  
Fuel Cell System ◽  
Optimal Energy ◽  
Load Following ◽  
Optimal Energy Management ◽  
Level 1

Dynamic characteristics of a proton exchange membrane fuel cell (PEMFC) system can impact fuel economy and load following performance of a fuel cell vehicle, especially if those dynamics are ignored in designing top-level energy management strategy. To quantify the effects of fuel cell system (FCS) dynamics on optimal energy management, dynamic programming (DP) is adopted in this study to derive optimal power split strategies at two levels: Level 1, where the FCS dynamics are ignored, and Level 2, where the FCS dynamics are incorporated. Analysis is performed to quantify the differences of these two resulting strategies to understand the effects of FCS dynamics. While Level 1 DP provides significant computational advantages, the resulting strategy leads to load following errors that need to be mitigated using battery or FCS itself. Our analysis shows that up to 5% fuel economy penalty on New York city cycle (NYCC) and 3% on supplemental federal test procedure (US06) can be resulted by ignoring FCS dynamics, when the dominant dynamics of the FCS has settling time as slow as 8 seconds.

scholarly journals Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5713
Author(s):  
Aaron Rabinowitz ◽  
Farhang Motallebi Araghi ◽  
Tushar Gaikwad ◽  
Zachary D. Asher ◽  
Thomas H. Bradley
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
High Fidelity ◽  
Energy Management Strategy ◽  
Optimal Energy ◽  
Upper Limit ◽  
Speed Prediction ◽  
Optimal Energy Management ◽  
Hybrid Electric

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

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