Online Weld Breakage Diagnosis for the Battery of Electric Vehicle: A Data-Driven Approach

2016 ◽  
Author(s):  
Yixin Cai ◽  
Languang Lu ◽  
Ping Shen ◽  
Xuning Feng ◽  
Hewu Wang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1950 ◽  
Author(s):  
Jaehyun Lee ◽  
Eunjung Lee ◽  
Jinho Kim
Keyword(s):  
Reinforcement Learning ◽  
Density Function ◽  
Electric Vehicle ◽  
Dynamic Pricing ◽  
Estimation Method ◽  
Data Driven ◽  
Usage Pattern ◽  
Load Shifting ◽  
Specific Location ◽  
Data Driven Approach

In the smart grid environment, the penetration of electric vehicle (EV) is increasing, and dynamic pricing and vehicle-to-grid technologies are being introduced. Consequently, automatic charging and discharging scheduling responding to electricity prices that change over time is required to reduce the charging cost of EVs, while increasing the grid reliability by moving charging loads from on-peak to off-peak periods. Hence, this study proposes a deep reinforcement learning-based, real-time EV charging and discharging algorithm. The proposed method utilizes kernel density estimation, particularly the nonparametric density function estimation method, to model the usage pattern of a specific charger at a specific location. Subsequently, the estimated density function is used to sample variables related to charger usage pattern so that the variables can be cast in the training process of a reinforcement learning agent. This ensures that the agent optimally learns the characteristics of the target charger. We analyzed the effectiveness of the proposed algorithm from two perspectives, i.e., charging cost and load shifting effect. Simulation results show that the proposed method outperforms the benchmarks that simply model usage pattern through general assumptions in terms of charging cost and load shifting effect. This means that when a reinforcement learning-based charging/discharging algorithm is deployed in a specific location, it is better to use data-driven approach to reflect the characteristics of the location, so that the charging cost reduction and the effect of load flattening are obtained.

Health Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach

2020 ◽  
Vol 25 (6) ◽  
pp. 2622-2632 ◽  
Author(s):  
Xiaosong Hu ◽  
Yunhong Che ◽  
Xianke Lin ◽  
Zhongwei Deng
Keyword(s):  
Electric Vehicle ◽  
Data Driven ◽  
Battery Packs ◽  
Data Driven Approach ◽  
Electric Vehicle Battery

scholarly journals Quantitive analysis of electric vehicle flexibility: A data-driven approach

2018 ◽  
Vol 95 ◽  
pp. 451-462 ◽  
Author(s):  
N. Sadeghianpourhamami ◽  
N. Refa ◽  
M. Strobbe ◽  
C. Develder
Keyword(s):  
Electric Vehicle ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Estimating the benefits of electric vehicle smart charging at non-residential locations: A data-driven approach

2015 ◽  
Vol 155 ◽  
pp. 515-525 ◽  
Author(s):  
Emre C. Kara ◽  
Jason S. Macdonald ◽  
Douglas Black ◽  
Mario Bérges ◽  
Gabriela Hug ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Data Driven ◽  
Residential Locations ◽  
Smart Charging ◽  
Data Driven Approach

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional
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