Battery Health-Conscious Plug-In Hybrid Electric Vehicle Grid Demand Prediction

2010 ◽  
Author(s):  
Saeid Bashash ◽  
Scott J. Moura ◽  
Hosam K. Fathy
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Peak Load ◽  
Demand Prediction ◽  
Battery Degradation ◽  
Hybrid Electric ◽  
Grid Load ◽  
Household Travel ◽  
National Household Travel Survey ◽  
At Home

This paper examines the problem of predicting the aggregate grid load imposed by battery health-conscious plug-in hybrid electric vehicle (PHEV) charging. The paper begins by generating a set of representative daily PHEV trips using the National Household Travel Survey (NHTS) and a set of federal and real-world drive cycles. Each trip is then used in a multiobjective genetic optimizer, along with a PHEV model and a battery degradation model, to simultaneously minimize PHEV energy cost and battery degradation. The optimization variables include the parameters of the PHEV charge pattern, defined as the timing and rate with which the PHEV receives electricity from the grid. For several weightings of the optimization objectives, total PHEV power demand is predicted by accumulating the charge patterns for individual PHEVs. Two charging scenarios, i.e., charging at home only versus charging at home and work, are examined. Results indicate that the main PHEV peak load occurs early in the morning (between 5.00–6.00a.m.), with approximately 45%–60% of vehicles simultaneously charging from the grid. Moreover, charging at work creates additional peaks in this load pattern.

Integrating battery degradation in a cost of ownership framework for hybrid electric vehicle design optimization

2018 ◽  
Vol 233 (6) ◽  
pp. 1507-1523 ◽  
Author(s):  
Ashish P Vora ◽  
Xing Jin ◽  
Vaidehi Hoshing ◽  
Gregory Shaver ◽  
Subbarao Varigonda ◽  
...  
Keyword(s):  
Design Optimization ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Net Present Value ◽  
Payback Period ◽  
Present Value ◽  
Cost Of Ownership ◽  
Battery Degradation ◽  
Hybrid Electric

Prior design optimization efforts do not capture the impact of battery degradation and replacement on the total cost of ownership, even though the battery is the most expensive and least robust powertrain component. A novel, comprehensive framework is presented for model-based parametric optimization of hybrid electric vehicle powertrains, while accounting for the degradation of the electric battery and its impact on fuel consumption and battery replacement. This is achieved by integrating a powertrain simulation model, an electrochemical battery model capable of predicting degradation, and a lifecycle economic analysis (including net present value, payback period, and internal rate of return). An example design study is presented here to optimize the sizing of the electric motor and battery pack for the North American transit bus application. The results show that the optimal design parameters depend on the metric of interest (i.e. net present value, payback period, etc.). Finally, it is also observed that the fuel consumption increases by up to 10% from “day 1” to the end of battery life. These results highlight the utility of the proposed framework in enabling better design decisions as compared to methods that do not capture the evolution of vehicle performance and fuel consumption as the battery degrades.

Sign in / Sign up

Export Citation Format

Share Document