Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model

Energy ◽  
2015 ◽  
Vol 92 ◽  
pp. 456-465 ◽  
Author(s):  
Branimir Škugor ◽  
Joško Deur
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Battery Model ◽  
Vehicle Fleet

scholarly journals Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4349
Author(s):  
Niklas Wulff ◽  
Fabia Miorelli ◽  
Hans Christian Gils ◽  
Patrick Jochem
Keyword(s):  
Open Source ◽  
Electric Vehicle ◽  
Full Range ◽  
Mobility Patterns ◽  
Electric Vehicle Charging ◽  
Survey Results ◽  
Vehicle Demand ◽  
Vehicle Fleet ◽  
Electric Loads

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

scholarly journals The remaining CO2 budget: a comparison of the CO2 emissions of diesel and BEV drivetrain technology

2021 ◽  
Author(s):  
Christian Böhmeke ◽  
Thomas Koch
Keyword(s):  
Electric Vehicle ◽  
Carbon Footprint ◽  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Battery Electric Vehicle ◽  
Useful Life ◽  
Vehicle Fleet ◽  
Real Time Simulations ◽  
Diesel Vehicle

AbstractThis paper describes the CO2 emissions of the additional electricity generation needed in Germany for battery electric vehicles. Different scenarios drawn up by the transmission system operators in past and for future years for expansion of the energy sources of electricity generation in Germany are considered. From these expansion scenarios, hourly resolved real-time simulations of the different years are created. Based on the calculations, it can be shown that even in 2035, the carbon footprint of a battery electric vehicle at a consumption of 22.5 kWh/100 km including losses and provision will be around 100 g CO2/km. Furthermore, it is shown why the often-mentioned German energy mix is not suitable for calculating the emissions of a battery electric vehicle fleet. Since the carbon footprint of a BEV improves significantly over the years due to the progressive expansion of renewable-energy sources, a comparison is drawn at the end of this work between a BEV (29.8 tons of CO2), a conventional diesel vehicle (34.4 tons of CO2), and a diesel vehicle with R33 fuel (25.8 tons of CO2) over the entire useful life.

Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model

2020 ◽  
Author(s):  
Sohel Anwar
Keyword(s):  
Electric Vehicle ◽  
Constant Current ◽  
Capacity Fade ◽  
State Of Health ◽  
Current Profile ◽  
Combined Model ◽  
Battery Model ◽  
Electric Vehicle Battery ◽  
Electrochemical Battery ◽  
Constant Current Charge

Abstract An electrochemical model based capacity fade estimation method for a Li-Ion battery is investigated in this paper. An empirical capacity fade model for estimating the state of health of a LiFePO4 electric vehicle battery was integrated with electrochemical battery model in Matlab/Simulink platform. This combined model was then validated against experimental data reported in the literature for constant current charge / discharge cycling. An HPPC current profile was then applied to the validated electrochemical-empirical battery prognosis model which reflected a real-time operating condition for charge and discharge current fluctuations in an electric vehicle battery. The combined model was simulated under the two different HPPC current inputs for three different cycle times. Additionally temperature was taken in account in estimating the cycle aging under the applied current profile to assess the present capacity remaining in the battery. The simulation results provided the state of health (SOH) of the battery for these cycling times which were comparable to the published experimental SOH values for constant current charge/discharge profiles. Thus this model can potentially be used to predict the capacity fade status of an electric vehicle battery.

scholarly journals Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle

2018 ◽  
Vol 9 (4) ◽  
pp. 45 ◽  
Author(s):  
Nicolas Sockeel ◽  
Jian Shi ◽  
Masood Shahverdi ◽  
Michael Mazzola
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Control Solution ◽  
Current Time ◽  
Battery Model ◽  
Hybrid Electric

Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging.

Energy Management System Optimal Strategy for Pure Electric Vehicle Based on the Dynamic Programming

2014 ◽  
Vol 556-562 ◽  
pp. 1472-1475 ◽  
Author(s):  
Bing Dong ◽  
Yan Tao Tian ◽  
Chang Jiu Zhou
Keyword(s):  
Dynamic Programming ◽  
Fuzzy Control ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management System ◽  
Control Method ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Energy Management System ◽  
Fuzzy Control Strategy

This thesis puts forward one optimal adaptive fuzzy control method based on the pure electric vehicle energy management system of the fuzzy control which has been founded already. By adding an optimizing researching model based on the conventional fuzzy control strategy, the thesis can pick up the valuable control rules based on the dynamic programming theory and also can adjust the parameter of the fuzzy controller automatically according to the system operating. These can make the sum of the energy loss reduce to the min. The experiment points out that this method makes the vehicle possess good economic performance in the same driving cycle.

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