scholarly journals Real-World Mobility and Environmental Data for the Assessment of In-Vehicle Battery Capacity Fade

2021 ◽  
Vol 12 (1) ◽  
pp. 48
Author(s):  
Elena Paffumi ◽  
Giorgio Martini
Keyword(s):  
Manganese Oxide ◽  
Electric Vehicles ◽  
Real World ◽  
Hybrid Electric Vehicles ◽  
Environmental Data ◽  
Mobility Patterns ◽  
Vehicle Performance ◽  
Battery Capacity ◽  
Capacity Drop ◽  
Environmental Temperatures

This work develops scenario-based analyses for predicting in-vehicle performance degradation of automotive traction batteries. It combines recent capacity performance-based models of NCM-LMO Li-ion (Nickel Cobalt Manganese Oxide—Lithium Manganese Oxide) variant batteries with real-world vehicle driving data from different geographical areas of Europe. The analysis addresses different battery and vehicle architectures (PHEVs (Plug-in Hybrid Electric Vehicles) and BEVs (Battery Electric Vehicles)) combined with different recharging strategies and mobility patterns and environmental temperatures. The mobility pattern datasets used in this analysis refer to six European cities and include up to 508,609 private vehicles, corresponding to 1.78 billion GPS records, 9.1 million trips and parking events and a total driven distance of 106.1 million kilometers. The results show the effect that the environmental temperature, the recharging power, and the driven kilometers have on the calendar and cycling aging. The majority of the combinations of the considered vehicle architectures and recharge strategies do not lead to battery capacity drop below 80% of its nominal value in less than five calendar years for a usage profile of up to 1000 km/month.

scholarly journals Thermoelectric Modeling and Online SOC Estimation of Li-Ion Battery for Plug-In Hybrid Electric Vehicles

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Aishwarya Panday ◽  
Hari Om Bansal ◽  
Pramod Srinivasan
Keyword(s):  
Electric Vehicles ◽  
Energy Demand ◽  
Hybrid Electric Vehicles ◽  
Environmental Concern ◽  
Open Circuit ◽  
Li Ion Battery ◽  
Vehicle Performance ◽  
Li Ion ◽  
Hybrid Electric ◽  
Thermoelectric Model

The increasing oil price, energy demand, and environmental concern are leading to a global switch towards Plug-In Hybrid Electric Vehicles (PHEVs). In a PHEV, Li-ion battery is considered as the primary propelling source. Therefore, an accurate battery model is required to predict theI-Vcharacteristic and dynamic behavior of a battery. This paper presents a highly effective thermoelectric model of Li-ion battery developed in Simulink. An algorithm is proposed for estimation of state of charge (SOC) and open circuit voltage (OCV) adaptively to notify the exact SOC level for better utilization of battery power and optimal vehicle performance. Thermal behavior of Li-ion battery is investigated for wide temperature range and its effect on resistance, capacity, and OCV is recorded. The minimum SOC level to which battery can get depleted is calculated using gradient method. The proposed simulation results are analyzed with those of earlier models and found to be better.

scholarly journals Comparisons of Real-World Vehicle Energy Efficiency with Dynamometer-Based Ratings and Simulation Models

2021 ◽  
Vol 12 (4) ◽  
pp. 161
Author(s):  
Karim Hamza ◽  
Kang-Ching Chu ◽  
Matthew Favetti ◽  
Peter Keene Benoliel ◽  
Vaishnavi Karanam ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Intensity ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Simulation Models ◽  
Hybrid Electric

Software tools for fuel economy simulations play an important role during design stages of advanced powertrains. However, calibration of vehicle models versus real-world driving data faces challenges owing to inherent variations in vehicle energy efficiency across different driving conditions and different vehicle owners. This work utilizes datasets of vehicles equipped with OBD/GPS loggers to validate and calibrate FASTSim (software originally developed by NREL) vehicle models. The results show that window-sticker ratings (derived from dynamometer tests) can be reasonably accurate when averaged across many trips by different vehicle owners, but successfully calibrated FASTSim models can have better fidelity. The results in this paper are shown for nine vehicle models, including the following: three battery-electric vehicles (BEVs), four plug-in hybrid electric vehicles (PHEVs), one hybrid electric vehicle (HEV), and one conventional internal combustion engine (CICE) vehicle. The calibrated vehicle models are able to successfully predict the average trip energy intensity within ±3% for an aggregate of trips across multiple vehicle owners, as opposed to within ±10% via window-sticker ratings or baseline FASTSim.

Design of coordinated control strategy during driving mode switching for parallel hybrid electric vehicles

2018 ◽  
Vol 41 (9) ◽  
pp. 2507-2520
Author(s):  
Jiangtao Fu ◽  
Shuzhong Song ◽  
Zhumu Fu ◽  
Jianwei Ma
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Coordinated Control ◽  
Mode Switching ◽  
Vehicle Performance ◽  
Driving Mode ◽  
Response Characteristics ◽  
Hybrid Electric

Hybrid electric vehicles (HEVs) require the power to drive the vehicle via a combination of internal combustion engine (ICE) and electric machine (EM). To improve the drivability, the smooth torque change during the driving mode switching is essential. This task can be achieved by using the coordinated control strategy. This paper presents a coordinated control strategy based on considering the different dynamic response characteristics of the ICE and the EM, which can effectively suppress the torque surge during the driving mode switching processes. The novelty lies in the proposed control is a motor active synchronization control strategy without clutch disengagement based on the mode switching classification. The coordinated control strategy is designed according to the classification of the driving modes. The objective is to minimize torque fluctuation and maintain or improve the driving performance of the vehicle. Results from the computer simulation demonstrate the effectiveness of this approach in reducing the torque surge without sacrificing vehicle performance.

scholarly journals Methodology for Finding Maximum Performance and Improvement Possibility of Rule-Based Control for Parallel Type-2 Hybrid Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1924 ◽  
Author(s):  
Haeseong Jeoung ◽  
Kiwook Lee ◽  
Namwook Kim
Keyword(s):  
Optimal Control ◽  
Electric Vehicles ◽  
Real World ◽  
Hybrid Electric Vehicles ◽  
Minimum Principle ◽  
Rule Based ◽  
Real World Applications ◽  
Hybrid Electric ◽  
Parallel Type

Hybrid electric vehicles (HEVs) require supervisory controllers to distribute the propulsion power from sources like an engine and motors. Control concepts based on optimal control theories such as dynamic programming (DP) and Pontryagin’s minimum principle (PMP) have been studied to maximize fuel efficiencies. These concepts are, however, not practical for real-world applications because they guarantee optimality only if future driving information is given prior to the actual driving. Instead, heuristic rule-based control concepts are widely used in real-world applications. Those concepts are not only simple enough to be designed based on existing vehicle control concepts, but also allow developers to easily intervene in the control to enhance other vital aspects of real-world vehicle performances, such as safety and drivability. In this study, a rule-based control for parallel type-2 HEVs is developed based on representative control concepts of real-world HEVs, and optimal control parameters are determined by optimization processes. The performance of the optimized rule-based control is evaluated by comparing it with the optimal results obtained by PMP, and it shows that the rule-based concepts can achieve high fuel efficiencies, which are close, typically within 4%, to the maximum values obtained by PMP.

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