Simulation and Analysis of the Effect of Real-World Driving Styles in an EV Battery Performance and Aging (original) (raw)
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IEEE Transactions on Transportation Electrification, 2018
In this paper, battery lifetime estimation of an electric vehicle (EV) using different driving styles on arterial roads integrating recharging scenarios in the neighborhood of the vehicle-to-grid (V2G) integration is studied. The real-world driving cycles from a fleet of connected vehicles are evaluated in an EV model with different charging options. Daily utility services are added to the simulations to explore the whole day performance of the battery and its daily degradation. Fifty driving cycles from different drivers on arterial roads are classified into aggressive, mild and gentle drivers based on their driving acceleration behavior. The standard level 1 and 2 chargers are considered for recharging and the frequency regulation, peak shaving and solar energy storage are assumed for the daily ancillary services. The results indicate that the aggressive driving and recharging behavior have significant effect on the battery life reduction. In addition, the daily utility services impose extra degradation of the battery. Also, the effect of temperature change on the battery degradation is explored. Simulation of active vs. passive thermal management systems in three different climates shows the significant impact of the battery temperature on its capacity fade. Highlights: Modeling Li-ion battery performance and cycle aging Generating daily driving patterns using real-world collected data V2G simulation using real ancillary services data Evaluation of cycle capacity fade in different daily scenarios Exploring the battery degradation in different climates I. INTRODUCTION The transportation sector is moving toward electrification due to the emerging energy and environmental issues [1-3]. Vehicle manufacturers are introducing their hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) versions to overtake and lead in this competitive market [4-6]. One of the key features of all these vehicles is their battery range and lifetime. Because
2013 World Electric Vehicle Symposium and Exhibition (EVS27), 2013
This paper presents a simulation study dealing with the influence of different factors on the energy consumption of an electric vehicle (EV). Due to the limited quantity of energy embedded in the battery, EVs are very sensitive to parameters which can influence their energy consumption and then can induce huge variations in their actual range. Among all these factors, driving conditions, auxiliaries' impact, driver's aggressiveness and braking energy recovery strategy are to be considered as the main factors influencing the EV energy consumption. The objective of this paper is thus to simulate and quantify the influence of each factor independently. For this, a virtual EV simulator has been created and validated through EVs experiments on a climatic 4WD chassis dyno in the frame of a project sponsored by the French ADEME and with the help of PSA, Renault and Tazzari car manufacturers. This simulator, validated thanks to a limited number of experimental results, has been then used on a very large range of operating conditions and hypotheses to extrapolate experimental results and help the analyses of influencing factors.
Driving cycle based battery rating selection and range analysis in EV applications
International Journal of Power Electronics and Drive Systems (IJPEDS), 2021
The energy consumption of electric vehicles (EVs) depends on traffic environment, terrain, resistive forces acting on vehicle, vehicle characteristics and driving habits of driver. The battery pack in EV is the main energy storage element and the energy capacity determines the range of vehicle. This paper discusses the behavior of battery when EV is subjected to different driving environments such as urban and highway. The battery rating is selected based on requirement of driving cycle. The MATLAB/Simulink model of battery energy storage system (BESS) consisting of battery, bidirectional DC/DC converter and electric propulsion system is built. The simulation is carried out and the performance of BESS is tested for standard driving cycles which emulate actual driving situations. It has been shown that, the amount of the energy recovered by battery during deceleration depends on the amount of regenerative energy available in the driving cycle. If the battery recovers more energy during deceleration, the effective energy consumed by it reduces and the range of the vehicle increases.
On the Sensitivity of the State of Batteries on Driver Behaviour within the EV Powertrain
Driveability of BEVs is subject to the driver behaviour and in more extreme cases the powertrain is unable to adapt. The behaviour of drivers is categorized and presented here with comparison against more standard drive cycles. The extremes of the driver behaviour and this distribution is based on real-world measurements. A driver grading mechanism is developed to capture the driver behaviour influence on the energy consumption of the vehicle. The impact of different driver grades is presented in regard to the SoC for a typical day. From this, the effect on attainable vehicle range and SoH is inferred. Driving range reductions of up to 50% for the more aggressive drivers are found.
An investigation on the effect of driver style and driving events on energy demand of a PHEV
Proceedings of the 26th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium EVS26, 2012
Environmental concerns, security of fuel supply and CO 2 regulations are driving innovation in the automotive industry towards electric and hybrid electric vehicles. The fuel economy and emission performance of hybrid electric vehicles (HEVs) strongly depends on the energy management system (EMS). Prior knowledge of driving information could be used to enhance the performance of a HEV.
Design and Assessment of Electric Vehicle Performance Parameters based on Drive Cycle
ITM Web of Conferences, 2021
Electric vehicle plays a significant role, in the future transportation across the world. EV has the potential to reduce air pollution and emission of Greenhouse gasses significantly compared to the existing fossil-fuel-based vehicles. Even though substantial progress can be expected in the area of embarked energy storage technologies, charging infrastructure, customer acceptance of Electric Vehicles is still limited due to the problems of Driving range anxiety and long battery charging time. We can solve most of these problems with the infrastructure development ,optimum sizing and design of the vehicle components and extensive study on vehicle dynamics under various real-time driving conditions. This research focuses on the Matlab software based co-simulation of Electric Vehicle system, including the battery pack and motor, to predict the vehicle performance parameters like driving range, efficiency, power requirement, and energy characteristics under different driving scenarios. ...
Journal of Power Sources, 2007
This paper proposes a methodology and approach to understand battery performance and life through driving cycle and duty cycle analyses from electric and hybrid vehicle (EHV) operation in real-world situations. Conducting driving cycle analysis with trip data collected from EHV operation in real life is very difficult and challenging. In fact, no comprehensive approach has been accepted to date, except those using standard driving cycles on a dynamometer or a track. Similarly, analyzing duty cycle performance of a battery under real-life operation faces the same challenge. A successful driving cycle analysis, however, can significantly enhance our understanding of EHV performance in real-life driving. Likewise, we also expect similar results through duty cycle analysis for batteries. Since 1995, we have been developing tools to analyze EHV and power source performance. In particular, we were able to collect data from a fleet of 15 Hyundai Santa Fe electric sports utility vehicles (e-SUVs) operated on Oahu, Hawaii; from July 2001 to June 2003 to allow driving and duty cycle analyses in order to understand battery pack performance from a variety of EHV operating conditions. We thus developed a comprehensive approach that comprises fuzzy logic pattern recognition (FL-PR) techniques to perform driving and duty cycle analyses. This approach has been successfully applied to EHV performance analysis via the creation of a compositional driving profile called "driving cycle profile" (DrCP) for each trip. The same approach was used to analyze battery performance via the construction of "duty cycle profile" (DuCP) to express battery usage under various operating conditions. The combination of the two analyses enables us to understand both the usage profile of EHV and battery performance in synergetic details and in a systematic manner using a pattern recognition technique.
Energies, 2011
Emerging green-energy transportation, such as hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs), has a great potential for reduction of fuel consumption and greenhouse emissions. The lithium-ion battery system used in these vehicles, however, is bulky, expensive and unreliable, and has been the primary roadblock for transportation electrification. Meanwhile, few studies have considered user-specific driving behavior and its significant impact on (P)HEV fuel efficiency, battery system lifetime, and the environment. This paper presents a detailed investigation of battery system modeling and real-world user-specific driving behavior analysis for emerging electric-drive vehicles. The proposed model is fast to compute and accurate for analyzing battery system run-time and long-term cycle life with a focus on temperature dependent battery system capacity fading and variation. The proposed solution is validated against physical measurement using real-world user driving studies, and has been adopted to facilitate battery system design and optimization. Using the collected real-world hybrid vehicle and run-time driving data, we have also conducted detailed analytical studies of users' specific driving patterns and their impacts on hybrid vehicle electric energy and fuel efficiency. This work provides a solid foundation for future energy control with emerging electric-drive applications.
A roadmap to understand battery performance in electric and hybrid vehicle operation
Journal of Power Sources, 2007
This work attempts to bridge laboratory and real-life battery testing data with a comprehensive analysis to provide a coherent approach for a realistic model to simulate battery performance, including life prediction. From electric vehicle field-testing results, we explain how to handle real-life data through driving cycle analysis to establish a scheme of "building blocks" that can be validated by test results obtained in the laboratory. We also show that a simple battery model can be built upon laboratory test data and validated by real-life duty cycles, therefore deriving a more realistic understanding and prediction of battery performance.
Li-ion battery performance and degradation in electric vehicles under different usage scenarios
International Journal of Energy Research, 2015
Lithium-ion (Li-ion) batteries are well known as an efficient energy storage solution for plug-in hybrid electric vehicles (PHEVs). However, performance and state of health of these batteries strictly depends on the usage scenario including operating temperature, power demand profile, and control strategy imposed by the battery management system. Also, in PHEVs equipped with electric climate control systems, climate control loads are imposed as additional loads on the battery, which results in a reduced all-electric range (AER) and increased battery capacity degradation. In this paper, vehicle AER, and fuel economy and life degradation of an aftermarket LiFePO 4 Li-ion battery cell are studied for a PHEV under several usage scenarios. Each scenario consists of a series and parallel PHEV powertrain layout developed in Autonomie software, climate condition, that is, hot and cold weather, and a daily driving and charging profile. For simulations, models of battery performance, heat generation, and degradation developed based on experimental results are integrated with a thermal vehicle cabin model. Impact of climate control loads and battery thermal preconditioning are incorporated in the simulations. It is observed that climate control loads significantly affect the AER (up to 20%), fuel economy (up to 65%), and battery degradation (up to 25%). On the other hand, thermal preconditioning could be used to reduce these impacts.