Forecasting the State of Health of Electric Vehicle Batteries to Evaluate the Viability of Car Sharing Practices

Šemanjski, Ivana; Gautama, Sidharta

doi:10.3390/en9121025

Cited by 18 publications

(16 citation statements)

References 49 publications

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“…At the same time, it should be borne in mind that for lithium batteries, narrowing the SOC ON-OFF operating area or increasing the SOC ON value (turning the generator before the battery discharges too deep) contributes to extending their service life (SOH) [40]. Figure 20 shows the SOC comparison for two different sets of generator sets installed on the RE.…”

Section: Energy Consumptionmentioning

confidence: 99%

Steam and Oxyhydrogen Addition Influence on Energy Usage by Range Extender—Battery Electric Vehicles

Łebkowski

2018

Energies

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The objective of this paper is to illustrate the benefits of the influence of the steam and oxyhydrogen gas (HHO) on the composition of emitted exhaust gases and energy usage of operating the internal combustion engine (ICE) that drives a generator-powered battery electric vehicle (BEV). The employed internal combustion generating sets can be used as trailer mounted electric energy sources allowing one to increase the range of BEV vehicles, mainly during long distance travel between cities. The basic configurations of hybrid and electric propulsion systems used in a given Electric Vehicles (xEV) includes all types of Hybrid Electric Vehicles (xHEV) and Battery Electric Vehicles (xBEV), which are discussed. Using the data collected during traction tests in real road traffic (an electric car with a trailer range extender (RE) fitted with ICE generators (5 kW petrol, 6.5 kW diesel), a mathematical model was developed in the Modelica package. The elaborated mathematical model takes into account the dynamic loads acting on the set of vehicles in motion and the electric drive system assisted by the work of RE. Conducted tests with steam and HHO additives for ICE have shown reduced (5–10%) fuel consumption and emissions (3–19%) of harmful gases into the atmosphere.

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Section: Energy Consumptionmentioning

confidence: 99%

Steam and Oxyhydrogen Addition Influence on Energy Usage by Range Extender—Battery Electric Vehicles

Łebkowski

2018

Energies

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“…but it is defined through multiple ways [7]. As and example, the State of Health (SoH) can be computed as the ratio between current and initial capacity [23] i.a. Besides this, manufacturers of EVs and Electronic Control Units (ECUs) keep their calculation approach secret [27].…”

Section: General Battery Degradationmentioning

confidence: 99%

“…Therefore, the practical operation needs to be considered also in order to understand the process of battery degradation. For this reason, EV performance data can be acquired from the EV Controller Area Network (CAN) bus [15,23,25], as done in this paper. Another approach, which is more accurate [24], uses a direct measurement of the battery parameters [24] i.a.…”

Section: General Battery Degradationmentioning

confidence: 99%

“…Here, the degradation is accelerated by internal temperature rise due to high current or power flow [16] i.a. For example, this strongly occurs during fast charging [12,18,23] which is why generally, fast charging should be avoided. However, it could be identified in a prior paper [6], that fast charging at low temperatures (< 17 • C) is battery-friendly.…”

Section: Static Recommendationsmentioning

confidence: 99%

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Dynamic EV Battery Health Recommendations

Eider

Berl

2018

Proceedings of the Ninth International Conference on Future Energy Systems

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Prolonging the lifetime of batteries in Electric Vehicles (EVs) becomes a more and more important issue for private users and fleet operators. In addition to the environmental point of view, a better battery health results in less cost, higher battery capacities and higher performance. To achieve this, the EV drivers or the fleet operators need to get proper information, which kind of actions will increase or decrease the batteries health. To this point, various tips and recommendations exist distributed over literature. Unfortunately, those kind of recommendations are hard to follow in the day-today routine. This paper suggests so called dynamic recommendations for battery health that are able to advise the user in specific situations with respect to battery use. Recommendations from literature are broken down into a list, which can be automatically computed. Recommendations will then be dynamically created in the current context of the EV and displayed to the user just in time. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models; Activity centered design; • Applied computing → Physics; • Information systems → Data analytics;

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“…Finally, other relevant topics, such as forecasting the state of health of electric vehicle batteries [18], a time series clustering based battery grouping method [19], financing innovations for the renewable energy transition in Europe [20], or clustering energy markets [21] have been analyzed and discussed within this special issue.…”

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confidence: 99%

Recent Advances in Energy Time Series Forecasting

2017

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This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI's Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results. Keywords: energy; time series; forecastingThis special issue has focused on the forecasting of time series, with particular emphasis on energy-related data. By energy, it was understood to mean any kind of energy, such as electrical, solar, or wind.Authors were invited to submit their original research and review articles exploring the issues and applications of energy time series and forecasting.Topics of primary interest included, but were not limited to:(1) Energy-related time series analysis. From all the submissions received, only those with very high quality scientific content and innovativeness were accepted, after rigorous peer review. A total of twenty-one papers were accepted, with the following author's geographical distribution:(1) China (9). (2) Spain (4). The submissions received can be broadly divided into the following topics. First, electricity demand forecasting has been addressed by using deep neural networks [1], cointegration techniques [2], random forests [3], imbalanced classification for outlying data [4], or non-linear autoregressive neural networks [5]. Another hot topic-that is, electricity price forecasting-has also been analyzed in this special issue by means of an empirical mode decomposition-based multiscale methodology [6] or by

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Forecasting the State of Health of Electric Vehicle Batteries to Evaluate the Viability of Car Sharing Practices

Cited by 18 publications

References 49 publications

Steam and Oxyhydrogen Addition Influence on Energy Usage by Range Extender—Battery Electric Vehicles

Steam and Oxyhydrogen Addition Influence on Energy Usage by Range Extender—Battery Electric Vehicles

Dynamic EV Battery Health Recommendations

Recent Advances in Energy Time Series Forecasting

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