Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects

Lai, Xin; Huang, Yunfeng; Gu, Huanghui; Han, Xiao; Feng, Xuning; Dai, Haifeng; Zheng, Yuejiu; Ouyang, Minggao

doi:10.1016/j.energy.2021.121754

Cited by 57 publications

(14 citation statements)

References 31 publications

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“…Methods for RDE estimation using load prediction are proposed in [3], [7]. These involve using a moving window to collect load data, which is clustered into distinct states, which represent a sequence of load levels.…”

Section: Prediction-based Methodsmentioning

confidence: 99%

Load Prediction Based Remaining Discharge Energy Estimation Using a Combined Online and Offline Prediction Framework

Hatherall

Marco

Barari

et al. 2022

2022 IEEE Conference on Control Technology and Applications (CCTA)

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Remaining discharge energy (RDE) indicates how much useful energy can be extracted from a battery before reaching the discharge limit. Future current loading on vehicle battery systems can be predicted to increase the accuracy of RDE estimations. This is done by using clustering techniques to group load measurements into states, and then using a probability-based framework, along with real-world data, to calculate the transitional probabilities between states. Here, an adapted K-means clustering method is used to cluster load profile data. Markov modelling is used to produce state transition probabilities. Two methods for load prediction are used, which are referred to as the offline-training method and the moving window method, where the offline-training method has not been implemented for this application before. Additional control logic is implemented to combine the proposed load prediction methods to produce a new hybrid load prediction method. This hybrid method shows improved RDE accuracy for a generalised load case. The robustness of the proposed technique is assessed in the presence of model errors, still showing good accuracy when compared to state-of-charge based calculations.

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Section: Prediction-based Methodsmentioning

confidence: 99%

Load Prediction Based Remaining Discharge Energy Estimation Using a Combined Online and Offline Prediction Framework

Hatherall

Marco

Barari

et al. 2022

2022 IEEE Conference on Control Technology and Applications (CCTA)

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“…[37,38]. So, to prevent failures and fres for upcoming BMSs in ESSs, it is expected that investigation to monitor and manage battery states will be continuously conducted [39,40].…”

Section: Real-time Implementationmentioning

confidence: 99%

A comprehensive state‐of‐the‐art review on reliability assessment and charging methodologies of grid‐integrated electric vehicles

Singh

Saket

Khan

2023

IET Electrical Syst in Trans

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This article presents a comprehensive review on the reliability assessment and charging methodologies of electric vehicles (EVs) connected to the grid. In the transportation industry, EVs are acknowledged as effective ideas for reducing the consumption of fossil fuels and the emission of greenhouse gases. The article presents a critical survey of the various types of EVs and their performance, generally depend on the proper interface between power electronics converters and energy storage devices. Bi‐directional converters are utilised to enhance efficiency and prolong battery life. This article explores an in‐depth analysis of the grid connected converter topologies for distribution networks, charging infrastructures and standards, battery charging technologies (merits/demerits), and most advanced off‐board and on‐board EV chargers. Evaluating the reliability of grid‐integrated EVs system is thus an important area of research. Then, the current research approaches vehicle‐to‐grid (V2G) and grid‐to‐vehicle (G2V) are demonstrated to describe travel behaviour, reliability parameters, and battery capacity associated with reliability and to assess the stability of the distribution system by incorporating EV discharging thresholds of both distribution networks and EVs. Finally, this article illustrates the positive as well as negative impacts of grid integration, issues related to EV converters, and specific suggestions for developing EV charging stations (CS) in the future.

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“…These factors discourage potential users from purchasing BEVs because of a negative association between anxiety and BEV use [54]. Accurate remaining discharge energy (RDE) estimation can relieve passenger anxiety for mileage, which is very important for EVs, especially for pure EVs [55]. Environmental concern is a psychological factor, according to Lai [49], which is an important deciding factor for the purchase of BEVs.…”

Section: Advantages and Disadvantages Of Battery Electric Vehiclesmentioning

confidence: 99%

Challenges and Opportunities for Future BEVs Adoption in Croatia

Emanović¹,

Jakara

Barić

2022

Sustainability

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Electric vehicles (EVs) represent a revolution and the beginning of a new era in the development of the automotive industry. This study investigates the advantages and disadvantages of battery electric vehicles (BEVs) and the possibilities of their better future adoption in the Republic of Croatia. Based on an in-depth analysis of the current status of BEVs in Croatia, the study shows that the number of passenger BEVs and charging stations are constantly increasing. However, despite the many advantages of BEVs, such as a reduction in urban air pollution, reduction in greenhouse gas emissions, noise-free, less dependence on oil as a fuel, etc., there also are certain disadvantages, such as a limited range of vehicles, the correlation with an insufficient number of charging stations on the transport network as a whole, storage of lithium-ion batteries, maintenance of electric vehicles, high prices, and the safety of BEVs in the road traffic system. The practical implications of BEVs’ positive and negative effects and challenges for increasing their implementation in Croatia are discussed. The results and findings from this research could present a base for policymakers and decisionmakers to formulate policies and strategies to improve the opportunities for the adoption of BEVs in Croatia.

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Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects

Cited by 57 publications

References 31 publications

Load Prediction Based Remaining Discharge Energy Estimation Using a Combined Online and Offline Prediction Framework

Load Prediction Based Remaining Discharge Energy Estimation Using a Combined Online and Offline Prediction Framework

A comprehensive state‐of‐the‐art review on reliability assessment and charging methodologies of grid‐integrated electric vehicles

Challenges and Opportunities for Future BEVs Adoption in Croatia

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