Performance evaluation strategy for battery pack of electric vehicles: Online estimation and offline evaluation

Hu, Lin; Yao, Ye; Bo, Ying; Huang, Jing; Tian, Qingtao; Yi, Xiaojian; Li, Qiqi

doi:10.1016/j.egyr.2022.02.026

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…This reduces the weight of the EVs as well as the cost. However, small batteries may suffer from degradation due to deep discharge and higher number of charge cycles [299]. This may result in the reduction in the overall life of the batteries.…”

Section: Discussion and Future Researchmentioning

confidence: 99%

Wireless charging systems for electric vehicles

Amjad

Farooq-i-Azam

et al. 2022

Renewable and Sustainable Energy Reviews

101

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Section: Discussion and Future Researchmentioning

confidence: 99%

Wireless charging systems for electric vehicles

Amjad

Farooq-i-Azam

et al. 2022

Renewable and Sustainable Energy Reviews

101

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“…This is an "offline estimation technique," which cannot be done in many BMS applications. Thus, online SoC estimation while the LIBs are in normal operating conditions is attracting more research efforts (L. Hu et al, 2022). For better comparison among all these frequent approaches in the following subsections, we have classified them into the main five groups as shown in Figure 9, and Table 5 summarizes the differences among them.…”

Section: Soc Estimation Methodsmentioning

confidence: 99%

A review of battery state of charge estimation and management systems: Models and future prospective

Hussein,

Aghmadi,

Abdelrahman

et al. 2024

WIREs Energy & Environment

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Batteries are considered critical elements in most applications nowadays due to their power and energy density features. However, uncontrolled charging and discharging will negatively affect their functions and might result in a catastrophic failure of their applications. Hence, a battery management system (BMS) is mandated for their proper operation. One of the critical elements of any BMS is the state of charge (SoC) estimation process, which highly determines the needed action to maintain the battery's health and efficiency. Several methods were used to estimate the Lithium‐ion batteries (LIBs) SoC, depending on the LIBs model or any other suitable technique. This article provides a critical review of the existing SoC estimation approaches and the main LIB models with their pros and cons, their possibility to integrate with each other's for precise estimation results, and the applicability of these techniques in electric vehicles and utility applications with the commonly used standards and codes in these sectors. Moreover, this study will also explore a future framework for integrating digital twins (DTs) with BMSs for improved and advanced management. Based on this comprehensive review, it can be concluded that merging the model‐based estimation techniques with the data‐driven approaches with their promising development to determine the dynamic patterns inside the battery can efficiently achieve precise estimation results while reducing the complexity of these models. This integration will align also with the integration of digital twin technology to provide complete and accurate supervision for the BMSs.This article is categorized under: Emerging Technologies > Energy Storage Emerging Technologies > Digitalization Energy and Power Systems > Distributed Generation

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“…In order to reduce environmental pollution and deal with the energy crisis, the development of clean energy vehicle technology has been vigorously promoted, so energy storage has also developed rapidly [1]. Lithium ion batteries have the characteristics of high output voltage, wide operating temperature range, long cycle life, and high energy density [2]. It has become a research hotspot of clean energy vehicle technology and energy storage.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi innovation Adaptive Robust Dual Kalman Filter Algorithm for Estimating Battery State

guan,

Yang,

et al. 2023

Preprint

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In response to the inaccurate estimation of State of Charge (SOC) in current power battery management systems, and considering that SOC may be subject to offset constraints from State of Health (SOH) when estimated separately. A method combining adaptive robust Kalman filtering with multiple innovation theories and online identification of dual Kalman filtering parameters is proposed. This control method is based on adaptive robust Kalman control. It corrects the estimated values using multiple innovation values and Kalman gains at different times. Dual Kalman filtering is used for online parameter identification and joint estimation of battery health status, which increases the amount of error information and provides an optimized method for accurate estimation of SOC and SOH. To verify the rationality of the algorithm, a second-order RC equivalent circuit model is used to characterize the dynamic characteristics of the battery, and experimental verification is carried out under different operating conditions. The experimental results show that the average error of SOC under three operating conditions: UDDS, FUDS, and US06 is 0.56\%, 0.31\%, and 1.23\%, respectively. The estimated error of SOH after stabilization is less than 1.73\%. The estimation error is the lowest among the five estimation algorithms. The proposed algorithm has been verified to have good accuracy and convergence. The multi-innovation adaptive robust dual Kalman filtering algorithm provides a theoretical basis for accurate state estimation and widespread application of lithium batteries.

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Performance evaluation strategy for battery pack of electric vehicles: Online estimation and offline evaluation

Cited by 12 publications

References 19 publications

Wireless charging systems for electric vehicles

Wireless charging systems for electric vehicles

A review of battery state of charge estimation and management systems: Models and future prospective

An Improved Multi innovation Adaptive Robust Dual Kalman Filter Algorithm for Estimating Battery State

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