Battery Aging Assessment for Real-World Electric Buses Based on Incremental Capacity Analysis and Radial Basis Function Neural Network

She, Chengqi; Wang, Zhenpo; Sun, Fuchun; Liu, Peng; Zhang, Lei

doi:10.1109/tii.2019.2951843

Cited by 176 publications

(83 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EMS control parameters optimized for specified driving cycles cannot meet all kinds of road situations. The degradation of power sources leads to the decrease of their performances and corresponding degradation rates will change along with SOHs [18]. Prices of hydrogen and power sources will also change along with their technology development on production, usage, investment, and operation.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Zhou

Gualous

et al. 2021

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. A road, health, and price-conscious optimal cost minimization strategy based on decision making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of vehicles including the hydrogen cost and degradation costs of fuel cell and battery. Second, a decision making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate health states of power sources and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach optimal results on the overall cost and charge sustaining of battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed.

show abstract

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Zhou

Gualous

et al. 2021

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

show abstract

“…Despite these advantages, careless uses of lithium-ion batteries such as overcharging and overdischarging can cause an internal shortcircuit [2], which increases the self-discharge rate, or a thermal runaway in severe cases [3]. Therefore, batterypowered devices have required a battery management system (BMS) [4] that estimates battery states such as an aging level [5], an internal short-circuit resistance [6], and remaining capacity to efficiently and safely control the charge/discharge current. Especially, a state-of-charge (SOC), representing the remaining capacity of the battery, is the key state affecting the reliability of the BMS [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions

et al. 2020

View full text Add to dashboard Cite

Load currents of batteries undergo real-time change depending on the operations of battery-powered devices, such as acceleration, deceleration, and sudden braking of electric vehicles. With the variation in current magnitude, parameters in an equivalent circuit model of the battery change depending on its chemical characteristics. Therefore, accurately identifying model parameters offline for various sizes and patterns of load currents with few experiments is difficult. Besides, there is a model uncertainty due to the difference between the linearity of the model structure and the nonlinearity of the real battery; hence, an experimentally obtained open-circuit voltage (OCV)-state-of-charge (SOC) curve has difficulty in providing an accurate OCV value for the model. Consequently, these difficulties lead to inaccuracies in the model and SOC estimation. In this paper, novel methods are proposed to reduce these inaccuracies under timevarying current conditions, where the magnitude of the load current changes in real time. These are (1) an OCV-SOC curve estimation for reducing the model uncertainty and (2) a current-adaptive extended Kalman filter algorithm for decreasing the effect of inaccurately identified model parameters. The experimental results show that the maximum-absolute-errors of the proposed SOC estimation were reduced to <1.5%. Consequently, battery-powered applications can obtain highly accurate SOC values even the magnitude of the load current changes in real time. INDEX TERMS Battery management system, electric vehicles, open-circuit voltage-state-of-charge curve estimation, adaptive extended Kalman filter, state-of-charge estimation.

show abstract

“…Additionally, knowing the degraded capacity aids in defining the SOH of the battery. The differential capacity rate (dQ/dV) has been used for detecting the aging process and assessing the degraded capacity in [26][27][28]. As this rate changes with the energy capability of the battery for a specific SOC range, this approach is considered one of the most effective techniques for capacity estimation.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter

Imran

Flaih³

2020

IEEE Access

View full text Add to dashboard Cite

Battery Aging Assessment for Real-World Electric Buses Based on Incremental Capacity Analysis and Radial Basis Function Neural Network

Cited by 176 publications

References 33 publications

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework

Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions

An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter

Contact Info

Product

Resources

About