Capacity fade and aging models for electric batteries and optimal charging strategy for electric vehicles

Fernández, Ismael Jaime Fernández; Calvillo, Christian; Sánchez-Miralles, Álvaro; Boal, Jaime

doi:10.1016/j.energy.2013.07.068

Cited by 120 publications

(63 citation statements)

References 17 publications

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“…Although the accurate prediction of battery E RDE is very crucial and challenging in EV onboard applications, there exist few studies on the E RDE prediction techniques to our knowledge and according to some critical reviewing publications [7][8][9], which is a relatively new topic compared with the battery SOC estimation [10][11][12][13], the SOH determination [14][15][16][17], and the state of power (SOP) prediction [18,19]. Traditionally, the E RDE is commonly seen as an extension of SOC estimation and directly determined in Eq.…”

Section: Introductionmentioning

confidence: 99%

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

Liu

Ouyang

et al. 2015

Applied Energy

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Section: Introductionmentioning

confidence: 99%

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

Liu

Ouyang

et al. 2015

Applied Energy

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“…Further, the condition of the cell (fed back in real-time) can be employed to influence the load profile. As discussed within [19,20,22], given that cell cycle ageing and performance degradation are inherently linked to its usage profile, there are obvious benefits in being able to emulate the real cell environment early within the ESS design life-cycle. The aim of this section is to discuss further the applicability of this CILS approach and the main areas for future research.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies [19][20][21][22] highlight the differential ageing that takes place between cells within a complete ESS and discuss the relative impact of cell depth of discharge (DOD), SOC, cell temperature and applied current (C-rate) on the degradation of the cell's energy capacity and power capability (i.e., the increase in internal impedance). One of the main challenges with experimental studies of battery ageing are the resource requirements (capital equipment and test time) required to age the cell ready for inclusion within a test programme.…”

Section: Introductionmentioning

confidence: 99%

A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

et al. 2015

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This research is aligned with the engineering challenge of scaling-up individual battery cells into a complete energy storage system (ESS). Manufacturing tolerances, coupled with thermal gradients and the differential electrical loading of adjacent cells, can result in significant variations in the rate of cell degradation, energy distribution and ESS performance. The uncertain transition from cell to system often manifests itself in over-engineered, non-optimal ESS designs within both the transport and energy sectors. To alleviate these issues, the authors propose a novel model-based framework for cell-in-the-loop simulation (CILS) in which a physical cell may be integrated within a complete model of an ESS and exercised against realistic electrical and thermal loads in real-time. This paper focuses on the electrical integration of both real and simulated cells within the CILS test environment. Validation of the CILS approach using real-world electric vehicle data is presented for an 18650 cell. The cell is integrated within a real-time simulation model of a series string of similar cells in a 4sp1 configuration. Results are presented that highlight the impact of cell variability (i.e., capacity and impedance) on the energy available from the multi-cell system and the useable capacity of the physical cell.

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“…With relatively small energy density compared to lithium battery, extending NI-MH battery life for purely electric application is a challenging problem [19,[21][22][23]. It mainly involves key issues as follows:…”

Section: Effective Methods Of Prolonging Battery Lifementioning

confidence: 99%

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

et al. 2018

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This paper discusses a design of a Battery Management System (BMS) solution for extending the life of Nickel-Metal Hydride (NI-MH) battery. Combined with application of electric boat, a State of Charge (SoC) optimal operation range control method based on high precision energy metering and online SoC correction is proposed. Firstly, a power metering scheme is introduced to reduce the original energy measurement error. Secondly, by establishing a model based parameter identification method and combining with Extended Kalman Filter (EKF) method, the estimation accuracy of SoC is guaranteed. Finally, SoC optimal operation range control method is presented to make battery running in the optimal range. After two years of operation, the battery managed by proposed method has much better status, compared to batteries that use AH integral method and fixed SoC operating range. Considering the SoC estimation of NI-MH battery is more difficult becausing special electrical characteristics, proposed method also would have a very good reference value for other types of battery management.

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Capacity fade and aging models for electric batteries and optimal charging strategy for electric vehicles

Cited by 120 publications

References 17 publications

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

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