Optimal energy management strategy for battery powered electric vehicles

Xi, Jiaqi; Li, Mian; Xu, Min

doi:10.1016/j.apenergy.2014.08.033

Cited by 28 publications

(7 citation statements)

References 17 publications

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“…Global warming, the petroleum crisis, and legislation pushing for higher fuel economy and lower emissions, are leading to the development of electric vehicles (EVs) [1,2]. As the key component of any electric vehicle, the energy storage system attracts more and more attention.…”

Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Hua

et al. 2015

Energies

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Estimation of state of charge (SOC) is of great importance for lithium-ion (Li-ion) batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF) and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO2 and LiFePO4 battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster.

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Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Hua

et al. 2015

Energies

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“…World Electric Vehicle Journal 2018, 9, x FOR PEER REVIEW 2 of 14 efficient and economic performance. Thus, it can be noted that the system's sizing and operation are key factors to ensure the fulfillment of the application requirements, the proper harnessing of the onboard energy sources, and the defined economic objectives [7,8]. In close-to-market applications, the economic issue becomes in an important decision factor as the solution has to be competitive and profitable both for the manufacturer and the end-users.…”

Section: Scenario Overviewmentioning

confidence: 99%

“…Despite the fact that, in a dual ESS based on BTs and SCs, the function of each energy storage unit during operation is quite defined (energy provided by the BT pack and power peak regulation by the SC pack), a proper methodology is still required to define their suitable sizing and to obtain efficient and economic performance. Thus, it can be noted that the system's sizing and operation are key factors to ensure the fulfillment of the application requirements, the proper harnessing of the onboard energy sources, and the defined economic objectives [7,8]. In close-to-market applications, the economic issue becomes in an important decision factor as the solution has to be competitive and profitable both for the manufacturer and the end-users.…”

Section: Introductionmentioning

confidence: 99%

Li-Ion Battery Lifetime Model’s Influence on the Economic Assessment of a Hybrid Electric Bus’s Operation

Martinez-Laserna

Herrera

Milo

et al. 2018

WEVJ

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The present paper is focused on the evaluation of the economic influence of a battery lifetime model upon the optimal sizing and energy management strategy of a dual energy storage system (ESS) composed of Lithium-ion batteries and supercapacitors. The operation of a Hybrid Bus is taken as a case study in order to evaluate the effects of battery lifetime models’ accuracy on ESS sizing and operation in a heavy-duty application. For this purpose, two different lifetime models (a Wöhler-curve-based model and a semi-empirical model) were applied in the multi-objective optimisation of a hybrid electric urban bus. Differences up to ca. 8% on the daily operation costs and ca. 25% on the dual ESS costs were estimated depending on the lifetime model considered for the optimisation.

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“…In order to reduce the usage of crude oil and protect the environment, the BEVs, as a promising substitute for conventional vehicles, have received much attention in the automobile market 1 . However, owing to the restriction of battery energy density and technology, EMSs have become the critical factor in improving the energy utilization rate and achieving higher driving range.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy optimal energy management for battery electric vehicles concerning equivalent speed

Pan

Chen

et al. 2020

Int Trans Electr Energ Syst

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Optimal energy management strategy for battery powered electric vehicles

Cited by 28 publications

References 17 publications

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Li-Ion Battery Lifetime Model’s Influence on the Economic Assessment of a Hybrid Electric Bus’s Operation

Fuzzy optimal energy management for battery electric vehicles concerning equivalent speed

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