An online state of charge estimation for Lithium-ion and supercapacitor in hybrid electric drive vehicle

Jarraya, Imen; Masmoudi, Ferdaous; Chabchoub, Mohamed Hedi; Trabelsi, Hafedh

doi:10.1016/j.est.2019.100946

Cited by 46 publications

(18 citation statements)

References 53 publications

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“…To this end, ICM is applied to derive optimal charging and discharging patterns of the storage system. ESS specifications are given in Table 1 [28,29].…”

Section: Energy Storage Systemmentioning

confidence: 99%

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

et al. 2020

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The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.Energies 2020, 13, 1706 2 of 25 microgrids is evolving quickly [1]. The advanced structure of the microgrids allows the distribution network operators (DSOs) to consider the EV battery, either as a load or distributed generator [2]. However, deployment of RESs and EVs creates some challenges, e.g., poor reliability and power quality problems. One of the most effective ways to mitigate these challenges is the deployment of energy storage systems (ESSs) in distribution grids. ESSs can alleviate the negative effects of uncertainties in RESs production [3]. On the other hand, despite the remarkable advantages of these units, there are some challenges in terms of adequate DERs capacity requirement, system configuration, and energy management and control. One of the most challenging issues is the optimal coordination of both supply and demand sides in microgrids with the main grid, while satisfying system constraints.Currently, extensive studies have been done on the impacts of the RESs, EVs, and ESSs on the network operation . In [4], a smart home energy management strategy is proposed to realize cost-effective energy systems for customers and provide reactive power compensation for home appliances using EV and ESS. In this paper, network-level studies are limited to investigating the power factor indicator, while the potential role of EVs and ESSs in distribution networks has not been considered. Researchers in [5] presented the centralized control strategy to manag...

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“…To this end, ICM is applied to derive optimal charging and discharging patterns of the storage system. ESS specifications are given in Table 1 [28,29].…”

Section: Energy Storage Systemmentioning

confidence: 99%

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

et al. 2020

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“…Thus Xiong, R.et al, designed a dual estimator framework including recursive least squares and adaptive H-infinite filter to promote the estimation efficiency [24]. Similarly, the research based on the model and filter observers are also reported in [25][26][27][28][29]. In summary, the method is provided with strong robustness, but the estimation performance highly relies on the accuracy of the model and preset matrixes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty

et al. 2022

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An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s.

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“…In order to improve the model accuracy, adaptive learning and online parameter identification methods in other fields are still applicable to the estimation of SOC [60][61][62]. Jarraya et al proposed a realtime estimation method for the SOC of lithium ion batteries based on extended Kalman filter [63]. Extended Kalman filter linearizes nonlinear systems and is therefore applied to the SOC estimation of supercapacitors [64][65][66][67][68].…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Wang

Liu

Sun³

et al. 2021

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This paper studies the state of charge (SOC) estimation of supercapacitors and lithium batteries in the hybrid energy storage system of electric vehicles. According to the energy storage principle of the electric vehicle composite energy storage system, the circuit models of supercapacitors and lithium batteries were established, respectively, and the model parameters were identified online using the recursive least square (RLS) method and Kalman filtering (KF) algorithm. Then, the online estimation of SOC was completed based on the Kalman filtering algorithm and unscented Kalman filtering algorithm. Finally, the experimental platform for SOC estimation was built and Matlab was used for calculation and analysis. The experimental results showed that the SOC estimation results reached a high accuracy, and the variation range of estimation error was [−0.94%, 0.34%]. For lithium batteries, the recursive least square method is combined with the 2RC model to obtain the optimal result, and the estimation error is within the range of [−1.16%, 0.85%] in the case of comprehensive weighing accuracy and calculation amount. Moreover, the system has excellent robustness and high reliability.

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An online state of charge estimation for Lithium-ion and supercapacitor in hybrid electric drive vehicle

Cited by 46 publications

References 53 publications

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

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