Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

Hu, Xiaosong; Sun, Fuchun; Zou, Yuan

doi:10.3390/en3091586

Cited by 241 publications

(114 citation statements)

References 35 publications

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“…It was recently introduced to estimate the SOC of batteries [14][15][16]. The equations of a Luenberger observer could be depicted as follows:…”

Section: The Luenberger Observer Soc Estimation Methodsmentioning

confidence: 99%

“…According to the literatures, some model-based methods can be the Luenberger observer SOC estimation method [13][14][15][16], the Sliding mode observer SOC estimation method [17][18][19], the Kalman filter SOC estimation method [20][21][22] and the Proportional integral observer (PIO) SOC estimation method [23][24][25], etc. Such estimation methods are so popular these days that evaluation studies of these methods should be carried out.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Zou

et al. 2014

Energies

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Abstract:Four model-based State of Charge (SOC) estimation methods for lithium-ion (Li-ion) batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS) current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods.

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“…It was recently introduced to estimate the SOC of batteries [14][15][16]. The equations of a Luenberger observer could be depicted as follows:…”

Section: The Luenberger Observer Soc Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Zou

et al. 2014

Energies

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“…SOC estimation is one of the most significant and difficult techniques to promote the commercialization of EVs [19]. Hu et al [20] presented an adaptive observer-based technique for estimating SOC of a lithium-ion battery pack used in an EV. In addition, a great deal of attention has been paid towards the influence of charging patterns on battery life for plug-in EVs [21,22].…”

Section: Introductionmentioning

confidence: 99%

The Concept of EV’s Intelligent Integrated Station and Its Energy Flow

et al. 2015

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Abstract:The increasing number of electric vehicles (EVs) connected to existing distribution networks as time-variant loads cause significant distortions in line current and voltage. A novel EV's intelligent integrated station (IIS) making full use of retired batteries is introduced in this paper to offer a potential solution for accommodating the charging demand of EVs. It proposes the concept of generalized energy in IIS, based on the energy/power flow between IIS and EVs, and between IIS and the power grid, to systematically evaluate the energy capacity of IIS. In order to derive a unique and satisfactory operation mode, information from both the grid (in terms of load level) and IIS (in terms of its energy capacity and EVs battery charging/exchanging requests) is merged. Then, based on the generalized energy of different systems, a novel charging/discharging control strategy is presented and whereby the operating status of the grid and energy capacity of IIS are monitored to make reasonable operation plans for IIS. Simulation results suggest that the proposed IIS offers peak load shifting when EV battery charging/exchanging requests are satisfied compared to existing charging stations. OPEN ACCESSEnergies 2015, 8 4189

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“…The core theory of the observer is to estimate the OCV online through a battery model [26], rather than waiting for a long setting time, and then use the OCV-based SOC to correct the SOC obtained through the CC method. The Luenberger observer [11] is the simplest state observer, of which the logical structure is shown in Figure 1a. It uses fixed gain for state feedback, and the selection of this gain-more precisely, the pole placement of the observer system [12]determines the convergence speed and noise filtering performance.…”

Section: Introductionmentioning

confidence: 99%

“…An accurate SOC estimation can not only provide the information of the remaining mileage, but also give assistance to active balancing and protection of the battery pack [5]. There are many different kinds of methods that focus on SOC estimation [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. However, due to the limited computational ability of the embedded processor and the large amount of batteries used in EVs, the SOC estimation algorithm needs to be simple and reasonably accurate.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

et al. 2016

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A battery's state-of-charge (SOC) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer from low accuracy and require tuning of the feedback gain by trial-and-error. In this study, an algorithm named lazy-extended Kalman filter (LEKF) is proposed, to allow the Luenberger observer to learn periodically from the extended Kalman filter (EKF) and solve the problems, while maintaining computational efficiency. We demonstrated the effectiveness and high performance of LEKF by both numerical simulation and experiments under different load conditions. The results show that LEKF can have 50% less computational complexity than the conventional EKF and a near-optimal estimation error of less than 2%.

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Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

Cited by 241 publications

References 35 publications

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

The Concept of EV’s Intelligent Integrated Station and Its Energy Flow

State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

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