State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering
TL;DR: A more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter supported by experimental data.
Abstract: In this paper, a more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter (EKF) supported by experimental data. A nonlinear battery model is constructed by separating the model into a nonlinear open circuit voltage and a two-order resistance-capacitance model. EKF is used to eliminate the measurement and process noise and remove the need of prior knowledge of initial SOC. A hardware-in-the-loop test bench was built to validate the method. The experimental results show that the proposed method can estimate the battery SOC with high accuracy.
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TL;DR: In this article, a comprehensive review of the battery state of charge estimation and its management system for the sustainable future electric vehicles (EVs) applications is presented, which can guarantee a reliable and safe operation and assess the battery SOC.
Abstract: Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.
1,150 citations
TL;DR: In this paper, the methods for monitoring the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications.
827 citations
TL;DR: In this article, a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs is presented, including the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models.
Abstract: With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.
494 citations
TL;DR: This paper proposes a co-estimation scheme of state of charge, state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles that is validated in a real battery management system with good real-time performance and convincible estimation accuracy.
Abstract: This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH, and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the extended Kalman filter. The battery parameters related with the battery SOH and SOF are online identified using the recursive least square algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real battery management system with good real-time performance and convincible estimation accuracy.
335 citations
TL;DR: A nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation.
Abstract: This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$ ) and lithium titanate ( $\text{LTO}$ ) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).
325 citations
Cites methods from "State of Charge Estimation of Lithi..."
...Lastly, this SOC estimate is further corrected using a KF like in [15]....
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References
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TL;DR: An accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment that accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response.
Abstract: Low power dissipation and maximum battery runtime are crucial in portable electronics. With accurate and efficient circuit and battery models in hand, circuit designers can predict and optimize battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment. This model accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4% runtime error and 30-mV maximum error voltage show that the proposed model predicts both the battery runtime and I-V performance accurately. The model can also be easily extended to other battery and power sourcing technologies.
1,986 citations
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...In this paper, a battery model and its model parameters are developed based on experimental data, which cover the whole spectrum of battery conditions when the vehicle is in operation....
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...Finally, Section VI gives the conclusions and summarizes the contributions of this paper....
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TL;DR: In this article, an extended Kalman filter (EKF) was used to estimate the battery state of charge, power fade, capacity fade, and instantaneous available power of a hybrid electric vehicle battery pack.
1,636 citations
TL;DR: In this article, extended Kalman filtering (EKF) is used to estimate battery state-of-charge, power fade, capacity fade, and instantaneous available power for hybrid-electric-vehicle battery packs.
1,587 citations
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...The Kalman filter is based on linear dynamic systems discretized in the time domain and is a recursive estimator, which means only the estimated state from the previous time step and the current measurements are needed to compute the estimation for the current state....
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TL;DR: In this paper, an extended Kalman filter (EKF) was proposed to estimate the battery state of charge, power fade, capacity fade, and instantaneous available power of a hybrid-electric-vehicle battery pack.
1,260 citations
Additional excerpts
...The Kalman filter is based on linear dynamic systems discretized in the time domain and is a recursive estimator, which means only the estimated state from the previous time step and the current measurements are needed to compute the estimation for the current state....
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TL;DR: In this article, the authors present a complete dynamic model of a lithium ion battery that is suitable for virtual prototyping of portable battery-powered systems, based on publicly available data such as the manufacturers' data sheets.
Abstract: Presents here a complete dynamic model of a lithium ion battery that is suitable for virtual-prototyping of portable battery-powered systems. The model accounts for nonlinear equilibrium potentials, rate- and temperature-dependencies, thermal effects and response to transient power demand. The model is based on publicly available data such as the manufacturers' data sheets. The Sony US18650 is used as an example. The model output agrees both with manufacturer's data and with experimental results. The model can be easily modified to fit data from different batteries and can be extended for wide dynamic ranges of different temperatures and current rates.
784 citations
Additional excerpts
...…of the experimental discharge curves with the modeling results confirmed that the parameters used to model a small-scale LIPB could be applied to a large-scale LIPB provided the materials and composition of the electrodes as well as the processes for manufacturing the batteries were the same....
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