A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter

Lei, Zheng; Dang, Xuanju; Jing, Benqin

doi:10.1109/access.2019.2910882

Cited by 32 publications

(24 citation statements)

References 37 publications

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“…36 2.2 | Improvement strategy of Ah method 2.2.1 | Improvement of SOC initial value correction OCV refers to the potential difference between positive and negative electrodes when the external circuit is disconnected. 25 After a certain period of shelving, the terminal voltage of the battery is approximately equal to the OCV. There is a relatively fixed functional relationship between the OCV and SOC, as shown in Figure 1.…”

Section: The Principle Of Traditional Ah Methodsmentioning

confidence: 99%

A novel practical state of charge estimation method: an adaptive improved ampere‐hour method based on composite correction factor

et al. 2020

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The research of the real-time state of charge (SOC) estimation method for lithium-ion battery is developing towards the trend of model diversification and algorithm complexity. However, due to the limitation of computing ability in the actual battery management system, the traditional ampere-hour (Ah) method is still widely used. First, temperature, charge-discharge current, and battery aging are considered as the main factors, which affect the estimation accuracy of the Ah method under the condition that detection accuracy of the current sensor is determined. Second, the relationship between the SOC and battery open-circuit voltage at different temperatures is analyzed, which is used to modify the initial SOC. Third, the influence mechanism of main factors on the effect of the Ah method is analyzed, and proposes a capacity composite correction factor to reflect the influence of charge-discharge efficiency, coulomb efficiency, and battery aging comprehensively, and then update its value in real-time. Lastly, the adaptive improved Ah formula and the complete SOC estimation model is designed, and the estimation effect of this model is verified by comparing with other SOC estimation methods in the experiment of dynamic cycle test. The results show that the estimation error of the adaptive improved method is less than 2% under two comprehensive working conditions, while the error of the traditional method is 5% to 10%, and compared with an extended kalman filter algorithm, it also gets a better SOC estimation performance, which proves that this method is scientific and effective.

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Section: The Principle Of Traditional Ah Methodsmentioning

confidence: 99%

A novel practical state of charge estimation method: an adaptive improved ampere‐hour method based on composite correction factor

et al. 2020

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“…More and more researchers adopted the UKF-based method in motion estimation for mobile robots and automated vehicles [29]. The standard UKF can be improved through combing with multi-innovation [30], [31], the historical innovations generated along the time. Multi-innovation theory was established by Ding and Chen [32].…”

Section: Related Workmentioning

confidence: 99%

“…In previous work, Xie et al [38] discussed the performance of KF with fading factors and raised that accumulative interference may occur if the effect of old measurements and new measurements both are equally weighted. According to the research of Liu et al [30], the influence of new measurement should be greater than that of old measurements. The maximum gain weight at the present time is guaranteed as follows,…”

Section: ) Multi-innovation Enhanced Ukfmentioning

confidence: 99%

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

Liu

Yuan

et al. 2020

IEEE Access

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Benefiting from high mobility and robust mechanical structure, ground mobile robots are widely adopted in the outdoor environment. The mobility of skid-steered mobile robots highly depends on the nonlinear and uncertain interaction between the tire and terrain. This paper introduces an approach to estimate the position, orientation, velocity, and wheel slip for the skid-steered mobile robots navigating on off-road terrains. More specifically, a Multi-Innovation Unscented Kalman Filter (MI-UKF) is developed to fusing different sensors' data. Historical innovations generated along the time sequence are merged into the update process of standard UKF to improve the accuracy of motion estimation. In the proposed estimator, an asymmetric ICR kinematic indicating wheel slip is taken into localization process. A four-wheeled prototype is introduced and three challenging test scenarios are designed. The improvements in orientation and velocity estimation are achieved according to results comparison. In the turning maneuver, the ICRs-based model operates more steady than the traditional wheel slip/skid model.

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“…Substituting (18), (20) and (40) into (39), one can obtain The scheme of the proposed SOC estimation method is shown in Fig. 3.…”

Section: B Nernst Equation For Correcting Lithium-ion Concentrationsmentioning

confidence: 99%

“…voltage (OCV) [20,21], electromotive force [22], internal resistance [23], and impedance spectroscopy [24]. Though generally simple and readily implementable, they have great strict requirements for experimental conditions and time.…”

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confidence: 99%

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

2020

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Lithium-ion batteries are generally regarded as a leading candidate for energy storage systems. The safe and reliable operation of lithium-ion batteries depends largely on accurate estimation of the state of charge (SOC), which requires an accurate battery model. Bearing strong mechanisms, the electrochemical model (EM) can mimic the battery dynamics with high fidelity, and thus the EM-based methods can produce more reliable SOC estimates. This paper proposes a novel EM-based SOC estimation method for lithium-ion batteries from the electrochemical mechanism perspective. Firstly, a single particle model is employed to gain a direct insight into the electrochemical reactions inside the battery, and it is found that the model output voltage and SOC are strongly related to the lithium-ion concentrations of solid phases. A simple negative voltage feedback module is then applied to observe the voltage error between the cell referenced terminal voltage and the model output voltage. To eliminate the voltage error and achieve a precise estimate, a quantitative relationship between the voltage error and corrected amount of lithium-ion concentrations is deduced based on the Nernst equation. The performance of proposed method has been systemically evaluated under different operating conditions, including various charging and discharging current rates, erroneous initial SOCs, and cell aging levels. Although an erroneous initial SOC of 50% is applied to the proposed algorithm, promising estimates with the mean absolute errors of 0.22% and 1.35% can be still achieved under the constant and dynamic loading conditions, respectively. INDEX TERMS Lithium-ion battery, state of charge (SOC), electrochemical model, Nernst equation I. INTRODUCTION With numerous advantages, such as high energy density, low self-discharging rate, no memory effects, and long lifespan, and so on, lithium-ion batteries are generally regarded as a leading energy storage candidate for electric vehicles, renewable energy systems, portable electronic devices, and many other applications [1-3]. In most lithium-ion battery energy storage systems, a battery management system (BMS) is employed to manage the performance and maintain the longevity of battery cells [1, 4, 5]. The functions of a BMS include mainly cell voltage detection and balancing [6, 7], estimation of state of charge (SOC) [8-11] and state of health (SOH) [12-14], and thermal management [15-18]. Defined as the ratio of the residual charge stored in a battery to its maximum available capacity, the SOC is one of the primary and critical parameters for the development of battery management strategies [19]. Accurate SOC estimation plays a vital role in controlling the safe charging and discharging of the battery while guaranteeing its efficiency. However, the SOC cannot be measured directly and accurately by sensors like ordinary physical quantities in practices. Despite the huge research efforts, the battery SOC estimation still poses a significant challenge due to the complexity and nonlinearity of e...

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A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter

Cited by 32 publications

References 37 publications

A novel practical state of charge estimation method: an adaptive improved ampere‐hour method based on composite correction factor

A novel practical state of charge estimation method: an adaptive improved ampere‐hour method based on composite correction factor

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

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