Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement

Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.

Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization

The performance of model-based state-of-charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the lithium-ion battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model-based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, partial least squares regression is able to establish a series of piecewise linear battery models automatically. One element state-space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the extended Kalman filter with two resistance and capacitance equivalent circuit model and the adaptive unscented Kalman filter with least squares support vector machines.

/pdf/a-simplified-model-based-state-of-charge-estimation-approach-4dago5hcr1.pdf

A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model

Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption.

Online Estimation of Power Capacity With Noise Effect Attenuation for Lithium-Ion Battery

The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This paper reviews efforts in the modeling and simulation of lithium-ion batteries and their use in the design of better batteries. Likely future directions in battery modeling and design including promising research opportunities are outlined.

http://ma.ecsdl.org/content/MA2010-02/4/226.full.pdf

Modeling and Simulation of Lithium-Ion Batteries from Systems Engineering Perspective

The advent of Energy Management (EM) and Electric Vehicles (EV) have completely changed the use of batteries. Accurately estimating the remaining power in batteries has become increasingly important. In order to estimate precise battery state of charge (SOC)/state of health (SOH) value, accurate parameter identification is essential when constructing an accurate battery model. Even though we are able to exactly identify battery parameters offline, the precision of online parameter identification usually suffers from measurement noise, which is an unavoidable phenomenon. In this paper we investigate how battery parameter identification is influenced by measurement noise. The selection of a low pass filter is also discussed and a fourth order Butterworth filter is adopted to effectively reject high frequency measurement noise. This algorithm can help with the identification of battery parameter that rejects measurement noise and maintains the accuracy of online battery parameter identification for future online model-based battery SOC/SOH estimation.

Li-ion battery parameter identification with low pass filter for measurement noise rejection

Batteries are becoming the main energy storage devices in following decades. Highly accurate battery models are needed to fulfill accurate controlling and monitoring purpose. For real-time operations, the calculation complexity of the battery model is an issue. Thevenin's circuit model is a tradeoff between fast calculation and accuracy. The model uses resistor and RC pairs to capture the dynamic of terminal voltage. Even though there are few components in the model, to accurately identify those components value is a difficult question. In this paper, an algorithm to separate effective data and ineffective data is proposed by comparing the condition number. This algorithm can help identify correct parameters for future model-based battery monitoring and controlling.

Accurate Thevenin's circuit-based battery model parameter identification

The maximum power point tracking (MPPT) controller can monitor the voltage and current of photovoltaic (PV) array in real-time to acquire the maximum power output for battery charging in PV system. In order to improve the accuracy and rapidity of tracking process, a bionic two-stage MPPT control strategy composed of fast positioning stage and precise determination stage is proposed in this paper for optimizing the duty cycle d of DC-DC converter. Firstly, an improved artificial bee colony algorithm with simplified probability selection mechanism and novel employed bee phase is presented to balances exploration and exploitation for excellent rapidity in positioning rough search region around global peak. Then, the simultaneous heat transfer search (SHTS) algorithm is adopted for accurately acquiring the global maximum power point in obtained search region. On the one hand, the SHTS can reduce the subjectivity in artificial setting of the parameters to ensure the universality and accuracy of search process. On the other hand, the parallel search in SHTS can decrease the optimization time effectively. Finally, extensive simulation results illustrate that this two-stage MPPT strategy shows excellent performance in precisely identifying the GMPP of PV system with phenomenal rapidity among multiple peaks. Moreover, it outperforms all the counterparts in tracking speed and accuracy under partial shading conditions. 

A two-stage MPPT controller for PV system based on the improved artificial bee colony and simultaneous heat transfer search algorithm.

This paper discusses the impact of calendar ageing on the anode and the concentration of lithium ions in the anode structure. It discusses the modeling of the calendar ageing, its implementation in the 3D First Principle Based Degradation Model (3DM) of the battery and the results that were observed as a result of calendar ageing over a period of 4–6 years. The paper also relates these physical degradation phenomena with the impact that they have on the parameters of the Equivalent Circuit Model of the battery. The paper uses the capacity degradation and the increase in the internal resistance of the battery to showcase the impact of calendar ageing on the State of Health of the battery.

Effect of calendar aging on li ion battery degradation and SOH

Safe and reliable management of rechargeable lithium-ion batteries relies on accurate state-of-charge (SOC) estimation. SOC estimation algorithms developed based on the conventional state-space model can be inaccurate when the slope of the battery SOC to open-circuit voltage ( $V_{\text{OC}}$ ) profile is close to zero, where the observability matrix of the conventional battery state-space model is ill-conditioned. This ill-conditioned observability issue is usually overlooked and results in unreliable SOC estimations. This article proposes a robust state-space model using a set of carefully selected states, making the proposed model well-conditioned over the full SOC range and insesitive to the slope of the SOC- $V_{\text{OC}}$ profile. The proposed model, therefore, solves the observability issue and improves the SOC estimation accuracy. To examine the performance of the proposed state-space model over the full SOC range, the battery is discharged using a standard electric vehicle driving profile. Experiments are further carried out in various environmental temperature conditions to validate the robustness of the proposed model. The results show that the proposed state-space model is robust against the change of the slope of the SOC- $V_{\text{OC}}$ profile and significantly improves the SOC estimation accuracy, rendering more than 30% improvement in various temperature conditions.

Cong-Sheng Huang

Papers

Li-ion battery parameter identification with low pass filter for measurement noise rejection

Accurate Thevenin's circuit-based battery model parameter identification

A two-stage MPPT controller for PV system based on the improved artificial bee colony and simultaneous heat transfer search algorithm.

Effect of calendar aging on li ion battery degradation and SOH

Robust State-of-Charge Estimation for Lithium-Ion Batteries Over Full SOC Range