A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles
Introduction
Lithium-ion battery (LiB), as an important on-board electric energy storage system, has been widely used in various electric vehicles (EVs). However, to satisfy the operation voltage and traction power requirements of electric vehicles, a battery pack has to be made with hundreds of cells connected in series or parallel to overcome the limitations of low energy density, low cell capacity and cell voltage [1], [2], [3]. Due to liquid electrolyte instability and flammability, lithium-ion cells have potential to ignite or even explode when over-charged or over-discharged. To avoid the potential safety hazard, an advanced battery management system (BMS) plays a vital role in improving battery performance, safety and reliability. For an efficient energy management of EVs, the most important key technique is to achieve an accurate State of Charge (SoC) estimate, which improves the power distribution efficiency and extends the calendar-life of the cells [4], [5]. However, since cell performance largely depends on its operation environments and aging states, it is still hard to control the battery cell accurately.
A number of studies have proposed methods for calculating battery SoC and each one has its own advantages. The most widely used method is the ampere–hour counting method. However, this open-loop calculation method can easily lead to accumulated calculation errors due to uncertain disturbances from the practice application and lack of necessary corrective resolution for the initial SoC offset [6]. Thus, to improve its performance, it is often recalibrated by other state-space based filters [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], Kalman filters being one of the most commonly used [7], [8], [9], [10], [11], [12]. The author in [7] used extended Kalman filtering (EKF) to estimate the LiPB cell SoC, the authors in [3] used multi-scale EKF to estimate the SoC of a battery pack. However, the performance of the EKF-based method has two fatal flaws. One is that this method greatly depends on the accuracy of the model; the other is that the noise information should be known in advance [9]. Authors in [9], [10], [11] used an adaptive extended Kalman filter (AEKF) algorithm to carry out the SoC estimation, which showed the prediction accuracy has been greatly improved even with an erroneous initial estimator state.
Common drawbacks with the above methods were that the cell parameter variances under different aging levels were ignored and the methods were verified under a narrow set of scenarios, without discussing different aging levels and loading profiles. In other words, the robustness of these SoC estimators against different driving cycles of varied health states of battery were not sufficiently discussed. To avoid this drawback, a data-driven parameter identification method was proposed to update the model parameters in real-time. In this method, the SoC was usually inferred by the estimated open circuit voltage (OCV) with the table of SoC vs. OCV. Authors in [11], [17] used the AEKF algorithm and recursive least squares (RLS) method to estimate OCVs and to infer SoCs. The results showed that this method had a desirable performance with an acceptable accuracy. Authors in [18] also used an online parameter identification method to estimate the OCV. To evaluate the OCV-based SoC estimation approach with the former SoC-based method, authors in [11] carried out the comparison of the SoC estimation between the AEKF-based OCV estimator and AEKF-based SoC estimator. The results showed that the latter has a better SoC estimation accuracy since its closed-loop feedback structure, but if the operation environment changed or the battery aged, the parameter variance on the battery would decrease the estimation accuracy remarkably.
A key contribution of this study is that a data-driven based adaptive SoC estimator was developed, thus the prone-error and time consuming periodic calibration of the model could be avoided. What’s more, the performance of the SoC estimator has been verified and evaluated by different loading profiles with different battery aging levels. In the proposed SoC estimator, the RLS method is employed to develop a data-driven based parameter identification method with the real-time measurement of battery current and voltage. In addition, the AEKF method is applied to establish the SoC estimator. It is noted that the AEKF algorithm can improve the prediction precision by adaptively updating the noise covariance, and the time-series based calculation process of the AEKF algorithm is shown in Table 1 [11].
The remainder of the paper is organized as follows: Section 2 describes the lumped parameter battery model and the implement flowchart of the RLS based online parameters identification method. A data-driven adaptive SoC estimator with AEKF algorithm is present in Section 3. To verify the proposed approach, different aging levels of 3.7 V/32 A h Lithium-ion polymer battery (LiPB) cells are used to execute the characteristic test and the experiment has been described Section 4. Section 5 verifies the proposed adaptive SoC estimator using the dynamic stress test (DST) and Federal Urban Driving Schedule (FUDS) test. In the final section, some conclusions and final remarks are given.
Section snippets
Battery model
Section 2.1 gives a detailed description of the lumped parameter battery model. Section 2.2 presents an implementation flowchart of the data-driven based parameter identification approach.
Adaptive extended Kalman filter-based SoC estimator
In our previous research in [6,11], the AEKF algorithm with a covariance matching technique was successfully applied to construct a reliable state estimation for a model-based battery management system application, this section focuses on building a data-driven based adaptive SoC estimator for achieving an accurate online SoC estimation against different aging levels and lading profiles. Section 3.1 gives a definition of the SoC calculation. The numerical implementation of the recursive
Data set of LiPB cells for verification
As an application case, the LiPB cells are used to verify the proposed approach. Section 4.1 gives a brief introduction for the test bench, and the test schedule is discussed in Section 4.2.
Verification and discussion
Herein we use two loading profiles to verify the proposed approach. Sections 5.1 DST loading profiles, 5.2 FUDS loading profiles verifies the proposed SoC estimator with DST and FUDS loading profiles respectively.
Conclusions
To model the dynamic voltage performance of LiPB accurately, the improved lumped parameter model has been proposed through optimizing the voltage source with an electrochemical function formed with SoC and capacity, which can improve the SoC estimation accuracy through an efficient closed-loop feedback between the SoC and open circuit voltage and achieve accurate open circuit voltage predictions against different battery aging levels.
To improve the dynamic voltage prediction precision of the
Acknowledgements
We would like to express our deep gratitude to Professor Chris Chunting Mi in the University of Michigan for many helpful discussions and Kathy McNamara for English editing. This work was supported by the National Natural Science Foundation of China (51276022) and the Higher school discipline innovation intelligence plan (“111”plan) of China.
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