Caiping Zhang

Robust and Adaptive Estimation of State of Charge for Lithium-Ion Batteries

The reliable operation of battery management systems depends critically on the accurate estimation of the state of charge (SOC) and characterizing parameters of a battery system. SOC estimation employs models that must be robust against variations in battery cell electrochemical features, aging, and operating conditions. This paper reveals that commonly used SOC estimation schemes are fundamentally flawed in providing the robustness of SOC estimation against model uncertainties. Parameter estimation methodologies and adaptive SOC estimation design are introduced in this paper to enhance SOC estimation accuracy and robustness. By a scrutiny of the impact of parameter variations on SOC estimation accuracy, the SOC-open-circuit-voltage mapping is identified to be the most critical function that must be accurately established. Identification algorithms are introduced, and their convergence properties are established. The integration of the identification algorithms and SOC estimation schemes lead to an adaptive SOC estimation framework that is superior over the existing methods in providing much improved accuracy and robustness. Experimental studies are conducted to validate the algorithms.

Evaluation of Acceptable Charging Current of Power Li-Ion Batteries Based on Polarization Characteristics

Battery polarization voltage characteristics of power Li-ion batteries are investigated at different charging rates. The polarization voltage is small and relatively stable in the middle state of charge (SOC) range, whereas at both ends of SOC, the polarization voltage is enlarged and varies rapidly. Meanwhile, there is an approximate linear relationship between polarization voltage and charging rate. Taking polarization time constant into account, an acceptable charging current curve is developed, which prevents charging current from being out of range. This curve takes polarization voltage and charge cutoff voltage as constraints to describe acceptable charging current value at different SOC. Based on this curve, the battery can be charged from 20% SOC to 80% SOC within 33 min. The average polarization and the maximum temperature rise are approximately equivalent to those of 0.5C constant current-constant voltage charging method. The experimental results demonstrate that the proposed charging mode can balance charging speed, charging polarization, and temperature rise.

An Optimal Charging Method for Li-Ion Batteries Using a Fuzzy-Control Approach Based on Polarization Properties

Battery charging is growing in importance as it has direct influence on battery performance and safety. This paper develops a generalized online estimation method for a charge-polarization-voltage-based resistance-capacitance ( RC) circuit model to simulate the charging behavior of Li-ion batteries. The effects of charging current, initial state of charge (SOC), initial polarization state, and aging on the charge polarization voltage are quantitatively analyzed in both time and SOC domains. It is demonstrated that the charge polarization voltage is nonlinearly related to these impact factors. In the SOC domain, the change in the charge polarization voltage is also analyzed with the gradient analytical method, and the relations between current, polarization voltage amplitude at the inflection point, and SOC are quantitatively established. This can be used to estimate battery SOC and polarization voltage accurately. A constant-polarization-based fuzzy-control charging method is proposed to adapt charging current acceptance with battery SOC stages. Experimental results demonstrate that the proposed charging method significantly shortens charging time with no obvious temperature rise compared with the traditional constant-current-constant-voltage charging method.

Balanced Control Strategies for Interconnected Heterogeneous Battery Systems

This paper develops new balanced charge/discharge strategies that distribute charge or discharge currents properly so that during operation, battery systems maintain uniform state-of-charge (SOC) all the time. The proposed balanced charge/discharge control strategies are useful for interconnected heterogeneous battery systems that can be built from battery modules with different types, ages, and power/capacity ratings. Both voltage-based and SOC-based balanced charge/discharge strategies are developed. Their convergence properties are rigorously established, and illustrative examples using production batteries demonstrate their convergence behavior under different charging current profiles. The approach will be especially useful for battery storage systems to support power grids with renewable energy sources where the battery systems are required to operate continuously.

Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise

State of charge (SOC) is one of the most important parameters in battery management system (BMS). There are numerous algorithms for SOC estimation, mostly of model-based observer/filter types such as Kalman filters, closed-loop observers, and robust observers. Modeling errors and measurement noises have critical impact on accuracy of SOC estimation in these algorithms. This paper is a comparative study of robustness of SOC estimation algorithms against modeling errors and measurement noises. By using a typical battery platform for vehicle applications with sensor noise and battery aging characterization, three popular and representative SOC estimation methods (extended Kalman filter, PI-controlled observer, and observer) are compared on such robustness. The simulation and experimental results demonstrate that deterioration of SOC estimation accuracy under modeling errors resulted from aging and larger measurement noise, which is quantitatively characterized. The findings of this paper provide useful information on the following aspects: (1) how SOC estimation accuracy depends on modeling reliability and voltage measurement accuracy; (2) pros and cons of typical SOC estimators in their robustness and reliability; (3) guidelines for requirements on battery system identification and sensor selections.

Robust Online State of Charge Estimation of Lithium-Ion Battery Pack Based on Error Sensitivity Analysis

Accurate and reliable state of charge (SOC) estimation is a key enabling technique for large format lithium-ion battery pack due to its vital role in battery safety and effective management. This paper tries to make three contributions to existing literatures through robust algorithms. (1) Observer based SOC estimation error model is established, where the crucial parameters on SOC estimation accuracy are determined by quantitative analysis, being a basis for parameters update. (2) The estimation method for a battery pack in which the inconsistency of cells is taken into consideration is proposed, ensuring all batteries’ SOC ranging from 0 to 1, effectively avoiding the battery overcharged/overdischarged. Online estimation of the parameters is also presented in this paper. (3) The SOC estimation accuracy of the battery pack is verified using the hardware-in-loop simulation platform. The experimental results at various dynamic test conditions, temperatures, and initial SOC difference between two cells demonstrate the efficacy of the proposed method.

Determination of SOC of a Battery Pack Used in Pure Electric Vehicles

Thevenins model having a RC network characterizing the battery polarization is used for SOC determination. Reverse current method is proposed to decrease the polarization elimination time. And the polarization voltage of the battery model is identified using the nonlinear least square technique. A maximum available capacity based SOC determination method considering the SOC limit of each cell for battery pack in series is presented in the paper. Discussed results demonstrate the efficacy of the proposed parameter identification technique and the SOC determination method for PEV application.

An optimal charging strategy of lithium-ion batteries based on polarization and temperature rise

The temperature rise characteristics of the Li-ion battery at different charging rate are investigated. Except the electrode tabs, the temperature rise of different positions at the battery surface is consistent, thus we can establish a battery particle thermal model to simulate the battery temperature variation at different charging rate. The temperature rise of simulation is compared with the temperature rise of experiment to verify the battery particle thermal model. Based on the battery thermal model, taking temperature rise as constraint, a boundary charging current curve is developed. Combined with the boundary charging current curve taking polarization voltage as constraint, a boundary charging current curve taking polarization voltage and temperature rise as constraint is developed. Based on the current curve, an optimal charging strategy is proposed. The experimental results demonstrate that the proposed charging strategy can not only shorten the charging time, but also enlarge the charging capacity.

A Novel Sliding Mode Observer for State of Charge Estimation of EV Lithium Batteries

A simple design for a sliding mode observer is proposed for EV lithium battery SOC estimation in this paper. The proposed observer does not have the limiting conditions of existing observers. Compared to the design of previous sliding mode observers, the new observer does not require a solving matrix equation and it does not need many observers for all of the state components. As a result, it is simple in terms of calculations and convenient for engineering applications. The new observer is suitable for both time-variant and time-invariant models of battery SOC estimation, and the robustness of the new observer is proved by Liapunov stability theorem. Battery tests are performed with simulated FUDS cycles. The proposed observer is used for the SOC estimation on both unchanging parameter and changing parameter models. The estimation results show that the new observer is robust and that the estimation precision can be improved base on a more accurate battery model.

A New Double Sliding Mode Observer for EV Lithium Battery SOC Estimation

A new sliding mode observer is proposed in this paper; compared with the existing sliding mode observer used for SOC estimation, the new observer has the advantages of simple design and good generality. The robustness of the new observer was proved by Lyapunov stability theorem. Taking the first-order Randle circuit model of the battery as an example, the new sliding mode observer was designed. Battery test was done with the simulated FUDS condition, and the robustness of the new observer was verified by the test. Because battery internal ohmic resistance is changing in battery working process, which has a significant effect on SOC estimation, a new double sliding mode observer was designed to identify the internal resistance. The tests results show that the battery internal ohmic resistance changes greatly when the SOC is low and the double observer can accurately identify the resistance which improves the accuracy of the battery model. The results also show that the new double observer is robust and can improve the precision of SOC estimation when the battery remaining capacity is low.