• Energy Research

AbstractWith the increasing application of lithium-ion batteries, the function of battery management system (BMS) comes to be more sophisticated. The state-of-energy (SOE) of lithium-ion batteries is a critical index for energy optimization and management in electric vehicles. The conventional power integral methods are easy to cause accumulated error due to current or voltage drift of sensors. Therefore the EKF method is employed in this study. A data-driven model is established to describe the relationship between the open-circuit voltage (OCV) and SOE based on the experimental data of a Li(Ni1/3Co1/3Mn1/3)O2 battery. The dynamic urban driving schedule of Wuhui city in China has been conducted on the lithium-ion battery to verify the accuracy of the proposed method. The results show that accurate SOE estimation results can be obtained by the proposed method.

Abstract The condition monitoring and fault diagnosis of the lithium-ion battery system are crucial issues for electric vehicles. The shocks, blows, twists, and vibrations during the electric vehicle driving process may cause the insulation fault. In order to ensure the safety of the drivers and passengers, a real-time monitor to detect the insulation state between the high voltage and ground is required. However, the conventional battery management system only provides very simple and coarse-grained measurements to detect the insulation resistance. In this work, a model-based insulation fault diagnosis method is proposed. Firstly, the equivalent circuit model for insulation fault diagnosis is established using a high-fidelity cell model. Then, the recursive least-squares method is employed to identify the model parameters. Considering the system nonlinear properties, measurement noise and unknown disturbance, the Kalman filter based state observer is designed for joint estimation of both the battery voltage and state-of-charge using the identified battery model. Finally, the positive and negative virtual insulation resistance are quantitatively assessed based on the prediction results of the state observer. Experiments under different loading profiles are performed to verify the proposed method.

Modern electric vehicles (EVs) and hybrid electric vehicles (HEVs) require a reliable battery management system (BMS). The remaining energy and the state-of-energy (SoE) are very important indexes for the embedded BMS used in both EV and HEV applications. As a case study in the embedded BMS, this paper presents the implementation of remaining energy prediction based on the μC/OS-II real time operating system (RTOS). In considering that there are accumulated errors caused by inevitable drift noise of the current or voltage sensors, a model based SoE estimator is developed based on a first-order RC equivalent circuit model. Moreover, the Bayesian learning technique is used for SoE estimation to get accurate and robustness estimation results. Lastly, two different kinds of batteries are carried out under laboratory experiments and real road test to verify the robustness of the proposed SoE estimation approach. The results indicate that the maximum absolute estimation error (MAEE) and the root-mean square error (RMSE) are within 2% and 1% for both LiFePO4 and Li(Ni1/3Co1/3Mn1/3)O2 batteries.

Abstract The hybrid energy storage system is a kind of complex system including state coupling, input coupling, environmental sensitivity, life degradation, and other characteristics. How to accurately estimate the internal state of the system, delay the battery life degradation, realize the coordinated and optimized control of power and energy have become the focus and difficulty of the hybrid energy storage system. With the application and popularization of hybrid energy storage systems in electric vehicles and smart grids, relevant theoretical and technological breakthroughs become more and more urgent. Many new achievements, new theories, new methods and new technologies from the fields of materials, information, energy, control and artificial intelligence have been put into this field. This paper comprehensively reviewed the key issues for control and management in hybrid energy storage systems from the aspects of multi-scale state estimation, aging mechanism investigation, life prediction, and energy optimization control of the hybrid energy storage system. Through the in-depth review of key scientific issues, the latest theoretical techniques and application results are presented. Finally some future research challenges and outlooks are presented, in hopes to provide some new ideas and inspirations for the future investigation of the hybrid energy storage systems.

Abstract The proton exchange membrane fuel cell has become the most widely used fuel cell in fuel cell vehicles. An effective and accurate control approach for its air supply system is crucial to ensure the performance and safety of the fuel cell system. In order to ensure safe and efficient operation of the air supply, this paper provides a novel modeling and control method based on Takagi-Sugeno fuzzy theory and predictive control. A local controlled autoregressive integrated moving average model for the air flow control is put forward, then the control-oriented T-S model is designed based on multi-model scheduling. The controller architecture is based on a fuzzy generalized predictive controller. The proposed controller can control the oxygen excess ratio in the ideal range and effectively suppress the fluctuation caused by the load change. In addition, an optimal control strategy is proposed aiming at avoiding the oxygen starvation and maximizing the system net power. According to the control results, the proposed method is proved to be able to accurately control the air supply at desire values. It enhances system output performance by fast response to better support the vehicle load variation, and improving the net power and system energy efficiency.

The state-of-charge (SOC) is a critical index in battery management system (BMS) for electric vehicles (EVs). However in the energy storage systems, the available energy also acts as a significant role. Through the estimating result of state-of-energy (SOE), we can further estimate how long the battery is going to last if we apply a low power demand, a high power demand, or even a dynamic power demand. Unlike the SOC, the SOE is not only the integral of current but also the integral of voltage which include the nonlinearity of Li-ion batteries. Since there are accumulated errors caused by current or voltage measurement noise, a joint estimator based on particle filter is proposed for the estimation of both SOC and SOE. Validation experiments are carried out based on IFP1865140-type batteries under both constant and dynamic current conditions. To further verify the robustness of the proposed method, experiments are performed under dynamic temperatures. The experiment results have verified that accurate and robust SOC and SOE estimation results can be obtained by the proposed method.

Abstract Estimation of state-of-charge (SOC) is a key criterion to evaluate the battery management system (BMS). Due to the flat open-circuit voltage (OCV)–SOC curve of LiFePO 4 batteries, it is a great challenge to estimate the SOC accurately. Here we present a dual-circuit state observer for SOC estimation of LiFePO 4 batteries. It is a combination of an open loop based current integrator and a proportional-integral (PI) based state observer. We also employed an easy but practical drifting current corrector to restrain the influence of the drifting current. The experiment results show that error of the estimated SOC is less than 2.5% by the proposed method when the initial SOC is unknown. We proved that with no matrix operations, the proposed method is lighted-weighted and high efficient, which is suitable for embedded systems.

Abstract The proton exchange membrane fuel cell has been widely used for industrial systems; however, its performance gradually degrades during use. Therefore, the study on the performance degradation prediction of fuel cells is helpful to extend its lifespan. In this paper, a novel hybrid approach using a combination of model-based adaptive Kalman filter and data-driven NARX neural network is proposed to predict the degradation of fuel cells. The overall degradation trend (i.e., irreversible degradation process) is captured by an empirical aging model and adaptive Kalman filter. Meanwhile, the detail degradation information (i.e., reversible degradation process) is depicted by the NARX neural network. Moreover, the correlation analysis of the reversible voltage time series is carried out to obtain the number of delays of the NARX neural network based on the autocorrelation function and the partial autocorrelation function. Then, the total degradation prediction is the sum of the overall degradation prediction and the detail degradation prediction. Finally, the prognostic capability of the proposed method is verified by two aging datasets, and the results show the effectiveness and superiority of the proposed method which can provide accurate degradation forecasting and remaining useful life.