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Accurate and rapid fault detection is essential for the safe operation of lithium-ion batteries in electric vehicles. However, conventional fault detection methods dependent on constant thresholds may have false alarms or missing alarms due to the inevitable disturbances resulted from the battery system modeling errors and measurement noises. In this paper, we design a multi-objective nonlinear fault detection observer for lithium-ion batteries, which is robust against disturbances but sensitive to battery multi-fault. We then perform formal stability and L ∞ / H _ performance analysis for the resultant estimation error system. Furthermore, tractable design procedures for the observer gain parameter and an adaptive threshold are derived. Then, via adaptive thresholding, a delicate three-step multi-fault detection scheme is developed to detect the occurrence of battery various faults, including short-circuit faults, current and voltage sensor faults. Finally, the efficacy of the proposed scheme is validated under several experimental case studies involving a variety of faults with their different levels of severity and erroneous SOC initialization. • A general battery model that incorporates both multi-faults and disturbances. • A multi-objective nonlinear observer with formal stability and performance analysis. • A three-step multi-fault detection scheme with an adaptive threshold. • Extensive experimental studies involving a variety of faults. [ABSTRACT FROM AUTHOR]

A bidirectional (Bi) DC/DC converter is one of the key components in a hybrid energy storage system for electric vehicles and plug-in electric vehicles. Based on the detailed analysis of the losses in the converter, this paper firstly develops a model to theoretically calculate the efficiency of the converter. Then, the influences of temperature, switching frequency, duty cycle and material of switching device on the converter’s efficiency are experimentally investigated. The analysis of the experimental results has shown that (1) The efficiency at the switching frequency of 15 kHz is about 2% higher than that of 25 kHz. (2) The efficiency at 25 °C is similar to that at 85 °C for the MOSFET SiC while the efficiency at 25 °C is 2% higher than that at 85 °C for the IGBT Si for both buck and boost modes. (3) In buck mode, when the duty cycles are decreasing from 66.7%, 50% to 33.33%, the peak efficiencies are also decreasing from 97.6%, 94.5% to 90.3%, respectively. In boost mode, when the duty cycle is increasing from 33.33%, 50% to 75%, the peak efficiency is decreasing from 96.9%, 96.5% to 92.4%, respectively. (4) The developed model can calculate the converter’s efficiency accurately [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity and complex time-variant property of batteries, the existing state of charge (SOC) estimation approaches based on a single equivalent circuit model (ECM) cannot provide the accurate SOC for the entire discharging period. This paper aims to present a novel SOC estimation approach based on a multiple ECMs fusion method for improving the practical application performance. In the proposed approach, three battery ECMs, namely the Thevenin model, the double polarization model and the 3rd order RC model, are selected to describe the dynamic voltage of lithium-ion batteries and the genetic algorithm is then used to determine the model parameters. The linear matrix inequality-based H-infinity technique is employed to estimate the SOC from the three models and the Bayes theorem-based probability method is employed to determine the optimal weights for synthesizing the SOCs estimated from the three models. Two types of lithium-ion batteries are used to verify the feasibility and robustness of the proposed approach. The results indicate that the proposed approach can improve the accuracy and reliability of the SOC estimation against uncertain battery materials and inaccurate initial states. [ABSTRACT FROM AUTHOR]

• An improved model with the voltage as input and current as output (VICO) is proposed. • The established VICO model is extended to an n -order VICO model. • The fault diagnosis method of current sensor is realized with the first-order VICO model. • The adaptability under different operating conditions and merit in detecting time are verified. Battery management systems (BMSs) are very important to ensure the safety of electric vehicles. The normal operation of BMSs is highly dependent on the accuracy of battery sensors. The present fault diagnosis efficiency of current sensors is much lower than that of voltage sensors due to model limitations in conventional methods. In this paper, a fault diagnosis method based on an improved model with voltage as input and current as output (VICO) is proposed to detect current sensor faults, where the least squares method combined with the unscented Kalman filter is used to estimate the fault current of current sensor. By comparing the estimated fault current with the diagnosis threshold, the fast fault diagnosis of current sensor is realized. The proposed method is verified under different operating conditions and compared with the methods based on state of charge and open-circuit voltage residuals. To highlight the importance of the proposed method, the influence and possible causes of minor faults and temperature on diagnosis are analyzed. The experimental results show that the method can detect the fault of the current sensor more accurately and quickly compared with the conventional methods, and has the ability to detect minor faults and adaptability under different operating conditions and temperatures. [ABSTRACT FROM AUTHOR]

• ESC behaviors at various temperatures are investigated experimentally. • ESC-induced heat generation and its impacts on electrical behaviors is modeled. • Distribution and anisotropy diffusion of ESC-caused heat generation is delineated. • An electro-thermal coupling model is proposed for batteries under ESCs. • Effectiveness of the proposed model is verified by experimental data. External short circuit (ESC) fault, which can cause large current and high temperature, is one of the main reasons for battery failure. Its analysis and diagnosis remains a challenging task due to complex electro-thermal characteristics of batteries under ESCs. In this paper, ESC experiments at various temperatures are conducted to investigate the impact of temperature on battery electro-thermal behaviors. Based on the analysis of the experimental data, heat generation inside a battery caused by ESC-induced high current and side reactions is modeled. The heat distribution and diffusion are also modeled by considering battery's internal jellyroll structure. The combination of the heat generation, distribution and diffusion models forms a novel electro-thermal coupling model, which is used to predict the complex thermal and electrical properties of a battery under ESCs. The presented model is simulated and verified by the test data. The maximum root mean square error of ESC current prediction is less than 1.73A and the maximum errors of the internal temperatures and the surface temperatures are only 1.771% and 3.915%, respectively. These results verify the effectivceness of the presented model. It is expected that the presented model is useful for safety analysis, temperature prediction and fault diagnosis applications of the lithium-ion batteries under ESC. [ABSTRACT FROM AUTHOR]