Due to the growing pressure of environmental pollution and energy crisis, electric vehicles (EVs) have become the future development trend. At the same time, due to
AI Customer ServiceAdvanced Energy Materials is your prime applied energy journal for research providing solutions to today''s global energy challenges. Abstract A variety of different methods
AI Customer ServiceThis paper leverages Baidu''s New Energy Vehicle (NEV) live operation data as the foundation for experimentation. Multiple sensors are implemented to monitor the new
AI Customer ServiceThe voltage abnormal fluctuation is a warning signal of short-circuit, over-voltage and under-voltage. This paper proposes a scheme of three-layer fault detection method for
AI Customer ServiceThe results show that the minimum detection time (DT) of voltage and current
AI Customer ServiceA concise and accurate method for estimating the state of health (SOH) of lithium-ion batteries in the on-board energy management system is critical.
AI Customer ServiceThis work mainly discusses the establishment of the battery voltage fault diagnosis mechanism of new energy vehicles using electronic diagnosis technology. Based on electronic diagnosis
AI Customer ServiceMilitary Standard Gbbz 24974-2012 Is the Standard for Military Battery Detection. the Design of Military Batteries, specific Requirements and Specifications Are Put
AI Customer ServicePDF | On Dec 16, 2023, Weisen ZHAO and others published Comparison of Multi-step Prediction Models for Voltage Difference of Energy Storage Battery Pack Based on Unified Computing
AI Customer ServiceSafety accidents in new energy electric vehicles caused by lithium-ion battery failures occur frequently, and the timely and accurate diagnosis of failures in battery packs is
AI Customer ServiceTo diagnose battery voltage fault, it is indispensable to set voltage abnormity thresholds. In this study, the voltage abnormity thresholds are set based on the statistics of voltage prediction errors and voltage difference
AI Customer ServiceThe first layer strategy is like the threshold-based fault detection method, if the battery voltage is lower than the discharge cut-off voltage, the battery is considered to have an
AI Customer ServiceXia et al. proposed a fault detection method for battery faults of short circuits based on the correlation coefficient. This method can utilize the direct voltage of the battery
AI Customer ServiceSafety accidents in new energy electric vehicles caused by lithium-ion battery
AI Customer ServiceNew energy vehicles use positioning bolts to fix the battery pack and power distribution copper row for fault maintenance. The distribution copper row obtains the single battery voltage in a
AI Customer ServiceTaking the leakage detection of byd-qin hybrid high-voltage system as an example, this paper analyzes the fault generation mechanism and puts forward the detection
AI Customer ServiceThis work mainly discusses the establishment of the battery voltage fault diagnosis mechanism
AI Customer ServiceTaking the leakage detection of byd-qin hybrid high-voltage system as an example, this paper analyzes the fault generation mechanism and puts forward the detection technology of new...
AI Customer ServiceNew energy vehicles use positioning bolts to fix the battery pack and power distribution copper
AI Customer ServiceTo diagnose battery voltage fault, it is indispensable to set voltage abnormity thresholds. In this study, the voltage abnormity thresholds are set based on the statistics of
AI Customer Servicenew energy vehicle offline detection standards. Keywords: insulation test;new energy vehicles; the biggest feature is the use of high-voltage battery technology. The new energy vehicle
AI Customer ServiceThe results show that the minimum detection time (DT) of voltage and current sensor fault is only 2 s and 26 s, also both the false detection rate (FDR) and missing
AI Customer ServiceTransportation electrification has been considered as a promising solution to environmental problems and has experienced rapid growth in recent years, leading to a global
AI Customer ServiceAccording to statistics, 60% of fire accidents in new energy vehicles are caused by power batteries. The development of advanced fault diagnosis technology for power battery
AI Customer ServiceThe voltage range of the battery-side DC bus of an ESS is 400–1500 V [19], and the DC bus voltage of an EV is above 300 V [20]. In Ref. [21], the analysis of many new
AI Customer ServiceThreshold-based fault diagnosis methods The battery overvoltage or undervoltage fault can be diagnosed using the threshold-based method. The voltage information collected by the voltage sensor is compared with the preset threshold. When the battery voltage exceeds the threshold, the fault occurrence state and fault occurrence time are defined .
The voltage abnormal fluctuation is a warning signal of short-circuit, over-voltage and under-voltage. This paper proposes a scheme of three-layer fault detection method for lithium-ion batteries based on statistical analysis. The first layer fault detection is based on the thresholds of over-charge and over-discharge of a battery pack.
In this paper, the fault diagnosis of battery systems in new energy vehicles is reviewed in detail. Firstly, the common failures of lithium-ion batteries are classified, and the triggering mechanism of battery cell failure is briefly analyzed. Next, the existing fault diagnosis methods are described and classified in detail.
To diagnose battery voltage fault, it is indispensable to set voltage abnormity thresholds. In this study, the voltage abnormity thresholds are set based on the statistics of voltage prediction errors and voltage difference between cells under different driving conditions.
The detection method of battery parameters in battery management system is simple and the accuracy is limited [, , ], but the accuracy of parameters is the direct factor affecting the fault diagnosis results. Wang et al. proposed a model-based insulation fault diagnosis method based on signal injection topology.
The above analysis proves that even the slight voltage abnormities of battery system during vehicular operation can be detected and diagnosed accurately by the method proposed in this work. Moreover, this method can achieve voltage fault diagnosis in advance when the voltage of the faulty cell still within the normal range.
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