The Review on Fault Diagnosis Methods for Power Batteries in Electric Vehicles

2013 ◽  
Vol 765-767 ◽  
pp. 2176-2179
Author(s):  
Zhen Po Wang ◽  
Yang Zhou
Keyword(s):  
Fault Diagnosis ◽  
Electric Vehicles ◽  
Power Source ◽  
Battery Pack ◽  
Power Battery ◽  
Diagnosis Method ◽  
Main Fault ◽  
Vehicle Security ◽  
Fault Source ◽  
Diagnostic Condition

As the only power source of electric vehicles (EVs), power battery pack is also the main fault source in EVs. The faults of power battery affect its performence and life, and even endanger vehicle security in extremly situation. Early fault diagnosis of power batteries can reduce losses, minimize maintenance, guarantee vehicle performence, security and realibility. In this study, the faults of power batteries are summarized, four fault diagnosis methods are concluded and analyzed, and the applicability of each method is compared and discussed. The principle of choosing the diagnosis method is that choosing according to the diagnostic condition.

A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles

2019 ◽  
Vol 34 (10) ◽  
pp. 9709-9718 ◽  
Author(s):  
Rui Xiong ◽  
Quanqing Yu ◽  
Weixiang Shen ◽  
Cheng Lin ◽  
Fengchun Sun
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery Pack ◽  
Sensor Fault ◽  
Diagnosis Method

scholarly journals A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19175-19186
Author(s):  
Jiuchun Jiang ◽  
Xinwei Cong ◽  
Shuowei Li ◽  
Caiping Zhang ◽  
Weige Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Diagnosis Method

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 443-451 ◽  
Author(s):  
Yujie Wang ◽  
Jiaqiang Tian ◽  
Zonghai Chen ◽  
Xingtao Liu
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Battery Pack ◽  
Model Based

Research on Automatic Replacing Technology of Power Battery Pack

2011 ◽  
Vol 308-310 ◽  
pp. 217-223 ◽  
Author(s):  
Zhen Po Wang ◽  
Hai Bin Han ◽  
Lu Zeng
Keyword(s):  
Electric Vehicles ◽  
Concrete Structure ◽  
Battery Pack ◽  
Charging Time ◽  
Power Battery ◽  
Replacement Process ◽  
Hydraulic Servo ◽  
Driving Range ◽  
Key Techniques ◽  
The Way

The short driving range and long charging time are two big problems for electric vehicles. A concept of battery pack automatic replacement is put forward in this paper to solve these problems, and a deep research on the key techniques is contained. This paper introduced the way of positioning and locking in replacement process, including the concrete structure of both replacing equipment and battery pack. For reliability problems of the connectors, two schemes are designed. Elastic jacks and coil are adopted to guarantee the reliability and automatically centering. On this basis, battery fast replacing system is designed, which controlled by PLC, driven by electro-hydraulic servo. This was proved to be a big success in practice.

scholarly journals Thermal Analysis and Improvements of the Power Battery Pack with Liquid Cooling for Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3045 ◽  
Author(s):  
Xia ◽  
Liu ◽  
Huang ◽  
Yang ◽  
Lai ◽  
...  
Keyword(s):  
Thermal Management ◽  
Electric Vehicles ◽  
Thermal Performance ◽  
Lithium Ion ◽  
Battery Pack ◽  
Maximum Temperature ◽  
Design Solution ◽  
Liquid Cooling ◽  
Power Battery ◽  
Reference Design

In order to ensure thermal safety and extended cycle life of Lithium-ion batteries (LIBs) used in electric vehicles (EVs), a typical thermal management scheme was proposed as a reference design for the power battery pack. Through the development of the model for theoretical analysis and numerical simulation combined with the thermal management test bench, the designed scheme could be evaluated. In particular, the three-dimensional transient thermal model was used as the type of model. The test result verified the accuracy and the rationality of the model, but it also showed that the reference design could not reach the qualified standard of thermal performance of the power battery pack. Based on the heat dissipation strategy of liquid cooling, a novel improved design solution was proposed. The results showed that the maximum temperature of the power battery pack dropped by 1 °C, and the temperature difference was reduced by 2 °C, which improved the thermal performance of the power battery pack and consequently provides guidance for the design of the battery thermal management system (BTMS).

scholarly journals A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1221
Author(s):  
Xinwei Cong ◽  
Caiping Zhang ◽  
Jiuchun Jiang ◽  
Weige Zhang ◽  
Yan Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Dominant Role ◽  
Lithium Ion ◽  
Window Size ◽  
Operational Reliability ◽  
Signal Features ◽  
Diagnosis Method

To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

scholarly journals Research on Intelligent Diagnosis Method for Large-Scale Ship Engine Fault in Non-Deterministic Environment

2017 ◽  
Vol 24 (s3) ◽  
pp. 200-206 ◽  
Author(s):  
Donghua Feng ◽  
Yahong Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Use Value ◽  
Intelligent Diagnosis ◽  
High Diagnostic Accuracy ◽  
Diagnosis Method ◽  
Main Fault ◽  
Diagnosis Model ◽  
Fault Mode

Abstract Aiming at the problem of inaccurate and time-consuming of the fault diagnosis method for large-scale ship engine, an intelligent diagnosis method for large-scale ship engine fault in non-deterministic environment based on neural network is proposed. First, the possible fault of the engine was analyzed, and the downtime fault of large-scale ship engine and the main fault mode were identified. On this basis, the fault diagnosis model for large-scale ship engine based on neural network is established, and the intelligent diagnosis of engine fault is completed. The experiment proved that the proposed method has high diagnostic accuracy, engine fault diagnosis takes only about 3s, with a higher use value.

scholarly journals Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhaona Lu ◽  
Junlong Wang ◽  
Chuanxing Wang ◽  
Guoqing Li
Keyword(s):  
Electric Vehicles ◽  
Time Integration ◽  
Estimation Method ◽  
Open Circuit ◽  
Battery Pack ◽  
Estimation Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Current Time ◽  
Power Battery

The state of charge estimation of a pure electric vehicle power battery pack is one of the important contents of the battery management system. Improving the estimation accuracy of the battery pack’s SOC is conducive to giving full play to its performance and preventing overcharge and discharge of a single battery. At present, the open-circuit voltage ampere-hour integral method is traditionally used to estimate the SOC value of the battery pack; however, this estimation method is not accurate enough to correct the initial value of SOC and cannot solve the problem of current time integration error between this correction and the next correction. As for the battery performance and characteristics of electric vehicles, it is pointed out that the size of the model value will affect the estimation accuracy of the Kalman signal value. Based on the analysis of the factors to be referred to in the calculation and estimation of SOC by Kalman for pure electric vehicles, the scheme is improved considering the change of battery model value, and the Kalman scheme is proposed. The feasibility and accuracy of the scheme are proved by several battery simulation experiments.

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