Battery-operated Data Research Articles

Voltage fault diagnosis and misdiagnosis analysis of battery systems using the modified Shannon entropy in real-world electric vehicles

Power battery systems fault diagnosis of electric vehicles (EVs) is a key technical approach to ensure the safety and reliable operation of the vehicle, of which avoiding misdiagnosis not only can reduce the driver's safety concerns, but also is a reflection of the reliability of the diagnostic method. This paper first proposes a modified Shannon entropy-based battery fault diagnosis method for identifying cells with abnormal voltage fluctuations in battery systems, and the method is implemented online by calculating the Shannon entropy of the voltage sequence in a moving time window. Then, the defined sensitivity factor (SF) can provide an efficient and accurate assessment of the extent of abnormal voltage fluctuations. Further, to improve the fault diagnosis accuracy of the method, we validated the diagnostic model by using battery operation data from a battery monitoring cloud platform with a sampling frequency of 0.1 Hz and eventually found a large number of false alarm cells in the diagnostic results. Based on this, we have analysed the fault diagnosis mechanisms of the model and thus obtained the causes of misdiagnosis. Finally, a solution to reduce misdiagnosis is proposed and its effectiveness is verified. To further test its comprehensive performance, we compared the accuracy of the method before and after optimization based on normal vehicle data, and the results showed a 81.9 % reduction in the average relative misdiagnosis rate of the model, thus greatly improving the reliability of this fault diagnosis strategy, creating conditions for the online application of the method and providing ideas for the analysis and improvement of the accuracy rate of other fault diagnosis methods.

A battery capacity estimation method based on the equivalent circuit model and quantile regression using vehicle real-world operation data

Battery capacity estimation is critical for evaluating the battery performance and safety. Currently, the sampling interval of battery operation data on the cloud is 10–30 s, and relatively poor data quality poses difficulties in battery capacity estimation. In this study, a battery capacity estimation method is proposed based on the battery equivalent circuit model and a quantile regression method using real-world vehicle operation data on the cloud. The battery parameters were identified based on the Thevenin battery model, and the state of charge (SOC) was estimated using a joint Kalman filter. Then, the discharge electric quantity–SOC data was fitted using quantile regression to obtain the preliminary capacity estimation results. To describe the battery capacity degradation along with driving mileage, this estimation result was further fitted using the Arrhenius empirical aging model and another quantile regression. The Nelder–Mead method was used to estimate the coefficients in quantile regression-related algorithms. Because quantile regression is not sensitive to the outliers of data points, it can reduce the impact of poor data quality on battery capacity estimation results. The proposed battery capacity estimation method was verified using real-world vehicle operation data, and the capacity estimation errors were within 3.2%.

Logging In-Operation Battery Data from Android Devices: A Possible Path to Sourcing Battery Operation Data

Operating Lithium-ion batteries requires a deep understanding of their performance. Laboratory experiments are conducted mostly under controlled ambient conditions and repetitive loads, whereas real-world operations feature a large spectrum of operating conditions. This leads to a large gap, which in-operation battery field data aims to close. Literature states the necessity for large datasets for field data studies; however, these are not available today. In this article, an overview of the possibilities of sourcing battery field data from Android devices is presented. Today, there are two billion Android devices in active use, and most of them are equipped with a lithium-ion battery and are also connected to the internet. An Android device study featuring multiple devices is conducted, evaluating signal quality and differences. Exemplary data analysis with a focus on state estimation algorithms is then tested on the sourced data.

Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

The development of a battery management algorithm is highly dependent on high-quality battery operation data, especially the data in extreme conditions such as low temperatures. The data in faults are also essential for failure and safety management research. This study developed a battery big data platform to realize vehicle operation, energy interaction and data management. First, we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously. Second, charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle. Third, the data transmission network was designed, a real-time monitoring interface was developed, and the self-developed battery management system was used to measure, collect, upload, and store battery operation data in real time. Finally, experimental validation was performed on the platform. Results demonstrate the efficiency and reliability of the platform. Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

A Novel Fusion Approach Consisting of GAN and State-of-Charge Estimator for Synthetic Battery Operation Data Generation

The recent success of machine learning has accelerated the development of data-driven lithium-ion battery state estimation and prediction. The lack of accessible battery operation data is one of the primary concerns with the data-driven approach. However, research on battery operation data augmentation is rare. When coping with data sparsity, one popular approach is to augment the dataset by producing synthetic data. In this paper, we propose a novel fusion method for synthetic battery operation data generation. It combines a generative, adversarial, network-based generation module and a state-of-charge estimator. The generation module generates battery operation features, namely the voltage, current, and temperature. The features are then fed into the state-of-charge estimator, which calculates the relevant state of charge. The results of the evaluation reveal that our method can produce synthetic data with distributions similar to the actual dataset and performs well in downstream tasks.

Health-Conscious vehicle battery state estimation based on deep transfer learning

Establishing an accurate mathematical model is fundamental to managing, monitoring, and protecting the battery pack in electric vehicles (EVs). The application of the deep learning algorithm-based state estimation method can significantly improve the accuracy and stability of the battery model but is hindered by the great demand for training data. This paper addresses the challenge of health-conscious battery modeling by utilizing multi-source data based on a novel deep transfer learning method. Firstly, a cloud-based battery management framework is designed, which is able to collect and process battery operation data from various EVs and provide a foundation for deploying the transfer learning method. Battery healthy state information in the collected dataset is labeled by a generic perception model, which can be commonly used to quantify the aging state of different battery packs and facilitate the knowledge transfer process. Additionally, a deep transfer learning method is developed to boost the training process of the battery model, where the operation data from different types of EVs can be used for establishing state estimators. The method is verified by the battery operation data collected from two types of electric buses. With the developed healthy state perception model and transfer learning method, battery model error can be limited to 2.43% and 1.27% in the whole life cycle.

Case Study of an Electric Vehicle Battery Thermal Runaway and Online Internal Short-Circuit Detection

A thermal runaway happened in a battery cell of an electric vehicle during driving, and the fire spreads to other batteries in a few minutes. Based on the recorded battery operation data, this letter analyzes the battery thermal runaway process, and identifies the critical time points of fault signals like cell voltages and temperatures, etc., which depict the start and spread of thermal runaway in the battery pack. By tracing back the clues, it is concluded that the cause of the fire is battery internal short circuit (ISC), and battery overcharge is a potential cause of ISC. Then, three different patterns of battery cell voltage abnormalities are compared, including battery external short circuit, ISC, and poor-signal wire connection. A cell voltage difference-based method is proposed to diagnose ISC online, which is simple and reliable to implement in engineering.

Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data – Part A: Storage operation

• A calendar ageing model is developed under the Gaussian process framework. • A new covariance function is composed, tailored to Li-ion battery ageing prediction. • Especial emphasis on the ability of the model to learn from new data observations. • Sensitivity analysis of the model, and analysis of the uncertainty quantification. • Validation with 32 cells, for broad range of static and dynamic storage conditions. Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. This first paper focusses on the systematic modelling and experimental verification of cell degradation through calendar ageing. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 32 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 18 tested cells achieves an overall mean-absolute-error of 0.53% in the capacity curves prediction, after being validated under a broad window of both dynamic and static temperature and SOC storage conditions.

Remaining useful life prediction for lithium-ion batteries using particle filter and artificial neural network

Purpose With the promotion of lithium-ion battery, it is more and more important to ensure the safety usage of the battery. The purpose of this paper is to analyze the battery operation data and estimate the remaining life of the battery, and provide effective information to the user to avoid the risk of battery accidents. Design/methodology/approach The particle filter (PF) algorithm is taken as the core, and the double-exponential model is used as the state equation and the artificial neural network is used as the observation equation. After the importance resampling process, the battery degradation curve is obtained after getting the posterior parameter, and then the system could estimate remaining useful life (RUL). Findings Experiments were carried out by using the public data set. The results show that the Bayesian-based posterior estimation model has a good predictive effect and fits the degradation curve of the battery well, and the prediction accuracy will increase gradually as the cycle increases. Originality/value This paper combines the advantages of the data-driven method and PF algorithm. The proposed method has good prediction accuracy and has an uncertain expression on the RUL of the battery. Besides, the method proposed is relatively easy to implement in the battery management system, which has high practical value and can effectively avoid battery using risk for driver safety.

Specimen Temperature Detection on a Clinical Laboratory Pre-Analytic Automation Track: Implications for Direct-from-Track Total Laboratory Automation (TLA) Systems.

Clinical laboratory regulations require temperature monitoring of facilities, reagent and specimen storage, as well as temperature-dependent equipment. Real-time specimen temperature detection has not yet been integrated into total laboratory automation (TLA) solutions. An infrared (IR) pyrometer was paired with a complementary metal oxide semiconductor (CMOS) laser sensor and connected to an embedded networked personal computer (PC) to create a modular temperature detection unit for closed, moving clinical laboratory specimens. Accuracy of the detector was assessed by comparing temperature measurements to those obtained from thermocouples connected to battery-operated data loggers. The temperature detector was then installed on a pre-analytic laboratory automation system to assess specimen temperature before and after processing on an integrated thawing and mixing (T/M) robotic workcell. The IR temperature detector was able to accurately record temperature of closed, moving specimens on a pre-analytic automation system. The effectiveness of the T/M workcell was independently verified using the temperature detector. Specimen reroute on the pre-analytic automation track was identified as a potential risk for frozen specimens being inadvertently delivered to future, connected instrumentation. Automated IR temperature detection can be used to verify specimen temperature prior to instrument loading and/or sampling. Such systems could be used to prevent frozen specimens from being inadvertently loaded onto analytical instrumentation in TLA solutions.

Data pieces-based parameter identification for lithium-ion battery

Abstract Battery characteristics vary with temperature and aging, it is necessary to identify battery parameters periodically for electric vehicles to ensure reliable State-of-Charge (SoC) estimation, battery equalization and safe operation. Aiming for on-board applications, this paper proposes a data pieces-based parameter identification (DPPI) method to identify comprehensive battery parameters including capacity, OCV (open circuit voltage)-Ah relationship and impedance-Ah relationship simultaneously only based on battery operation data. First a vehicle field test was conducted and battery operation data was recorded, then the DPPI method is elaborated based on vehicle test data, parameters of all 97 cells of the battery package are identified and compared. To evaluate the adaptability of the proposed DPPI method, it is used to identify battery parameters of different aging levels and different temperatures based on battery aging experiment data. Then a concept of “OCV-Ah aging database” is proposed, based on which battery capacity can be identified even though the battery was never fully charged or discharged. Finally, to further examine the effectiveness of the identified battery parameters, they are used to perform SoC estimation for the test vehicle with adaptive extended Kalman filter (AEKF). The result shows good accuracy and reliability.

A Flow-Through Sampler for Semivolatile Organic Compounds in Air

A widely acknowledged limitation of current passive air sampling designs for semivolatile organic chemicals is their relatively low sampling rate, severely constraining the temporal resolution that can be achieved. Addressing the need for an improved sampling design which achieves significantly faster uptake while maintaining the capability of providing quantitative information, a new sampler has been developed that provides greatly increased sampling rates by forcing the wind to blow through the sampling medium. The sampler consists of a horizontally oriented, aerodynamically shaped, stainless steel flow tube mounted on a post with ball bearings, which turns into the wind with the help of vanes. A series of polyurethane foam (PUF) discs with relatively large porosity mounted inside the flow tube serve as the sampling medium. The sampled air volume is calculated from wind speed, which is measured outside the sampler and after passage through the sampling medium using precalibrated vortex rotor and turbine anemometers mounted on top of the sampler and at the exit of the flow tube, respectively. Small battery-operated data loggers are used for data storage. Under typical wind speed conditions, the sampler can collect 100 m(3)/ day, which is approaching the sampling rates of conventional high volume samplers. Controlled experiments in the laboratory and frontal chromatography theory yield the theoretical plate number and breakthrough volumes for polychlorinated biphenyls and polycyclic aromatic hydrocarbons in the PUF plugs and allow for the estimation of breakthrough levels for relatively volatile organic chemicals. After correction for breakthrough, the air concentration obtained with the new flow-through sampler are independent of sampling length and volume and compare favorably with those obtained from conventional pumped high volume samples.

Prolonged ambulatory monitoring of colonic motor activity in the pig.

The aim of this study was to develop a chronic model suitable for repeated, long-term studies of the interaction of behavior and colonic function in unrestrained pigs. Cecostomies were created in three 20-30 kg micropigs under general anesthesia. Fistulas were created by suturing the bowel to the abdominal wall. Recordings were made by passing a small (8F) solid-state pressure transducer through the fistula into the proximal bowel and connecting it to a battery-operated data logger worn in a vest on the pig's back. Cecostomies have remained patent and trouble-free for over 18 months. No serious infections have occurred. Preliminary data from a total of thirteen 24-h recording sessions showed 54% of all contractile activity to be in the 2-4 cpm frequency range. Increased motility was seen following meals and upon morning awakening. Motility was minimal during the night. Infrequent (10.31 +/- 2.05/24 h; mean +/- SD) propagated contractions were also noted. These contractions were generally of low amplitude (33.24 +/- 3.81 mmHg). These techniques allow prolonged, intraluminal recordings to be made from the colon of the unrestrained pig.

Method for prolonged ambulatory monitoring of high-amplitude propagated contractions from colon

This study monitored high-amplitude propagated contractions (HAPCs) in ambulating subjects over a 24-h period using a new ambulatory recording system. Twelve healthy volunteers aged 34 +/- 5.96 yr participated. Approximately 12 h after a Colyte bowel prep, a small catheter (OD less than 3 mm), containing three solid-state pressure transducers spaced 5 cm apart, was positioned by flexible sigmoidoscope at 40-50 cm from the anal verge. A battery-operated data recorder sampled the pressure at each port at 1 Hz and stored the values on all ports if any port exceeded 75 mmHg. At the conclusion of the 24-h period, an X-ray was taken to confirm the location of the catheter. Fifty-four percent of all HAPCs preceded a bowel movement by less than or equal to 1 h. Forty-nine percent of all HAPCs occurred within 1 h after a high-fat meal, and 33% occurred within 1 h of morning awakening. Reverse propagated waves, not previously described in the colon, were observed in three individuals. Spontaneous high-amplitude caudally propagated contractions occur 6.9 +/- 1.5 times/24 h in the sigmoid colon in ambulating asymptomatic individuals and are temporally related to defecation and meals. Peristaltic activity is decreased during sleep. This recording technique was reliable and well tolerated in all participants.