Multi‐objective design optimization for mini‐channel cooling battery thermal management system in an electric vehicle

Lithium‐ion battery packs have been generally used as the power source for electric vehicles. Heat generated during discharge and limited space in the battery pack may bring safety issues and negative effect on the battery pack. Battery thermal management system is indispensable since it can effectively moderate the temperature rise by using a simple system, thereby improving the safety of battery packs. However, the comprehensive investigation on the optimal design of battery thermal management system with liquid cooling is still rare. This article develops a comprehensive methodology to design an efficient mini‐channel cooling system, which comprises thermodynamics, fluid dynamics, and structural analysis. The developed methodology mainly contains four steps: the design of the mini‐channel cooling system and computational fluid dynamics analysis, the design of experiments and selection of surrogate models, formulation of optimization model, and multi‐objective optimization for selection of the optimum scheme for mini‐channel cooling battery thermal management system. The findings in the study display that the temperature difference decreases from 8.0878 to 7.6267 K by 5.70%, the standard temperature deviation decreases from 2.1346 to 2.1172 K by 0.82%, and the pressure drop decreases from 302.14 to 167.60 Pa by 44.53%. The developed methodology could be extended for industrial battery pack design process to enhance cooling effect thermal performance and decrease power consumption.     

Multi‐fault detection and diagnosis of HVAC systems: an experimental study

The objective of this study is to detect faults due to multiple element failures in HVAC systems occurring concurrently. To classify and detect single as well as multiple faults, measurements were made of supply air temperature, OA‐damper position, supply fan pressure, indoor temperature and airflow rate in a variable air volume heating ventilating and air conditioning test facility. Experimental results show that three types of patterns emerge in the analysis of multi‐fault problems. To solve the multi‐fault problem, a new strategy based on pattern classification and the use of residual ratios is presented. It is shown that the residual ratio can be used to diagnose and accurately identify and detect multiple‐faults occurring in HVAC systems. Copyright © 2005 John Wiley & Sons, Ltd.     

A model‐based fault detection and diagnosis strategy for HVAC systems

A strategy of fault detection and diagnosis (FDD) for HVAC sub‐systems at the system level is presented in this paper. In the strategy, performance indices (PIs) are proposed to indicate the health condition of different sub‐systems including cooling tower system, chiller system, secondary pump systems before heat exchangers, heat exchanger system and secondary pump system after heat exchangers. The regression models are used to estimate the PIs as benchmarks for comparison with monitored PIs. The online adaptive threshold determined by training data and monitored data is used to determine whether the PI residuals between the estimation and calculation or monitoring are in the normal working range. A dynamic simulation platform is used to simulate the faults of different sub‐systems and generate data for training and validation. The proposed FDD strategy is validated using the simulation data and proven to be effective in the FDD of heating, ventilating and air‐conditioning (HVAC) sub‐systems. Copyright © 2009 John Wiley & Sons, Ltd.     

Multi‐agent‐based sharing power economy for a smart community

In this paper, a multi‐agent‐based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small‐scale battery ESSs (each owned by community users), which are connected together to form a unified‐ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified‐ESS. This decision is based on the price of electricity at ‘time of use’ and ‘state of charge’ of unified‐ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of ‘no profit, no loss’. The objective of the proposed PDH is to limit the purchase of electricity on ‘high priced’ hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi‐agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on‐peak, off‐peak, and mid‐level (mid‐peak) prices of a real‐time price signal during 24 h of a day. Results clearly show vital financial benefits of ‘sharing power economy’ for end users and efficient use of power within the smart community. Copyright © 2017 John Wiley & Sons, Ltd.     

Multi‐scale computational study of the molten salt based recycling of spent nuclear fuels

By applying a rigorous computational procedure combining first principles density functional theory (DFT) calculations and statistical mechanics, we acquire thermochemical properties of materials for a pyroprocessing system recycling spent nuclear fuels. Cluster expansions to DFT obtained energies parameterize atomic interaction potentials of Cl‐Cl and Cl‐U adsorbed on W(110) surface from a molten salt (KCL‐LiCl). Using these databases of the long‐range and multibody interactions, Monte Carlo simulations identify thermodynamically stable configurations of the adsorbates on the W(110) surface in grand canonical open system at T = 773 K. Our results indicate that Cl atoms adsorbed at the interface of the molten salt and W(110) surface substantially drive electrochemical deposition of U ions at low chemical potential of Cl. This behavior, however, stops after approximately 1/3 ML coverage of U because the atomic sites on W(110) surface are mostly blocked by adsorbed Cl, which implies that the attractive interactions of Cl‐W are stronger than Cl‐U as well as the repulsive interactions between U atoms are effective at these coverage ranges. We also predict the solubility limit of U ion in the molten LiCl‐KCl phases at T = 773 K should be about 5 atomic percent, which well agrees with previous reports by experimental measurements. This study indicates that accurate characterization of the stable Cl structure at the interface is vital for understanding the fundamental mechanisms of recycling spent nuclear fuels and for screening high functional electrode materials in the pyroprocessing system. Copyright © 2014 John Wiley & Sons, Ltd.     

Heat exchanger: from micro‐ to multi‐scale design optimization

This paper presents a consideration of micro‐heat exchangers design optimization for the aim of process intensification. Two examples are discussed to illustrate different ways of heat transfer intensification in micro‐heat exchangers. To solve the key issue of the link between the micro‐scale and the macro‐scale, a multi‐scale design optimization method using fractal and constructal approaches is introduced. The concept of a novel constructal heat exchanger is also proposed. Copyright © 2007 John Wiley & Sons, Ltd.     

Single and multi‐objective optimization for the performance enhancement of lead–acid battery cell

Electric energy storage systems are used considerably in industries and daily applications. The demand for batteries with high energy content has increased because of their use in hybrid vehicles. Lead–acid batteries have wide applications because of their advantages such as high safety factor and low cost of production. The major shortcoming of lead–acid batteries is low energy content and high dimension and weight. Nowadays, a common method to increase the energy content of lead–acid battery is the experimental method with trial and error, which is time consuming and expensive. In this paper, non‐isothermal one‐dimensional numerical simulation of lead–acid battery with finite volume method is performed. In addition, a cell with higher energy content and lower thickness is designed by using particle swarm optimization algorithm based on developed simulation code. The results of single objective optimization show that an optimal battery that has 27.6% higher energy can be made with the same cell dimension. The results also show that an optimum cell battery can be obtained with a decrease of 24% in thickness while keeping the energy the same. Moreover, a multi‐objective optimization algorithm is utilized to find Pareto optimal solutions while considering the energy content and thickness objectives simultaneously. Copyright © 2016 John Wiley & Sons, Ltd.     

The effects of vehicle‐to‐grid systems on wind power integration

Renewable energy portfolio standards have created a large increase in the amount of renewable electricity production, and one technology that has benefited greatly from these standards is wind power. The uncertainty inherent in wind electricity production dictates that additional amounts of conventional generation resources be kept in reserve, should wind electricity output suddenly dip. The introduction of plug‐in hybrid electric vehicles into the transportation fleet presents an possible solution to this problem through the concept of vehicle‐to‐grid power. The ability of vehicle‐to‐grid power systems to help solve the variability and uncertainty issuess in systems with large amounts of wind power capacity is examined through a multiparadigm simulation model. The problem is examined from the perspectives of three different stakeholders: policy makers, the electricity system operator and plug‐in hybrid electric vehicle owners. Additionally, a preliminary economic analysis of the technology is performed, and a comparison made with generation technologies that perform similar functions. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.     

Diagnosis method based on wavelet coefficient scale relativity correlation dimension for fault

Correlation dimension as a tool to describe machinery condition is introduced. Vibration signals of the fan under different working conditions are analyzed using a threshold filtering algorithm based on the region relativity of the wavelet coefficients for reducing noise. The result shows that the characteristics of the signal could be preserved completely. The correlation dimension is able to identify conditions of the fan with faults compared with the normal condition, thereby providing an effective technology for condition monitoring and fault diagnosis of mechanical equipment. __________ Translated from Journal of North China Electric Power University, 2007, 34(1): 59–62 [译自: 华北电力大学学报]     

基于机车故障诊断专家系统的故障树优化设计

针对基于故障树的机车故障诊断专家系统,通过引入二叉故障树的概念,将传统故障树合成为一棵二叉故障树模型,并以此为基础,对其知识库的存储和故障诊断方式进行了研究,达到了优化系统的目的。     

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.     

Distribution of size in multi‐evaporator air conditioning systems

This paper reports the thermodynamic optimization of air conditioning systems with one or more evaporators, for one or more rooms. First, the paper shows that the minimization of the total power requirement leads to the same results as the minimization of entropy generation in the whole system, which includes the insulation between the cold space and the ambient. The results also show that there is a trade‐off between the power lost by fluid friction in the distribution network and the power required by the refrigerant. This trade‐off determines the optimal allocation of hardware in the installation. The optimum diameters of the refrigerant pipelines are independent of their lengths. Copyright © 2013 John Wiley & Sons, Ltd.     

Vibration‐based wind turbine planetary gearbox fault diagnosis using spectral averaging

Planetary gearboxes (PGBs) are widely used in the drivetrain of wind turbines. Any PGB failure could lead to a significant breakdown or major loss of a wind turbine. Therefore, PGB fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and lifespan of wind turbines. The wind energy industry currently utilizes vibratory analysis as a standard method for PGB condition monitoring and fault diagnosis. Among them, the vibration separation is considered as one of the well‐established vibratory analysis techniques. However, the drawbacks of the vibration separation technique as reported in the literature include the following: potential sun gear fault diagnosis limitation, multiple sensors and large data requirement, and vulnerability to external noise. This paper presents a new method using a single vibration sensor for PGB fault diagnosis using spectral averaging. It combines the techniques of enveloping, Welch's spectral averaging, and data mining‐based fault classifiers. Using the presented approach, vibration fault features for wind turbine PGB are extracted as condition indicators for fault diagnosis and condition indicators are used as inputs to fault classifiers for PGB fault diagnosis. The method is validated on a set of seeded localized faults on all gears: sun gear, planetary gear, and ring gear. The results have shown a promising PGB fault diagnosis performance with the presented method. Copyright © 2015 John Wiley & Sons, Ltd.     

Research on heat dissipation performance and flow characteristics of air‐cooled battery pack

With the depletion of fossil fuels and the aggravation of environmental pollution, the research and development speed of electric vehicles has been accelerating, and the thermal management of battery pack has become increasingly important. This paper selects the electric vehicle battery pack with natural air cooling as the study subject, conducts simulation analysis of the heat dissipation performance of battery packs with and without vents. Then this paper researches on the influence of internal flow field and external flow field. Field synergy principle is used to analyze the effect of velocity field and temperature field amplitude. The results show the following: it is found that the maximum temperature rise and the internal maximum temperature difference of the battery pack with vents are reduced by about 23.1% and 19.9%, raising speed value can improve the heat dissipation performance, and raising temperature value can decrease the heat dissipation performance. Reasonable design of the vents can make the inner and outer flow field work synergistically to achieve the best cooling effect. Then the reference basis for the air cooling heat dissipation performance analysis of electric vehicle, battery pack structure arrangement, and air‐inlet and air‐outlet pattern choosing are offered.     

Parameter estimation of an electrochemistry‐based lithium‐ion battery model using a two‐step procedure and a parameter sensitivity analysis

Lithium‐ion batteries are indispensable in various applications owing to their high specific energy and long service life. Lithium‐ion battery models are used for investigating the behavior of the battery and enabling power control in applications. The Doyle‐Fuller‐Newman (DFN) model is a popular electrochemistry‐based model, which characterizes the dynamics in the battery through diffusions in solid and electrolyte and predicts current/voltage response. However, the DFN model contains a large number of parameters that need to be estimated to obtain an accurate battery model. In this paper, a computationally feasible two‐step estimation approach is proposed that only uses voltage and current measurements of the battery under consideration. In the two‐step procedure, the parameters are divided into 2 groups. The first group contains thermodynamic parameters, which are estimated using low‐current discharges, while the second group contains kinetic parameters, which are estimated using a well‐designed highly‐dynamic pulse (dis‐)charge current. A parameter sensitivity analysis is done to find a subset of parameters that can be reliably estimated using current and voltage measurements only. Experimental data are collected for 12 Ah nickel cobalt aluminum pouch lithium‐ion cell. The voltage predictions of the identified model are compared with several experimental data sets to validate the model. A root mean square error between model predictions and experimental data smaller than 16 mV is achieved.     

Investigation of nickel‐63 radioisotope‐powered GaN betavoltaic nuclear battery

This work describes the theoretical and experimental investigation of an in‐house produced ⁶³Ni radioisotope‐powered GaN‐based direct conversion (betavoltaic) nuclear battery. GaN p‐n junction device with 1‐mm² area was fabricated and irradiated by the ⁶³Ni plate source. Short‐circuit current and open‐circuit voltage of the battery were measured, and current‐voltage curves were plotted. The energy stored in battery, maximum power, and efficiency parameters were calculated. Monte Carlo modelling was used to investigate radioisotope's self‐absorption effect, the optimization of semiconductor and source thickness, transport, and penetration of beta particles in semiconductor junction. A large fraction of beta particle energy emitted from ⁶³Ni source is absorbed within 1 μm of the semiconductor junction on the basis of the simulation results. Epitaxial growth of GaN was performed using metal‐organic chemical vapour deposition (MOCVD) system. Monte Carlo simulation with MCNPX was used to determine optimum ⁶³Ni radioactive film thickness. ⁶³Ni film was electroplated on one face of 1‐mm² copper plate and mounted 1 mm over the semiconductor device. A ⁶³Ni source with an apparent activity of 0.31 mCi produced 0.1 ± 0.001 nA short‐circuit current (I_sc), 0.65 V ± 0.0022 open‐circuit voltage (V_oc), and 0.016 nW ± 0.0002 maximum power (P_max) in the semiconductor device. The filling factor (FF) of the betavoltaic cell was 25%, and the conversion efficiency (?) was 0.05%. Finally, experimental results were compared with theoretical calculations.     

基于小波分析的燃机排气温度传感器组故障诊断方法研究

为提高燃机排气温度传感器输出信号的准确性,确保燃机系统安全稳定运行,本文研究了小波分析在传感器故障诊断中的应用。通过检测小波变换的模极大值是否超限判断传感器是否发生故障;分析故障信号的奇异度,求取信号的李氏指数,对故障进行辩识;在缓变信号的辩识中,又借助相关性分析方法进一步寻找信号缓变的真正原因。对温度传感器的故障半物理模拟实验表明了小波分析诊断传感器故障的有效性。     

Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle

To improve the economic performance of dual‐motor battery electric vehicles, a novel driving pattern recognition–based energy management strategy (NDPREMS) is proposed in this paper. The NDPREMS firstly employs principal component analysis method to reduce the dimension of characteristic parameters of driving patterns and uses hierarchical cluster method for classifying driving patterns to construct a database of typical driving patterns, based on which a driving pattern recognizer is achieved using generalized regression neural network (GRNN) and the accuracy of this recognizer reaches 96.08%. In order to reasonably allocate the power between two motors, on the basis of rule‐based energy management strategy (REMS), a dynamic programming–based energy management strategy (DPEMS) under typical driving patterns is formulated. By doing so, the logic thresholds of REMS are optimized, and thus, the NDPREMS is achieved. Comparison simulations of control effect concerning the REMS, DPEMS, and NDPREMS are performed under typical driving patterns. Results indicate that the proposed NDPREMS exhibits greater energy conservation compared with REMS, the economic improvement under urban driving pattern is the most obvious at 11.04%, the improvement under the comprehensive test driving pattern is 5.65%, and the performance of the NDPREMS is similar to that of DPEMS.     

Thermal management system of lithium‐ion battery module based on micro heat pipe array

Temperature affects the performance of electric vehicle battery. To solve this problem, micro heat pipe arrays are utilized in a thermal management system that cools and heats battery modules. In the present study, the heat generation of a battery module during a charge‐discharge cycle under a constant current of 36 A (2C) was computed. Then, the cooling area of the condenser was calculated and experimentally validated. At rates of 1C and 2C, the thermal management system effectively reduced the temperature of the module to less than 40°C, and the temperature difference was controlled less than 5°C between battery surfaces of the module. A heating plate with 30‐W power effectively improved charge performance at low temperature within a short heating time and with uniform temperature distribution. Charge capacity obviously increased after heating when battery temperature was below 0°C. This study presents a new way to enhance the stability and safety of a battery module during the continuous charge‐discharge cycle at high temperatures and low temperatures accordingly.