Semi-supervised estimation of capacity degradation for lithium ion batteries with electrochemical impedance spectroscopy

Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems.However,they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities,leading to prohibitive costs and efforts for data collection.In response to this issue,this study proposes a convolutional neural network(CNN)based method to perform end-to-end capacity estimation by taking only raw impedance spectra as input.More importantly,an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process,thereby significantly alleviating the cost of collecting training data.Two large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed method.The results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are given.However,the estimation performance of supervised machine learning algorithms sharply deteriorates when fewer samples with measured capacities are available.In this case,the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%.A further validation under different current rates and states of charge confirms the effectiveness of the proposed method.Our method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks.

Multi-objective Optimization of Differential Steering System of Electric Vehicle with Motorized Wheels

A differential steering system is presented for electric vehicle with motorized wheels and a dynamic model of three-freedom car is built.Based on these models,the quantitative expressions of the road feel,sensitivity,and operation stability of the steering are derived.Then,according to the features of multi-constrained optimization of multi-objective function,a multi-island genetic algorithm(MIGA)is designed.Taking the road feel and the sensitivity of the steering as optimization objectives and the operation stability of the steering as a constraint,the system parameters are optimized.The simulation results show that the system optimized with MIGA can improve the steering road feel,and guarantee the operation stability and steering sensibility.

Integrated Control System of Vehicle ABS/ASR/ACC Based onMC9S12DP256

The integrated control system of vehicle ABS/ASR/ACC has been developed using the MC9S12DP256 single chip, which is the new Motorola 16-bit product in HSC12 family. The system including the main control module, the data collection module and the drive and fault diagnosis module is demonstrated and its data collection function is presented in detail. The system designed by the modularization can supervise the data, drive the valves and pump. The program can be debugged on line, which is steady and reliable validated by the large numbers of vehicle road tests.

Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries.The main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation models.In addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging data.We incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV curves.Correlation analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional flexibility.The validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 mAh.Achieving this level of precision for OCV estimation requires only around 50 s collection of partial charging data.Further validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed method.Additional cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV estimation.Our method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

Effect of sinter layer porosity distribution on flow and temperature fields in a sinter cooler

When sinters are filled into the sinter cooler from the sintering machine, it is commonly seen that, due to segregation effects, sinters of larger size usually accumulate closer to the inner wall of the sinter cooler, whereas those of smaller size are to the outer wall. This nonuniform distribution of sinters has led to uneven cooling effect throughout the cooler. This causes the sinters leaving the cooler at a large temperature difference. This undesired temperature difference leads to the deformation and even the destruction of the conveyors. The computational fluid dynamics （CFD） technique was used in the present work to investigate the heat and fluid flow phenomena within the sinter cooler corresponding to the different distribution of sinter layer porosity, which was highly dependent on the arrangement and orientation of sinters within the sinter cooler. It is confirmed that a high mass flow rate within the sinter layer causes a low temperature region and vice versa. The flow fields for vertically reducing porosity distribution and random distribution are almost identical indicating the relative insignificance of convective heat transfer mechanism.

Rotary ultrasonic-assisted milling of brittle materials

In order to improve the machining efficiency of ultrasonic milling,the easiest and most effective approach was started with the improvement of tool design.The main objective of this research was to utilize rotary ultrasonic machining (RUM's) effectiveness in removing brittle materials to extend the applications of this independent,innovative manufacturing method (self-driving rotary ultrasonic machining),and to experimentally investigate its milling application on brittle materials.The designed tool was used in the conjunction with previously established RUM machine tools,and glass was selected as workpiece for experiments.The interrelationship between feed rate and depth of cut was discussed.By measuring the surface roughness of workpiece,the overall efficacy of utilizing RUM for milling was evaluated and presented.Ultrasonic assisted milling results in the reduction of milling resistance,which leads to a greater process rate.

H∞ control of novel active steering integrated with electric power steering function

Based on the traditional active steering system, a novel active steering system integrated with electric power steering function was introduced, which can achieve the functions of both active steering and electric power steering. In view of the interference from road random signal and sensor noise in the novel active steering system, the H∞ control model of the novel active steering system was built. With satisfying steering feel, good robust performance and steering stability being the control objectives, the H∞ controller for the novel active front steering (AFS) system was designed. The simulation results show that the novel AFS system with H∞ control strategy can attenuate the road interference quickly, and there is no resonance peak in the bode diagram. It can make the driver obtain more useful information in the low frequency range, and attenuate the road interference better in the high frequency range, thus the driver can get more satisfying road feeling. Therefore, the designed H∞ controller can synthesize the advantages of both robust performance and robust stability, and has certain contribution to the design of novel AFS system.

A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries

State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC estimation.In this article,we compare five state-of-the-art FOMs in terms of SOC estimation.To this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM parameters.Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy.The model R(RQ)W shows superior identification accuracy than the other four FOMs.Secondly,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current drifts.The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs.Although more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory accuracy.Validation results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other models.Moreover,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.

Multi-objective optimization of active steering system with force and displacement coupled control

A novel active steering system with force and displacement coupled control(the novel AFS system) was introduced,which has functions of both the active steering and electric power steering.Based on the model of the novel AFS system and the vehicle three-degree of freedom system,the concept and quantitative formulas of the novel AFS system steering performance were proposed.The steering road feel and steering portability were set as the optimizing targets with the steering stability and steering portability as the constraint conditions.According to the features of constrained optimization of multi-variable function,a multi-variable genetic algorithm for the system parameter optimization was designed.The simulation results show that based on parametric optimization of the multi-objective genetic algorithm,the novel AFS system can improve the steering road feel,steering portability and steering stability,thus the optimization method can provide a theoretical basis for the design and optimization of the novel AFS system.

VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

The development of Vehicular Ad-hoc Network(VANET)technology is helping Intelligent Transportation System(ITS)services to become a reality.Vehicles can use VANETs to communicate safety messages on the road(while driving)and can inform their location and share road condition information in real-time.However,intentional and unintentional(e.g.,packet/frame collision)wireless signal jamming can occur,which will degrade the quality of communication over the channel,preventing the reception of safety messages,and thereby posing a safety hazard to the vehicle’s passengers.In this paper,VANET jamming detection applying Support Vector Machine(SVM)machine learning technology is used to classify jamming and non-jamming situations.The analysis is based on two cases which include normal traffic and heavy traffic conditions,where the results show that the probability of packet dropping will increase when many vehicles are using the wireless channel simultaneously.When using SVM classification,the most appropriate feature set applied in determining a jamming situation shows an accuracy of 98%or higher.Furthermore,more advanced jamming attacks need to be considered for preparation of more reliable and safer autonomous ITS services.Such research can use vehicular communication transmission and reception data based on selected published datasets.In this paper,an additional adversarial defense algorithm using the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)method is proposed,which assumes that evolutionary attacks of the jammer will attempt to confuse the trained classifier.The simulation results show that applying DBSCAN can improve the accuracy by elimination of outliers before conducting classification testing.

A Study of Motor Bearing Fault Diagnosis using Modulation Signal Bispectrum Analysis of Motor Current Signals

Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new approach to detection and diagnosis of motor bearing faults based on induction motor stator current analysis. Tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high nose levels, a modulation signal bispectrum (MSB) is used in this paper to detect and diagnose different motor bearing defects. The results show that bearing faults can induced a detestable amplitude increases at its characteristic frequencies. MSB peaks show a clear difference at these frequencies whereas conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also show that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component.

Principle and Control of Active Engine Mount Based on Magnetostrictive Actuator

Engine mount system affects the automobile NVH performance.Active mounts would achieve excellent vibration isolation and relative displacement control performance in a broad frequency bandwidth by outputting controlled force to the mounting system.The actuator and control method of the active mounts determine the system performance.In this paper,an active mount based on the smart material,i.e.,Terfenol-D rod,is proposed,which mainly includes three parts:rubber spring,magnetostrictive actuator(MA),and hydraulic amplification mechanism(HAM).Dynamic model of the active mount is correspondingly established.A state feedback control method based on x-LMS(Least-Mean-Square)algorithm is proposed as well.Specifically,with the consideration of the unmeasurable state parameters in the active mounting system,an x-LMS state feedback controller with the system state as the reference signal is constructed by employing Sage-Husa Kalman filter to realize the state estimation of the active mounting system.Then a detailed analysis of the proposed control method is conducted,with deriving iterative formula of tap-weight vector.Sequentially,the problem of the dependence on the excitation signal in the x-LMS algorithm is addressed.The feasibility and capability of the proposed control method are verified and evaluated by simulation of a two-degree-offreedom active mounting system.

Control of Hydraulic Power System by Mixed Neural Network PID in Unmanned Walking Platform

To speedily regulate and precisely control a hydraulic power system in a unmanned walking platform(UWP),based on the brief analysis of digital PID and its shortcomings,dual control parameters in a hydraulic power system are given for the precision requirement,and a control strategy for dual relative control parameters in the dual loop PID is put forward,a load and throttle rotation-speed response model for variable pump and gasoline engine is provided according to a physical process,a simplified neural network structure PID is introduced,and formed mixed neural network PID(MNN PID)to control rotation speed of engine and pressure of variable pump,calculation using the back propagation(BP)algorithm and a self-adapted learning step is made,including a mathematic principle and a calculation flow scheme,the BP algorithm of neural network PID is trained and the control effect of system is simulated in Matlab environment,real control effects of engine rotation speed and variable pump pressure are verified in the experimental bench.Results show that algorithm effect of MNN PID is stable and MNN PID can meet the adjusting requirement of control parameters.

Inverse Problems for an Euler-Bernoulli Beam: Identification of Bending Rigidity and External Loads

We present a method for identifying the flexural rigidity and external loads acting on a beam using the finite-element method. We used mixed beam elements possessing transverse deflection and the bending moment as the primary degrees of freedom. The first step is to determine the bending moment from the transverse deflection and boundary conditions. The second step is to substitute the bending moment into the final equations with respect to the unknown parameters (flexural rigidity or external load). The final step solves the resulting system of equations. We apply this method to some inverse beam problems and provide an accurate estimation. Several numerical examples are performed and show that present method gives excellent results for identifying bending stiffness and distributed load of beam.

Determination of Shear Center of Arbitrary Cross-Section

In structural analysis, it is often necessary to determine the geometrical properties of cross section. The location of the shear center is greater importance for an arbitrary cross section. In this study, the problems of coupled shearing and torsional were analyzed by using the finite element method. Namely, the simultaneous equations with respect to the warping, shear deflection, angle of torsion and Lagrange’s multipliers are derived by finite element approximation. Solving them numerically, the matrix of the shearing rigidity and torsional rigidity is obtained. This matrix indicates the coupled shearing and torsional deflection. The shear center can be obtained determining the coordinate axes so as to eliminate the non-diagonal terms. Several numerical examples are performed and show that the present method gives excellent results for an arbitrary cross section.

The Construction of a Combined OBE-based Teaching Model

This paper proposes a teaching model based on the concept of outcome-based education(OBE).OBE is meant to be a student-centered learning model.Based on OBE,the characteristics of project-based learning(PBL)and lecture-based teaching approach are analyzed.Then,a combined teaching model is constructed based on Acharya’s research findings and PBL.This proposed model integrates PBL and lecture-based teaching approach to help achieve ideal learning outcomes.It enhances learners’learning initiative and creativity while ensuring holistic and systematic learning outcomes.

Deep Reinforcement Learning Based Decision‑Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments

Uncertain environment on multi-lane highway,e.g.,the stochastic lane-change maneuver of surrounding vehicles,is a big challenge for achieving safe automated highway driving.To improve the driving safety,a heuristic reinforcement learning decision-making framework with integrated risk assessment is proposed.First,the framework includes a long short-term memory model to predict the trajectory of surrounding vehicles and a future integrated risk assessment model to estimate the possible driving risk.Second,a heuristic decaying state entropy deep reinforcement learning algorithm is introduced to address the exploration and exploitation dilemma of reinforcement learning.Finally,the framework also includes a rule-based vehicle decision model for interaction decision problems with surrounding vehicles.The proposed framework is validated in both low-density and high-density traffic scenarios.The results show that the traffic efficiency and vehicle safety are both improved compared to the common dueling double deep Q-Network method and rule-based method.

Study on Rollover Index and Stability for a Triaxle Bus

Vehicle rollover, and its resulting fatalities, is an actively researched topic especially for multi-axle vehicles in the field of vehicle dynamics and control. This paper first presents a new rollover index for a triaxle bus to accurately evaluate its rollover possibility and then discusses the influence laws of the vehicle rollover dynamics to explore the mechanism of its stability. First, a six degree of freedom rollover model of the triaxle bus is developed, including lateral, yaw, roll motion of the sprung mass of the front/rear axle, and roll motion of the unsprung mass of the front/rear axle. Next, some key parameters of the vehicle rollover model are identified. A new rollover index is deduced according to the basics of vehicle dynamics, to predict vehicle rollover risk for the triaxle bus, which is verified by TruckSim. Furthermore, the influence laws of vehicle rollover dynamics by vehicle parameters and road parameters are discussed based on the simulation results. More importantly, the results show that the new method of modeling can precisely describe the rollover dynamics of the studied bus, and the proposed new index can e ectively evaluate the rollover possibility. Therefore, this study provides a theoretical basis to improve anti-rollover ability for triaxle buses.

On-board Diagnosis of Soft Short Circuit Fault in Lithium-ion Battery Packs for Electric Vehicles Using an Extended Kalman Filter

The safety of lithium-ion batteries in electric vehicles(EVs)is attracting more attention.To ensure battery safety,early detection is necessary of a soft short circuit(SC)which may evolve into severe SC faults,leading to fire or thermal runaway.This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter(EKF)for on-board applications in EVs.In the proposed method,the EKF is used to estimate the state of charge(SOC)of the faulty cell by adjusting a gain matrix based on real-time measured voltages.The SOC difference between the estimated SOC and the calculated SOC through coulomb counting for the faulty cell is employed to detect soft SC faults,and the soft SC resistance values are further identified to indicate the degree of fault severity.Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values.The experimental data are acquired to validate the proposed soft SC fault diagnosis method.The results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.