SOC distribution-based modeling for lithium-ion battery for electric vehicles using numerical optimization

In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.

Arc-search in numerical optimization

Determining the search direction and the search step are the two main steps of the nonlinear optimization algorithm,in which the derivatives of the objective and constraint functions are used to determine the search direction,the one-dimensional search and the trust domain methods are used to determine the step length along the search direction.One dimensional line search has been widely discussed in various textbooks and references.However,there is a lessknown techniquearc-search method,which is relatively new and may generate more efficient algorithms in some cases.In this paper,we will survey this technique,discuss its applications in different optimization problems,and explain its potential improvements over traditional line search method.

Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm

The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field.The main objective of this operation is to minimize the unit production cost.This paper proposes a Gaussian quantum-behaved bat algorithm(GQBA)to solve the problem of multi-pass turning operation.The proposed algorithm mainly includes the following two improvements.The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification.The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of the quantum-behaved bats,thus performing a more accurate search and avoiding premature convergence.The performance of the presented GQBA is demonstrated through numerical benchmark functions and amulti-pass turning operation problem.Thirteen classical benchmark functions are utilized in the comparison experiments,and the experimental results for accuracy and convergence speed demonstrate that,in most cases,the GQBA can provide a better search capability than other algorithms.Furthermore,GQBA is applied to an optimization problem formulti-pass turning,which is designed tominimize the production cost while considering many practical machining constraints in the machining process.The experimental results indicate that the GQBA outperforms other comparison algorithms in terms of cost reduction,which proves the effectiveness of the GQBA.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems

The artificial bee colony (ABC) algorithm is a swarm-based metaheuristic optimization technique, developed by inspiring foraging and dance behaviors of honey bee colonies. ABC consists of four phases named as initialization, employed bee, onlooker bee and scout bee. The employed bees try to improve their solution in employed bees phase. If an employed bee cannot improve self-solution in a certain time, it becomes a scout bee. This alteration is done in the scout bee phase. The onlooker bee phase is placed where information sharing is done. Although a candidate solution improved by onlookers is chosen among the employed bee population according to fitness values of the employed bees, neighbor of candidate solution is randomly selected. In this paper, we propose a selection mechanism for neighborhood of the candidate solutions in the onlooker bee phase. The proposed selection mechanism was based on information shared by the employed bees. Average fitness value obtained by the employed bees is calculated and those better than the aver- age fitness value are written to memory board. Therefore, the onlooker bees select a neighbor from the memory board. In this paper, the proposed ABC-based method called as iABC were applied to both five numerical benchmark functions and an estimation of energy demand problem. Obtained results for the problems show that iABC is better than the basic ABC in terms of solution quality.

Radial Basis Function Interpolation and Galerkin Projection for Direct Trajectory Optimization and Costate Estimation

This work presents a novel approach combining radial basis function(RBF)interpolation with Galerkin projection to efficiently solve general optimal control problems.The goal is to develop a highly flexible solution to optimal control problems,especially nonsmooth problems involving discontinuities,while accounting for trajectory accuracy and computational efficiency simultaneously.The proposed solution,called the RBF-Galerkin method,offers a highly flexible framework for direct transcription by using any interpolant functions from the broad class of global RBFs and any arbitrary discretization points that do not necessarily need to be on a mesh of points.The RBF-Galerkin costate mapping theorem is developed that describes an exact equivalency between the Karush-Kuhn-Tucker(KKT)conditions of the nonlinear programming problem resulted from the RBF-Galerkin method and the discretized form of the first-order necessary conditions of the optimal control problem,if a set of discrete conditions holds.The efficacy of the proposed method along with the accuracy of the RBF-Galerkin costate mapping theorem is confirmed against an analytical solution for a bang-bang optimal control problem.In addition,the proposed approach is compared against both local and global polynomial methods for a robot motion planning problem to verify its accuracy and computational efficiency.

On THE NUMERICAL INVERSION OF THE LAPLACE TRANSFORM BY THE USE OF AN OPTIMIZED LEGENDRE POLYNOMIALS

A method for inverting the Laplace transform is presented, using a finite series of the classical Legendre polynomials. The method recovers a real valued function f(t) in a finite interval of the positive real axis when f(t) belongs to a certain class βand requires the knowledge of its Laplace transform F(s) only at a finite number of discrete points on the real axis s>0. The choice of these points will be carefully considered so as to improve the approximation error as well as to minimize the number of steps needed in the evaluations. The method is tested on few examples, with particular emphasis on the estimation of the error bounds involved.

Weighted Sparse Image Classification Based on Low Rank Representation

The conventional sparse representation-based image classification usually codes the samples independently,which will ignore the correlation information existed in the data.Hence,if we can explore the correlation information hidden in the data,the classification result will be improved significantly.To this end,in this paper,a novel weighted supervised spare coding method is proposed to address the image classification problem.The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation.And then,it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in a supervised way.Experimental results show that the proposed method is superiority to many conventional image classification methods.

Infinity Norm Measurement of Sensitivity for Closed-Loop Systems Using Chirp as Excitation

A simple identification method based on a closed-loop experiment is proposed to measure the infinity norm of sensitivity function.A chirp signal,modified to have desired band-limited characteristic and finite duration,is used as the excitation in the experiment,and the sensitivity function is calculated using Fourier transform of input and error signals before the infinity norm is evaluated through maximization of the magnitude of sensitivity function.With an additional feature of providing values of gain margin and phase margin at a little extra effort,this method can be used in the identification step of a controller auto-tuning procedure,as having been supported by simulation results showing its capability of providing fast and accurate estimates for a large variety of processes.

Development of a mixed fruit beverage and pulsed light treatment thereof to obtain a microbially safe and enzymatically stable product

A mixed fruit beverage was formulated according to D-optimal mixture design using apple ber juice(X_(1),45-59%v/v)-an underexplored fruit,carambola juice(X_(2),1-20%v/v),and black table grape juice(X3,35-40%v/v).The crisp overall acceptability(COA)from 16 semi-trained panelists and overall hedonic score(OSH)from consumers were estimated.The best formulation was apple ber:carambola:black table grape juices=59.0:3.5:37.5 v/v with COA and OSH values of 3.23(on a scale of 5)and 7.55(on a scale of 9),respectively.Further,the efficacy of pulsed light(PL)(18-30 W cm^(-2) for 33-167 s;total fluence 600-5000 J cm^(-2))and thermal treatments(90℃ for 1-5 min)were studied on various attributes of the mixed beverage.The beverages processed at 90℃ for 5 min(thermal treatment)and 5000 J cm^(-2)(PL treatment at 30 W cm^(-2) for 167 s)showed no microbial count with a complete enzyme inactivation.The thermally pasteurized sample(90℃/5 min)was more brown(browning index=70.15)than the PL pasteurized beverage(browning index=65.98).Interestingly,the PL pasteurized(5000 J cm^(-2))beverage showed 25%,27%,and 19% more vitamin C,antioxidants,and phenolic compounds,respectively,than the thermally pasteurized(90℃/5 min)beverage.The PL-treated beverage had a greater sensory appeal than the thermally treated beverage.PL-treatment can be an efficient non-thermal pasteurization treatment for ensuring complete microbial and enzymatic inactivation with maximal retention of nutritional and sensory attributes of the blended beverage.

Quasi-Developable B-Spline Surface Design with Control Rulings

We propose a method for generating a ruled B-spline surface fitting to a sequence of pre-defined ruling lines and the generated surface is required to be as-developable-as-possible.Specifically,the terminal ruling lines are treated as hard constraints.Different from existing methods that compute a quasi-developable surface from two boundary curves and cannot achieve explicit ruling control,our method controls ruling lines in an intuitive way and serves as an effective tool for computing quasi-developable surfaces from freely-designed rulings.We treat this problem from the point of view of numerical optimization and solve for surfaces meeting the distance error tolerance allowed in applications.The performance and the efficacy of the proposed method are demonstrated by the experiments on a variety of models including an application of the method for path planning in 5-axis computer numerical control(CNC)flank milling.

Light-weighting in aerospace component and system design

Light-weighting involves the use of advanced materials and engineering methods to enable structural elements to deliver the same,or enhanced,technical performance while using less material.The concept has been extensively explored and utilised in many industries from automotive applications to fashion and packaging and offers significant potential in the aviation sector.Typical implementations of light-weighting have involved use of high performance materials such as composites and optimisation of structures using computational aided engineering approaches with production enabled by advanced manufacturing methods such as additive manufacture,foam metals and hot forming.This paper reviews the principal approaches used in light-weighting,along with the scope for application of light-weighting in aviation applications from power-plants to airframe components.A particular area identified as warranting attention and amenable to the use of lightweighting approaches is the design of solar powered aircraft wings.The high aspect ratio typically used for these can be associated with insufficient stiffness,giving rise to non-linear deformation,aileron reversal,flutter and rigid-elastic coupling.Additional applications considered include ultralight aviation components and sub-systems,UAVs,and rockets.Advanced optimisation approaches can be applied to optimise the layout of structural elements,as well as geometrical parameters in order to maximise structural stiffness,minimise mass and enable incorporation of energy storage features.The use of additive manufacturing technologies,some capable of producing composite or multi-material components is an enabler for light-weighting,as features formally associated with one principal function can be designed to fulfil multiple functionalities。