Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

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Closed-loop optimization of fast-charging protocols for batteries with machine learning.

In recent years solar energy has received worldwide attention in the field of renewable energy systems Among the various research thrusts in solar PV, the most proverbial area is extracting maximum power from solar PV system Application dof Maximum Power Point Tracking (MPPT) for extracting maximum power is very much appreciated and holds the key in developing efficient solar PV system In this paper, a state of the art review on various maximum power point techniques for solar PV systems covering timeworn conventional methods and latest soft computing algorithms is presented To date critical analysis on each of the method in terms of (1) tracking speed, (2) algorithm complexity, (3) Dynamic tracking under partial shading and (4) hardware implementation is not been carried out In this regard the authors have attempted to compile a comprehensive review on various solar PV MPPT techniques based on the above criteria Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for practicing engineers as well as for new researchers

A comprehensive review on solar PV maximum power point tracking techniques

Solar photovoltaic (PV) systems under partial shading conditions (PSCs) have a nonmonotonic P – V characteristic with multiple local maximum power points, which makes the existing maximum power point tracking (MPPT) algorithms unsatisfactory performance for global MPPT, if not invalid. This paper proposes a novel overall distribution (OD) MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithm to improve the accuracy of MPPT. Through simulations and experimentations, the higher effectiveness and accuracy of the proposed OD-PSO MPPT algorithm in solar PV systems is demonstrated in comparison to two existing artificial intelligence MPPT algorithms.

An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading

Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

/pdf/a-brief-review-on-key-technologies-in-the-battery-management-epnx1tchkf.pdf

A brief review on key technologies in the battery management system of electric vehicles

Unfilled gap of prolonged energy demand by conventional energy sources and consent of global warming as its vulnerable outcome provides a vent to search compatible option. Augmentation in use of solar energy reveled through last 3 decades portrays its heterogeneous rewards in the prevailing energy scenario. Nevertheless solar PV system arises as viable option in the critical power system era its low efficiency energy conversion attribute necessitates an efficient power conversion system. The nonlinearity of I–V (current–voltage) characteristic and its alteration for an assorted insolation and temperature values may enable the alteration in terminal voltage. This may deviates maximum power point due to which the available maximum power delivery to load can be differed. Literature of this field reiterated that the uniform insolation and partial shading condition demands undeniable need of maximum power point tracking. Nonetheless through investigation in this direction furnishes the availability of a bunch of such techniques; each of them posses its own pros and cones. This ubiquitous trait of available maximum power point tracking (MPPT) techniques unfolds the complexity in its precise selection. To diminish such complexity this paper offers a state of art of various MPPT technique and their comprehensive comparative analysis based on 110 standard research articles. The focus of this paper is to offer a better commencement and to furnish valued information for investigators of this field.

Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems

A photovoltaic (PV) generation system (PGS) is becoming increasingly important as renewable energy sources due to its advantages such as absence of fuel cost, low maintenance requirement, and environmental friendliness. For large PGS, the probability for partially shaded condition (PSC) to occur is also high. Under PSC, the P-V curve of PGS exhibits multiple peaks, which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods. In this paper, a particle swarm optimization (PSO)-based MPPT algorithm for PGS operating under PSC is proposed. The standard version of PSO is modified to meet the practical consideration of PGS operating under PSC. The problem formulation, design procedure, and parameter setting method which takes the hardware limitation into account are described and explained in detail. The proposed method boasts the advantages such as easy to implement, system-independent, and high tracking efficiency. To validate the correctness of the proposed method, simulation, and experimental results of a 500-W PGS will also be provided to demonstrate the effectiveness of the proposed technique.

A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions

Lithium-ion (Li-ion) batteries have emerged as the major power source for today's electronic products because they can offer advantages such as high energy density, low maintenance requirement, high open-circuit voltage, low self-discharge rate, and lack of memory effect. The performance and lifespan of Li-ion batteries are closely related to the quality of the charging pattern. Therefore, an optimal charging pattern is essential for Li-ion batteries to achieve shorter charging time and longer cycle life. In this paper, a Taguchi-based algorithm is presented. Consecutive orthogonal array techniques are utilized to determine the optimal charging pattern for the five-step constant current charging method. The problem formulation, implementation procedure, and parameter setting method are described and explained in detail. Experimental results show that the obtained charging pattern is capable of charging the Li-ion batteries to 95% capacity. Comparing to the conventional constant current-constant voltage method, the benefits of the obtained pattern are faster and safer charging and longer battery cycle life.

Search for an Optimal Five-Step Charging Pattern for Li-Ion Batteries Using Consecutive Orthogonal Arrays

Lithium-ion (Li-ion) batteries have emerged as the major power source for today's electronic products because they can offer advantages such as high energy density, low maintenance requirement, high open-circuit voltage, low self-discharge rate, and lack of memory effect. The performance and lifespan of Li-ion bat- teries are closely related to the quality of the charging pattern. Therefore, an optimal charging pattern is essential for Li-ion bat- teries to achieve shorter charging time and longer cycle life. In this paper, a Taguchi-based algorithm is presented. Consecutive orthogonal array techniques are utilized to determine the opti- mal charging pattern for the five-step constant current charging method. The problem formulation, implementation procedure, and parameter setting method are described and explained in detail. Experimental results show that the obtained charging pattern is capable of charging the Li-ion batteries to 95% capacity. Compar- ing to the conventional constant current-constant voltage method, the benefits of the obtained pattern are faster and safer charging and longer battery cycle life.

Yi-Hwa Liu

Papers

A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions

Search for an Optimal Five-Step Charging Pattern for Li-Ion Batteries Using Consecutive Orthogonal Arrays

Search for an Optimal Five-Step Charging Pattern for Li-Ion Batteries Using Consecutive