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The selection of component parameters plays an important role in the energy optimization of dual-motor drive electric buses (DMDEBs). To design a set of parameters that can be robust to different road slopes, passenger masses, and bus routes, a Taguchi-based robust design optimization method is proposed in this paper for DMDEBs. First, the dynamic programming (DP) is designed as a part of the Taguchi method to explore the optimal solution (electricity consumption) corresponding to each set of component parameters. Then, by taking the optimal electricity consumption obtained by DP as the response, the Taguchi method is utilized to find the robust component parameters that are insensitive to the noise factors. To evaluate the robustness and reliability of the designed component parameters, a six sigma analysis based on Monte Carlo sampling is employed considering three kinds of stochastic disturbances. Finally, simulation results demonstrate that the proposed robust design optimization method is effective and the designed component parameters have better robustness than the initial component parameters. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is widely used in a variety of applications including habitat monitoring, military surveillance, environmental monitoring, scientific applications, etc. The major limitation of WSN is that sometimes it is not feasible to replace or recharge the battery once it gets fully exhausted and thus, it limits the lifetime of WSN. One of the possible solutions to overcome this limitation is to incorporate any energy harvesting device, which can use the alternative energy sources to charge the battery. However, the processing temperature and the performance of energy harvesting devices limit their applications. In this paper, low temperature and high performance single-sided silicon heterojunction (SHJ) solar cells are fabricated with 13% efficiency using hot-wire chemical vapor deposition (HWCVD) method. This paper also describes an energy management model that successfully addresses the various issues in the existing energy harvesting models. In order to implement the proposed model, the results show that the high efficiency SHJ solar cells are best suitable candidate as an energy harvesting device that can be incorporated inside the node. The subsequent analysis shows that the consumed power per day by the node can be successfully recovered from the SHJ solar cells, if the sunlight is available only for 25 min in a day with 100 mW/cm2 intensity. This clearly indicates that the node's battery will remain fully charged if the above said condition is satisfied, which seems to be very feasible. Finally, one can conclude that the node functioning will remain active till the battery lifetime i.e., approximately 30 years for Li-ion battery. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management strategy for a hybrid electric vehicle (HEV) with a turbocharged diesel engine. By introducing turbocharger to the HEV powertrain, air path dynamics of engine becomes extremely complex and critical to engine torque response during transient processes. Traditional strategy that adopts steady-state-map based engine model may not work properly in this situation as a result of its incapability of accurately capturing torque response. Thus, in this paper, a physical-law based air path model is utilized to simulate turbo "lag" phenomenon and predict air charge in cylinder. Meanwhile, engine torque boundaries are obtained on the basis of predicted air charge. A receding horizon structure is then implemented in optimal supervisory controller to generate torque split strategy for the HEV. Simulations are conducted for three cases: the first one is rule-based torque-split energy management strategy without optimization; the second one is online optimal control strategy using map-based engine model; and the third one is online optimal control strategy combining air path loop model. The comparison of the results shows that the proposed third method has the best fuel economy of all and demonstrates considerable improvements of fuel saving on the other two methods. [ABSTRACT FROM AUTHOR]

This paper examines the problem of controlling the exchange of current in photovoltaic-plus-storage systems to achieve photovoltaic (PV) maximum power point tracking (MPPT). This work is motivated by the need for MPPT algorithms that are less costly and complex to implement in PV farms with integrated battery energy storage. We study the online optimal control of a "hybrid" PV/lithium (Li)-ion battery integration topology that is self-balancing in nature. The self-balancing behavior ensures that the state of charge (SOC) across different cells balances to the same stable equilibrium value without needing any balancing power electronics, thereby significantly reducing the integration cost. The DC-DC converters in this hybrid system are controlled to achieve PV MPPT that maximizes energy generation and storage. However, sensing needs for traditional MPPT controllers can render the hybrid system unnecessarily complex and costly. We surmount this problem by: (i) developing a novel model-based PV power estimation algorithm that only requires voltage measurement, and (ii) using this algorithm together with extremum-seeking (ES) control to achieve closed-loop, estimation-based PV MPPT. Simulation case studies show that this estimation-based MPPT controller is able to harness more than 99% of the maximum available solar energy under different irradiation profiles. [ABSTRACT FROM AUTHOR]

The need for less fuel consumption and the trend of higher level of autonomy together urge the power optimization in multihybrid autonomous vehicles. Both the multivehicle coordination control and the hybrid powertrain energy management should be optimized to maximize fuel savings. In this paper, we intend to have a computationally efficient framework to optimize them individually and then evaluate the overall control performance. The optimization is conducted in series. First is at the multivehicle system's level where the distributed locally optimal solution is given for vehicles with nonlinear dynamics. Second, the powertrain management optimization is conducted at the hybrid powertrain level. We provide an analytical formulation of the powertrain optimization for each hybrid vehicle by using Pontryagin's minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations, which enables the computationally efficient real-time implementation. To justify the applicability of the methodology in real-time, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach. An off-road example is given in the simulation, although the framework developed can be applied to on-road scenario as well. [ABSTRACT FROM AUTHOR]

This paper presents a study of the energy-efficient operation of all-electric vehicles leveraging route information, such as road grade, to adjust the velocity trajectory. First, Pontryagin's maximum principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only five modes are required to achieve minimum energy consumption: full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. Then, the minimum energy consumption problem is reformulated and solved in the distance domain using dynamic programming to find the optimal speed profiles. Various simulation results are shown for a lightweight autonomous military vehicle. The sensitivity of energy consumption to regenerative-braking power limits and trip time is investigated. These studies provide important information that can be used in designing component size and scheduling operation to achieve the desired vehicle range. [ABSTRACT FROM AUTHOR]

The increasing significance on the development of high-performance lithium-ion (Li-ion) batteries is calling for new battery materials, theoretical models, and simulation tools. Lithiation-induced deformation in electrodes calls attention to study the multiphysics coupling between mechanics and electrochemistry. In this paper, a simultaneous multiscale and multiphysics model to study the coupled electrochemistry and mechanics in the continuum battery cell level and the microscale particle level was developed and implemented in comsolmultiphysics. In the continuum scale, the porous electrode theory and the classical mechanics model were applied. In the microscale, the specific particle structure has been incorporated into the model. This model was demonstrated to study the effects of mechanical constraints, charging rate, and silicon/C ratio, on the electrochemical performance. This model provides a powerful tool to perform simultaneous multiscale and multiphysics design on Li-ion batteries, from the particle level to full-cell level. [ABSTRACT FROM AUTHOR]

The control of shared energy assets within building clusters has traditionally been confined to a discrete action space, owing in part to a computationally intractable decision space. In this work, we leverage the current state of the art in reinforcement learning (RL) for continuous control tasks, the deep deterministic policy gradient (DDPG) algorithm, toward addressing this limitation. The goals of this paper are twofold: (i) to design an efficient charged/discharged dispatch policy for a shared battery system within a building cluster and (ii) to address the continuous domain task of determining how much energy should be charged/discharged at each decision cycle. Experimentally, our results demonstrate an ability to exploit factors such as energy arbitrage, along with the continuous action space toward demand peak minimization. This approach is shown to be computationally tractable, achieving efficient results after only 5 h of simulation. Additionally, the agent showed an ability to adapt to different building clusters, designing unique control strategies to address the energy demands of the clusters studied. [ABSTRACT FROM AUTHOR]

This paper presents a novel design for a test platform to determine the state of health (SOH) of lithium-ion batteries (LIBs). The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18,650 Li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry are stored and displayed in LabVIEW to obtain the charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify that the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb counting method in LabVIEW. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly identified. [ABSTRACT FROM AUTHOR]

Battery thermal management system (BTMS) is a complex and highly integrated system, which is used to control the battery thermal conditions in electric vehicles (EVs). The BTMS consists of many subsystems that belong to different disciplines, which poses challenges to BTMS optimization using conventional methods. This paper develops a general variable fidelity-based multidisciplinary design optimization (MDO) architecture and optimizes the BTMS by considering different systems/disciplines from the systemic perspective. Four subsystems and/or subdisciplines are modeled, including the battery thermodynamics, fluid dynamics, structure, and lifetime model. To perform the variable fidelity-based MDO of the BTMS, two computational fluid dynamics (CFD) models with different levels of fidelity are developed. A low fidelity surrogate model and a tuned low fidelity model are also developed using an automatic surrogate model selection method, the concurrent surrogate model selection (COSMOS). An adaptive model switching (AMS) method is utilized to realize the adaptive switch between variable-fidelity models. The objectives are to maximize the battery lifetime and to minimize the battery volume, the fan's power, and the temperature difference among different cells. The results show that the variable-fidelity MDO can balance the characteristics of the low fidelity mathematical models and the computationally expensive simulations, and find the optimal solutions efficiently and accurately. [ABSTRACT FROM AUTHOR]

Traditionally viewed as mere energy consumers, buildings have adapted, capitalizing on smart grid technologies and distributed energy resources to efficiently use and trade energy, as evident in net-zero energy buildings (NZEBs). In this paper, we examine the opportunities presented by applying net-zero to building communities (clusters). This paper makes two main contributions: one, it presents a framework for generating Pareto optimal operational strategies for building clusters; two, it examines the energy tradeoffs resulting from adaptive decisions in response to dynamic operation conditions. Using a building cluster simulator, the proposed approach is shown to adaptively and significantly reduce total energy cost. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) hybrid systems and their optimal energy management are still being actively studied. PV systems must be positioned at optimal tilt and azimuth angles to obtain maximum system performance. This paper presents the effect of energy management of a grid-connected PV-battery-load system on optimal PV placement using a linear programing (LP) method. The optimal placement of PV arrays aims to minimize the electricity cost of a fixed PV system for investors by considering monthly average daily global radiation, ambient temperature, wind speed, demand management constraints, and electricity tariffs in Turkey. The analysis is extended to consider 68 locations across Turkey. The analysis reveals that the optimal placement (optimal tilt and azimuth angle) can be different from that of previous studies because of multiple objective considerations. The optimal tilt angle results are lower than the local latitude in the range of 0 deg-8 deg in all regions. Furthermore, the optimal azimuth angle results are in the range of 6 deg-24 deg westbound throughout Turkey. These results can play a significant role for investments in the design of grid-connected PV-battery-load systems. [ABSTRACT FROM AUTHOR]

This paper investigates the supervisory-level, fault tolerant control of a 2004 Prius powertrain. The fault considered is an interturn short circuit (ITSC) fault in the traction drive (a surface mount permanent magnet synchronous machine (SPMSM) for which its rotor is part of the vehicle's driveline). ITSC faults arise from electrical insulation failures in the stator windings where part of a phase winding remains functional while the remaining decoupled windings form a self-contained loop. Because the permanent magnets on the rotor (driveline) shaft are able to induce very large eddy currents in this self-contained loop if its rotational velocity is left unchecked, the maximum allowable driveline speed, and consequently, vehicle speed, must be reduced to avoid exceeding the drive's operational thermal limits. A method for detecting these ITSC faults and the induced eddy current in an SPMSM using a moving horizon observer (MHO) is reviewed. These parameters then determine which previously computed, fault-level-dependent SPMSM input-output power efficiency map and maximum safe operating speed is utilized by the supervisory-level controller. The fault tolerant control is demonstrated by simulating a Prius over a 40 s drive velocity profile with fault levels of 0.5%, 1%, 2%, and 5% detected at the midpoint of the profile. For comparison, the Prius is also simulated without a traction motor fault. Results show that the control reduces vehicle velocity upon detection of a fault to an appropriate safe value. [ABSTRACT FROM AUTHOR]

This paper presents experimental investigation results of an electric variable valve timing (EVVT) actuator using linear parameter varying (LPV) system identification and control. For the LPV system identification, a number of local system identification tests were carried out to obtain a family of linear time-invariant (LTI) models at fixed engine speed and battery voltage. Using engine speed and battery voltage as time-varying scheduling parameters, the family of local LTI models is translated into a single LPV model. Then, a robust gain-scheduling (RGS) dynamic output-feedback (DOF) controller with guaranteed H∞ performance was synthesized and validated experimentally. In contrast to the vast majority of gain-scheduling literature, scheduling parameters are assumed to be polluted by measurement noises and the engine speed and battery voltage are modeled as noisy scheduling parameters. Experimental and simulation results show the effectiveness of the developed approach. [ABSTRACT FROM AUTHOR]

Discussion of big data (BD) has been about data, software, and methods with an emphasis on retail and personalization of services and products. Big data also has impacted engineering and manufacturing and has resulted in better and more efficient manufacturing operations, improved quality, and more personalized products. A less apparent effect is that big data have changed problem solving: the problems we choose to solve, the strategy we seek, and the tools we employ. This paper illustrates this point by showing how the big data style of thinking enabled the development of a new quality assurance philosophy called process monitoring for quality (PMQ). PMQ is a blend of process monitoring and quality control (QC) that is founded on big data and big model (BDBM), which are catalysts for the next step in the evolution of the quality movement. Process monitoring (PM) for quality was used to evaluate the performance of the ultrasonically welded battery tabs in the new Chevrolet Volt, an extended range electric vehicle. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) have been integrated with wind turbines to mitigate wind intermittence and make wind power dispatchable as traditional power sources. This paper presents two phases of optimizations, namely, power scheduling and real-time control that allows an integrated wind turbine and BESS to provide the grid with consistent power within each dispatch interval. In the power scheduling phase, the desired battery state of charge (SOC) under each wind speed is first determined by conducting an offline probabilistic analysis on historical wind data. With this information, a computationally efficient one-step-ahead model predictive approach is developed for scheduling the integrated system power output for the next dispatch interval. In the real-time control phase, novel control algorithms are developed to make the actual system power output match the scheduled target. A wind turbine active power controller is proposed to track the reference power set point obtained by a steady-state optimization approach. By combining an internal integral torque control and a gain-scheduled pitch control, the proposed active power controller can operate around a desired tip speed ratio (TSR) without an accurate knowledge of turbine power coefficient curve. Compared to the conventional power scheduling and real-time controller, implementing the new methodology significantly reduces the ramp rate, generator torque changing rate, battery charging rate, and the power output deviation from the scheduled target. BESSs with various capacities and different wind profiles are considered to demonstrate the effectiveness of the proposed algorithms on battery sizing. [ABSTRACT FROM AUTHOR]

This paper introduces a new method to monitor battery state of health (SOH). In particular, the side reaction current density is estimated as a direct SOH indicator for the first time and its estimation is formulated as an inaccessible subsystem identification problem, where the battery health subsystem is treated as an inaccessible subsystem with the side reaction current density as the output. Inaccessibility in this context refers to the fact that the inputs and outputs of the subsystem are not measurable in situ. This subsystem is identified using retrospective-cost subsystem identification (RCSI) algorithm, and the output of the identified battery health subsystem provides an estimate for the side reaction current density. Using an example parameter set for a LiFePO4 battery, simulations are performed to obtain estimates under various current profiles. These simulations show promising results in identifying the battery health subsystem and estimating the side reaction current density with RCSI under ideal conditions. Robustness of the algorithm under nonideal conditions is analyzed. Estimation of the side reaction current density using RCSI is shown to be sensitive to nonideal conditions that cause errors in the measurement or estimation of the battery voltage. A method for quantitatively assessing the impact of nonideal conditions on the side reaction current estimation accuracy is provided. The proposed estimation technique, including the method for estimating the side reaction current density using RCSI and the framework analyzing its robustness, can also be applied to other parameter sets and other battery chemistries to monitor the SOH change resulting from any electrochemical-based degradation mechanism that consumes cyclable Li-ions. [ABSTRACT FROM AUTHOR]

Developing and parameterizing models that accurately predict the battery voltage and temperature in a vehicle battery pack are challenging due to the complex geometries of the airflow that influence the convective heat transfer. This paper addresses the difficulty in parameterizing low-order models which rely on coupling with finite element simulations. First, we propose a methodology to couple the parameterization of an equivalent circuit model (ECM) for both the electrical and thermal battery behavior with a finite element model (FEM) for the parameterization of the convective cooling of the airflow. In air-cooled battery packs with complex geometries and cooling channels, an FEM can provide the physics basis for the parameterization of the ECM that might have different convective coefficients between the cells depending on the airflow patterns. The second major contribution of this work includes validation of the ECM against the data collected from a three-cell fixture that emulates a segment of the pack with relevant cooling conditions for a hybrid vehicle. The validation is performed using an array of thin film temperature sensors covering the surface of the cell. Experiments with pulsing currents and drive cycles are used for validation over a wide range of operating conditions (ambient temperature, state of charge, current amplitude, and pulse width). [ABSTRACT FROM AUTHOR]

Car sharing services promise "green" transportation systems. Two vehicle technologies offer marketable, sustainable sharing: autonomous vehicles (AVs) eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero emissions. Designing an autonomous electric vehicle (AEV) fleet must account for the relationships among fleet operations, charging station (CS) operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four subsystem problems: fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. We also compare an AEV service and autonomous vehicle (AV) service with gasoline engines. A case study for an autonomous fleet operating in Ann Arbor, MI, is used to examine AEV and AV sharing systems profitability and feasibility for a variety of market scenarios. The results provide practical insights for service system decision makers. [ABSTRACT FROM AUTHOR]