Modeling Discharge Behavior of Multicell Battery Jiucai Zhang; Song Ci; Sharif, Hamid ...
IEEE transactions on energy conversion,
2010-Dec., 2010-12-00, 20101201, Letnik:
25, Številka:
4
Journal Article
Recenzirano
Multicell battery has been widely used in various electrical and electronics devices. To achieve the optimal multicell battery design, accurately modeling battery discharge behavior of multicell ...battery is crucial. However, modeling discharge behavior of multicell battery is very challenging due to the nonlinear battery effects, nonuniform cell quality, and various cell connections. In this paper, we develop a circuit-based multicell battery model to accurately model the multicell battery discharge behavior in terms of available capacity, output voltage, and internal resistance with consideration of nonlinear battery effect and nonuniform cell quality. It also characterizes the cell discharge current distribution of cell strings in parallel connection. The proposed multicell battery model has been validated by extensive simulation and experimental results under various load conditions.
Enabling robots to provide effective assistance yet still accommodating the operator’s commands for telemanipulation of an object is very challenging because robot’s assistance is not always ...intuitive for human operators and human behaviors and preferences are sometimes ambiguous for the robot to interpret. Due to the difference in hand structures, some motion assistance from the robot may surprise the operator with counter-intuitive movements, which could introduce more burden to the human to correct the actions and/or reduce the operator’s sense of system control. To address these problems, we developed a novel preference-aware assistance knowledge learning approach. An assistance preference model learns what assistance is preferred by a human, and a stage-wise model updating method ensures the learning stability while dealing with the ambiguity of human preference data. Such a preference-aware assistance knowledge enables a teleoperated robot hand to provide more active yet preferred assistance toward manipulation success. We also developed knowledge transfer methods to transfer the preference knowledge across different robot hand structures to avoid extensive robot-specific training. Experiments to telemanipulate a 3-finger hand and 2-finger hand, respectively, to use, move, and hand over a cup have been conducted. Results demonstrated that the methods enabled the robots to effectively learn the preference knowledge and allowed knowledge transfer between robots with less training effort.
Battery performance prediction is crucial for battery-aware power management, battery maintenance, and multi-cell battery design. However, the existing battery models cannot capture the circuit ...characteristics and nonlinear battery effects, especially recovery effect. This paper aims to fill this gap by developing an enhanced circuit-based model for single-cell battery. The proposed model is validated by comparing simulation results with experimental data collected through battery testbed. The comparison shows that the proposed model can accurately characterize and predict the single-cell battery performance with considerations of various nonlinear battery effects under both constant and variable loads.
Many electronic systems such as robotics, battery-powered electric vehicles, and mobile computing devices are powered by multi-cell battery with limited energy capacity. Therefore, maximizing the ...battery performance such as operating time, available capacity, and lifetime is one of the major battery design challenges. Traditional approaches such as dynamic power management to maximize the battery discharge performance have treated multi-cell battery as a pure passive component with a fixed configuration. Thus, the multi-cell battery performance is determined by the weakest battery cell, leading to a low utilization of battery energy. In this paper, we propose a novel multi-cell battery design to dynamically reconfigure the cell topology of a multi-cell battery, which also interacts with the power management module of a battery-powered system to maximize the battery discharge performance. Then, the dynamic reconfiguration problem of the multi-cell battery is formulated as a Lagrangian Relaxation problem and solved by dynamic programming. Both simulation and experimental results show that the proposed design can significantly enhance the multi-cell battery operating time and useable capacity. Moreover, the proposed design can automatically exclude the failure or malfunction cells through reconfiguration, which can greatly improve the multi-cell battery safety.
Dexterous manipulation tasks usually have multiple objectives. The priorities of these objectives may vary at different phases of a manipulation task. Current methods do not consider the objective ...priority and its change during the task, making a robot have a hard time or even fail to learn a good policy. In this work, we develop a novel Adaptive Hierarchical Curriculum to guide the robot to learn manipulation tasks with multiple prioritized objectives. Our method determines the objective priorities during the learning process and updates the learning sequence of the objectives to adapt to the changing priorities at different phases. A smooth transition function is developed to mitigate the effects on the learning stability when updating the learning sequence. The proposed method is validated in a multi-objective manipulation task with a JACO robot arm in which the robot needs to manipulate a target with obstacles surrounded. The simulation and physical experiment results show that the proposed method outperforms the baseline methods with a 92.5% success rate in 40 tests and on average takes 36.4% less time to finish the task.
The battery technology literature is reviewed, with an emphasis on key elements that limit extreme fast charging. Key gaps in existing elements of the technology are presented as well as ...developmental needs. Among these needs are advanced models and methods to detect and prevent lithium plating; new positive-electrode materials which are less prone to stress-induced failure; better electrode designs to accommodate very rapid diffusion in and out of the electrode; measure temperature distributions during fast charge to enable/validate models; and develop thermal management and pack designs to accommodate the higher operating voltage.
•Key gaps in lithium-based battery technology are presented viz. extremely fast charging.•At cell level, lithium plating on anode remains an issue.•At cell level, stress-induced cracking of cathode material may be an issue.•Safety at pack level must be explored.
Battery thermal barriers are reviewed with regards to extreme fast charging. Present-day thermal management systems for battery electric vehicles are inadequate in limiting the maximum temperature ...rise of the battery during extreme fast charging. If the battery thermal management system is not designed correctly, the temperature of the cells could reach abuse temperatures and potentially send the cells into thermal runaway. Furthermore, the cell and battery interconnect design needs to be improved to meet the lifetime expectations of the consumer. Each of these aspects is explored and addressed as well as outlining where the heat is generated in a cell, the efficiencies of power and energy cells, and what type of battery thermal management solutions are available in today's market. Thermal management is not a limiting condition with regard to extreme fast charging, but many factors need to be addressed especially for future high specific energy density cells to meet U.S. Department of Energy cost and volume goals.
•Aggressive thermal management will be required during extreme fast charging.•Present high energy density cells will need to increase their charge efficiency.•Cell design will have an impact on the temperature variation within the cell.•Battery interconnects will need to be robust and may require a redesign.
Managed charging of electric vehicle (EV) loads has the potential to use renewable energy more effectively, shave peak demand, and fill demand valleys while serving transportation needs. However, the ...potential value to the grid from managed charging has not been fully quantified. This paper adopts the tools used in the National Renewable Energy Laboratory's California Low Carbon Grid Study to quantify value to the grid from managed charging by using three levels of managed loads for 13 TW·h of annual load from three million EVs in a 2030 California grid scenario. Simulation results show that management of the EV fleet's aggregate load from unmanaged to 100% managed results in savings between 210 million and 660 million annually in generation system costs, depending on grid conditions. The simulation results also suggest that targeted EV supply equipment (EVSE) deployments at workplaces and other mid-day parking locations will be needed to support managed charging in a high-renewables California and enable the identified value to the grid. Although the value of generation to the grid from managed EV load paired with high renewables seems substantial, we estimate that the installed cost of an EVSE must be between 1000 and 3000 for a ten-year life to be cost neutral, depending on grid conditions.
The ability to charge battery electric vehicles (BEVs) on a time scale that is on par with the time to fuel an internal combustion engine vehicle (ICEV) would remove a significant barrier to the ...adoption of BEVs. However, for viability, fast charging at this time scale needs to also occur at a price that is acceptable to consumers. Therefore, the cost drivers for both BEV owners and charging station providers are analyzed. In addition, key infrastructure considerations are examined, including grid stability and delivery of power, the design of fast charging stations and the design and use of electric vehicle service equipment. Each of these aspects have technical barriers that need to be addressed, and are directly linked to economic impacts to use and implementation. This discussion focuses on both the economic and infrastructure issues which exist and need to be addressed for the effective implementation of fast charging at 400 kW and above. In so doing, it has been found that there is a distinct need to effectively manage the intermittent, high power demand of fast charging, strategically plan infrastructure corridors, and to further understand the cost of operation of charging infrastructure and BEVs.
•Management of intermittent, high power demand is crucial.•Planning is needed for XFC including siting future corridors.•Planning needs to include cost of charging equipment, operation and installation.•Increased coordination needs to occur between governing authorities.•Safety, cyber physical security, interoperability and compatibility will impact use.
To achieve a successful increase in the plug-in battery electric vehicle (BEV) market, it is anticipated that a significant improvement in battery performance is required to increase the range that ...BEVs can travel and the rate at which they can be recharged. While the range that BEVs can travel on a single recharge is improving, the recharge rate is still much slower than the refueling rate of conventional internal combustion engine vehicles. To achieve comparable recharge times, we explore the vehicle considerations of charge rates of at least 400 kW. Faster recharge is expected to significantly mitigate the perceived deficiencies for long-distance transportation, to provide alternative charging in densely populated areas where overnight charging at home may not be possible, and to reduce range anxiety for travel within a city when unplanned charging may be required. This substantial increase in charging rate is expected to create technical issues in the design of the battery system and the vehicle's electrical architecture that must be resolved. This work focuses on vehicle system design and total recharge time to meet the goals of implementing improved charge rates and the impacts of these expected increases on system voltage and vehicle components.
•BEV refueling time requires 4–6 C-rate charging and large battery capacities.•Peak charge rate less important than average rate for 150–200 mile range recharge.•XFC significantly impacts BEV voltage design, which may impact other EVs.•BEV-charging infrastructure coordination must provide consistent charge experience.
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