A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+

This roadmap presents the transformational research ideas proposed by “BATTERY 2030+,” the European large-scale research initiative for future battery chemistries. A “chemistry-neutral” roadmap to advance battery research, particularly at low technology readiness levels, is outlined, with a time horizon of more than ten years. The roadmap is centered around six themes: 1) accelerated materials discovery platform, 2) battery interface genome, with the integration of smart functionalities such as 3) sensing and 4) self-healing processes. Beyond chemistry related aspects also include crosscutting research regarding 5) manufacturability and 6) recyclability. This roadmap should be seen as an enabling complement to the global battery roadmaps which focus on expected ultrahigh battery performance, especially for the future of transport. Batteries are used in many applications and are considered to be one technology necessary to reach the climate goals. Currently the market is dominated by lithium-ion batteries, which perform well, but despite new generations coming in the near future, they will soon approach their performance limits. Without major breakthroughs, battery performance and production requirements will not be sufficient to enable the building of a climate-neutral society. Through this “chemistry neutral” approach a generic toolbox transforming the way batteries are developed, designed and manufactured, will be created

Universal Programmable Battery Charger with Optional Battery Management System

This report demonstrates improvements made in battery charging and battery management technology through the design of a universal programmable battery charger with optional battery management system attachment. This charger offers improvements in charge efficiency and unique battery charging algorithms to charge a variety of battery chemistries with variety of power requirements. Improvements in efficiency result from a synchronous Buck Controller topology as compared to previous universal chargers that use asynchronous Buck-Boost Converter topologies. This battery charger also surpasses current universal battery chargers by offering different charge modes for different battery chemistries. Charge modes provide the user an option between extending the life of the battery by selecting a mode with a slower, less stressful charge rate or a shorter charge time with a fast, more stressful charging mode. The user can also choose a charge mode in which the battery charges to full capacity, resulting in maximum runtime or a less than full capacity, which puts less stress on the battery thus extending the lifetime. Ultimately, this system permits weighing the performance tradeoff of battery lifetime and charge time. The optional battery management system attachment offers more precise monitoring of each cell and cell balancing for Li-Ion batteries. This further enhances the performance of the charger when integrated, but is not necessary for charger operation. The battery charger consists of three subcircuits: A microcontroller unit, a power stage, and a current sensing circuit. A C2000 Piccolo F28069 microcontroller controls a LM5117 Buck Controller by injecting a pulse-width modulated signal into the feedback node controlling the output of the buck to set a constant current or constant voltage thus creating a programmable battery charger. The pulse-width modulated signal changes according to charge algorithms created in software for specific battery chemistries and charge requirements. An analog-to-digital converter on the microcontroller monitors battery voltage by using a voltage divider and an INA169 current shunt monitor, which outputs a voltage corresponding to the charge current to another analog-to-digital converter on the microcontroller, monitors the charge current. This allows the charger program to maintain correct and safe charging conditions for each charge mode in addition to measuring output power. Lights on the microcontroller display a real-time status to the user of which portion of the charge profile the charger is in. A solid red light means the charger is in the constant current portion of the charge profile. A blinking red light means the charger is in the constant voltage portion. No red light means the battery charger finished and the battery is currently charged above nominal voltage. The battery charger works with the battery management system in the next section to provide ultimate battery charging and managing capabilities. The battery management system consists of two subcircuits: A microcontroller and a battery monitoring circuit. The MSP430FR5969 microcontroller unit communicates with BQ76PL536 battery management integrated circuits to create a battery management system that monitors data such as cell voltage, pack voltage, pack temperature, state of charge, fault statuses, alert statuses, and a variety of other useful cell parameters. This data displays on a liquid crystal display screen through different menu options. The user scrolls through the menus using a capacitive touch slider on the microcontroller unit and selects a given option using the option select button. A cell balance mode allows the user to check the balance of the cells and allows cell balancing if the cells differ by more than a set threshold

Taking stock of large-scale lithium-ion battery production using life cycle assessment

Battery electric vehicles are being increasingly favored as an alternative to internal combustion engine vehicles (ICEVs). This is mainly due to their lower environmental impact when compared to ICEVs over the vehicle’s lifetime. Life cycle assessment (LCA) studies focusing specifically on battery electric vehicles (BEVs) have identified battery cell production as an environmental hotspot in the BEV’s life cycle. However, lack of primary or industrial data, different technical scopes, and varying data quality, limit a thorough understanding of the environmental impacts of cell production. Further, with scaling-up of battery production (to meet the rising demand for BEVs), the source and level of impacts are expected to change. In response, the main aim of this thesis is to explore and understand the implications of upscaling in battery production. An example of such a change is provided at the mining sites where raw materials for lithium used in batteries are extracted and produced. As mining continues, over time, the ore grades at these sites decline. Thus, this thesis also aims to investigate the effect of declining ore grades on the overall impacts from cell production. A sub-goal is to understand the relevance of background data in LCA studies and its effect on overall results.The technical scope of this thesis is the production of a graphite-NMC:811 21700 type cylindrical cell. To assess the environmental impacts of upscaling, production in a small-scale facility is compared to production in a large-scale facility. Next, the impact of declining ore grades on overall cell production is estimated by analyzing the data from multiple mining sites for lithium, with varying ore grades and different types of sources – spodumene and brine. To assess the effect of background database on overall results, the LCA model for cell production was coupled with different versions of the Ecoinvent background database. Lastly, a physics-based model platform, developed in cross-disciplinary collaboration, is proposed with the objective of filling data gaps in LCA of lithium-ion batteries (LIBs). The model platform will help link the cell design aspects such as power or energy optimization to changes in the individual cell production processes. Further, the model platform will help expand the technical scope to broadened set of cell geometries and chemistries, and increase the precision in use phase modeling as well.The results show that the upscaling leads to a reduction in environmental impacts from cell production. This is due to higher energy and material efficiency of cell production at large scale. Further, when low-carbon intensive sources are used, then the impacts from cell production shift almost entirely to the raw material extraction and production phase. In the context of declining ore grades, the type of source and grade of lithium account for 5-15% of the global warming impacts from cell production. This implies that future environmental impacts from LIB production could increase, due to increased chemical and energy inputs, in response to declining ore grades at mining sites. The changes in the background data have a significant bearing on the overall results. These are due to evolving technical systems and an improved representation of these systems in terms of data quality and geo-spatial representativeness. Lastly, preliminary results from the physics-based model platform show that accounting for variations in cell design can further add variability in results

A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation

The final publication is available at Elsevier via http://doi.org/10.1016/j.jpowsour.2014.01.057 © 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, we survey two kinds of mathematics-based battery models intended for use in hybrid and electric vehicle simulation. The first is circuit-based, which is founded upon the electrical behaviour of the battery, and abstracts away the electrochemistry into equivalent electrical components. The second is chemistry-based, which is founded upon the electrochemical equations of the battery chemistry.Natural Sciences and Engineering Research Council (NSERC) of Canada, Toyota, and MapleSoft

Lithium-ion battery data and where to find it

Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core of transformational developments in battery design, modelling and management is data. In this work, the datasets associated with lithium batteries in the public domain are summarised. We review the data by mode of experimental testing, giving particular attention to test variables and data provided. Alongside highlighted tools and platforms, over 30 datasets are reviewed

Meetod elektrisõiduki aku laetuse taseme täpsemaks hindamiseks

The electric vehicle (EV) is a complex, safety-critical system, which must ensure the safety of the operator and the reliability and longevity of the device. The battery management system (BMS) of an EV is an embedded system, whose main responsibility is the protection and safety of the high-voltage battery pack. The BMS must ensure that the requirements for health, status and deliverable power are met by maintaining the battery pack within the defined operational conditions for the defined lifetime of the battery. The state of charge (SOC) of a cell describes the ratio of its current capacity (amount of charge stored) to the nominal capacity as defined by the manufacturer. SOC estimation is a crucial, but not trivial BMS task as it can not be measured directly, but must be estimated from known and measured parameters, such as the cell terminal voltage, current, temperature, and the static and dynamic behavior of the cell in different conditions. Many different SOC estimation methods exist, out of which (currently) the most practical methods for implementing on a real-time embedded system are adaptive methods, which adapt to different internal and external conditions. This thesis proposes the use of the sigma point Kalman filter (SPKF) for non-linear systems as an equivalentcircuit model-based state estimator to be used in one of the current series production EV projects developed by Rimac Automobili. The estimator has been implemented and validated to yield better results than the currently used SOC estimation method, and will be deployed on the BMS of a high-performance hybrid-electric vehicle

A Study on Advanced Lithium-Based Battery Cell Chemistries to Enhance Lunar Exploration Missions

NASAs Exploration Technology Development Program (ETDP) Energy Storage Project conducted an advanced lithium-based battery chemistry feasibility study to determine the best advanced chemistry to develop for the Altair Lunar Lander and the Extravehicular Activities (EVA) advanced Lunar surface spacesuit. These customers require safe, reliable batteries with extremely high specific energy as compared to state-of-the-art. The specific energy goals for the development project are 220 watt-hours per kilogram (Wh/kg) delivered at the battery-level at 0 degrees Celsius ( C) at a C/10 discharge rate. Continuous discharge rates between C/5 and C/2, operation between 0 and 30 C and 200 cycles are targeted. Electrode materials that were considered include layered metal oxides, spinel oxides, and olivine-type cathode materials, and lithium metal, lithium alloy, and silicon-based composite anode materials. Advanced cell chemistry options were evaluated with respect to multiple quantitative and qualitative attributes while considering their projected performance at the end of the available development timeframe. Following a rigorous ranking process, a chemistry that combines a lithiated nickel manganese cobalt oxide Li(LiNMC)O2 cathode with a silicon-based composite anode was selected as the technology that can potentially offer the best combination of safety, specific energy, energy density, and likelihood of success

On battery recovery effect in wireless sensor nodes

With the perennial demand for longer runtime of battery-powered Wireless Sensor Nodes (WSNs), several techniques have been proposed to increase the battery runtime. One such class of techniques exploiting the battery recovery effect phenomenon claims that performing an intermittent discharge instead of a continuous discharge will increase the usable battery capacity. Several works in the areas of embedded systems and wireless sensor networks have assumed the existence of this recovery effect and proposed different power management techniques in the form of power supply architectures (multiple battery setup) and communication protocols (burst mode transmission) in order to exploit it. However, until now, a systematic experimental evaluation of the recovery effect has not been performed with real battery cells, using high accuracy battery testers to confirm the existence of this recovery phenomenon. In this paper, a systematic evaluation procedure is developed to verify the existence of this battery recovery effect. Using our evaluation procedure we investigated Alkaline, Nickel-Metal Hydride (NiMH) and Lithium-Ion (Li-Ion) battery chemistries, which are commonly used as power supplies for WSN applications. Our experimental results do not show any evidence of the aforementioned recovery effect in these battery chemistries. In particular, our results show a significant deviation from the stochastic battery models, which were used by many power management techniques. Therefore, the existing power management approaches that rely on this recovery effect do not hold in practice. Instead of a battery recovery effect, our experimental results show the existence of the rate capacity effect, which is the reduction of usable battery capacity with higher discharge power, to be the dominant electrochemical phenomenon that should be considered for maximizing the runtime of WSN applications. We outline power management techniques that minimize the rate capacity effect in order to obtain a higher energy output from the battery